R version 3.5.2 (2018-12-20) -- "Eggshell Igloo"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

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'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

> library(limma)
> 
> set.seed(0); u <- runif(100)
> 
> ### strsplit2
> 
> x <- c("ab;cd;efg","abc;def","z","")
> strsplit2(x,split=";")
     [,1]  [,2]  [,3] 
[1,] "ab"  "cd"  "efg"
[2,] "abc" "def" ""   
[3,] "z"   ""    ""   
[4,] ""    ""    ""   
> 
> ### removeext
> 
> removeExt(c("slide1.spot","slide.2.spot"))
[1] "slide1"  "slide.2"
> removeExt(c("slide1.spot","slide"))
[1] "slide1.spot" "slide"      
> 
> ### printorder
> 
> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6),ndups=2,start="topright",npins=4)
$printorder
  [1]   6   5   4   3   2   1  12  11  10   9   8   7  18  17  16  15  14  13
 [19]  24  23  22  21  20  19  30  29  28  27  26  25  36  35  34  33  32  31
 [37]  42  41  40  39  38  37  48  47  46  45  44  43   6   5   4   3   2   1
 [55]  12  11  10   9   8   7  18  17  16  15  14  13  24  23  22  21  20  19
 [73]  30  29  28  27  26  25  36  35  34  33  32  31  42  41  40  39  38  37
 [91]  48  47  46  45  44  43   6   5   4   3   2   1  12  11  10   9   8   7
[109]  18  17  16  15  14  13  24  23  22  21  20  19  30  29  28  27  26  25
[127]  36  35  34  33  32  31  42  41  40  39  38  37  48  47  46  45  44  43
[145]   6   5   4   3   2   1  12  11  10   9   8   7  18  17  16  15  14  13
[163]  24  23  22  21  20  19  30  29  28  27  26  25  36  35  34  33  32  31
[181]  42  41  40  39  38  37  48  47  46  45  44  43  54  53  52  51  50  49
[199]  60  59  58  57  56  55  66  65  64  63  62  61  72  71  70  69  68  67
[217]  78  77  76  75  74  73  84  83  82  81  80  79  90  89  88  87  86  85
[235]  96  95  94  93  92  91  54  53  52  51  50  49  60  59  58  57  56  55
[253]  66  65  64  63  62  61  72  71  70  69  68  67  78  77  76  75  74  73
[271]  84  83  82  81  80  79  90  89  88  87  86  85  96  95  94  93  92  91
[289]  54  53  52  51  50  49  60  59  58  57  56  55  66  65  64  63  62  61
[307]  72  71  70  69  68  67  78  77  76  75  74  73  84  83  82  81  80  79
[325]  90  89  88  87  86  85  96  95  94  93  92  91  54  53  52  51  50  49
[343]  60  59  58  57  56  55  66  65  64  63  62  61  72  71  70  69  68  67
[361]  78  77  76  75  74  73  84  83  82  81  80  79  90  89  88  87  86  85
[379]  96  95  94  93  92  91 102 101 100  99  98  97 108 107 106 105 104 103
[397] 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121
[415] 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139
[433] 102 101 100  99  98  97 108 107 106 105 104 103 114 113 112 111 110 109
[451] 120 119 118 117 116 115 126 125 124 123 122 121 132 131 130 129 128 127
[469] 138 137 136 135 134 133 144 143 142 141 140 139 102 101 100  99  98  97
[487] 108 107 106 105 104 103 114 113 112 111 110 109 120 119 118 117 116 115
[505] 126 125 124 123 122 121 132 131 130 129 128 127 138 137 136 135 134 133
[523] 144 143 142 141 140 139 102 101 100  99  98  97 108 107 106 105 104 103
[541] 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121
[559] 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139
[577] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157
[595] 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[613] 186 185 184 183 182 181 192 191 190 189 188 187 150 149 148 147 146 145
[631] 156 155 154 153 152 151 162 161 160 159 158 157 168 167 166 165 164 163
[649] 174 173 172 171 170 169 180 179 178 177 176 175 186 185 184 183 182 181
[667] 192 191 190 189 188 187 150 149 148 147 146 145 156 155 154 153 152 151
[685] 162 161 160 159 158 157 168 167 166 165 164 163 174 173 172 171 170 169
[703] 180 179 178 177 176 175 186 185 184 183 182 181 192 191 190 189 188 187
[721] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157
[739] 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[757] 186 185 184 183 182 181 192 191 190 189 188 187

$plate
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

$plate.r
  [1]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4
 [26]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  3  3
 [51]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3
 [76]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  2  2  2  2
[101]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
[126]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  1  1  1  1  1  1
[151]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[176]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  8  8  8  8  8  8  8  8
[201]  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8
[226]  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  7  7  7  7  7  7  7  7  7  7
[251]  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7
[276]  7  7  7  7  7  7  7  7  7  7  7  7  7  6  6  6  6  6  6  6  6  6  6  6  6
[301]  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6
[326]  6  6  6  6  6  6  6  6  6  6  6  5  5  5  5  5  5  5  5  5  5  5  5  5  5
[351]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
[376]  5  5  5  5  5  5  5  5  5 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[401] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[426] 12 12 12 12 12 12 12 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[451] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[476] 11 11 11 11 11 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[501] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[526] 10 10 10  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
[551]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
[576]  9 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
[601] 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 15
[626] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
[651] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 14 14 14
[676] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
[701] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 13 13 13 13 13
[726] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
[751] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13

$plate.c
  [1]  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15
 [26] 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3
 [51]  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14
 [76] 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2
[101]  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13
[126] 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1
[151]  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18
[176] 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6
[201]  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17
[226] 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5
[251]  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16
[276] 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4
[301]  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21
[326] 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9
[351]  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20
[376] 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8
[401]  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19
[426] 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7
[451] 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24
[476] 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12
[501] 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23
[526] 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11
[551] 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22
[576] 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10
[601] 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3
[626]  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15
[651] 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2
[676]  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14
[701] 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1
[726]  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13
[751] 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22

$plateposition
  [1] "p1D03" "p1D03" "p1D02" "p1D02" "p1D01" "p1D01" "p1D06" "p1D06" "p1D05"
 [10] "p1D05" "p1D04" "p1D04" "p1D09" "p1D09" "p1D08" "p1D08" "p1D07" "p1D07"
 [19] "p1D12" "p1D12" "p1D11" "p1D11" "p1D10" "p1D10" "p1D15" "p1D15" "p1D14"
 [28] "p1D14" "p1D13" "p1D13" "p1D18" "p1D18" "p1D17" "p1D17" "p1D16" "p1D16"
 [37] "p1D21" "p1D21" "p1D20" "p1D20" "p1D19" "p1D19" "p1D24" "p1D24" "p1D23"
 [46] "p1D23" "p1D22" "p1D22" "p1C03" "p1C03" "p1C02" "p1C02" "p1C01" "p1C01"
 [55] "p1C06" "p1C06" "p1C05" "p1C05" "p1C04" "p1C04" "p1C09" "p1C09" "p1C08"
 [64] "p1C08" "p1C07" "p1C07" "p1C12" "p1C12" "p1C11" "p1C11" "p1C10" "p1C10"
 [73] "p1C15" "p1C15" "p1C14" "p1C14" "p1C13" "p1C13" "p1C18" "p1C18" "p1C17"
 [82] "p1C17" "p1C16" "p1C16" "p1C21" "p1C21" "p1C20" "p1C20" "p1C19" "p1C19"
 [91] "p1C24" "p1C24" "p1C23" "p1C23" "p1C22" "p1C22" "p1B03" "p1B03" "p1B02"
[100] "p1B02" "p1B01" "p1B01" "p1B06" "p1B06" "p1B05" "p1B05" "p1B04" "p1B04"
[109] "p1B09" "p1B09" "p1B08" "p1B08" "p1B07" "p1B07" "p1B12" "p1B12" "p1B11"
[118] "p1B11" "p1B10" "p1B10" "p1B15" "p1B15" "p1B14" "p1B14" "p1B13" "p1B13"
[127] "p1B18" "p1B18" "p1B17" "p1B17" "p1B16" "p1B16" "p1B21" "p1B21" "p1B20"
[136] "p1B20" "p1B19" "p1B19" "p1B24" "p1B24" "p1B23" "p1B23" "p1B22" "p1B22"
[145] "p1A03" "p1A03" "p1A02" "p1A02" "p1A01" "p1A01" "p1A06" "p1A06" "p1A05"
[154] "p1A05" "p1A04" "p1A04" "p1A09" "p1A09" "p1A08" "p1A08" "p1A07" "p1A07"
[163] "p1A12" "p1A12" "p1A11" "p1A11" "p1A10" "p1A10" "p1A15" "p1A15" "p1A14"
[172] "p1A14" "p1A13" "p1A13" "p1A18" "p1A18" "p1A17" "p1A17" "p1A16" "p1A16"
[181] "p1A21" "p1A21" "p1A20" "p1A20" "p1A19" "p1A19" "p1A24" "p1A24" "p1A23"
[190] "p1A23" "p1A22" "p1A22" "p1H03" "p1H03" "p1H02" "p1H02" "p1H01" "p1H01"
[199] "p1H06" "p1H06" "p1H05" "p1H05" "p1H04" "p1H04" "p1H09" "p1H09" "p1H08"
[208] "p1H08" "p1H07" "p1H07" "p1H12" "p1H12" "p1H11" "p1H11" "p1H10" "p1H10"
[217] "p1H15" "p1H15" "p1H14" "p1H14" "p1H13" "p1H13" "p1H18" "p1H18" "p1H17"
[226] "p1H17" "p1H16" "p1H16" "p1H21" "p1H21" "p1H20" "p1H20" "p1H19" "p1H19"
[235] "p1H24" "p1H24" "p1H23" "p1H23" "p1H22" "p1H22" "p1G03" "p1G03" "p1G02"
[244] "p1G02" "p1G01" "p1G01" "p1G06" "p1G06" "p1G05" "p1G05" "p1G04" "p1G04"
[253] "p1G09" "p1G09" "p1G08" "p1G08" "p1G07" "p1G07" "p1G12" "p1G12" "p1G11"
[262] "p1G11" "p1G10" "p1G10" "p1G15" "p1G15" "p1G14" "p1G14" "p1G13" "p1G13"
[271] "p1G18" "p1G18" "p1G17" "p1G17" "p1G16" "p1G16" "p1G21" "p1G21" "p1G20"
[280] "p1G20" "p1G19" "p1G19" "p1G24" "p1G24" "p1G23" "p1G23" "p1G22" "p1G22"
[289] "p1F03" "p1F03" "p1F02" "p1F02" "p1F01" "p1F01" "p1F06" "p1F06" "p1F05"
[298] "p1F05" "p1F04" "p1F04" "p1F09" "p1F09" "p1F08" "p1F08" "p1F07" "p1F07"
[307] "p1F12" "p1F12" "p1F11" "p1F11" "p1F10" "p1F10" "p1F15" "p1F15" "p1F14"
[316] "p1F14" "p1F13" "p1F13" "p1F18" "p1F18" "p1F17" "p1F17" "p1F16" "p1F16"
[325] "p1F21" "p1F21" "p1F20" "p1F20" "p1F19" "p1F19" "p1F24" "p1F24" "p1F23"
[334] "p1F23" "p1F22" "p1F22" "p1E03" "p1E03" "p1E02" "p1E02" "p1E01" "p1E01"
[343] "p1E06" "p1E06" "p1E05" "p1E05" "p1E04" "p1E04" "p1E09" "p1E09" "p1E08"
[352] "p1E08" "p1E07" "p1E07" "p1E12" "p1E12" "p1E11" "p1E11" "p1E10" "p1E10"
[361] "p1E15" "p1E15" "p1E14" "p1E14" "p1E13" "p1E13" "p1E18" "p1E18" "p1E17"
[370] "p1E17" "p1E16" "p1E16" "p1E21" "p1E21" "p1E20" "p1E20" "p1E19" "p1E19"
[379] "p1E24" "p1E24" "p1E23" "p1E23" "p1E22" "p1E22" "p1L03" "p1L03" "p1L02"
[388] "p1L02" "p1L01" "p1L01" "p1L06" "p1L06" "p1L05" "p1L05" "p1L04" "p1L04"
[397] "p1L09" "p1L09" "p1L08" "p1L08" "p1L07" "p1L07" "p1L12" "p1L12" "p1L11"
[406] "p1L11" "p1L10" "p1L10" "p1L15" "p1L15" "p1L14" "p1L14" "p1L13" "p1L13"
[415] "p1L18" "p1L18" "p1L17" "p1L17" "p1L16" "p1L16" "p1L21" "p1L21" "p1L20"
[424] "p1L20" "p1L19" "p1L19" "p1L24" "p1L24" "p1L23" "p1L23" "p1L22" "p1L22"
[433] "p1K03" "p1K03" "p1K02" "p1K02" "p1K01" "p1K01" "p1K06" "p1K06" "p1K05"
[442] "p1K05" "p1K04" "p1K04" "p1K09" "p1K09" "p1K08" "p1K08" "p1K07" "p1K07"
[451] "p1K12" "p1K12" "p1K11" "p1K11" "p1K10" "p1K10" "p1K15" "p1K15" "p1K14"
[460] "p1K14" "p1K13" "p1K13" "p1K18" "p1K18" "p1K17" "p1K17" "p1K16" "p1K16"
[469] "p1K21" "p1K21" "p1K20" "p1K20" "p1K19" "p1K19" "p1K24" "p1K24" "p1K23"
[478] "p1K23" "p1K22" "p1K22" "p1J03" "p1J03" "p1J02" "p1J02" "p1J01" "p1J01"
[487] "p1J06" "p1J06" "p1J05" "p1J05" "p1J04" "p1J04" "p1J09" "p1J09" "p1J08"
[496] "p1J08" "p1J07" "p1J07" "p1J12" "p1J12" "p1J11" "p1J11" "p1J10" "p1J10"
[505] "p1J15" "p1J15" "p1J14" "p1J14" "p1J13" "p1J13" "p1J18" "p1J18" "p1J17"
[514] "p1J17" "p1J16" "p1J16" "p1J21" "p1J21" "p1J20" "p1J20" "p1J19" "p1J19"
[523] "p1J24" "p1J24" "p1J23" "p1J23" "p1J22" "p1J22" "p1I03" "p1I03" "p1I02"
[532] "p1I02" "p1I01" "p1I01" "p1I06" "p1I06" "p1I05" "p1I05" "p1I04" "p1I04"
[541] "p1I09" "p1I09" "p1I08" "p1I08" "p1I07" "p1I07" "p1I12" "p1I12" "p1I11"
[550] "p1I11" "p1I10" "p1I10" "p1I15" "p1I15" "p1I14" "p1I14" "p1I13" "p1I13"
[559] "p1I18" "p1I18" "p1I17" "p1I17" "p1I16" "p1I16" "p1I21" "p1I21" "p1I20"
[568] "p1I20" "p1I19" "p1I19" "p1I24" "p1I24" "p1I23" "p1I23" "p1I22" "p1I22"
[577] "p1P03" "p1P03" "p1P02" "p1P02" "p1P01" "p1P01" "p1P06" "p1P06" "p1P05"
[586] "p1P05" "p1P04" "p1P04" "p1P09" "p1P09" "p1P08" "p1P08" "p1P07" "p1P07"
[595] "p1P12" "p1P12" "p1P11" "p1P11" "p1P10" "p1P10" "p1P15" "p1P15" "p1P14"
[604] "p1P14" "p1P13" "p1P13" "p1P18" "p1P18" "p1P17" "p1P17" "p1P16" "p1P16"
[613] "p1P21" "p1P21" "p1P20" "p1P20" "p1P19" "p1P19" "p1P24" "p1P24" "p1P23"
[622] "p1P23" "p1P22" "p1P22" "p1O03" "p1O03" "p1O02" "p1O02" "p1O01" "p1O01"
[631] "p1O06" "p1O06" "p1O05" "p1O05" "p1O04" "p1O04" "p1O09" "p1O09" "p1O08"
[640] "p1O08" "p1O07" "p1O07" "p1O12" "p1O12" "p1O11" "p1O11" "p1O10" "p1O10"
[649] "p1O15" "p1O15" "p1O14" "p1O14" "p1O13" "p1O13" "p1O18" "p1O18" "p1O17"
[658] "p1O17" "p1O16" "p1O16" "p1O21" "p1O21" "p1O20" "p1O20" "p1O19" "p1O19"
[667] "p1O24" "p1O24" "p1O23" "p1O23" "p1O22" "p1O22" "p1N03" "p1N03" "p1N02"
[676] "p1N02" "p1N01" "p1N01" "p1N06" "p1N06" "p1N05" "p1N05" "p1N04" "p1N04"
[685] "p1N09" "p1N09" "p1N08" "p1N08" "p1N07" "p1N07" "p1N12" "p1N12" "p1N11"
[694] "p1N11" "p1N10" "p1N10" "p1N15" "p1N15" "p1N14" "p1N14" "p1N13" "p1N13"
[703] "p1N18" "p1N18" "p1N17" "p1N17" "p1N16" "p1N16" "p1N21" "p1N21" "p1N20"
[712] "p1N20" "p1N19" "p1N19" "p1N24" "p1N24" "p1N23" "p1N23" "p1N22" "p1N22"
[721] "p1M03" "p1M03" "p1M02" "p1M02" "p1M01" "p1M01" "p1M06" "p1M06" "p1M05"
[730] "p1M05" "p1M04" "p1M04" "p1M09" "p1M09" "p1M08" "p1M08" "p1M07" "p1M07"
[739] "p1M12" "p1M12" "p1M11" "p1M11" "p1M10" "p1M10" "p1M15" "p1M15" "p1M14"
[748] "p1M14" "p1M13" "p1M13" "p1M18" "p1M18" "p1M17" "p1M17" "p1M16" "p1M16"
[757] "p1M21" "p1M21" "p1M20" "p1M20" "p1M19" "p1M19" "p1M24" "p1M24" "p1M23"
[766] "p1M23" "p1M22" "p1M22"

> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6))
$printorder
  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
 [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2
 [51]  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
 [76] 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4
[101]  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
[126] 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6
[151]  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
[176] 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8
[201]  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
[226] 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10
[251] 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
[276] 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12
[301] 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
[326] 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14
[351] 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
[376] 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16
[401] 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
[426] 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18
[451] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
[476] 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
[501] 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
[526] 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22
[551] 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
[576] 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
[601] 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1
[626]  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
[651] 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3
[676]  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
[701] 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5
[726]  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
[751] 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

$plate
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[334] 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2
[371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[408] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1
[445] 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1
[482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[519] 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2
[556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[593] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1
[630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[667] 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2
[704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[741] 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

$plate.r
  [1]  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  4
 [26]  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3  3
 [51]  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3  3
 [76]  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2  2
[101]  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2  2
[126]  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1  1
[151]  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1  5
[176]  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  4  4  4  4  4  4  8  8
[201]  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  4  4  4  4  4  4  8  8  8
[226]  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3  3  3  3  3  3  7  7  7  7
[251]  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3  3  3  3  3  7  7  7  7  7
[276]  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2  2  2  2  6  6  6  6  6  6
[301] 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2  2  2  6  6  6  6  6  6 10
[326] 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1  1  5  5  5  5  5  5  9  9
[351]  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9
[376]  9  9  9 13 13 13 13 13 13  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12
[401] 12 12 16 16 16 16 16 16  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12
[426] 12 16 16 16 16 16 16  3  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11
[451] 15 15 15 15 15 15  3  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15
[476] 15 15 15 15 15  2  2  2  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14
[501] 14 14 14 14  2  2  2  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14
[526] 14 14 14  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13
[551] 13 13  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13
[576] 13  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16
[601]  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3
[626]  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3
[651]  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2
[676]  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2
[701]  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1
[726]  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1
[751]  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13

$plate.c
  [1]  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1
 [26]  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5
 [51]  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9
 [76] 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13
[101] 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17
[126] 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21
[151]  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1
[176]  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  2  6 10 14 18 22  2  6
[201] 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10
[226] 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14
[251] 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18
[276] 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22
[301]  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2
[326]  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6
[351] 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10
[376] 14 18 22  2  6 10 14 18 22  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15
[401] 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19
[426] 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23
[451]  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3
[476]  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7
[501] 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11
[526] 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15
[551] 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19
[576] 23  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24
[601]  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4
[626]  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8
[651] 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12
[676] 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16
[701] 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20
[726] 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24
[751]  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24

$plateposition
  [1] "p1D01" "p1D05" "p1D09" "p1D13" "p1D17" "p1D21" "p1H01" "p1H05" "p1H09"
 [10] "p1H13" "p1H17" "p1H21" "p1L01" "p1L05" "p1L09" "p1L13" "p1L17" "p1L21"
 [19] "p1P01" "p1P05" "p1P09" "p1P13" "p1P17" "p1P21" "p2D01" "p2D05" "p2D09"
 [28] "p2D13" "p2D17" "p2D21" "p2H01" "p2H05" "p2H09" "p2H13" "p2H17" "p2H21"
 [37] "p2L01" "p2L05" "p2L09" "p2L13" "p2L17" "p2L21" "p2P01" "p2P05" "p2P09"
 [46] "p2P13" "p2P17" "p2P21" "p1C01" "p1C05" "p1C09" "p1C13" "p1C17" "p1C21"
 [55] "p1G01" "p1G05" "p1G09" "p1G13" "p1G17" "p1G21" "p1K01" "p1K05" "p1K09"
 [64] "p1K13" "p1K17" "p1K21" "p1O01" "p1O05" "p1O09" "p1O13" "p1O17" "p1O21"
 [73] "p2C01" "p2C05" "p2C09" "p2C13" "p2C17" "p2C21" "p2G01" "p2G05" "p2G09"
 [82] "p2G13" "p2G17" "p2G21" "p2K01" "p2K05" "p2K09" "p2K13" "p2K17" "p2K21"
 [91] "p2O01" "p2O05" "p2O09" "p2O13" "p2O17" "p2O21" "p1B01" "p1B05" "p1B09"
[100] "p1B13" "p1B17" "p1B21" "p1F01" "p1F05" "p1F09" "p1F13" "p1F17" "p1F21"
[109] "p1J01" "p1J05" "p1J09" "p1J13" "p1J17" "p1J21" "p1N01" "p1N05" "p1N09"
[118] "p1N13" "p1N17" "p1N21" "p2B01" "p2B05" "p2B09" "p2B13" "p2B17" "p2B21"
[127] "p2F01" "p2F05" "p2F09" "p2F13" "p2F17" "p2F21" "p2J01" "p2J05" "p2J09"
[136] "p2J13" "p2J17" "p2J21" "p2N01" "p2N05" "p2N09" "p2N13" "p2N17" "p2N21"
[145] "p1A01" "p1A05" "p1A09" "p1A13" "p1A17" "p1A21" "p1E01" "p1E05" "p1E09"
[154] "p1E13" "p1E17" "p1E21" "p1I01" "p1I05" "p1I09" "p1I13" "p1I17" "p1I21"
[163] "p1M01" "p1M05" "p1M09" "p1M13" "p1M17" "p1M21" "p2A01" "p2A05" "p2A09"
[172] "p2A13" "p2A17" "p2A21" "p2E01" "p2E05" "p2E09" "p2E13" "p2E17" "p2E21"
[181] "p2I01" "p2I05" "p2I09" "p2I13" "p2I17" "p2I21" "p2M01" "p2M05" "p2M09"
[190] "p2M13" "p2M17" "p2M21" "p1D02" "p1D06" "p1D10" "p1D14" "p1D18" "p1D22"
[199] "p1H02" "p1H06" "p1H10" "p1H14" "p1H18" "p1H22" "p1L02" "p1L06" "p1L10"
[208] "p1L14" "p1L18" "p1L22" "p1P02" "p1P06" "p1P10" "p1P14" "p1P18" "p1P22"
[217] "p2D02" "p2D06" "p2D10" "p2D14" "p2D18" "p2D22" "p2H02" "p2H06" "p2H10"
[226] "p2H14" "p2H18" "p2H22" "p2L02" "p2L06" "p2L10" "p2L14" "p2L18" "p2L22"
[235] "p2P02" "p2P06" "p2P10" "p2P14" "p2P18" "p2P22" "p1C02" "p1C06" "p1C10"
[244] "p1C14" "p1C18" "p1C22" "p1G02" "p1G06" "p1G10" "p1G14" "p1G18" "p1G22"
[253] "p1K02" "p1K06" "p1K10" "p1K14" "p1K18" "p1K22" "p1O02" "p1O06" "p1O10"
[262] "p1O14" "p1O18" "p1O22" "p2C02" "p2C06" "p2C10" "p2C14" "p2C18" "p2C22"
[271] "p2G02" "p2G06" "p2G10" "p2G14" "p2G18" "p2G22" "p2K02" "p2K06" "p2K10"
[280] "p2K14" "p2K18" "p2K22" "p2O02" "p2O06" "p2O10" "p2O14" "p2O18" "p2O22"
[289] "p1B02" "p1B06" "p1B10" "p1B14" "p1B18" "p1B22" "p1F02" "p1F06" "p1F10"
[298] "p1F14" "p1F18" "p1F22" "p1J02" "p1J06" "p1J10" "p1J14" "p1J18" "p1J22"
[307] "p1N02" "p1N06" "p1N10" "p1N14" "p1N18" "p1N22" "p2B02" "p2B06" "p2B10"
[316] "p2B14" "p2B18" "p2B22" "p2F02" "p2F06" "p2F10" "p2F14" "p2F18" "p2F22"
[325] "p2J02" "p2J06" "p2J10" "p2J14" "p2J18" "p2J22" "p2N02" "p2N06" "p2N10"
[334] "p2N14" "p2N18" "p2N22" "p1A02" "p1A06" "p1A10" "p1A14" "p1A18" "p1A22"
[343] "p1E02" "p1E06" "p1E10" "p1E14" "p1E18" "p1E22" "p1I02" "p1I06" "p1I10"
[352] "p1I14" "p1I18" "p1I22" "p1M02" "p1M06" "p1M10" "p1M14" "p1M18" "p1M22"
[361] "p2A02" "p2A06" "p2A10" "p2A14" "p2A18" "p2A22" "p2E02" "p2E06" "p2E10"
[370] "p2E14" "p2E18" "p2E22" "p2I02" "p2I06" "p2I10" "p2I14" "p2I18" "p2I22"
[379] "p2M02" "p2M06" "p2M10" "p2M14" "p2M18" "p2M22" "p1D03" "p1D07" "p1D11"
[388] "p1D15" "p1D19" "p1D23" "p1H03" "p1H07" "p1H11" "p1H15" "p1H19" "p1H23"
[397] "p1L03" "p1L07" "p1L11" "p1L15" "p1L19" "p1L23" "p1P03" "p1P07" "p1P11"
[406] "p1P15" "p1P19" "p1P23" "p2D03" "p2D07" "p2D11" "p2D15" "p2D19" "p2D23"
[415] "p2H03" "p2H07" "p2H11" "p2H15" "p2H19" "p2H23" "p2L03" "p2L07" "p2L11"
[424] "p2L15" "p2L19" "p2L23" "p2P03" "p2P07" "p2P11" "p2P15" "p2P19" "p2P23"
[433] "p1C03" "p1C07" "p1C11" "p1C15" "p1C19" "p1C23" "p1G03" "p1G07" "p1G11"
[442] "p1G15" "p1G19" "p1G23" "p1K03" "p1K07" "p1K11" "p1K15" "p1K19" "p1K23"
[451] "p1O03" "p1O07" "p1O11" "p1O15" "p1O19" "p1O23" "p2C03" "p2C07" "p2C11"
[460] "p2C15" "p2C19" "p2C23" "p2G03" "p2G07" "p2G11" "p2G15" "p2G19" "p2G23"
[469] "p2K03" "p2K07" "p2K11" "p2K15" "p2K19" "p2K23" "p2O03" "p2O07" "p2O11"
[478] "p2O15" "p2O19" "p2O23" "p1B03" "p1B07" "p1B11" "p1B15" "p1B19" "p1B23"
[487] "p1F03" "p1F07" "p1F11" "p1F15" "p1F19" "p1F23" "p1J03" "p1J07" "p1J11"
[496] "p1J15" "p1J19" "p1J23" "p1N03" "p1N07" "p1N11" "p1N15" "p1N19" "p1N23"
[505] "p2B03" "p2B07" "p2B11" "p2B15" "p2B19" "p2B23" "p2F03" "p2F07" "p2F11"
[514] "p2F15" "p2F19" "p2F23" "p2J03" "p2J07" "p2J11" "p2J15" "p2J19" "p2J23"
[523] "p2N03" "p2N07" "p2N11" "p2N15" "p2N19" "p2N23" "p1A03" "p1A07" "p1A11"
[532] "p1A15" "p1A19" "p1A23" "p1E03" "p1E07" "p1E11" "p1E15" "p1E19" "p1E23"
[541] "p1I03" "p1I07" "p1I11" "p1I15" "p1I19" "p1I23" "p1M03" "p1M07" "p1M11"
[550] "p1M15" "p1M19" "p1M23" "p2A03" "p2A07" "p2A11" "p2A15" "p2A19" "p2A23"
[559] "p2E03" "p2E07" "p2E11" "p2E15" "p2E19" "p2E23" "p2I03" "p2I07" "p2I11"
[568] "p2I15" "p2I19" "p2I23" "p2M03" "p2M07" "p2M11" "p2M15" "p2M19" "p2M23"
[577] "p1D04" "p1D08" "p1D12" "p1D16" "p1D20" "p1D24" "p1H04" "p1H08" "p1H12"
[586] "p1H16" "p1H20" "p1H24" "p1L04" "p1L08" "p1L12" "p1L16" "p1L20" "p1L24"
[595] "p1P04" "p1P08" "p1P12" "p1P16" "p1P20" "p1P24" "p2D04" "p2D08" "p2D12"
[604] "p2D16" "p2D20" "p2D24" "p2H04" "p2H08" "p2H12" "p2H16" "p2H20" "p2H24"
[613] "p2L04" "p2L08" "p2L12" "p2L16" "p2L20" "p2L24" "p2P04" "p2P08" "p2P12"
[622] "p2P16" "p2P20" "p2P24" "p1C04" "p1C08" "p1C12" "p1C16" "p1C20" "p1C24"
[631] "p1G04" "p1G08" "p1G12" "p1G16" "p1G20" "p1G24" "p1K04" "p1K08" "p1K12"
[640] "p1K16" "p1K20" "p1K24" "p1O04" "p1O08" "p1O12" "p1O16" "p1O20" "p1O24"
[649] "p2C04" "p2C08" "p2C12" "p2C16" "p2C20" "p2C24" "p2G04" "p2G08" "p2G12"
[658] "p2G16" "p2G20" "p2G24" "p2K04" "p2K08" "p2K12" "p2K16" "p2K20" "p2K24"
[667] "p2O04" "p2O08" "p2O12" "p2O16" "p2O20" "p2O24" "p1B04" "p1B08" "p1B12"
[676] "p1B16" "p1B20" "p1B24" "p1F04" "p1F08" "p1F12" "p1F16" "p1F20" "p1F24"
[685] "p1J04" "p1J08" "p1J12" "p1J16" "p1J20" "p1J24" "p1N04" "p1N08" "p1N12"
[694] "p1N16" "p1N20" "p1N24" "p2B04" "p2B08" "p2B12" "p2B16" "p2B20" "p2B24"
[703] "p2F04" "p2F08" "p2F12" "p2F16" "p2F20" "p2F24" "p2J04" "p2J08" "p2J12"
[712] "p2J16" "p2J20" "p2J24" "p2N04" "p2N08" "p2N12" "p2N16" "p2N20" "p2N24"
[721] "p1A04" "p1A08" "p1A12" "p1A16" "p1A20" "p1A24" "p1E04" "p1E08" "p1E12"
[730] "p1E16" "p1E20" "p1E24" "p1I04" "p1I08" "p1I12" "p1I16" "p1I20" "p1I24"
[739] "p1M04" "p1M08" "p1M12" "p1M16" "p1M20" "p1M24" "p2A04" "p2A08" "p2A12"
[748] "p2A16" "p2A20" "p2A24" "p2E04" "p2E08" "p2E12" "p2E16" "p2E20" "p2E24"
[757] "p2I04" "p2I08" "p2I12" "p2I16" "p2I20" "p2I24" "p2M04" "p2M08" "p2M12"
[766] "p2M16" "p2M20" "p2M24"

> 
> ### merge.rglist
> 
> R <- G <- matrix(11:14,4,2)
> rownames(R) <- rownames(G) <- c("a","a","b","c")
> RG1 <- new("RGList",list(R=R,G=G))
> R <- G <- matrix(21:24,4,2)
> rownames(R) <- rownames(G) <- c("b","a","a","c")
> RG2 <- new("RGList",list(R=R,G=G))
> merge(RG1,RG2)
An object of class "RGList"
$R
  [,1] [,2] [,3] [,4]
a   11   11   22   22
a   12   12   23   23
b   13   13   21   21
c   14   14   24   24

$G
  [,1] [,2] [,3] [,4]
a   11   11   22   22
a   12   12   23   23
b   13   13   21   21
c   14   14   24   24

> merge(RG2,RG1)
An object of class "RGList"
$R
  [,1] [,2] [,3] [,4]
b   21   21   13   13
a   22   22   11   11
a   23   23   12   12
c   24   24   14   14

$G
  [,1] [,2] [,3] [,4]
b   21   21   13   13
a   22   22   11   11
a   23   23   12   12
c   24   24   14   14

> 
> ### background correction
> 
> RG <- new("RGList", list(R=c(1,2,3,4),G=c(1,2,3,4),Rb=c(2,2,2,2),Gb=c(2,2,2,2)))
> backgroundCorrect(RG)
An object of class "RGList"
$R
     [,1]
[1,]   -1
[2,]    0
[3,]    1
[4,]    2

$G
     [,1]
[1,]   -1
[2,]    0
[3,]    1
[4,]    2

> backgroundCorrect(RG, method="half")
An object of class "RGList"
$R
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

$G
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

> backgroundCorrect(RG, method="minimum")
An object of class "RGList"
$R
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

$G
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

> backgroundCorrect(RG, offset=5)
An object of class "RGList"
$R
     [,1]
[1,]    4
[2,]    5
[3,]    6
[4,]    7

$G
     [,1]
[1,]    4
[2,]    5
[3,]    6
[4,]    7

> 
> ### loessFit
> 
> x <- 1:100
> y <- rnorm(100)
> out <- loessFit(y,x)
> f1 <- quantile(out$fitted)
> r1 <- quantile(out$residual)
> w <- rep(1,100)
> w[1:50] <- 0.5
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f2 <- quantile(out$fitted)
> r2 <- quantile(out$residual)
> out <- loessFit(y,x,weights=w,method="locfit")
> f3 <- quantile(out$fitted)
> r3 <- quantile(out$residual)
> out <- loessFit(y,x,weights=w,method="loess")
> f4 <- quantile(out$fitted)
> r4 <- quantile(out$residual)
> w <- rep(1,100)
> w[2*(1:50)] <- 0
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f5 <- quantile(out$fitted)
> r5 <- quantile(out$residual)
> data.frame(f1,f2,f3,f4,f5)
              f1           f2          f3          f4          f5
0%   -0.78835384 -0.687432210 -0.78957137 -0.76756060 -0.63778292
25%  -0.18340154 -0.179683572 -0.18979269 -0.16773223 -0.38064318
50%  -0.11492924 -0.114796040 -0.12087983 -0.07185314 -0.15971879
75%   0.01507921 -0.008145125 -0.01857508  0.04030634  0.07839396
100%  0.21653837  0.145106033  0.19214597  0.21417361  0.51836274
> data.frame(r1,r2,r3,r4,r5)
              r1          r2          r3           r4          r5
0%   -2.04434053 -2.05132680 -2.02404318 -2.101242874 -2.22280633
25%  -0.59321065 -0.57200209 -0.58975649 -0.577887481 -0.71037756
50%   0.05874864  0.04514326  0.08335198 -0.001769806  0.06785517
75%   0.56010750  0.55124530  0.57618740  0.561454370  0.65383830
100%  2.57936026  2.64549799  2.57549257  2.402324533  2.28648835
> 
> ### normalizeWithinArrays
> 
> RG <- new("RGList",list())
> RG$R <- matrix(rexp(100*2),100,2)
> RG$G <- matrix(rexp(100*2),100,2)
> RG$Rb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RG$Gb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="saddle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
       V1                V2                V3               V4        
 Min.   :0.01626   Min.   :0.01213   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.35497   1st Qu.:0.29133   1st Qu.:0.2745   1st Qu.:0.3953  
 Median :0.71793   Median :0.70294   Median :0.6339   Median :0.8223  
 Mean   :0.90184   Mean   :1.00122   Mean   :0.9454   Mean   :1.1324  
 3rd Qu.:1.16891   3rd Qu.:1.33139   3rd Qu.:1.4059   3rd Qu.:1.4221  
 Max.   :4.56267   Max.   :6.37947   Max.   :5.0486   Max.   :6.6295  
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="mle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
       V1                V2                V3               V4        
 Min.   :0.01701   Min.   :0.01255   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.35423   1st Qu.:0.29118   1st Qu.:0.2745   1st Qu.:0.3953  
 Median :0.71719   Median :0.70280   Median :0.6339   Median :0.8223  
 Mean   :0.90118   Mean   :1.00110   Mean   :0.9454   Mean   :1.1324  
 3rd Qu.:1.16817   3rd Qu.:1.33124   3rd Qu.:1.4059   3rd Qu.:1.4221  
 Max.   :4.56193   Max.   :6.37932   Max.   :5.0486   Max.   :6.6295  
> MA <- normalizeWithinArrays(RGb,method="loess")
> summary(MA$M)
       V1                 V2          
 Min.   :-5.88044   Min.   :-5.66985  
 1st Qu.:-1.18483   1st Qu.:-1.57014  
 Median :-0.21632   Median : 0.04823  
 Mean   : 0.03487   Mean   :-0.05481  
 3rd Qu.: 1.49669   3rd Qu.: 1.45113  
 Max.   : 7.07324   Max.   : 6.19744  
> #MA <- normalizeWithinArrays(RG[,1:2], mouse.setup, method="robustspline")
> #MA$M[1:5,]
> #MA <- normalizeWithinArrays(mouse.data, mouse.setup)
> #MA$M[1:5,]
> 
> ### normalizeBetweenArrays
> 
> MA2 <- normalizeBetweenArrays(MA,method="scale")
> MA$M[1:5,]
           [,1]       [,2]
[1,] -1.1689588  4.5558123
[2,]  0.8971363  0.3296544
[3,]  2.8247439  1.4249960
[4,] -1.8533240  0.4804851
[5,]  1.9158459 -5.5087631
> MA$A[1:5,]
            [,1]       [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200  0.2071043
[4,]  0.09281027 -1.3880965
[5,]  0.22385828 -3.0855818
> MA2 <- normalizeBetweenArrays(MA,method="quantile")
> MA$M[1:5,]
           [,1]       [,2]
[1,] -1.1689588  4.5558123
[2,]  0.8971363  0.3296544
[3,]  2.8247439  1.4249960
[4,] -1.8533240  0.4804851
[5,]  1.9158459 -5.5087631
> MA$A[1:5,]
            [,1]       [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200  0.2071043
[4,]  0.09281027 -1.3880965
[5,]  0.22385828 -3.0855818
> 
> ### unwrapdups
> 
> M <- matrix(1:12,6,2)
> unwrapdups(M,ndups=1)
     [,1] [,2]
[1,]    1    7
[2,]    2    8
[3,]    3    9
[4,]    4   10
[5,]    5   11
[6,]    6   12
> unwrapdups(M,ndups=2)
     [,1] [,2] [,3] [,4]
[1,]    1    2    7    8
[2,]    3    4    9   10
[3,]    5    6   11   12
> unwrapdups(M,ndups=3)
     [,1] [,2] [,3] [,4] [,5] [,6]
[1,]    1    2    3    7    8    9
[2,]    4    5    6   10   11   12
> unwrapdups(M,ndups=2,spacing=3)
     [,1] [,2] [,3] [,4]
[1,]    1    4    7   10
[2,]    2    5    8   11
[3,]    3    6    9   12
> 
> ### trigammaInverse
> 
> trigammaInverse(c(1e-6,NA,5,1e6))
[1] 1.000000e+06           NA 4.961687e-01 1.000001e-03
> 
> ### lmFit, eBayes, topTable
> 
> M <- matrix(rnorm(10*6,sd=0.3),10,6)
> rownames(M) <- LETTERS[1:10]
> M[1,1:3] <- M[1,1:3] + 2
> design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
> contrast.matrix <- cbind(First3=c(1,0),Last3=c(0,1),"Last3-First3"=c(-1,1))
> fit <- lmFit(M,design)
> fit2 <- eBayes(contrasts.fit(fit,contrasts=contrast.matrix))
> topTable(fit2)
       First3       Last3 Last3.First3      AveExpr           F      P.Value
A  1.77602021  0.06025114  -1.71576906  0.918135675 50.91471061 7.727200e-23
D -0.05454069  0.39127869   0.44581938  0.168369004  2.51638838 8.075072e-02
F -0.16249607 -0.33009728  -0.16760121 -0.246296671  2.18256779 1.127516e-01
G  0.30852468 -0.06873462  -0.37725930  0.119895035  1.61088775 1.997102e-01
H -0.16942269  0.20578118   0.37520387  0.018179245  1.14554368 3.180510e-01
J  0.21417623  0.07074940  -0.14342683  0.142462814  0.82029274 4.403027e-01
C -0.12236781  0.15095948   0.27332729  0.014295836  0.60885003 5.439761e-01
B -0.11982833  0.13529287   0.25512120  0.007732271  0.52662792 5.905931e-01
E  0.01897934  0.10434934   0.08536999  0.061664340  0.18136849 8.341279e-01
I -0.04720963  0.03996397   0.08717360 -0.003622829  0.06168476 9.401792e-01
     adj.P.Val
A 7.727200e-22
D 3.758388e-01
F 3.758388e-01
G 4.992756e-01
H 6.361019e-01
J 7.338379e-01
C 7.382414e-01
B 7.382414e-01
E 9.268088e-01
I 9.401792e-01
> topTable(fit2,coef=3,resort.by="logFC")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="p")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,sort="logFC",resort.by="t")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="B")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,lfc=1)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p=0.2)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p=0.2,lfc=0.5)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p=0.2,lfc=0.5,sort="none")
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> 
> designlist <- list(Null=matrix(1,6,1),Two=design,Three=cbind(1,c(0,0,1,1,0,0),c(0,0,0,0,1,1)))
> out <- selectModel(M,designlist)
> table(out$pref)

 Null   Two Three 
    5     3     2 
> 
> ### marray object
> 
> #suppressMessages(suppressWarnings(gotmarray <- require(marray,quietly=TRUE)))
> #if(gotmarray) {
> #	data(swirl)
> #	snorm = maNorm(swirl)
> #	fit <- lmFit(snorm, design = c(1,-1,-1,1))
> #	fit <- eBayes(fit)
> #	topTable(fit,resort.by="AveExpr")
> #}
> 
> ### duplicateCorrelation
> 
> cor.out <- duplicateCorrelation(M)
> cor.out$consensus.correlation
[1] -0.09290714
> cor.out$atanh.correlations
[1] -0.4419130  0.4088967 -0.1964978 -0.6093769  0.3730118
> 
> ### gls.series
> 
> fit <- gls.series(M,design,correlation=cor.out$cor)
> fit$coefficients
     First3Arrays Last3Arrays
[1,]   0.82809594  0.09777201
[2,]  -0.08845425  0.27111909
[3,]  -0.07175836 -0.11287397
[4,]   0.06955100  0.06852328
[5,]   0.08348330  0.05535668
> fit$stdev.unscaled
     First3Arrays Last3Arrays
[1,]    0.3888215   0.3888215
[2,]    0.3888215   0.3888215
[3,]    0.3888215   0.3888215
[4,]    0.3888215   0.3888215
[5,]    0.3888215   0.3888215
> fit$sigma
[1] 0.7630059 0.2152728 0.3350370 0.3227781 0.3405473
> fit$df.residual
[1] 10 10 10 10 10
> 
> ### mrlm
> 
> fit <- mrlm(M,design)
Warning message:
In rlm.default(x = X, y = y, weights = w, ...) :
  'rlm' failed to converge in 20 steps
> fit$coef
  First3Arrays Last3Arrays
A   1.75138894  0.06025114
B  -0.11982833  0.10322039
C  -0.09302502  0.15095948
D  -0.05454069  0.33700045
E   0.07927938  0.10434934
F  -0.16249607 -0.34010852
G   0.30852468 -0.06873462
H  -0.16942269  0.24392984
I  -0.04720963  0.03996397
J   0.21417623 -0.05679272
> fit$stdev.unscaled
  First3Arrays Last3Arrays
A    0.5933418   0.5773503
B    0.5773503   0.6096497
C    0.6017444   0.5773503
D    0.5773503   0.6266021
E    0.6307703   0.5773503
F    0.5773503   0.5846707
G    0.5773503   0.5773503
H    0.5773503   0.6544564
I    0.5773503   0.5773503
J    0.5773503   0.6689776
> fit$sigma
 [1] 0.2894294 0.2679396 0.2090236 0.1461395 0.2309018 0.2827476 0.2285945
 [8] 0.2267556 0.3537469 0.2172409
> fit$df.residual
 [1] 4 4 4 4 4 4 4 4 4 4
> 
> # Similar to Mette Langaas 19 May 2004
> set.seed(123)
> narrays <- 9
> ngenes <- 5
> mu <- 0
> alpha <- 2
> beta <- -2
> epsilon <- matrix(rnorm(narrays*ngenes,0,1),ncol=narrays)
> X <- cbind(rep(1,9),c(0,0,0,1,1,1,0,0,0),c(0,0,0,0,0,0,1,1,1))
> dimnames(X) <- list(1:9,c("mu","alpha","beta"))
> yvec <- mu*X[,1]+alpha*X[,2]+beta*X[,3]
> ymat <- matrix(rep(yvec,ngenes),ncol=narrays,byrow=T)+epsilon
> ymat[5,1:2] <- NA
> fit <- lmFit(ymat,design=X)
> test.contr <- cbind(c(0,1,-1),c(1,1,0),c(1,0,1))
> dimnames(test.contr) <- list(c("mu","alpha","beta"),c("alpha-beta","mu+alpha","mu+beta"))
> fit2 <- contrasts.fit(fit,contrasts=test.contr)
> eBayes(fit2)
An object of class "MArrayLM"
$coefficients
     alpha-beta mu+alpha   mu+beta
[1,]   3.537333 1.677465 -1.859868
[2,]   4.355578 2.372554 -1.983024
[3,]   3.197645 1.053584 -2.144061
[4,]   2.697734 1.611443 -1.086291
[5,]   3.502304 2.051995 -1.450309

$stdev.unscaled
     alpha-beta  mu+alpha   mu+beta
[1,]  0.8164966 0.5773503 0.5773503
[2,]  0.8164966 0.5773503 0.5773503
[3,]  0.8164966 0.5773503 0.5773503
[4,]  0.8164966 0.5773503 0.5773503
[5,]  1.1547005 0.8368633 0.8368633

$sigma
[1] 1.3425032 0.4647155 1.1993444 0.9428569 0.9421509

$df.residual
[1] 6 6 6 6 4

$cov.coefficients
           alpha-beta     mu+alpha       mu+beta
alpha-beta  0.6666667 3.333333e-01 -3.333333e-01
mu+alpha    0.3333333 3.333333e-01  5.551115e-17
mu+beta    -0.3333333 5.551115e-17  3.333333e-01

$rank
[1] 3

$Amean
[1]  0.2034961  0.1954604 -0.2863347  0.1188659  0.1784593

$method
[1] "ls"

$design
  mu alpha beta
1  1     0    0
2  1     0    0
3  1     0    0
4  1     1    0
5  1     1    0
6  1     1    0
7  1     0    1
8  1     0    1
9  1     0    1

$contrasts
      alpha-beta mu+alpha mu+beta
mu             0        1       1
alpha          1        1       0
beta          -1        0       1

$df.prior
[1] 9.306153

$s2.prior
[1] 0.923179

$var.prior
[1] 17.33142 17.33142 12.26855

$proportion
[1] 0.01

$s2.post
[1] 1.2677996 0.6459499 1.1251558 0.9097727 0.9124980

$t
     alpha-beta mu+alpha   mu+beta
[1,]   3.847656 2.580411 -2.860996
[2,]   6.637308 5.113018 -4.273553
[3,]   3.692066 1.720376 -3.500994
[4,]   3.464003 2.926234 -1.972606
[5,]   3.175181 2.566881 -1.814221

$df.total
[1] 15.30615 15.30615 15.30615 15.30615 13.30615

$p.value
       alpha-beta     mu+alpha      mu+beta
[1,] 1.529450e-03 0.0206493481 0.0117123495
[2,] 7.144893e-06 0.0001195844 0.0006385076
[3,] 2.109270e-03 0.1055117477 0.0031325769
[4,] 3.381970e-03 0.0102514264 0.0668844448
[5,] 7.124839e-03 0.0230888584 0.0922478630

$lods
     alpha-beta  mu+alpha    mu+beta
[1,]  -1.013417 -3.702133 -3.0332393
[2,]   3.981496  1.283349 -0.2615911
[3,]  -1.315036 -5.168621 -1.7864101
[4,]  -1.757103 -3.043209 -4.6191869
[5,]  -2.257358 -3.478267 -4.5683738

$F
[1]  7.421911 22.203107  7.608327  6.227010  5.060579

$F.p.value
[1] 5.581800e-03 2.988923e-05 5.080726e-03 1.050148e-02 2.320274e-02

> 
> ### uniquegenelist
> 
> uniquegenelist(letters[1:8],ndups=2)
[1] "a" "c" "e" "g"
> uniquegenelist(letters[1:8],ndups=2,spacing=2)
[1] "a" "b" "e" "f"
> 
> ### classifyTests
> 
> tstat <- matrix(c(0,5,0, 0,2.5,0, -2,-2,2, 1,1,1), 4, 3, byrow=TRUE)
> classifyTestsF(tstat)
TestResults matrix
     [,1] [,2] [,3]
[1,]    0    1    0
[2,]    0    0    0
[3,]   -1   -1    1
[4,]    0    0    0
> classifyTestsF(tstat,fstat.only=TRUE)
[1] 8.333333 2.083333 4.000000 1.000000
attr(,"df1")
[1] 3
attr(,"df2")
[1] Inf
> limma:::.classifyTestsP(tstat)
TestResults matrix
     [,1] [,2] [,3]
[1,]    0    1    0
[2,]    0    1    0
[3,]    0    0    0
[4,]    0    0    0
> 
> ### avereps
> 
> x <- matrix(rnorm(8*3),8,3)
> colnames(x) <- c("S1","S2","S3")
> rownames(x) <- c("b","a","a","c","c","b","b","b")
> avereps(x)
          S1         S2         S3
b -0.2353018  0.5220094  0.2302895
a -0.4347701  0.6453498 -0.6758914
c  0.3482980 -0.4820695 -0.3841313
> 
> ### roast
> 
> y <- matrix(rnorm(100*4),100,4)
> sigma <- sqrt(2/rchisq(100,df=7))
> y <- y*sigma
> design <- cbind(Intercept=1,Group=c(0,0,1,1))
> iset1 <- 1:5
> y[iset1,3:4] <- y[iset1,3:4]+3
> iset2 <- 6:10
> roast(y=y,iset1,design,contrast=2)
         Active.Prop     P.Value
Down               0 0.996498249
Up                 1 0.004002001
UpOrDown           1 0.008000000
Mixed              1 0.008000000
> roast(y=y,iset1,design,contrast=2,array.weights=c(0.5,1,0.5,1))
         Active.Prop    P.Value
Down               0 0.99899950
Up                 1 0.00150075
UpOrDown           1 0.00300000
Mixed              1 0.00300000
> w <- matrix(runif(100*4),100,4)
> roast(y=y,iset1,design,contrast=2,weights=w)
         Active.Prop   P.Value
Down               0 0.9994997
Up                 1 0.0010005
UpOrDown           1 0.0020000
Mixed              1 0.0020000
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,gene.weights=runif(100))
     NGenes PropDown PropUp Direction PValue   FDR PValue.Mixed FDR.Mixed
set1      5        0      1        Up  0.008 0.015        0.008     0.015
set2      5        0      0        Up  0.959 0.959        0.687     0.687
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,array.weights=c(0.5,1,0.5,1))
     NGenes PropDown PropUp Direction PValue   FDR PValue.Mixed FDR.Mixed
set1      5        0      1        Up  0.004 0.007        0.004     0.007
set2      5        0      0        Up  0.679 0.679        0.658     0.658
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w)
     NGenes PropDown PropUp Direction PValue   FDR PValue.Mixed FDR.Mixed
set1      5      0.0      1        Up  0.003 0.005        0.003     0.005
set2      5      0.2      0      Down  0.950 0.950        0.250     0.250
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes PropDown PropUp Direction PValue   FDR PValue.Mixed FDR.Mixed
set1      5        0      1        Up  0.001 0.001        0.001     0.001
set2      5        0      0      Down  0.791 0.791        0.146     0.146
> fry(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes Direction       PValue         FDR PValue.Mixed    FDR.Mixed
set1      5        Up 0.0007432594 0.001486519 1.820548e-05 3.641096e-05
set2      5      Down 0.8208140511 0.820814051 2.211837e-01 2.211837e-01
> rownames(y) <- paste0("Gene",1:100)
> iset1A <- rownames(y)[1:5]
> fry(y=y,index=iset1A,design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes Direction       PValue PValue.Mixed
set1      5        Up 0.0007432594 1.820548e-05
> 
> ### camera
> 
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1),allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction      PValue
set1      5  -0.2481655        Up 0.001050253
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction       PValue        FDR
set1      5  -0.2481655        Up 0.0009047749 0.00180955
set2      5   0.1719094      Down 0.9068364378 0.90683644
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1))
     NGenes Direction       PValue
set1      5        Up 1.105329e-10
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2)
     NGenes Direction       PValue          FDR
set1      5        Up 7.334400e-12 1.466880e-11
set2      5      Down 8.677115e-01 8.677115e-01
> camera(y=y,iset1A,design,contrast=2)
     NGenes Direction     PValue
set1      5        Up 7.3344e-12
> 
> ### with EList arg
> 
> y <- new("EList",list(E=y))
> roast(y=y,iset1,design,contrast=2)
         Active.Prop     P.Value
Down               0 0.997498749
Up                 1 0.003001501
UpOrDown           1 0.006000000
Mixed              1 0.006000000
> camera(y=y,iset1,design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction       PValue
set1      5  -0.2481655        Up 0.0009047749
> camera(y=y,iset1,design,contrast=2)
     NGenes Direction     PValue
set1      5        Up 7.3344e-12
> 
> ### eBayes with trend
> 
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
           logFC     AveExpr         t      P.Value  adj.P.Val          B
Gene2   3.729512  1.73488969  4.865697 0.0004854886 0.02902331  0.1596831
Gene3   3.488703  1.03931081  4.754954 0.0005804663 0.02902331 -0.0144071
Gene4   2.696676  1.74060725  3.356468 0.0063282637 0.21094212 -2.3434702
Gene1   2.391846  1.72305203  3.107124 0.0098781268 0.24695317 -2.7738874
Gene33 -1.492317 -0.07525287 -2.783817 0.0176475742 0.29965463 -3.3300835
Gene5   2.387967  1.63066783  2.773444 0.0179792778 0.29965463 -3.3478204
Gene80 -1.839760 -0.32802306 -2.503584 0.0291489863 0.37972679 -3.8049642
Gene39  1.366141 -0.27360750  2.451133 0.0320042242 0.37972679 -3.8925860
Gene95 -1.907074  1.26297763 -2.414217 0.0341754107 0.37972679 -3.9539571
Gene50  1.034777  0.01608433  2.054690 0.0642289403 0.59978803 -4.5350317
> fit$df.prior
[1] 9.098442
> fit$s2.prior
    Gene1     Gene2     Gene3     Gene4     Gene5     Gene6     Gene7     Gene8 
0.6901845 0.6977354 0.3860494 0.7014122 0.6341068 0.2926337 0.3077620 0.3058098 
    Gene9    Gene10    Gene11    Gene12    Gene13    Gene14    Gene15    Gene16 
0.2985145 0.2832520 0.3232434 0.3279710 0.2816081 0.2943502 0.3127994 0.2894802 
   Gene17    Gene18    Gene19    Gene20    Gene21    Gene22    Gene23    Gene24 
0.2812758 0.2840051 0.2839124 0.2954261 0.2838592 0.2812704 0.3157029 0.2844541 
   Gene25    Gene26    Gene27    Gene28    Gene29    Gene30    Gene31    Gene32 
0.4778832 0.2818242 0.2930360 0.2940957 0.2941862 0.3234399 0.3164779 0.2853510 
   Gene33    Gene34    Gene35    Gene36    Gene37    Gene38    Gene39    Gene40 
0.2988244 0.3450090 0.3048596 0.3089086 0.3104534 0.4551549 0.3220008 0.2813286 
   Gene41    Gene42    Gene43    Gene44    Gene45    Gene46    Gene47    Gene48 
0.2826027 0.2822504 0.2823330 0.3170673 0.3146173 0.3146793 0.2916540 0.2975003 
   Gene49    Gene50    Gene51    Gene52    Gene53    Gene54    Gene55    Gene56 
0.3538946 0.2907240 0.3199596 0.2816641 0.2814293 0.2996822 0.2812885 0.2896157 
   Gene57    Gene58    Gene59    Gene60    Gene61    Gene62    Gene63    Gene64 
0.2955317 0.2815907 0.2919420 0.2849675 0.3540805 0.3491713 0.2975019 0.2939325 
   Gene65    Gene66    Gene67    Gene68    Gene69    Gene70    Gene71    Gene72 
0.2986943 0.3265466 0.3402343 0.3394927 0.2813283 0.2814440 0.3089669 0.3030850 
   Gene73    Gene74    Gene75    Gene76    Gene77    Gene78    Gene79    Gene80 
0.2859286 0.2813216 0.3475231 0.3334419 0.2949550 0.3108702 0.2959688 0.3295294 
   Gene81    Gene82    Gene83    Gene84    Gene85    Gene86    Gene87    Gene88 
0.3413700 0.2946268 0.3029565 0.2920284 0.2926205 0.2818046 0.3425116 0.2882936 
   Gene89    Gene90    Gene91    Gene92    Gene93    Gene94    Gene95    Gene96 
0.2945459 0.3077919 0.2892134 0.2823787 0.3048049 0.2961408 0.4590012 0.2812784 
   Gene97    Gene98    Gene99   Gene100 
0.2846345 0.2819651 0.3137551 0.2856081 
> summary(fit$s2.post)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.2335  0.2603  0.2997  0.3375  0.3655  0.7812 
> 
> y$E[1,1] <- NA
> y$E[1,3] <- NA
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
           logFC     AveExpr         t      P.Value  adj.P.Val          B
Gene3   3.488703  1.03931081  4.604490 0.0007644061 0.07644061 -0.2333915
Gene2   3.729512  1.73488969  4.158038 0.0016033158 0.08016579 -0.9438583
Gene4   2.696676  1.74060725  2.898102 0.0145292666 0.44537707 -3.0530813
Gene33 -1.492317 -0.07525287 -2.784004 0.0178150826 0.44537707 -3.2456324
Gene5   2.387967  1.63066783  2.495395 0.0297982959 0.46902627 -3.7272957
Gene80 -1.839760 -0.32802306 -2.491115 0.0300256116 0.46902627 -3.7343584
Gene39  1.366141 -0.27360750  2.440729 0.0328318388 0.46902627 -3.8172597
Gene1   2.638272  1.47993643  2.227507 0.0530016060 0.58890673 -3.9537576
Gene95 -1.907074  1.26297763 -2.288870 0.0429197808 0.53649726 -4.0642439
Gene50  1.034777  0.01608433  2.063663 0.0635275235 0.60439978 -4.4204731
> fit$df.residual[1]
[1] 0
> fit$df.prior
[1] 8.971891
> fit$s2.prior
  [1] 0.7014084 0.9646561 0.4276287 0.9716476 0.8458852 0.2910492 0.3097052
  [8] 0.3074225 0.2985517 0.2786374 0.3267121 0.3316013 0.2766404 0.2932679
 [15] 0.3154347 0.2869186 0.2761395 0.2799884 0.2795119 0.2946468 0.2794412
 [22] 0.2761282 0.3186442 0.2806092 0.4596465 0.2767847 0.2924541 0.2939204
 [29] 0.2930568 0.3269177 0.3194905 0.2814293 0.2989389 0.3483845 0.3062977
 [36] 0.3110287 0.3127934 0.4418052 0.3254067 0.2761732 0.2780422 0.2773311
 [43] 0.2776653 0.3201314 0.3174515 0.3175199 0.2897731 0.2972785 0.3567262
 [50] 0.2885556 0.3232426 0.2767207 0.2762915 0.3000062 0.2761306 0.2870975
 [57] 0.2947817 0.2766152 0.2901489 0.2813183 0.3568982 0.3724440 0.2972804
 [64] 0.2927300 0.2987764 0.3301406 0.3437962 0.3430762 0.2761729 0.2763094
 [71] 0.3110958 0.3041715 0.2822004 0.2761654 0.3507694 0.3371214 0.2940441
 [78] 0.3132660 0.2953388 0.3331880 0.3448949 0.2946558 0.3040162 0.2902616
 [85] 0.2910320 0.2769211 0.3459946 0.2859057 0.2935193 0.3097398 0.2865663
 [92] 0.2774968 0.3062327 0.2955576 0.5425422 0.2761214 0.2808585 0.2771484
 [99] 0.3164981 0.2817725
> summary(fit$s2.post)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.2296  0.2581  0.3003  0.3453  0.3652  0.9158 
> 
> ### voom
> 
> y <- matrix(rpois(100*4,lambda=20),100,4)
> design <- cbind(Int=1,x=c(0,0,1,1))
> v <- voom(y,design)
> names(v)
[1] "E"       "weights" "design"  "targets"
> summary(v$E)
       V1              V2              V3              V4       
 Min.   :12.25   Min.   :12.58   Min.   :12.19   Min.   :12.24  
 1st Qu.:13.13   1st Qu.:13.07   1st Qu.:13.15   1st Qu.:13.03  
 Median :13.29   Median :13.30   Median :13.30   Median :13.27  
 Mean   :13.28   Mean   :13.29   Mean   :13.29   Mean   :13.28  
 3rd Qu.:13.49   3rd Qu.:13.51   3rd Qu.:13.50   3rd Qu.:13.50  
 Max.   :14.23   Max.   :14.28   Max.   :13.97   Max.   :13.96  
> summary(v$weights)
       V1               V2               V3               V4        
 Min.   : 5.935   Min.   : 5.935   Min.   : 5.935   Min.   : 5.935  
 1st Qu.: 6.788   1st Qu.: 7.049   1st Qu.: 7.207   1st Qu.: 6.825  
 Median :11.066   Median :10.443   Median :10.606   Median :10.414  
 Mean   :10.421   Mean   :10.485   Mean   :10.571   Mean   :10.532  
 3rd Qu.:13.485   3rd Qu.:14.155   3rd Qu.:13.859   3rd Qu.:14.121  
 Max.   :15.083   Max.   :15.101   Max.   :15.095   Max.   :15.063  
> 
> ### goana
> 
> EB <- c("133746","1339","134","1340","134083","134111","134147","134187","134218","134266",
+ "134353","134359","134391","134429","134430","1345","134510","134526","134549","1346",
+ "134637","1347","134701","134728","1348","134829","134860","134864","1349","134957",
+ "135","1350","1351","135112","135114","135138","135152","135154","1352","135228",
+ "135250","135293","135295","1353","135458","1355","1356","135644","135656","1357",
+ "1358","135892","1359","135924","135935","135941","135946","135948","136","1360",
+ "136051","1361","1362","136227","136242","136259","1363","136306","136319","136332",
+ "136371","1364","1365","136541","1366","136647","1368","136853","1369","136991",
+ "1370","137075","1371","137209","1373","137362","1374","137492","1375","1376",
+ "137682","137695","137735","1378","137814","137868","137872","137886","137902","137964")
> go <- goana(fit,FDR=0.8,geneid=EB)
> topGO(go,n=10,truncate.term=30)
                                     Term Ont  N Up Down        P.Up
GO:0070062          extracellular exosome  CC  8  0    4 1.000000000
GO:0043230        extracellular organelle  CC  8  0    4 1.000000000
GO:1903561          extracellular vesicle  CC  8  0    4 1.000000000
GO:0072359 circulatory system developm...  BP  2  0    2 1.000000000
GO:0007507              heart development  BP  2  0    2 1.000000000
GO:0032501 multicellular organismal pr...  BP 31  6    7 0.796992878
GO:0098609             cell-cell adhesion  BP  5  4    0 0.009503355
GO:0097190    apoptotic signaling pathway  BP  3  3    0 0.010952381
GO:0031252              cell leading edge  CC  3  3    0 0.010952381
GO:0006897                    endocytosis  BP  3  3    0 0.010952381
                P.Down
GO:0070062 0.003047199
GO:0043230 0.003047199
GO:1903561 0.003047199
GO:0072359 0.009090909
GO:0007507 0.009090909
GO:0032501 0.009111120
GO:0098609 1.000000000
GO:0097190 1.000000000
GO:0031252 1.000000000
GO:0006897 1.000000000
> topGO(go,n=10,truncate.term=30,sort="down")
                                     Term Ont  N Up Down      P.Up      P.Down
GO:0070062          extracellular exosome  CC  8  0    4 1.0000000 0.003047199
GO:0043230        extracellular organelle  CC  8  0    4 1.0000000 0.003047199
GO:1903561          extracellular vesicle  CC  8  0    4 1.0000000 0.003047199
GO:0072359 circulatory system developm...  BP  2  0    2 1.0000000 0.009090909
GO:0007507              heart development  BP  2  0    2 1.0000000 0.009090909
GO:0032501 multicellular organismal pr...  BP 31  6    7 0.7969929 0.009111120
GO:0032502          developmental process  BP 25  4    6 0.8946593 0.014492712
GO:0031982                        vesicle  CC 18  1    5 0.9946677 0.015552466
GO:0009887     animal organ morphogenesis  BP  3  0    2 1.0000000 0.025788497
GO:0055082 cellular chemical homeostas...  BP  3  1    2 0.5476190 0.025788497
> 
> proc.time()
   user  system elapsed 
   2.01    0.18    2.21 

R version 3.5.2 (2018-12-20) -- "Eggshell Igloo"
Copyright (C) 2018 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(limma)
> 
> set.seed(0); u <- runif(100)
> 
> ### strsplit2
> 
> x <- c("ab;cd;efg","abc;def","z","")
> strsplit2(x,split=";")
     [,1]  [,2]  [,3] 
[1,] "ab"  "cd"  "efg"
[2,] "abc" "def" ""   
[3,] "z"   ""    ""   
[4,] ""    ""    ""   
> 
> ### removeext
> 
> removeExt(c("slide1.spot","slide.2.spot"))
[1] "slide1"  "slide.2"
> removeExt(c("slide1.spot","slide"))
[1] "slide1.spot" "slide"      
> 
> ### printorder
> 
> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6),ndups=2,start="topright",npins=4)
$printorder
  [1]   6   5   4   3   2   1  12  11  10   9   8   7  18  17  16  15  14  13
 [19]  24  23  22  21  20  19  30  29  28  27  26  25  36  35  34  33  32  31
 [37]  42  41  40  39  38  37  48  47  46  45  44  43   6   5   4   3   2   1
 [55]  12  11  10   9   8   7  18  17  16  15  14  13  24  23  22  21  20  19
 [73]  30  29  28  27  26  25  36  35  34  33  32  31  42  41  40  39  38  37
 [91]  48  47  46  45  44  43   6   5   4   3   2   1  12  11  10   9   8   7
[109]  18  17  16  15  14  13  24  23  22  21  20  19  30  29  28  27  26  25
[127]  36  35  34  33  32  31  42  41  40  39  38  37  48  47  46  45  44  43
[145]   6   5   4   3   2   1  12  11  10   9   8   7  18  17  16  15  14  13
[163]  24  23  22  21  20  19  30  29  28  27  26  25  36  35  34  33  32  31
[181]  42  41  40  39  38  37  48  47  46  45  44  43  54  53  52  51  50  49
[199]  60  59  58  57  56  55  66  65  64  63  62  61  72  71  70  69  68  67
[217]  78  77  76  75  74  73  84  83  82  81  80  79  90  89  88  87  86  85
[235]  96  95  94  93  92  91  54  53  52  51  50  49  60  59  58  57  56  55
[253]  66  65  64  63  62  61  72  71  70  69  68  67  78  77  76  75  74  73
[271]  84  83  82  81  80  79  90  89  88  87  86  85  96  95  94  93  92  91
[289]  54  53  52  51  50  49  60  59  58  57  56  55  66  65  64  63  62  61
[307]  72  71  70  69  68  67  78  77  76  75  74  73  84  83  82  81  80  79
[325]  90  89  88  87  86  85  96  95  94  93  92  91  54  53  52  51  50  49
[343]  60  59  58  57  56  55  66  65  64  63  62  61  72  71  70  69  68  67
[361]  78  77  76  75  74  73  84  83  82  81  80  79  90  89  88  87  86  85
[379]  96  95  94  93  92  91 102 101 100  99  98  97 108 107 106 105 104 103
[397] 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121
[415] 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139
[433] 102 101 100  99  98  97 108 107 106 105 104 103 114 113 112 111 110 109
[451] 120 119 118 117 116 115 126 125 124 123 122 121 132 131 130 129 128 127
[469] 138 137 136 135 134 133 144 143 142 141 140 139 102 101 100  99  98  97
[487] 108 107 106 105 104 103 114 113 112 111 110 109 120 119 118 117 116 115
[505] 126 125 124 123 122 121 132 131 130 129 128 127 138 137 136 135 134 133
[523] 144 143 142 141 140 139 102 101 100  99  98  97 108 107 106 105 104 103
[541] 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121
[559] 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139
[577] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157
[595] 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[613] 186 185 184 183 182 181 192 191 190 189 188 187 150 149 148 147 146 145
[631] 156 155 154 153 152 151 162 161 160 159 158 157 168 167 166 165 164 163
[649] 174 173 172 171 170 169 180 179 178 177 176 175 186 185 184 183 182 181
[667] 192 191 190 189 188 187 150 149 148 147 146 145 156 155 154 153 152 151
[685] 162 161 160 159 158 157 168 167 166 165 164 163 174 173 172 171 170 169
[703] 180 179 178 177 176 175 186 185 184 183 182 181 192 191 190 189 188 187
[721] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157
[739] 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[757] 186 185 184 183 182 181 192 191 190 189 188 187

$plate
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

$plate.r
  [1]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4
 [26]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  3  3
 [51]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3
 [76]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  2  2  2  2
[101]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
[126]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  1  1  1  1  1  1
[151]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[176]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  8  8  8  8  8  8  8  8
[201]  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8
[226]  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  7  7  7  7  7  7  7  7  7  7
[251]  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7
[276]  7  7  7  7  7  7  7  7  7  7  7  7  7  6  6  6  6  6  6  6  6  6  6  6  6
[301]  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6
[326]  6  6  6  6  6  6  6  6  6  6  6  5  5  5  5  5  5  5  5  5  5  5  5  5  5
[351]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
[376]  5  5  5  5  5  5  5  5  5 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[401] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[426] 12 12 12 12 12 12 12 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[451] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[476] 11 11 11 11 11 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[501] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[526] 10 10 10  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
[551]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
[576]  9 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
[601] 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 15
[626] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
[651] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 14 14 14
[676] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
[701] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 13 13 13 13 13
[726] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
[751] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13

$plate.c
  [1]  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15
 [26] 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3
 [51]  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14
 [76] 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2
[101]  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13
[126] 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1
[151]  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18
[176] 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6
[201]  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17
[226] 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5
[251]  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16
[276] 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4
[301]  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21
[326] 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9
[351]  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20
[376] 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8
[401]  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19
[426] 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7
[451] 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24
[476] 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12
[501] 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23
[526] 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11
[551] 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22
[576] 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10
[601] 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3
[626]  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15
[651] 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2
[676]  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14
[701] 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1
[726]  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13
[751] 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22

$plateposition
  [1] "p1D03" "p1D03" "p1D02" "p1D02" "p1D01" "p1D01" "p1D06" "p1D06" "p1D05"
 [10] "p1D05" "p1D04" "p1D04" "p1D09" "p1D09" "p1D08" "p1D08" "p1D07" "p1D07"
 [19] "p1D12" "p1D12" "p1D11" "p1D11" "p1D10" "p1D10" "p1D15" "p1D15" "p1D14"
 [28] "p1D14" "p1D13" "p1D13" "p1D18" "p1D18" "p1D17" "p1D17" "p1D16" "p1D16"
 [37] "p1D21" "p1D21" "p1D20" "p1D20" "p1D19" "p1D19" "p1D24" "p1D24" "p1D23"
 [46] "p1D23" "p1D22" "p1D22" "p1C03" "p1C03" "p1C02" "p1C02" "p1C01" "p1C01"
 [55] "p1C06" "p1C06" "p1C05" "p1C05" "p1C04" "p1C04" "p1C09" "p1C09" "p1C08"
 [64] "p1C08" "p1C07" "p1C07" "p1C12" "p1C12" "p1C11" "p1C11" "p1C10" "p1C10"
 [73] "p1C15" "p1C15" "p1C14" "p1C14" "p1C13" "p1C13" "p1C18" "p1C18" "p1C17"
 [82] "p1C17" "p1C16" "p1C16" "p1C21" "p1C21" "p1C20" "p1C20" "p1C19" "p1C19"
 [91] "p1C24" "p1C24" "p1C23" "p1C23" "p1C22" "p1C22" "p1B03" "p1B03" "p1B02"
[100] "p1B02" "p1B01" "p1B01" "p1B06" "p1B06" "p1B05" "p1B05" "p1B04" "p1B04"
[109] "p1B09" "p1B09" "p1B08" "p1B08" "p1B07" "p1B07" "p1B12" "p1B12" "p1B11"
[118] "p1B11" "p1B10" "p1B10" "p1B15" "p1B15" "p1B14" "p1B14" "p1B13" "p1B13"
[127] "p1B18" "p1B18" "p1B17" "p1B17" "p1B16" "p1B16" "p1B21" "p1B21" "p1B20"
[136] "p1B20" "p1B19" "p1B19" "p1B24" "p1B24" "p1B23" "p1B23" "p1B22" "p1B22"
[145] "p1A03" "p1A03" "p1A02" "p1A02" "p1A01" "p1A01" "p1A06" "p1A06" "p1A05"
[154] "p1A05" "p1A04" "p1A04" "p1A09" "p1A09" "p1A08" "p1A08" "p1A07" "p1A07"
[163] "p1A12" "p1A12" "p1A11" "p1A11" "p1A10" "p1A10" "p1A15" "p1A15" "p1A14"
[172] "p1A14" "p1A13" "p1A13" "p1A18" "p1A18" "p1A17" "p1A17" "p1A16" "p1A16"
[181] "p1A21" "p1A21" "p1A20" "p1A20" "p1A19" "p1A19" "p1A24" "p1A24" "p1A23"
[190] "p1A23" "p1A22" "p1A22" "p1H03" "p1H03" "p1H02" "p1H02" "p1H01" "p1H01"
[199] "p1H06" "p1H06" "p1H05" "p1H05" "p1H04" "p1H04" "p1H09" "p1H09" "p1H08"
[208] "p1H08" "p1H07" "p1H07" "p1H12" "p1H12" "p1H11" "p1H11" "p1H10" "p1H10"
[217] "p1H15" "p1H15" "p1H14" "p1H14" "p1H13" "p1H13" "p1H18" "p1H18" "p1H17"
[226] "p1H17" "p1H16" "p1H16" "p1H21" "p1H21" "p1H20" "p1H20" "p1H19" "p1H19"
[235] "p1H24" "p1H24" "p1H23" "p1H23" "p1H22" "p1H22" "p1G03" "p1G03" "p1G02"
[244] "p1G02" "p1G01" "p1G01" "p1G06" "p1G06" "p1G05" "p1G05" "p1G04" "p1G04"
[253] "p1G09" "p1G09" "p1G08" "p1G08" "p1G07" "p1G07" "p1G12" "p1G12" "p1G11"
[262] "p1G11" "p1G10" "p1G10" "p1G15" "p1G15" "p1G14" "p1G14" "p1G13" "p1G13"
[271] "p1G18" "p1G18" "p1G17" "p1G17" "p1G16" "p1G16" "p1G21" "p1G21" "p1G20"
[280] "p1G20" "p1G19" "p1G19" "p1G24" "p1G24" "p1G23" "p1G23" "p1G22" "p1G22"
[289] "p1F03" "p1F03" "p1F02" "p1F02" "p1F01" "p1F01" "p1F06" "p1F06" "p1F05"
[298] "p1F05" "p1F04" "p1F04" "p1F09" "p1F09" "p1F08" "p1F08" "p1F07" "p1F07"
[307] "p1F12" "p1F12" "p1F11" "p1F11" "p1F10" "p1F10" "p1F15" "p1F15" "p1F14"
[316] "p1F14" "p1F13" "p1F13" "p1F18" "p1F18" "p1F17" "p1F17" "p1F16" "p1F16"
[325] "p1F21" "p1F21" "p1F20" "p1F20" "p1F19" "p1F19" "p1F24" "p1F24" "p1F23"
[334] "p1F23" "p1F22" "p1F22" "p1E03" "p1E03" "p1E02" "p1E02" "p1E01" "p1E01"
[343] "p1E06" "p1E06" "p1E05" "p1E05" "p1E04" "p1E04" "p1E09" "p1E09" "p1E08"
[352] "p1E08" "p1E07" "p1E07" "p1E12" "p1E12" "p1E11" "p1E11" "p1E10" "p1E10"
[361] "p1E15" "p1E15" "p1E14" "p1E14" "p1E13" "p1E13" "p1E18" "p1E18" "p1E17"
[370] "p1E17" "p1E16" "p1E16" "p1E21" "p1E21" "p1E20" "p1E20" "p1E19" "p1E19"
[379] "p1E24" "p1E24" "p1E23" "p1E23" "p1E22" "p1E22" "p1L03" "p1L03" "p1L02"
[388] "p1L02" "p1L01" "p1L01" "p1L06" "p1L06" "p1L05" "p1L05" "p1L04" "p1L04"
[397] "p1L09" "p1L09" "p1L08" "p1L08" "p1L07" "p1L07" "p1L12" "p1L12" "p1L11"
[406] "p1L11" "p1L10" "p1L10" "p1L15" "p1L15" "p1L14" "p1L14" "p1L13" "p1L13"
[415] "p1L18" "p1L18" "p1L17" "p1L17" "p1L16" "p1L16" "p1L21" "p1L21" "p1L20"
[424] "p1L20" "p1L19" "p1L19" "p1L24" "p1L24" "p1L23" "p1L23" "p1L22" "p1L22"
[433] "p1K03" "p1K03" "p1K02" "p1K02" "p1K01" "p1K01" "p1K06" "p1K06" "p1K05"
[442] "p1K05" "p1K04" "p1K04" "p1K09" "p1K09" "p1K08" "p1K08" "p1K07" "p1K07"
[451] "p1K12" "p1K12" "p1K11" "p1K11" "p1K10" "p1K10" "p1K15" "p1K15" "p1K14"
[460] "p1K14" "p1K13" "p1K13" "p1K18" "p1K18" "p1K17" "p1K17" "p1K16" "p1K16"
[469] "p1K21" "p1K21" "p1K20" "p1K20" "p1K19" "p1K19" "p1K24" "p1K24" "p1K23"
[478] "p1K23" "p1K22" "p1K22" "p1J03" "p1J03" "p1J02" "p1J02" "p1J01" "p1J01"
[487] "p1J06" "p1J06" "p1J05" "p1J05" "p1J04" "p1J04" "p1J09" "p1J09" "p1J08"
[496] "p1J08" "p1J07" "p1J07" "p1J12" "p1J12" "p1J11" "p1J11" "p1J10" "p1J10"
[505] "p1J15" "p1J15" "p1J14" "p1J14" "p1J13" "p1J13" "p1J18" "p1J18" "p1J17"
[514] "p1J17" "p1J16" "p1J16" "p1J21" "p1J21" "p1J20" "p1J20" "p1J19" "p1J19"
[523] "p1J24" "p1J24" "p1J23" "p1J23" "p1J22" "p1J22" "p1I03" "p1I03" "p1I02"
[532] "p1I02" "p1I01" "p1I01" "p1I06" "p1I06" "p1I05" "p1I05" "p1I04" "p1I04"
[541] "p1I09" "p1I09" "p1I08" "p1I08" "p1I07" "p1I07" "p1I12" "p1I12" "p1I11"
[550] "p1I11" "p1I10" "p1I10" "p1I15" "p1I15" "p1I14" "p1I14" "p1I13" "p1I13"
[559] "p1I18" "p1I18" "p1I17" "p1I17" "p1I16" "p1I16" "p1I21" "p1I21" "p1I20"
[568] "p1I20" "p1I19" "p1I19" "p1I24" "p1I24" "p1I23" "p1I23" "p1I22" "p1I22"
[577] "p1P03" "p1P03" "p1P02" "p1P02" "p1P01" "p1P01" "p1P06" "p1P06" "p1P05"
[586] "p1P05" "p1P04" "p1P04" "p1P09" "p1P09" "p1P08" "p1P08" "p1P07" "p1P07"
[595] "p1P12" "p1P12" "p1P11" "p1P11" "p1P10" "p1P10" "p1P15" "p1P15" "p1P14"
[604] "p1P14" "p1P13" "p1P13" "p1P18" "p1P18" "p1P17" "p1P17" "p1P16" "p1P16"
[613] "p1P21" "p1P21" "p1P20" "p1P20" "p1P19" "p1P19" "p1P24" "p1P24" "p1P23"
[622] "p1P23" "p1P22" "p1P22" "p1O03" "p1O03" "p1O02" "p1O02" "p1O01" "p1O01"
[631] "p1O06" "p1O06" "p1O05" "p1O05" "p1O04" "p1O04" "p1O09" "p1O09" "p1O08"
[640] "p1O08" "p1O07" "p1O07" "p1O12" "p1O12" "p1O11" "p1O11" "p1O10" "p1O10"
[649] "p1O15" "p1O15" "p1O14" "p1O14" "p1O13" "p1O13" "p1O18" "p1O18" "p1O17"
[658] "p1O17" "p1O16" "p1O16" "p1O21" "p1O21" "p1O20" "p1O20" "p1O19" "p1O19"
[667] "p1O24" "p1O24" "p1O23" "p1O23" "p1O22" "p1O22" "p1N03" "p1N03" "p1N02"
[676] "p1N02" "p1N01" "p1N01" "p1N06" "p1N06" "p1N05" "p1N05" "p1N04" "p1N04"
[685] "p1N09" "p1N09" "p1N08" "p1N08" "p1N07" "p1N07" "p1N12" "p1N12" "p1N11"
[694] "p1N11" "p1N10" "p1N10" "p1N15" "p1N15" "p1N14" "p1N14" "p1N13" "p1N13"
[703] "p1N18" "p1N18" "p1N17" "p1N17" "p1N16" "p1N16" "p1N21" "p1N21" "p1N20"
[712] "p1N20" "p1N19" "p1N19" "p1N24" "p1N24" "p1N23" "p1N23" "p1N22" "p1N22"
[721] "p1M03" "p1M03" "p1M02" "p1M02" "p1M01" "p1M01" "p1M06" "p1M06" "p1M05"
[730] "p1M05" "p1M04" "p1M04" "p1M09" "p1M09" "p1M08" "p1M08" "p1M07" "p1M07"
[739] "p1M12" "p1M12" "p1M11" "p1M11" "p1M10" "p1M10" "p1M15" "p1M15" "p1M14"
[748] "p1M14" "p1M13" "p1M13" "p1M18" "p1M18" "p1M17" "p1M17" "p1M16" "p1M16"
[757] "p1M21" "p1M21" "p1M20" "p1M20" "p1M19" "p1M19" "p1M24" "p1M24" "p1M23"
[766] "p1M23" "p1M22" "p1M22"

> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6))
$printorder
  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
 [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2
 [51]  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
 [76] 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4
[101]  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
[126] 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6
[151]  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
[176] 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8
[201]  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
[226] 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10
[251] 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
[276] 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12
[301] 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
[326] 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14
[351] 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
[376] 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16
[401] 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
[426] 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18
[451] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
[476] 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
[501] 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
[526] 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22
[551] 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
[576] 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
[601] 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1
[626]  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
[651] 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3
[676]  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
[701] 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5
[726]  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
[751] 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

$plate
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[334] 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2
[371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[408] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1
[445] 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1
[482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[519] 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2
[556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[593] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1
[630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[667] 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2
[704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[741] 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

$plate.r
  [1]  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  4
 [26]  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3  3
 [51]  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3  3
 [76]  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2  2
[101]  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2  2
[126]  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1  1
[151]  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1  5
[176]  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  4  4  4  4  4  4  8  8
[201]  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  4  4  4  4  4  4  8  8  8
[226]  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3  3  3  3  3  3  7  7  7  7
[251]  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3  3  3  3  3  7  7  7  7  7
[276]  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2  2  2  2  6  6  6  6  6  6
[301] 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2  2  2  6  6  6  6  6  6 10
[326] 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1  1  5  5  5  5  5  5  9  9
[351]  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9
[376]  9  9  9 13 13 13 13 13 13  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12
[401] 12 12 16 16 16 16 16 16  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12
[426] 12 16 16 16 16 16 16  3  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11
[451] 15 15 15 15 15 15  3  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15
[476] 15 15 15 15 15  2  2  2  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14
[501] 14 14 14 14  2  2  2  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14
[526] 14 14 14  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13
[551] 13 13  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13
[576] 13  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16
[601]  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3
[626]  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3
[651]  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2
[676]  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2
[701]  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1
[726]  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1
[751]  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13

$plate.c
  [1]  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1
 [26]  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5
 [51]  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9
 [76] 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13
[101] 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17
[126] 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21
[151]  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1
[176]  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  2  6 10 14 18 22  2  6
[201] 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10
[226] 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14
[251] 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18
[276] 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22
[301]  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2
[326]  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6
[351] 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10
[376] 14 18 22  2  6 10 14 18 22  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15
[401] 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19
[426] 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23
[451]  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3
[476]  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7
[501] 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11
[526] 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15
[551] 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19
[576] 23  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24
[601]  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4
[626]  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8
[651] 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12
[676] 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16
[701] 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20
[726] 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24
[751]  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24

$plateposition
  [1] "p1D01" "p1D05" "p1D09" "p1D13" "p1D17" "p1D21" "p1H01" "p1H05" "p1H09"
 [10] "p1H13" "p1H17" "p1H21" "p1L01" "p1L05" "p1L09" "p1L13" "p1L17" "p1L21"
 [19] "p1P01" "p1P05" "p1P09" "p1P13" "p1P17" "p1P21" "p2D01" "p2D05" "p2D09"
 [28] "p2D13" "p2D17" "p2D21" "p2H01" "p2H05" "p2H09" "p2H13" "p2H17" "p2H21"
 [37] "p2L01" "p2L05" "p2L09" "p2L13" "p2L17" "p2L21" "p2P01" "p2P05" "p2P09"
 [46] "p2P13" "p2P17" "p2P21" "p1C01" "p1C05" "p1C09" "p1C13" "p1C17" "p1C21"
 [55] "p1G01" "p1G05" "p1G09" "p1G13" "p1G17" "p1G21" "p1K01" "p1K05" "p1K09"
 [64] "p1K13" "p1K17" "p1K21" "p1O01" "p1O05" "p1O09" "p1O13" "p1O17" "p1O21"
 [73] "p2C01" "p2C05" "p2C09" "p2C13" "p2C17" "p2C21" "p2G01" "p2G05" "p2G09"
 [82] "p2G13" "p2G17" "p2G21" "p2K01" "p2K05" "p2K09" "p2K13" "p2K17" "p2K21"
 [91] "p2O01" "p2O05" "p2O09" "p2O13" "p2O17" "p2O21" "p1B01" "p1B05" "p1B09"
[100] "p1B13" "p1B17" "p1B21" "p1F01" "p1F05" "p1F09" "p1F13" "p1F17" "p1F21"
[109] "p1J01" "p1J05" "p1J09" "p1J13" "p1J17" "p1J21" "p1N01" "p1N05" "p1N09"
[118] "p1N13" "p1N17" "p1N21" "p2B01" "p2B05" "p2B09" "p2B13" "p2B17" "p2B21"
[127] "p2F01" "p2F05" "p2F09" "p2F13" "p2F17" "p2F21" "p2J01" "p2J05" "p2J09"
[136] "p2J13" "p2J17" "p2J21" "p2N01" "p2N05" "p2N09" "p2N13" "p2N17" "p2N21"
[145] "p1A01" "p1A05" "p1A09" "p1A13" "p1A17" "p1A21" "p1E01" "p1E05" "p1E09"
[154] "p1E13" "p1E17" "p1E21" "p1I01" "p1I05" "p1I09" "p1I13" "p1I17" "p1I21"
[163] "p1M01" "p1M05" "p1M09" "p1M13" "p1M17" "p1M21" "p2A01" "p2A05" "p2A09"
[172] "p2A13" "p2A17" "p2A21" "p2E01" "p2E05" "p2E09" "p2E13" "p2E17" "p2E21"
[181] "p2I01" "p2I05" "p2I09" "p2I13" "p2I17" "p2I21" "p2M01" "p2M05" "p2M09"
[190] "p2M13" "p2M17" "p2M21" "p1D02" "p1D06" "p1D10" "p1D14" "p1D18" "p1D22"
[199] "p1H02" "p1H06" "p1H10" "p1H14" "p1H18" "p1H22" "p1L02" "p1L06" "p1L10"
[208] "p1L14" "p1L18" "p1L22" "p1P02" "p1P06" "p1P10" "p1P14" "p1P18" "p1P22"
[217] "p2D02" "p2D06" "p2D10" "p2D14" "p2D18" "p2D22" "p2H02" "p2H06" "p2H10"
[226] "p2H14" "p2H18" "p2H22" "p2L02" "p2L06" "p2L10" "p2L14" "p2L18" "p2L22"
[235] "p2P02" "p2P06" "p2P10" "p2P14" "p2P18" "p2P22" "p1C02" "p1C06" "p1C10"
[244] "p1C14" "p1C18" "p1C22" "p1G02" "p1G06" "p1G10" "p1G14" "p1G18" "p1G22"
[253] "p1K02" "p1K06" "p1K10" "p1K14" "p1K18" "p1K22" "p1O02" "p1O06" "p1O10"
[262] "p1O14" "p1O18" "p1O22" "p2C02" "p2C06" "p2C10" "p2C14" "p2C18" "p2C22"
[271] "p2G02" "p2G06" "p2G10" "p2G14" "p2G18" "p2G22" "p2K02" "p2K06" "p2K10"
[280] "p2K14" "p2K18" "p2K22" "p2O02" "p2O06" "p2O10" "p2O14" "p2O18" "p2O22"
[289] "p1B02" "p1B06" "p1B10" "p1B14" "p1B18" "p1B22" "p1F02" "p1F06" "p1F10"
[298] "p1F14" "p1F18" "p1F22" "p1J02" "p1J06" "p1J10" "p1J14" "p1J18" "p1J22"
[307] "p1N02" "p1N06" "p1N10" "p1N14" "p1N18" "p1N22" "p2B02" "p2B06" "p2B10"
[316] "p2B14" "p2B18" "p2B22" "p2F02" "p2F06" "p2F10" "p2F14" "p2F18" "p2F22"
[325] "p2J02" "p2J06" "p2J10" "p2J14" "p2J18" "p2J22" "p2N02" "p2N06" "p2N10"
[334] "p2N14" "p2N18" "p2N22" "p1A02" "p1A06" "p1A10" "p1A14" "p1A18" "p1A22"
[343] "p1E02" "p1E06" "p1E10" "p1E14" "p1E18" "p1E22" "p1I02" "p1I06" "p1I10"
[352] "p1I14" "p1I18" "p1I22" "p1M02" "p1M06" "p1M10" "p1M14" "p1M18" "p1M22"
[361] "p2A02" "p2A06" "p2A10" "p2A14" "p2A18" "p2A22" "p2E02" "p2E06" "p2E10"
[370] "p2E14" "p2E18" "p2E22" "p2I02" "p2I06" "p2I10" "p2I14" "p2I18" "p2I22"
[379] "p2M02" "p2M06" "p2M10" "p2M14" "p2M18" "p2M22" "p1D03" "p1D07" "p1D11"
[388] "p1D15" "p1D19" "p1D23" "p1H03" "p1H07" "p1H11" "p1H15" "p1H19" "p1H23"
[397] "p1L03" "p1L07" "p1L11" "p1L15" "p1L19" "p1L23" "p1P03" "p1P07" "p1P11"
[406] "p1P15" "p1P19" "p1P23" "p2D03" "p2D07" "p2D11" "p2D15" "p2D19" "p2D23"
[415] "p2H03" "p2H07" "p2H11" "p2H15" "p2H19" "p2H23" "p2L03" "p2L07" "p2L11"
[424] "p2L15" "p2L19" "p2L23" "p2P03" "p2P07" "p2P11" "p2P15" "p2P19" "p2P23"
[433] "p1C03" "p1C07" "p1C11" "p1C15" "p1C19" "p1C23" "p1G03" "p1G07" "p1G11"
[442] "p1G15" "p1G19" "p1G23" "p1K03" "p1K07" "p1K11" "p1K15" "p1K19" "p1K23"
[451] "p1O03" "p1O07" "p1O11" "p1O15" "p1O19" "p1O23" "p2C03" "p2C07" "p2C11"
[460] "p2C15" "p2C19" "p2C23" "p2G03" "p2G07" "p2G11" "p2G15" "p2G19" "p2G23"
[469] "p2K03" "p2K07" "p2K11" "p2K15" "p2K19" "p2K23" "p2O03" "p2O07" "p2O11"
[478] "p2O15" "p2O19" "p2O23" "p1B03" "p1B07" "p1B11" "p1B15" "p1B19" "p1B23"
[487] "p1F03" "p1F07" "p1F11" "p1F15" "p1F19" "p1F23" "p1J03" "p1J07" "p1J11"
[496] "p1J15" "p1J19" "p1J23" "p1N03" "p1N07" "p1N11" "p1N15" "p1N19" "p1N23"
[505] "p2B03" "p2B07" "p2B11" "p2B15" "p2B19" "p2B23" "p2F03" "p2F07" "p2F11"
[514] "p2F15" "p2F19" "p2F23" "p2J03" "p2J07" "p2J11" "p2J15" "p2J19" "p2J23"
[523] "p2N03" "p2N07" "p2N11" "p2N15" "p2N19" "p2N23" "p1A03" "p1A07" "p1A11"
[532] "p1A15" "p1A19" "p1A23" "p1E03" "p1E07" "p1E11" "p1E15" "p1E19" "p1E23"
[541] "p1I03" "p1I07" "p1I11" "p1I15" "p1I19" "p1I23" "p1M03" "p1M07" "p1M11"
[550] "p1M15" "p1M19" "p1M23" "p2A03" "p2A07" "p2A11" "p2A15" "p2A19" "p2A23"
[559] "p2E03" "p2E07" "p2E11" "p2E15" "p2E19" "p2E23" "p2I03" "p2I07" "p2I11"
[568] "p2I15" "p2I19" "p2I23" "p2M03" "p2M07" "p2M11" "p2M15" "p2M19" "p2M23"
[577] "p1D04" "p1D08" "p1D12" "p1D16" "p1D20" "p1D24" "p1H04" "p1H08" "p1H12"
[586] "p1H16" "p1H20" "p1H24" "p1L04" "p1L08" "p1L12" "p1L16" "p1L20" "p1L24"
[595] "p1P04" "p1P08" "p1P12" "p1P16" "p1P20" "p1P24" "p2D04" "p2D08" "p2D12"
[604] "p2D16" "p2D20" "p2D24" "p2H04" "p2H08" "p2H12" "p2H16" "p2H20" "p2H24"
[613] "p2L04" "p2L08" "p2L12" "p2L16" "p2L20" "p2L24" "p2P04" "p2P08" "p2P12"
[622] "p2P16" "p2P20" "p2P24" "p1C04" "p1C08" "p1C12" "p1C16" "p1C20" "p1C24"
[631] "p1G04" "p1G08" "p1G12" "p1G16" "p1G20" "p1G24" "p1K04" "p1K08" "p1K12"
[640] "p1K16" "p1K20" "p1K24" "p1O04" "p1O08" "p1O12" "p1O16" "p1O20" "p1O24"
[649] "p2C04" "p2C08" "p2C12" "p2C16" "p2C20" "p2C24" "p2G04" "p2G08" "p2G12"
[658] "p2G16" "p2G20" "p2G24" "p2K04" "p2K08" "p2K12" "p2K16" "p2K20" "p2K24"
[667] "p2O04" "p2O08" "p2O12" "p2O16" "p2O20" "p2O24" "p1B04" "p1B08" "p1B12"
[676] "p1B16" "p1B20" "p1B24" "p1F04" "p1F08" "p1F12" "p1F16" "p1F20" "p1F24"
[685] "p1J04" "p1J08" "p1J12" "p1J16" "p1J20" "p1J24" "p1N04" "p1N08" "p1N12"
[694] "p1N16" "p1N20" "p1N24" "p2B04" "p2B08" "p2B12" "p2B16" "p2B20" "p2B24"
[703] "p2F04" "p2F08" "p2F12" "p2F16" "p2F20" "p2F24" "p2J04" "p2J08" "p2J12"
[712] "p2J16" "p2J20" "p2J24" "p2N04" "p2N08" "p2N12" "p2N16" "p2N20" "p2N24"
[721] "p1A04" "p1A08" "p1A12" "p1A16" "p1A20" "p1A24" "p1E04" "p1E08" "p1E12"
[730] "p1E16" "p1E20" "p1E24" "p1I04" "p1I08" "p1I12" "p1I16" "p1I20" "p1I24"
[739] "p1M04" "p1M08" "p1M12" "p1M16" "p1M20" "p1M24" "p2A04" "p2A08" "p2A12"
[748] "p2A16" "p2A20" "p2A24" "p2E04" "p2E08" "p2E12" "p2E16" "p2E20" "p2E24"
[757] "p2I04" "p2I08" "p2I12" "p2I16" "p2I20" "p2I24" "p2M04" "p2M08" "p2M12"
[766] "p2M16" "p2M20" "p2M24"

> 
> ### merge.rglist
> 
> R <- G <- matrix(11:14,4,2)
> rownames(R) <- rownames(G) <- c("a","a","b","c")
> RG1 <- new("RGList",list(R=R,G=G))
> R <- G <- matrix(21:24,4,2)
> rownames(R) <- rownames(G) <- c("b","a","a","c")
> RG2 <- new("RGList",list(R=R,G=G))
> merge(RG1,RG2)
An object of class "RGList"
$R
  [,1] [,2] [,3] [,4]
a   11   11   22   22
a   12   12   23   23
b   13   13   21   21
c   14   14   24   24

$G
  [,1] [,2] [,3] [,4]
a   11   11   22   22
a   12   12   23   23
b   13   13   21   21
c   14   14   24   24

> merge(RG2,RG1)
An object of class "RGList"
$R
  [,1] [,2] [,3] [,4]
b   21   21   13   13
a   22   22   11   11
a   23   23   12   12
c   24   24   14   14

$G
  [,1] [,2] [,3] [,4]
b   21   21   13   13
a   22   22   11   11
a   23   23   12   12
c   24   24   14   14

> 
> ### background correction
> 
> RG <- new("RGList", list(R=c(1,2,3,4),G=c(1,2,3,4),Rb=c(2,2,2,2),Gb=c(2,2,2,2)))
> backgroundCorrect(RG)
An object of class "RGList"
$R
     [,1]
[1,]   -1
[2,]    0
[3,]    1
[4,]    2

$G
     [,1]
[1,]   -1
[2,]    0
[3,]    1
[4,]    2

> backgroundCorrect(RG, method="half")
An object of class "RGList"
$R
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

$G
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

> backgroundCorrect(RG, method="minimum")
An object of class "RGList"
$R
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

$G
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

> backgroundCorrect(RG, offset=5)
An object of class "RGList"
$R
     [,1]
[1,]    4
[2,]    5
[3,]    6
[4,]    7

$G
     [,1]
[1,]    4
[2,]    5
[3,]    6
[4,]    7

> 
> ### loessFit
> 
> x <- 1:100
> y <- rnorm(100)
> out <- loessFit(y,x)
> f1 <- quantile(out$fitted)
> r1 <- quantile(out$residual)
> w <- rep(1,100)
> w[1:50] <- 0.5
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f2 <- quantile(out$fitted)
> r2 <- quantile(out$residual)
> out <- loessFit(y,x,weights=w,method="locfit")
> f3 <- quantile(out$fitted)
> r3 <- quantile(out$residual)
> out <- loessFit(y,x,weights=w,method="loess")
> f4 <- quantile(out$fitted)
> r4 <- quantile(out$residual)
> w <- rep(1,100)
> w[2*(1:50)] <- 0
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f5 <- quantile(out$fitted)
> r5 <- quantile(out$residual)
> data.frame(f1,f2,f3,f4,f5)
              f1           f2          f3          f4          f5
0%   -0.78835384 -0.687432210 -0.78957137 -0.76756060 -0.63778292
25%  -0.18340154 -0.179683572 -0.18979269 -0.16773223 -0.38064318
50%  -0.11492924 -0.114796040 -0.12087983 -0.07185314 -0.15971879
75%   0.01507921 -0.008145125 -0.01857508  0.04030634  0.07839396
100%  0.21653837  0.145106033  0.19214597  0.21417361  0.51836274
> data.frame(r1,r2,r3,r4,r5)
              r1          r2          r3           r4          r5
0%   -2.04434053 -2.05132680 -2.02404318 -2.101242874 -2.22280633
25%  -0.59321065 -0.57200209 -0.58975649 -0.577887481 -0.71037756
50%   0.05874864  0.04514326  0.08335198 -0.001769806  0.06785517
75%   0.56010750  0.55124530  0.57618740  0.561454370  0.65383830
100%  2.57936026  2.64549799  2.57549257  2.402324533  2.28648835
> 
> ### normalizeWithinArrays
> 
> RG <- new("RGList",list())
> RG$R <- matrix(rexp(100*2),100,2)
> RG$G <- matrix(rexp(100*2),100,2)
> RG$Rb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RG$Gb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="saddle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
       V1                V2                V3               V4        
 Min.   :0.01626   Min.   :0.01213   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.35497   1st Qu.:0.29133   1st Qu.:0.2745   1st Qu.:0.3953  
 Median :0.71793   Median :0.70294   Median :0.6339   Median :0.8223  
 Mean   :0.90184   Mean   :1.00122   Mean   :0.9454   Mean   :1.1324  
 3rd Qu.:1.16891   3rd Qu.:1.33139   3rd Qu.:1.4059   3rd Qu.:1.4221  
 Max.   :4.56267   Max.   :6.37947   Max.   :5.0486   Max.   :6.6295  
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="mle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
       V1                V2                V3               V4        
 Min.   :0.01701   Min.   :0.01255   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.35423   1st Qu.:0.29118   1st Qu.:0.2745   1st Qu.:0.3953  
 Median :0.71719   Median :0.70280   Median :0.6339   Median :0.8223  
 Mean   :0.90118   Mean   :1.00110   Mean   :0.9454   Mean   :1.1324  
 3rd Qu.:1.16817   3rd Qu.:1.33124   3rd Qu.:1.4059   3rd Qu.:1.4221  
 Max.   :4.56193   Max.   :6.37932   Max.   :5.0486   Max.   :6.6295  
> MA <- normalizeWithinArrays(RGb,method="loess")
> summary(MA$M)
       V1                 V2          
 Min.   :-5.88044   Min.   :-5.66985  
 1st Qu.:-1.18483   1st Qu.:-1.57014  
 Median :-0.21632   Median : 0.04823  
 Mean   : 0.03487   Mean   :-0.05481  
 3rd Qu.: 1.49669   3rd Qu.: 1.45113  
 Max.   : 7.07324   Max.   : 6.19744  
> #MA <- normalizeWithinArrays(RG[,1:2], mouse.setup, method="robustspline")
> #MA$M[1:5,]
> #MA <- normalizeWithinArrays(mouse.data, mouse.setup)
> #MA$M[1:5,]
> 
> ### normalizeBetweenArrays
> 
> MA2 <- normalizeBetweenArrays(MA,method="scale")
> MA$M[1:5,]
           [,1]       [,2]
[1,] -1.1689588  4.5558123
[2,]  0.8971363  0.3296544
[3,]  2.8247439  1.4249960
[4,] -1.8533240  0.4804851
[5,]  1.9158459 -5.5087631
> MA$A[1:5,]
            [,1]       [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200  0.2071043
[4,]  0.09281027 -1.3880965
[5,]  0.22385828 -3.0855818
> MA2 <- normalizeBetweenArrays(MA,method="quantile")
> MA$M[1:5,]
           [,1]       [,2]
[1,] -1.1689588  4.5558123
[2,]  0.8971363  0.3296544
[3,]  2.8247439  1.4249960
[4,] -1.8533240  0.4804851
[5,]  1.9158459 -5.5087631
> MA$A[1:5,]
            [,1]       [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200  0.2071043
[4,]  0.09281027 -1.3880965
[5,]  0.22385828 -3.0855818
> 
> ### unwrapdups
> 
> M <- matrix(1:12,6,2)
> unwrapdups(M,ndups=1)
     [,1] [,2]
[1,]    1    7
[2,]    2    8
[3,]    3    9
[4,]    4   10
[5,]    5   11
[6,]    6   12
> unwrapdups(M,ndups=2)
     [,1] [,2] [,3] [,4]
[1,]    1    2    7    8
[2,]    3    4    9   10
[3,]    5    6   11   12
> unwrapdups(M,ndups=3)
     [,1] [,2] [,3] [,4] [,5] [,6]
[1,]    1    2    3    7    8    9
[2,]    4    5    6   10   11   12
> unwrapdups(M,ndups=2,spacing=3)
     [,1] [,2] [,3] [,4]
[1,]    1    4    7   10
[2,]    2    5    8   11
[3,]    3    6    9   12
> 
> ### trigammaInverse
> 
> trigammaInverse(c(1e-6,NA,5,1e6))
[1] 1.000000e+06           NA 4.961687e-01 1.000001e-03
> 
> ### lmFit, eBayes, topTable
> 
> M <- matrix(rnorm(10*6,sd=0.3),10,6)
> rownames(M) <- LETTERS[1:10]
> M[1,1:3] <- M[1,1:3] + 2
> design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
> contrast.matrix <- cbind(First3=c(1,0),Last3=c(0,1),"Last3-First3"=c(-1,1))
> fit <- lmFit(M,design)
> fit2 <- eBayes(contrasts.fit(fit,contrasts=contrast.matrix))
> topTable(fit2)
       First3       Last3 Last3.First3      AveExpr           F      P.Value
A  1.77602021  0.06025114  -1.71576906  0.918135675 50.91471061 7.727200e-23
D -0.05454069  0.39127869   0.44581938  0.168369004  2.51638838 8.075072e-02
F -0.16249607 -0.33009728  -0.16760121 -0.246296671  2.18256779 1.127516e-01
G  0.30852468 -0.06873462  -0.37725930  0.119895035  1.61088775 1.997102e-01
H -0.16942269  0.20578118   0.37520387  0.018179245  1.14554368 3.180510e-01
J  0.21417623  0.07074940  -0.14342683  0.142462814  0.82029274 4.403027e-01
C -0.12236781  0.15095948   0.27332729  0.014295836  0.60885003 5.439761e-01
B -0.11982833  0.13529287   0.25512120  0.007732271  0.52662792 5.905931e-01
E  0.01897934  0.10434934   0.08536999  0.061664340  0.18136849 8.341279e-01
I -0.04720963  0.03996397   0.08717360 -0.003622829  0.06168476 9.401792e-01
     adj.P.Val
A 7.727200e-22
D 3.758388e-01
F 3.758388e-01
G 4.992756e-01
H 6.361019e-01
J 7.338379e-01
C 7.382414e-01
B 7.382414e-01
E 9.268088e-01
I 9.401792e-01
> topTable(fit2,coef=3,resort.by="logFC")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="p")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,sort="logFC",resort.by="t")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="B")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,lfc=1)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p=0.2)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p=0.2,lfc=0.5)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p=0.2,lfc=0.5,sort="none")
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> 
> designlist <- list(Null=matrix(1,6,1),Two=design,Three=cbind(1,c(0,0,1,1,0,0),c(0,0,0,0,1,1)))
> out <- selectModel(M,designlist)
> table(out$pref)

 Null   Two Three 
    5     3     2 
> 
> ### marray object
> 
> #suppressMessages(suppressWarnings(gotmarray <- require(marray,quietly=TRUE)))
> #if(gotmarray) {
> #	data(swirl)
> #	snorm = maNorm(swirl)
> #	fit <- lmFit(snorm, design = c(1,-1,-1,1))
> #	fit <- eBayes(fit)
> #	topTable(fit,resort.by="AveExpr")
> #}
> 
> ### duplicateCorrelation
> 
> cor.out <- duplicateCorrelation(M)
> cor.out$consensus.correlation
[1] -0.09290714
> cor.out$atanh.correlations
[1] -0.4419130  0.4088967 -0.1964978 -0.6093769  0.3730118
> 
> ### gls.series
> 
> fit <- gls.series(M,design,correlation=cor.out$cor)
> fit$coefficients
     First3Arrays Last3Arrays
[1,]   0.82809594  0.09777201
[2,]  -0.08845425  0.27111909
[3,]  -0.07175836 -0.11287397
[4,]   0.06955100  0.06852328
[5,]   0.08348330  0.05535668
> fit$stdev.unscaled
     First3Arrays Last3Arrays
[1,]    0.3888215   0.3888215
[2,]    0.3888215   0.3888215
[3,]    0.3888215   0.3888215
[4,]    0.3888215   0.3888215
[5,]    0.3888215   0.3888215
> fit$sigma
[1] 0.7630059 0.2152728 0.3350370 0.3227781 0.3405473
> fit$df.residual
[1] 10 10 10 10 10
> 
> ### mrlm
> 
> fit <- mrlm(M,design)
Warning message:
In rlm.default(x = X, y = y, weights = w, ...) :
  'rlm' failed to converge in 20 steps
> fit$coef
  First3Arrays Last3Arrays
A   1.75138894  0.06025114
B  -0.11982833  0.10322039
C  -0.09302502  0.15095948
D  -0.05454069  0.33700045
E   0.07927938  0.10434934
F  -0.16249607 -0.34010852
G   0.30852468 -0.06873462
H  -0.16942269  0.24392984
I  -0.04720963  0.03996397
J   0.21417623 -0.05679272
> fit$stdev.unscaled
  First3Arrays Last3Arrays
A    0.5933418   0.5773503
B    0.5773503   0.6096497
C    0.6017444   0.5773503
D    0.5773503   0.6266021
E    0.6307703   0.5773503
F    0.5773503   0.5846707
G    0.5773503   0.5773503
H    0.5773503   0.6544564
I    0.5773503   0.5773503
J    0.5773503   0.6689776
> fit$sigma
 [1] 0.2894294 0.2679396 0.2090236 0.1461395 0.2309018 0.2827476 0.2285945
 [8] 0.2267556 0.3537469 0.2172409
> fit$df.residual
 [1] 4 4 4 4 4 4 4 4 4 4
> 
> # Similar to Mette Langaas 19 May 2004
> set.seed(123)
> narrays <- 9
> ngenes <- 5
> mu <- 0
> alpha <- 2
> beta <- -2
> epsilon <- matrix(rnorm(narrays*ngenes,0,1),ncol=narrays)
> X <- cbind(rep(1,9),c(0,0,0,1,1,1,0,0,0),c(0,0,0,0,0,0,1,1,1))
> dimnames(X) <- list(1:9,c("mu","alpha","beta"))
> yvec <- mu*X[,1]+alpha*X[,2]+beta*X[,3]
> ymat <- matrix(rep(yvec,ngenes),ncol=narrays,byrow=T)+epsilon
> ymat[5,1:2] <- NA
> fit <- lmFit(ymat,design=X)
> test.contr <- cbind(c(0,1,-1),c(1,1,0),c(1,0,1))
> dimnames(test.contr) <- list(c("mu","alpha","beta"),c("alpha-beta","mu+alpha","mu+beta"))
> fit2 <- contrasts.fit(fit,contrasts=test.contr)
> eBayes(fit2)
An object of class "MArrayLM"
$coefficients
     alpha-beta mu+alpha   mu+beta
[1,]   3.537333 1.677465 -1.859868
[2,]   4.355578 2.372554 -1.983024
[3,]   3.197645 1.053584 -2.144061
[4,]   2.697734 1.611443 -1.086291
[5,]   3.502304 2.051995 -1.450309

$stdev.unscaled
     alpha-beta  mu+alpha   mu+beta
[1,]  0.8164966 0.5773503 0.5773503
[2,]  0.8164966 0.5773503 0.5773503
[3,]  0.8164966 0.5773503 0.5773503
[4,]  0.8164966 0.5773503 0.5773503
[5,]  1.1547005 0.8368633 0.8368633

$sigma
[1] 1.3425032 0.4647155 1.1993444 0.9428569 0.9421509

$df.residual
[1] 6 6 6 6 4

$cov.coefficients
           alpha-beta     mu+alpha       mu+beta
alpha-beta  0.6666667 3.333333e-01 -3.333333e-01
mu+alpha    0.3333333 3.333333e-01  5.551115e-17
mu+beta    -0.3333333 5.551115e-17  3.333333e-01

$rank
[1] 3

$Amean
[1]  0.2034961  0.1954604 -0.2863347  0.1188659  0.1784593

$method
[1] "ls"

$design
  mu alpha beta
1  1     0    0
2  1     0    0
3  1     0    0
4  1     1    0
5  1     1    0
6  1     1    0
7  1     0    1
8  1     0    1
9  1     0    1

$contrasts
      alpha-beta mu+alpha mu+beta
mu             0        1       1
alpha          1        1       0
beta          -1        0       1

$df.prior
[1] 9.306153

$s2.prior
[1] 0.923179

$var.prior
[1] 17.33142 17.33142 12.26855

$proportion
[1] 0.01

$s2.post
[1] 1.2677996 0.6459499 1.1251558 0.9097727 0.9124980

$t
     alpha-beta mu+alpha   mu+beta
[1,]   3.847656 2.580411 -2.860996
[2,]   6.637308 5.113018 -4.273553
[3,]   3.692066 1.720376 -3.500994
[4,]   3.464003 2.926234 -1.972606
[5,]   3.175181 2.566881 -1.814221

$df.total
[1] 15.30615 15.30615 15.30615 15.30615 13.30615

$p.value
       alpha-beta     mu+alpha      mu+beta
[1,] 1.529450e-03 0.0206493481 0.0117123495
[2,] 7.144893e-06 0.0001195844 0.0006385076
[3,] 2.109270e-03 0.1055117477 0.0031325769
[4,] 3.381970e-03 0.0102514264 0.0668844448
[5,] 7.124839e-03 0.0230888584 0.0922478630

$lods
     alpha-beta  mu+alpha    mu+beta
[1,]  -1.013417 -3.702133 -3.0332393
[2,]   3.981496  1.283349 -0.2615911
[3,]  -1.315036 -5.168621 -1.7864101
[4,]  -1.757103 -3.043209 -4.6191869
[5,]  -2.257358 -3.478267 -4.5683738

$F
[1]  7.421911 22.203107  7.608327  6.227010  5.060579

$F.p.value
[1] 5.581800e-03 2.988923e-05 5.080726e-03 1.050148e-02 2.320274e-02

> 
> ### uniquegenelist
> 
> uniquegenelist(letters[1:8],ndups=2)
[1] "a" "c" "e" "g"
> uniquegenelist(letters[1:8],ndups=2,spacing=2)
[1] "a" "b" "e" "f"
> 
> ### classifyTests
> 
> tstat <- matrix(c(0,5,0, 0,2.5,0, -2,-2,2, 1,1,1), 4, 3, byrow=TRUE)
> classifyTestsF(tstat)
TestResults matrix
     [,1] [,2] [,3]
[1,]    0    1    0
[2,]    0    0    0
[3,]   -1   -1    1
[4,]    0    0    0
> classifyTestsF(tstat,fstat.only=TRUE)
[1] 8.333333 2.083333 4.000000 1.000000
attr(,"df1")
[1] 3
attr(,"df2")
[1] Inf
> limma:::.classifyTestsP(tstat)
TestResults matrix
     [,1] [,2] [,3]
[1,]    0    1    0
[2,]    0    1    0
[3,]    0    0    0
[4,]    0    0    0
> 
> ### avereps
> 
> x <- matrix(rnorm(8*3),8,3)
> colnames(x) <- c("S1","S2","S3")
> rownames(x) <- c("b","a","a","c","c","b","b","b")
> avereps(x)
          S1         S2         S3
b -0.2353018  0.5220094  0.2302895
a -0.4347701  0.6453498 -0.6758914
c  0.3482980 -0.4820695 -0.3841313
> 
> ### roast
> 
> y <- matrix(rnorm(100*4),100,4)
> sigma <- sqrt(2/rchisq(100,df=7))
> y <- y*sigma
> design <- cbind(Intercept=1,Group=c(0,0,1,1))
> iset1 <- 1:5
> y[iset1,3:4] <- y[iset1,3:4]+3
> iset2 <- 6:10
> roast(y=y,iset1,design,contrast=2)
         Active.Prop     P.Value
Down               0 0.996498249
Up                 1 0.004002001
UpOrDown           1 0.008000000
Mixed              1 0.008000000
> roast(y=y,iset1,design,contrast=2,array.weights=c(0.5,1,0.5,1))
         Active.Prop    P.Value
Down               0 0.99899950
Up                 1 0.00150075
UpOrDown           1 0.00300000
Mixed              1 0.00300000
> w <- matrix(runif(100*4),100,4)
> roast(y=y,iset1,design,contrast=2,weights=w)
         Active.Prop   P.Value
Down               0 0.9994997
Up                 1 0.0010005
UpOrDown           1 0.0020000
Mixed              1 0.0020000
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,gene.weights=runif(100))
     NGenes PropDown PropUp Direction PValue   FDR PValue.Mixed FDR.Mixed
set1      5        0      1        Up  0.008 0.015        0.008     0.015
set2      5        0      0        Up  0.959 0.959        0.687     0.687
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,array.weights=c(0.5,1,0.5,1))
     NGenes PropDown PropUp Direction PValue   FDR PValue.Mixed FDR.Mixed
set1      5        0      1        Up  0.004 0.007        0.004     0.007
set2      5        0      0        Up  0.679 0.679        0.658     0.658
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w)
     NGenes PropDown PropUp Direction PValue   FDR PValue.Mixed FDR.Mixed
set1      5      0.0      1        Up  0.003 0.005        0.003     0.005
set2      5      0.2      0      Down  0.950 0.950        0.250     0.250
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes PropDown PropUp Direction PValue   FDR PValue.Mixed FDR.Mixed
set1      5        0      1        Up  0.001 0.001        0.001     0.001
set2      5        0      0      Down  0.791 0.791        0.146     0.146
> fry(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes Direction       PValue         FDR PValue.Mixed    FDR.Mixed
set1      5        Up 0.0007432594 0.001486519 1.820548e-05 3.641096e-05
set2      5      Down 0.8208140511 0.820814051 2.211837e-01 2.211837e-01
> rownames(y) <- paste0("Gene",1:100)
> iset1A <- rownames(y)[1:5]
> fry(y=y,index=iset1A,design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes Direction       PValue PValue.Mixed
set1      5        Up 0.0007432594 1.820548e-05
> 
> ### camera
> 
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1),allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction      PValue
set1      5  -0.2481655        Up 0.001050253
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction       PValue        FDR
set1      5  -0.2481655        Up 0.0009047749 0.00180955
set2      5   0.1719094      Down 0.9068364378 0.90683644
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1))
     NGenes Direction       PValue
set1      5        Up 1.105329e-10
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2)
     NGenes Direction       PValue          FDR
set1      5        Up 7.334400e-12 1.466880e-11
set2      5      Down 8.677115e-01 8.677115e-01
> camera(y=y,iset1A,design,contrast=2)
     NGenes Direction     PValue
set1      5        Up 7.3344e-12
> 
> ### with EList arg
> 
> y <- new("EList",list(E=y))
> roast(y=y,iset1,design,contrast=2)
         Active.Prop     P.Value
Down               0 0.997498749
Up                 1 0.003001501
UpOrDown           1 0.006000000
Mixed              1 0.006000000
> camera(y=y,iset1,design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction       PValue
set1      5  -0.2481655        Up 0.0009047749
> camera(y=y,iset1,design,contrast=2)
     NGenes Direction     PValue
set1      5        Up 7.3344e-12
> 
> ### eBayes with trend
> 
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
           logFC     AveExpr         t      P.Value  adj.P.Val          B
Gene2   3.729512  1.73488969  4.865697 0.0004854886 0.02902331  0.1596831
Gene3   3.488703  1.03931081  4.754954 0.0005804663 0.02902331 -0.0144071
Gene4   2.696676  1.74060725  3.356468 0.0063282637 0.21094212 -2.3434702
Gene1   2.391846  1.72305203  3.107124 0.0098781268 0.24695317 -2.7738874
Gene33 -1.492317 -0.07525287 -2.783817 0.0176475742 0.29965463 -3.3300835
Gene5   2.387967  1.63066783  2.773444 0.0179792778 0.29965463 -3.3478204
Gene80 -1.839760 -0.32802306 -2.503584 0.0291489863 0.37972679 -3.8049642
Gene39  1.366141 -0.27360750  2.451133 0.0320042242 0.37972679 -3.8925860
Gene95 -1.907074  1.26297763 -2.414217 0.0341754107 0.37972679 -3.9539571
Gene50  1.034777  0.01608433  2.054690 0.0642289403 0.59978803 -4.5350317
> fit$df.prior
[1] 9.098442
> fit$s2.prior
    Gene1     Gene2     Gene3     Gene4     Gene5     Gene6     Gene7     Gene8 
0.6901845 0.6977354 0.3860494 0.7014122 0.6341068 0.2926337 0.3077620 0.3058098 
    Gene9    Gene10    Gene11    Gene12    Gene13    Gene14    Gene15    Gene16 
0.2985145 0.2832520 0.3232434 0.3279710 0.2816081 0.2943502 0.3127994 0.2894802 
   Gene17    Gene18    Gene19    Gene20    Gene21    Gene22    Gene23    Gene24 
0.2812758 0.2840051 0.2839124 0.2954261 0.2838592 0.2812704 0.3157029 0.2844541 
   Gene25    Gene26    Gene27    Gene28    Gene29    Gene30    Gene31    Gene32 
0.4778832 0.2818242 0.2930360 0.2940957 0.2941862 0.3234399 0.3164779 0.2853510 
   Gene33    Gene34    Gene35    Gene36    Gene37    Gene38    Gene39    Gene40 
0.2988244 0.3450090 0.3048596 0.3089086 0.3104534 0.4551549 0.3220008 0.2813286 
   Gene41    Gene42    Gene43    Gene44    Gene45    Gene46    Gene47    Gene48 
0.2826027 0.2822504 0.2823330 0.3170673 0.3146173 0.3146793 0.2916540 0.2975003 
   Gene49    Gene50    Gene51    Gene52    Gene53    Gene54    Gene55    Gene56 
0.3538946 0.2907240 0.3199596 0.2816641 0.2814293 0.2996822 0.2812885 0.2896157 
   Gene57    Gene58    Gene59    Gene60    Gene61    Gene62    Gene63    Gene64 
0.2955317 0.2815907 0.2919420 0.2849675 0.3540805 0.3491713 0.2975019 0.2939325 
   Gene65    Gene66    Gene67    Gene68    Gene69    Gene70    Gene71    Gene72 
0.2986943 0.3265466 0.3402343 0.3394927 0.2813283 0.2814440 0.3089669 0.3030850 
   Gene73    Gene74    Gene75    Gene76    Gene77    Gene78    Gene79    Gene80 
0.2859286 0.2813216 0.3475231 0.3334419 0.2949550 0.3108702 0.2959688 0.3295294 
   Gene81    Gene82    Gene83    Gene84    Gene85    Gene86    Gene87    Gene88 
0.3413700 0.2946268 0.3029565 0.2920284 0.2926205 0.2818046 0.3425116 0.2882936 
   Gene89    Gene90    Gene91    Gene92    Gene93    Gene94    Gene95    Gene96 
0.2945459 0.3077919 0.2892134 0.2823787 0.3048049 0.2961408 0.4590012 0.2812784 
   Gene97    Gene98    Gene99   Gene100 
0.2846345 0.2819651 0.3137551 0.2856081 
> summary(fit$s2.post)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.2335  0.2603  0.2997  0.3375  0.3655  0.7812 
> 
> y$E[1,1] <- NA
> y$E[1,3] <- NA
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
           logFC     AveExpr         t      P.Value  adj.P.Val          B
Gene3   3.488703  1.03931081  4.604490 0.0007644061 0.07644061 -0.2333915
Gene2   3.729512  1.73488969  4.158038 0.0016033158 0.08016579 -0.9438583
Gene4   2.696676  1.74060725  2.898102 0.0145292666 0.44537707 -3.0530813
Gene33 -1.492317 -0.07525287 -2.784004 0.0178150826 0.44537707 -3.2456324
Gene5   2.387967  1.63066783  2.495395 0.0297982959 0.46902627 -3.7272957
Gene80 -1.839760 -0.32802306 -2.491115 0.0300256116 0.46902627 -3.7343584
Gene39  1.366141 -0.27360750  2.440729 0.0328318388 0.46902627 -3.8172597
Gene1   2.638272  1.47993643  2.227507 0.0530016060 0.58890673 -3.9537576
Gene95 -1.907074  1.26297763 -2.288870 0.0429197808 0.53649726 -4.0642439
Gene50  1.034777  0.01608433  2.063663 0.0635275235 0.60439978 -4.4204731
> fit$df.residual[1]
[1] 0
> fit$df.prior
[1] 8.971891
> fit$s2.prior
  [1] 0.7014084 0.9646561 0.4276287 0.9716476 0.8458852 0.2910492 0.3097052
  [8] 0.3074225 0.2985517 0.2786374 0.3267121 0.3316013 0.2766404 0.2932679
 [15] 0.3154347 0.2869186 0.2761395 0.2799884 0.2795119 0.2946468 0.2794412
 [22] 0.2761282 0.3186442 0.2806092 0.4596465 0.2767847 0.2924541 0.2939204
 [29] 0.2930568 0.3269177 0.3194905 0.2814293 0.2989389 0.3483845 0.3062977
 [36] 0.3110287 0.3127934 0.4418052 0.3254067 0.2761732 0.2780422 0.2773311
 [43] 0.2776653 0.3201314 0.3174515 0.3175199 0.2897731 0.2972785 0.3567262
 [50] 0.2885556 0.3232426 0.2767207 0.2762915 0.3000062 0.2761306 0.2870975
 [57] 0.2947817 0.2766152 0.2901489 0.2813183 0.3568982 0.3724440 0.2972804
 [64] 0.2927300 0.2987764 0.3301406 0.3437962 0.3430762 0.2761729 0.2763094
 [71] 0.3110958 0.3041715 0.2822004 0.2761654 0.3507694 0.3371214 0.2940441
 [78] 0.3132660 0.2953388 0.3331880 0.3448949 0.2946558 0.3040162 0.2902616
 [85] 0.2910320 0.2769211 0.3459946 0.2859057 0.2935193 0.3097398 0.2865663
 [92] 0.2774968 0.3062327 0.2955576 0.5425422 0.2761214 0.2808585 0.2771484
 [99] 0.3164981 0.2817725
> summary(fit$s2.post)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.2296  0.2581  0.3003  0.3453  0.3652  0.9158 
> 
> ### voom
> 
> y <- matrix(rpois(100*4,lambda=20),100,4)
> design <- cbind(Int=1,x=c(0,0,1,1))
> v <- voom(y,design)
> names(v)
[1] "E"       "weights" "design"  "targets"
> summary(v$E)
       V1              V2              V3              V4       
 Min.   :12.25   Min.   :12.58   Min.   :12.19   Min.   :12.24  
 1st Qu.:13.13   1st Qu.:13.07   1st Qu.:13.15   1st Qu.:13.03  
 Median :13.29   Median :13.30   Median :13.30   Median :13.27  
 Mean   :13.28   Mean   :13.29   Mean   :13.29   Mean   :13.28  
 3rd Qu.:13.49   3rd Qu.:13.51   3rd Qu.:13.50   3rd Qu.:13.50  
 Max.   :14.23   Max.   :14.28   Max.   :13.97   Max.   :13.96  
> summary(v$weights)
       V1               V2               V3               V4        
 Min.   : 5.935   Min.   : 5.935   Min.   : 5.935   Min.   : 5.935  
 1st Qu.: 6.788   1st Qu.: 7.049   1st Qu.: 7.207   1st Qu.: 6.825  
 Median :11.066   Median :10.443   Median :10.606   Median :10.414  
 Mean   :10.421   Mean   :10.485   Mean   :10.571   Mean   :10.532  
 3rd Qu.:13.485   3rd Qu.:14.155   3rd Qu.:13.859   3rd Qu.:14.121  
 Max.   :15.083   Max.   :15.101   Max.   :15.095   Max.   :15.063  
> 
> ### goana
> 
> EB <- c("133746","1339","134","1340","134083","134111","134147","134187","134218","134266",
+ "134353","134359","134391","134429","134430","1345","134510","134526","134549","1346",
+ "134637","1347","134701","134728","1348","134829","134860","134864","1349","134957",
+ "135","1350","1351","135112","135114","135138","135152","135154","1352","135228",
+ "135250","135293","135295","1353","135458","1355","1356","135644","135656","1357",
+ "1358","135892","1359","135924","135935","135941","135946","135948","136","1360",
+ "136051","1361","1362","136227","136242","136259","1363","136306","136319","136332",
+ "136371","1364","1365","136541","1366","136647","1368","136853","1369","136991",
+ "1370","137075","1371","137209","1373","137362","1374","137492","1375","1376",
+ "137682","137695","137735","1378","137814","137868","137872","137886","137902","137964")
> go <- goana(fit,FDR=0.8,geneid=EB)
> topGO(go,n=10,truncate.term=30)
                                     Term Ont  N Up Down        P.Up
GO:0070062          extracellular exosome  CC  8  0    4 1.000000000
GO:0043230        extracellular organelle  CC  8  0    4 1.000000000
GO:1903561          extracellular vesicle  CC  8  0    4 1.000000000
GO:0072359 circulatory system developm...  BP  2  0    2 1.000000000
GO:0007507              heart development  BP  2  0    2 1.000000000
GO:0032501 multicellular organismal pr...  BP 31  6    7 0.796992878
GO:0098609             cell-cell adhesion  BP  5  4    0 0.009503355
GO:0097190    apoptotic signaling pathway  BP  3  3    0 0.010952381
GO:0031252              cell leading edge  CC  3  3    0 0.010952381
GO:0006897                    endocytosis  BP  3  3    0 0.010952381
                P.Down
GO:0070062 0.003047199
GO:0043230 0.003047199
GO:1903561 0.003047199
GO:0072359 0.009090909
GO:0007507 0.009090909
GO:0032501 0.009111120
GO:0098609 1.000000000
GO:0097190 1.000000000
GO:0031252 1.000000000
GO:0006897 1.000000000
> topGO(go,n=10,truncate.term=30,sort="down")
                                     Term Ont  N Up Down      P.Up      P.Down
GO:0070062          extracellular exosome  CC  8  0    4 1.0000000 0.003047199
GO:0043230        extracellular organelle  CC  8  0    4 1.0000000 0.003047199
GO:1903561          extracellular vesicle  CC  8  0    4 1.0000000 0.003047199
GO:0072359 circulatory system developm...  BP  2  0    2 1.0000000 0.009090909
GO:0007507              heart development  BP  2  0    2 1.0000000 0.009090909
GO:0032501 multicellular organismal pr...  BP 31  6    7 0.7969929 0.009111120
GO:0032502          developmental process  BP 25  4    6 0.8946593 0.014492712
GO:0031982                        vesicle  CC 18  1    5 0.9946677 0.015552466
GO:0009887     animal organ morphogenesis  BP  3  0    2 1.0000000 0.025788497
GO:0055082 cellular chemical homeostas...  BP  3  1    2 0.5476190 0.025788497
> 
> proc.time()
   user  system elapsed 
   2.01    0.18    2.21 

R Under development (unstable) (2019-03-09 r76216) -- "Unsuffered Consequences"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(limma)
> 
> set.seed(0); u <- runif(100)
> 
> ### strsplit2
> 
> x <- c("ab;cd;efg","abc;def","z","")
> strsplit2(x,split=";")
     [,1]  [,2]  [,3] 
[1,] "ab"  "cd"  "efg"
[2,] "abc" "def" ""   
[3,] "z"   ""    ""   
[4,] ""    ""    ""   
> 
> ### removeext
> 
> removeExt(c("slide1.spot","slide.2.spot"))
[1] "slide1"  "slide.2"
> removeExt(c("slide1.spot","slide"))
[1] "slide1.spot" "slide"      
> 
> ### printorder
> 
> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6),ndups=2,start="topright",npins=4)
$printorder
  [1]   6   5   4   3   2   1  12  11  10   9   8   7  18  17  16  15  14  13
 [19]  24  23  22  21  20  19  30  29  28  27  26  25  36  35  34  33  32  31
 [37]  42  41  40  39  38  37  48  47  46  45  44  43   6   5   4   3   2   1
 [55]  12  11  10   9   8   7  18  17  16  15  14  13  24  23  22  21  20  19
 [73]  30  29  28  27  26  25  36  35  34  33  32  31  42  41  40  39  38  37
 [91]  48  47  46  45  44  43   6   5   4   3   2   1  12  11  10   9   8   7
[109]  18  17  16  15  14  13  24  23  22  21  20  19  30  29  28  27  26  25
[127]  36  35  34  33  32  31  42  41  40  39  38  37  48  47  46  45  44  43
[145]   6   5   4   3   2   1  12  11  10   9   8   7  18  17  16  15  14  13
[163]  24  23  22  21  20  19  30  29  28  27  26  25  36  35  34  33  32  31
[181]  42  41  40  39  38  37  48  47  46  45  44  43  54  53  52  51  50  49
[199]  60  59  58  57  56  55  66  65  64  63  62  61  72  71  70  69  68  67
[217]  78  77  76  75  74  73  84  83  82  81  80  79  90  89  88  87  86  85
[235]  96  95  94  93  92  91  54  53  52  51  50  49  60  59  58  57  56  55
[253]  66  65  64  63  62  61  72  71  70  69  68  67  78  77  76  75  74  73
[271]  84  83  82  81  80  79  90  89  88  87  86  85  96  95  94  93  92  91
[289]  54  53  52  51  50  49  60  59  58  57  56  55  66  65  64  63  62  61
[307]  72  71  70  69  68  67  78  77  76  75  74  73  84  83  82  81  80  79
[325]  90  89  88  87  86  85  96  95  94  93  92  91  54  53  52  51  50  49
[343]  60  59  58  57  56  55  66  65  64  63  62  61  72  71  70  69  68  67
[361]  78  77  76  75  74  73  84  83  82  81  80  79  90  89  88  87  86  85
[379]  96  95  94  93  92  91 102 101 100  99  98  97 108 107 106 105 104 103
[397] 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121
[415] 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139
[433] 102 101 100  99  98  97 108 107 106 105 104 103 114 113 112 111 110 109
[451] 120 119 118 117 116 115 126 125 124 123 122 121 132 131 130 129 128 127
[469] 138 137 136 135 134 133 144 143 142 141 140 139 102 101 100  99  98  97
[487] 108 107 106 105 104 103 114 113 112 111 110 109 120 119 118 117 116 115
[505] 126 125 124 123 122 121 132 131 130 129 128 127 138 137 136 135 134 133
[523] 144 143 142 141 140 139 102 101 100  99  98  97 108 107 106 105 104 103
[541] 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121
[559] 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139
[577] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157
[595] 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[613] 186 185 184 183 182 181 192 191 190 189 188 187 150 149 148 147 146 145
[631] 156 155 154 153 152 151 162 161 160 159 158 157 168 167 166 165 164 163
[649] 174 173 172 171 170 169 180 179 178 177 176 175 186 185 184 183 182 181
[667] 192 191 190 189 188 187 150 149 148 147 146 145 156 155 154 153 152 151
[685] 162 161 160 159 158 157 168 167 166 165 164 163 174 173 172 171 170 169
[703] 180 179 178 177 176 175 186 185 184 183 182 181 192 191 190 189 188 187
[721] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157
[739] 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[757] 186 185 184 183 182 181 192 191 190 189 188 187

$plate
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

$plate.r
  [1]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4
 [26]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  3  3
 [51]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3
 [76]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  2  2  2  2
[101]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
[126]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  1  1  1  1  1  1
[151]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[176]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  8  8  8  8  8  8  8  8
[201]  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8
[226]  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  7  7  7  7  7  7  7  7  7  7
[251]  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7
[276]  7  7  7  7  7  7  7  7  7  7  7  7  7  6  6  6  6  6  6  6  6  6  6  6  6
[301]  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6
[326]  6  6  6  6  6  6  6  6  6  6  6  5  5  5  5  5  5  5  5  5  5  5  5  5  5
[351]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
[376]  5  5  5  5  5  5  5  5  5 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[401] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[426] 12 12 12 12 12 12 12 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[451] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[476] 11 11 11 11 11 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[501] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[526] 10 10 10  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
[551]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
[576]  9 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
[601] 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 15
[626] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
[651] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 14 14 14
[676] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
[701] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 13 13 13 13 13
[726] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
[751] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13

$plate.c
  [1]  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15
 [26] 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3
 [51]  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14
 [76] 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2
[101]  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13
[126] 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1
[151]  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18
[176] 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6
[201]  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17
[226] 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5
[251]  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16
[276] 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4
[301]  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21
[326] 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9
[351]  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20
[376] 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8
[401]  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19
[426] 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7
[451] 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24
[476] 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12
[501] 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23
[526] 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11
[551] 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22
[576] 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10
[601] 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3
[626]  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15
[651] 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2
[676]  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14
[701] 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1
[726]  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13
[751] 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22

$plateposition
  [1] "p1D03" "p1D03" "p1D02" "p1D02" "p1D01" "p1D01" "p1D06" "p1D06" "p1D05"
 [10] "p1D05" "p1D04" "p1D04" "p1D09" "p1D09" "p1D08" "p1D08" "p1D07" "p1D07"
 [19] "p1D12" "p1D12" "p1D11" "p1D11" "p1D10" "p1D10" "p1D15" "p1D15" "p1D14"
 [28] "p1D14" "p1D13" "p1D13" "p1D18" "p1D18" "p1D17" "p1D17" "p1D16" "p1D16"
 [37] "p1D21" "p1D21" "p1D20" "p1D20" "p1D19" "p1D19" "p1D24" "p1D24" "p1D23"
 [46] "p1D23" "p1D22" "p1D22" "p1C03" "p1C03" "p1C02" "p1C02" "p1C01" "p1C01"
 [55] "p1C06" "p1C06" "p1C05" "p1C05" "p1C04" "p1C04" "p1C09" "p1C09" "p1C08"
 [64] "p1C08" "p1C07" "p1C07" "p1C12" "p1C12" "p1C11" "p1C11" "p1C10" "p1C10"
 [73] "p1C15" "p1C15" "p1C14" "p1C14" "p1C13" "p1C13" "p1C18" "p1C18" "p1C17"
 [82] "p1C17" "p1C16" "p1C16" "p1C21" "p1C21" "p1C20" "p1C20" "p1C19" "p1C19"
 [91] "p1C24" "p1C24" "p1C23" "p1C23" "p1C22" "p1C22" "p1B03" "p1B03" "p1B02"
[100] "p1B02" "p1B01" "p1B01" "p1B06" "p1B06" "p1B05" "p1B05" "p1B04" "p1B04"
[109] "p1B09" "p1B09" "p1B08" "p1B08" "p1B07" "p1B07" "p1B12" "p1B12" "p1B11"
[118] "p1B11" "p1B10" "p1B10" "p1B15" "p1B15" "p1B14" "p1B14" "p1B13" "p1B13"
[127] "p1B18" "p1B18" "p1B17" "p1B17" "p1B16" "p1B16" "p1B21" "p1B21" "p1B20"
[136] "p1B20" "p1B19" "p1B19" "p1B24" "p1B24" "p1B23" "p1B23" "p1B22" "p1B22"
[145] "p1A03" "p1A03" "p1A02" "p1A02" "p1A01" "p1A01" "p1A06" "p1A06" "p1A05"
[154] "p1A05" "p1A04" "p1A04" "p1A09" "p1A09" "p1A08" "p1A08" "p1A07" "p1A07"
[163] "p1A12" "p1A12" "p1A11" "p1A11" "p1A10" "p1A10" "p1A15" "p1A15" "p1A14"
[172] "p1A14" "p1A13" "p1A13" "p1A18" "p1A18" "p1A17" "p1A17" "p1A16" "p1A16"
[181] "p1A21" "p1A21" "p1A20" "p1A20" "p1A19" "p1A19" "p1A24" "p1A24" "p1A23"
[190] "p1A23" "p1A22" "p1A22" "p1H03" "p1H03" "p1H02" "p1H02" "p1H01" "p1H01"
[199] "p1H06" "p1H06" "p1H05" "p1H05" "p1H04" "p1H04" "p1H09" "p1H09" "p1H08"
[208] "p1H08" "p1H07" "p1H07" "p1H12" "p1H12" "p1H11" "p1H11" "p1H10" "p1H10"
[217] "p1H15" "p1H15" "p1H14" "p1H14" "p1H13" "p1H13" "p1H18" "p1H18" "p1H17"
[226] "p1H17" "p1H16" "p1H16" "p1H21" "p1H21" "p1H20" "p1H20" "p1H19" "p1H19"
[235] "p1H24" "p1H24" "p1H23" "p1H23" "p1H22" "p1H22" "p1G03" "p1G03" "p1G02"
[244] "p1G02" "p1G01" "p1G01" "p1G06" "p1G06" "p1G05" "p1G05" "p1G04" "p1G04"
[253] "p1G09" "p1G09" "p1G08" "p1G08" "p1G07" "p1G07" "p1G12" "p1G12" "p1G11"
[262] "p1G11" "p1G10" "p1G10" "p1G15" "p1G15" "p1G14" "p1G14" "p1G13" "p1G13"
[271] "p1G18" "p1G18" "p1G17" "p1G17" "p1G16" "p1G16" "p1G21" "p1G21" "p1G20"
[280] "p1G20" "p1G19" "p1G19" "p1G24" "p1G24" "p1G23" "p1G23" "p1G22" "p1G22"
[289] "p1F03" "p1F03" "p1F02" "p1F02" "p1F01" "p1F01" "p1F06" "p1F06" "p1F05"
[298] "p1F05" "p1F04" "p1F04" "p1F09" "p1F09" "p1F08" "p1F08" "p1F07" "p1F07"
[307] "p1F12" "p1F12" "p1F11" "p1F11" "p1F10" "p1F10" "p1F15" "p1F15" "p1F14"
[316] "p1F14" "p1F13" "p1F13" "p1F18" "p1F18" "p1F17" "p1F17" "p1F16" "p1F16"
[325] "p1F21" "p1F21" "p1F20" "p1F20" "p1F19" "p1F19" "p1F24" "p1F24" "p1F23"
[334] "p1F23" "p1F22" "p1F22" "p1E03" "p1E03" "p1E02" "p1E02" "p1E01" "p1E01"
[343] "p1E06" "p1E06" "p1E05" "p1E05" "p1E04" "p1E04" "p1E09" "p1E09" "p1E08"
[352] "p1E08" "p1E07" "p1E07" "p1E12" "p1E12" "p1E11" "p1E11" "p1E10" "p1E10"
[361] "p1E15" "p1E15" "p1E14" "p1E14" "p1E13" "p1E13" "p1E18" "p1E18" "p1E17"
[370] "p1E17" "p1E16" "p1E16" "p1E21" "p1E21" "p1E20" "p1E20" "p1E19" "p1E19"
[379] "p1E24" "p1E24" "p1E23" "p1E23" "p1E22" "p1E22" "p1L03" "p1L03" "p1L02"
[388] "p1L02" "p1L01" "p1L01" "p1L06" "p1L06" "p1L05" "p1L05" "p1L04" "p1L04"
[397] "p1L09" "p1L09" "p1L08" "p1L08" "p1L07" "p1L07" "p1L12" "p1L12" "p1L11"
[406] "p1L11" "p1L10" "p1L10" "p1L15" "p1L15" "p1L14" "p1L14" "p1L13" "p1L13"
[415] "p1L18" "p1L18" "p1L17" "p1L17" "p1L16" "p1L16" "p1L21" "p1L21" "p1L20"
[424] "p1L20" "p1L19" "p1L19" "p1L24" "p1L24" "p1L23" "p1L23" "p1L22" "p1L22"
[433] "p1K03" "p1K03" "p1K02" "p1K02" "p1K01" "p1K01" "p1K06" "p1K06" "p1K05"
[442] "p1K05" "p1K04" "p1K04" "p1K09" "p1K09" "p1K08" "p1K08" "p1K07" "p1K07"
[451] "p1K12" "p1K12" "p1K11" "p1K11" "p1K10" "p1K10" "p1K15" "p1K15" "p1K14"
[460] "p1K14" "p1K13" "p1K13" "p1K18" "p1K18" "p1K17" "p1K17" "p1K16" "p1K16"
[469] "p1K21" "p1K21" "p1K20" "p1K20" "p1K19" "p1K19" "p1K24" "p1K24" "p1K23"
[478] "p1K23" "p1K22" "p1K22" "p1J03" "p1J03" "p1J02" "p1J02" "p1J01" "p1J01"
[487] "p1J06" "p1J06" "p1J05" "p1J05" "p1J04" "p1J04" "p1J09" "p1J09" "p1J08"
[496] "p1J08" "p1J07" "p1J07" "p1J12" "p1J12" "p1J11" "p1J11" "p1J10" "p1J10"
[505] "p1J15" "p1J15" "p1J14" "p1J14" "p1J13" "p1J13" "p1J18" "p1J18" "p1J17"
[514] "p1J17" "p1J16" "p1J16" "p1J21" "p1J21" "p1J20" "p1J20" "p1J19" "p1J19"
[523] "p1J24" "p1J24" "p1J23" "p1J23" "p1J22" "p1J22" "p1I03" "p1I03" "p1I02"
[532] "p1I02" "p1I01" "p1I01" "p1I06" "p1I06" "p1I05" "p1I05" "p1I04" "p1I04"
[541] "p1I09" "p1I09" "p1I08" "p1I08" "p1I07" "p1I07" "p1I12" "p1I12" "p1I11"
[550] "p1I11" "p1I10" "p1I10" "p1I15" "p1I15" "p1I14" "p1I14" "p1I13" "p1I13"
[559] "p1I18" "p1I18" "p1I17" "p1I17" "p1I16" "p1I16" "p1I21" "p1I21" "p1I20"
[568] "p1I20" "p1I19" "p1I19" "p1I24" "p1I24" "p1I23" "p1I23" "p1I22" "p1I22"
[577] "p1P03" "p1P03" "p1P02" "p1P02" "p1P01" "p1P01" "p1P06" "p1P06" "p1P05"
[586] "p1P05" "p1P04" "p1P04" "p1P09" "p1P09" "p1P08" "p1P08" "p1P07" "p1P07"
[595] "p1P12" "p1P12" "p1P11" "p1P11" "p1P10" "p1P10" "p1P15" "p1P15" "p1P14"
[604] "p1P14" "p1P13" "p1P13" "p1P18" "p1P18" "p1P17" "p1P17" "p1P16" "p1P16"
[613] "p1P21" "p1P21" "p1P20" "p1P20" "p1P19" "p1P19" "p1P24" "p1P24" "p1P23"
[622] "p1P23" "p1P22" "p1P22" "p1O03" "p1O03" "p1O02" "p1O02" "p1O01" "p1O01"
[631] "p1O06" "p1O06" "p1O05" "p1O05" "p1O04" "p1O04" "p1O09" "p1O09" "p1O08"
[640] "p1O08" "p1O07" "p1O07" "p1O12" "p1O12" "p1O11" "p1O11" "p1O10" "p1O10"
[649] "p1O15" "p1O15" "p1O14" "p1O14" "p1O13" "p1O13" "p1O18" "p1O18" "p1O17"
[658] "p1O17" "p1O16" "p1O16" "p1O21" "p1O21" "p1O20" "p1O20" "p1O19" "p1O19"
[667] "p1O24" "p1O24" "p1O23" "p1O23" "p1O22" "p1O22" "p1N03" "p1N03" "p1N02"
[676] "p1N02" "p1N01" "p1N01" "p1N06" "p1N06" "p1N05" "p1N05" "p1N04" "p1N04"
[685] "p1N09" "p1N09" "p1N08" "p1N08" "p1N07" "p1N07" "p1N12" "p1N12" "p1N11"
[694] "p1N11" "p1N10" "p1N10" "p1N15" "p1N15" "p1N14" "p1N14" "p1N13" "p1N13"
[703] "p1N18" "p1N18" "p1N17" "p1N17" "p1N16" "p1N16" "p1N21" "p1N21" "p1N20"
[712] "p1N20" "p1N19" "p1N19" "p1N24" "p1N24" "p1N23" "p1N23" "p1N22" "p1N22"
[721] "p1M03" "p1M03" "p1M02" "p1M02" "p1M01" "p1M01" "p1M06" "p1M06" "p1M05"
[730] "p1M05" "p1M04" "p1M04" "p1M09" "p1M09" "p1M08" "p1M08" "p1M07" "p1M07"
[739] "p1M12" "p1M12" "p1M11" "p1M11" "p1M10" "p1M10" "p1M15" "p1M15" "p1M14"
[748] "p1M14" "p1M13" "p1M13" "p1M18" "p1M18" "p1M17" "p1M17" "p1M16" "p1M16"
[757] "p1M21" "p1M21" "p1M20" "p1M20" "p1M19" "p1M19" "p1M24" "p1M24" "p1M23"
[766] "p1M23" "p1M22" "p1M22"

> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6))
$printorder
  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
 [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2
 [51]  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
 [76] 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4
[101]  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
[126] 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6
[151]  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
[176] 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8
[201]  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
[226] 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10
[251] 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
[276] 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12
[301] 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
[326] 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14
[351] 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
[376] 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16
[401] 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
[426] 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18
[451] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
[476] 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
[501] 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
[526] 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22
[551] 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
[576] 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
[601] 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1
[626]  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
[651] 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3
[676]  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
[701] 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5
[726]  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
[751] 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

$plate
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[334] 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2
[371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[408] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1
[445] 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1
[482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[519] 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2
[556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[593] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1
[630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[667] 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2
[704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[741] 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

$plate.r
  [1]  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  4
 [26]  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3  3
 [51]  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3  3
 [76]  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2  2
[101]  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2  2
[126]  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1  1
[151]  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1  5
[176]  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  4  4  4  4  4  4  8  8
[201]  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  4  4  4  4  4  4  8  8  8
[226]  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3  3  3  3  3  3  7  7  7  7
[251]  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3  3  3  3  3  7  7  7  7  7
[276]  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2  2  2  2  6  6  6  6  6  6
[301] 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2  2  2  6  6  6  6  6  6 10
[326] 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1  1  5  5  5  5  5  5  9  9
[351]  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9
[376]  9  9  9 13 13 13 13 13 13  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12
[401] 12 12 16 16 16 16 16 16  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12
[426] 12 16 16 16 16 16 16  3  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11
[451] 15 15 15 15 15 15  3  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15
[476] 15 15 15 15 15  2  2  2  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14
[501] 14 14 14 14  2  2  2  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14
[526] 14 14 14  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13
[551] 13 13  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13
[576] 13  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16
[601]  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3
[626]  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3
[651]  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2
[676]  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2
[701]  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1
[726]  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1
[751]  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13

$plate.c
  [1]  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1
 [26]  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5
 [51]  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9
 [76] 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13
[101] 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17
[126] 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21
[151]  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1
[176]  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  2  6 10 14 18 22  2  6
[201] 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10
[226] 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14
[251] 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18
[276] 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22
[301]  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2
[326]  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6
[351] 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10
[376] 14 18 22  2  6 10 14 18 22  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15
[401] 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19
[426] 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23
[451]  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3
[476]  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7
[501] 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11
[526] 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15
[551] 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19
[576] 23  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24
[601]  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4
[626]  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8
[651] 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12
[676] 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16
[701] 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20
[726] 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24
[751]  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24

$plateposition
  [1] "p1D01" "p1D05" "p1D09" "p1D13" "p1D17" "p1D21" "p1H01" "p1H05" "p1H09"
 [10] "p1H13" "p1H17" "p1H21" "p1L01" "p1L05" "p1L09" "p1L13" "p1L17" "p1L21"
 [19] "p1P01" "p1P05" "p1P09" "p1P13" "p1P17" "p1P21" "p2D01" "p2D05" "p2D09"
 [28] "p2D13" "p2D17" "p2D21" "p2H01" "p2H05" "p2H09" "p2H13" "p2H17" "p2H21"
 [37] "p2L01" "p2L05" "p2L09" "p2L13" "p2L17" "p2L21" "p2P01" "p2P05" "p2P09"
 [46] "p2P13" "p2P17" "p2P21" "p1C01" "p1C05" "p1C09" "p1C13" "p1C17" "p1C21"
 [55] "p1G01" "p1G05" "p1G09" "p1G13" "p1G17" "p1G21" "p1K01" "p1K05" "p1K09"
 [64] "p1K13" "p1K17" "p1K21" "p1O01" "p1O05" "p1O09" "p1O13" "p1O17" "p1O21"
 [73] "p2C01" "p2C05" "p2C09" "p2C13" "p2C17" "p2C21" "p2G01" "p2G05" "p2G09"
 [82] "p2G13" "p2G17" "p2G21" "p2K01" "p2K05" "p2K09" "p2K13" "p2K17" "p2K21"
 [91] "p2O01" "p2O05" "p2O09" "p2O13" "p2O17" "p2O21" "p1B01" "p1B05" "p1B09"
[100] "p1B13" "p1B17" "p1B21" "p1F01" "p1F05" "p1F09" "p1F13" "p1F17" "p1F21"
[109] "p1J01" "p1J05" "p1J09" "p1J13" "p1J17" "p1J21" "p1N01" "p1N05" "p1N09"
[118] "p1N13" "p1N17" "p1N21" "p2B01" "p2B05" "p2B09" "p2B13" "p2B17" "p2B21"
[127] "p2F01" "p2F05" "p2F09" "p2F13" "p2F17" "p2F21" "p2J01" "p2J05" "p2J09"
[136] "p2J13" "p2J17" "p2J21" "p2N01" "p2N05" "p2N09" "p2N13" "p2N17" "p2N21"
[145] "p1A01" "p1A05" "p1A09" "p1A13" "p1A17" "p1A21" "p1E01" "p1E05" "p1E09"
[154] "p1E13" "p1E17" "p1E21" "p1I01" "p1I05" "p1I09" "p1I13" "p1I17" "p1I21"
[163] "p1M01" "p1M05" "p1M09" "p1M13" "p1M17" "p1M21" "p2A01" "p2A05" "p2A09"
[172] "p2A13" "p2A17" "p2A21" "p2E01" "p2E05" "p2E09" "p2E13" "p2E17" "p2E21"
[181] "p2I01" "p2I05" "p2I09" "p2I13" "p2I17" "p2I21" "p2M01" "p2M05" "p2M09"
[190] "p2M13" "p2M17" "p2M21" "p1D02" "p1D06" "p1D10" "p1D14" "p1D18" "p1D22"
[199] "p1H02" "p1H06" "p1H10" "p1H14" "p1H18" "p1H22" "p1L02" "p1L06" "p1L10"
[208] "p1L14" "p1L18" "p1L22" "p1P02" "p1P06" "p1P10" "p1P14" "p1P18" "p1P22"
[217] "p2D02" "p2D06" "p2D10" "p2D14" "p2D18" "p2D22" "p2H02" "p2H06" "p2H10"
[226] "p2H14" "p2H18" "p2H22" "p2L02" "p2L06" "p2L10" "p2L14" "p2L18" "p2L22"
[235] "p2P02" "p2P06" "p2P10" "p2P14" "p2P18" "p2P22" "p1C02" "p1C06" "p1C10"
[244] "p1C14" "p1C18" "p1C22" "p1G02" "p1G06" "p1G10" "p1G14" "p1G18" "p1G22"
[253] "p1K02" "p1K06" "p1K10" "p1K14" "p1K18" "p1K22" "p1O02" "p1O06" "p1O10"
[262] "p1O14" "p1O18" "p1O22" "p2C02" "p2C06" "p2C10" "p2C14" "p2C18" "p2C22"
[271] "p2G02" "p2G06" "p2G10" "p2G14" "p2G18" "p2G22" "p2K02" "p2K06" "p2K10"
[280] "p2K14" "p2K18" "p2K22" "p2O02" "p2O06" "p2O10" "p2O14" "p2O18" "p2O22"
[289] "p1B02" "p1B06" "p1B10" "p1B14" "p1B18" "p1B22" "p1F02" "p1F06" "p1F10"
[298] "p1F14" "p1F18" "p1F22" "p1J02" "p1J06" "p1J10" "p1J14" "p1J18" "p1J22"
[307] "p1N02" "p1N06" "p1N10" "p1N14" "p1N18" "p1N22" "p2B02" "p2B06" "p2B10"
[316] "p2B14" "p2B18" "p2B22" "p2F02" "p2F06" "p2F10" "p2F14" "p2F18" "p2F22"
[325] "p2J02" "p2J06" "p2J10" "p2J14" "p2J18" "p2J22" "p2N02" "p2N06" "p2N10"
[334] "p2N14" "p2N18" "p2N22" "p1A02" "p1A06" "p1A10" "p1A14" "p1A18" "p1A22"
[343] "p1E02" "p1E06" "p1E10" "p1E14" "p1E18" "p1E22" "p1I02" "p1I06" "p1I10"
[352] "p1I14" "p1I18" "p1I22" "p1M02" "p1M06" "p1M10" "p1M14" "p1M18" "p1M22"
[361] "p2A02" "p2A06" "p2A10" "p2A14" "p2A18" "p2A22" "p2E02" "p2E06" "p2E10"
[370] "p2E14" "p2E18" "p2E22" "p2I02" "p2I06" "p2I10" "p2I14" "p2I18" "p2I22"
[379] "p2M02" "p2M06" "p2M10" "p2M14" "p2M18" "p2M22" "p1D03" "p1D07" "p1D11"
[388] "p1D15" "p1D19" "p1D23" "p1H03" "p1H07" "p1H11" "p1H15" "p1H19" "p1H23"
[397] "p1L03" "p1L07" "p1L11" "p1L15" "p1L19" "p1L23" "p1P03" "p1P07" "p1P11"
[406] "p1P15" "p1P19" "p1P23" "p2D03" "p2D07" "p2D11" "p2D15" "p2D19" "p2D23"
[415] "p2H03" "p2H07" "p2H11" "p2H15" "p2H19" "p2H23" "p2L03" "p2L07" "p2L11"
[424] "p2L15" "p2L19" "p2L23" "p2P03" "p2P07" "p2P11" "p2P15" "p2P19" "p2P23"
[433] "p1C03" "p1C07" "p1C11" "p1C15" "p1C19" "p1C23" "p1G03" "p1G07" "p1G11"
[442] "p1G15" "p1G19" "p1G23" "p1K03" "p1K07" "p1K11" "p1K15" "p1K19" "p1K23"
[451] "p1O03" "p1O07" "p1O11" "p1O15" "p1O19" "p1O23" "p2C03" "p2C07" "p2C11"
[460] "p2C15" "p2C19" "p2C23" "p2G03" "p2G07" "p2G11" "p2G15" "p2G19" "p2G23"
[469] "p2K03" "p2K07" "p2K11" "p2K15" "p2K19" "p2K23" "p2O03" "p2O07" "p2O11"
[478] "p2O15" "p2O19" "p2O23" "p1B03" "p1B07" "p1B11" "p1B15" "p1B19" "p1B23"
[487] "p1F03" "p1F07" "p1F11" "p1F15" "p1F19" "p1F23" "p1J03" "p1J07" "p1J11"
[496] "p1J15" "p1J19" "p1J23" "p1N03" "p1N07" "p1N11" "p1N15" "p1N19" "p1N23"
[505] "p2B03" "p2B07" "p2B11" "p2B15" "p2B19" "p2B23" "p2F03" "p2F07" "p2F11"
[514] "p2F15" "p2F19" "p2F23" "p2J03" "p2J07" "p2J11" "p2J15" "p2J19" "p2J23"
[523] "p2N03" "p2N07" "p2N11" "p2N15" "p2N19" "p2N23" "p1A03" "p1A07" "p1A11"
[532] "p1A15" "p1A19" "p1A23" "p1E03" "p1E07" "p1E11" "p1E15" "p1E19" "p1E23"
[541] "p1I03" "p1I07" "p1I11" "p1I15" "p1I19" "p1I23" "p1M03" "p1M07" "p1M11"
[550] "p1M15" "p1M19" "p1M23" "p2A03" "p2A07" "p2A11" "p2A15" "p2A19" "p2A23"
[559] "p2E03" "p2E07" "p2E11" "p2E15" "p2E19" "p2E23" "p2I03" "p2I07" "p2I11"
[568] "p2I15" "p2I19" "p2I23" "p2M03" "p2M07" "p2M11" "p2M15" "p2M19" "p2M23"
[577] "p1D04" "p1D08" "p1D12" "p1D16" "p1D20" "p1D24" "p1H04" "p1H08" "p1H12"
[586] "p1H16" "p1H20" "p1H24" "p1L04" "p1L08" "p1L12" "p1L16" "p1L20" "p1L24"
[595] "p1P04" "p1P08" "p1P12" "p1P16" "p1P20" "p1P24" "p2D04" "p2D08" "p2D12"
[604] "p2D16" "p2D20" "p2D24" "p2H04" "p2H08" "p2H12" "p2H16" "p2H20" "p2H24"
[613] "p2L04" "p2L08" "p2L12" "p2L16" "p2L20" "p2L24" "p2P04" "p2P08" "p2P12"
[622] "p2P16" "p2P20" "p2P24" "p1C04" "p1C08" "p1C12" "p1C16" "p1C20" "p1C24"
[631] "p1G04" "p1G08" "p1G12" "p1G16" "p1G20" "p1G24" "p1K04" "p1K08" "p1K12"
[640] "p1K16" "p1K20" "p1K24" "p1O04" "p1O08" "p1O12" "p1O16" "p1O20" "p1O24"
[649] "p2C04" "p2C08" "p2C12" "p2C16" "p2C20" "p2C24" "p2G04" "p2G08" "p2G12"
[658] "p2G16" "p2G20" "p2G24" "p2K04" "p2K08" "p2K12" "p2K16" "p2K20" "p2K24"
[667] "p2O04" "p2O08" "p2O12" "p2O16" "p2O20" "p2O24" "p1B04" "p1B08" "p1B12"
[676] "p1B16" "p1B20" "p1B24" "p1F04" "p1F08" "p1F12" "p1F16" "p1F20" "p1F24"
[685] "p1J04" "p1J08" "p1J12" "p1J16" "p1J20" "p1J24" "p1N04" "p1N08" "p1N12"
[694] "p1N16" "p1N20" "p1N24" "p2B04" "p2B08" "p2B12" "p2B16" "p2B20" "p2B24"
[703] "p2F04" "p2F08" "p2F12" "p2F16" "p2F20" "p2F24" "p2J04" "p2J08" "p2J12"
[712] "p2J16" "p2J20" "p2J24" "p2N04" "p2N08" "p2N12" "p2N16" "p2N20" "p2N24"
[721] "p1A04" "p1A08" "p1A12" "p1A16" "p1A20" "p1A24" "p1E04" "p1E08" "p1E12"
[730] "p1E16" "p1E20" "p1E24" "p1I04" "p1I08" "p1I12" "p1I16" "p1I20" "p1I24"
[739] "p1M04" "p1M08" "p1M12" "p1M16" "p1M20" "p1M24" "p2A04" "p2A08" "p2A12"
[748] "p2A16" "p2A20" "p2A24" "p2E04" "p2E08" "p2E12" "p2E16" "p2E20" "p2E24"
[757] "p2I04" "p2I08" "p2I12" "p2I16" "p2I20" "p2I24" "p2M04" "p2M08" "p2M12"
[766] "p2M16" "p2M20" "p2M24"

> 
> ### merge.rglist
> 
> R <- G <- matrix(11:14,4,2)
> rownames(R) <- rownames(G) <- c("a","a","b","c")
> RG1 <- new("RGList",list(R=R,G=G))
> R <- G <- matrix(21:24,4,2)
> rownames(R) <- rownames(G) <- c("b","a","a","c")
> RG2 <- new("RGList",list(R=R,G=G))
> merge(RG1,RG2)
An object of class "RGList"
$R
  [,1] [,2] [,3] [,4]
a   11   11   22   22
a   12   12   23   23
b   13   13   21   21
c   14   14   24   24

$G
  [,1] [,2] [,3] [,4]
a   11   11   22   22
a   12   12   23   23
b   13   13   21   21
c   14   14   24   24

> merge(RG2,RG1)
An object of class "RGList"
$R
  [,1] [,2] [,3] [,4]
b   21   21   13   13
a   22   22   11   11
a   23   23   12   12
c   24   24   14   14

$G
  [,1] [,2] [,3] [,4]
b   21   21   13   13
a   22   22   11   11
a   23   23   12   12
c   24   24   14   14

> 
> ### background correction
> 
> RG <- new("RGList", list(R=c(1,2,3,4),G=c(1,2,3,4),Rb=c(2,2,2,2),Gb=c(2,2,2,2)))
> backgroundCorrect(RG)
An object of class "RGList"
$R
     [,1]
[1,]   -1
[2,]    0
[3,]    1
[4,]    2

$G
     [,1]
[1,]   -1
[2,]    0
[3,]    1
[4,]    2

> backgroundCorrect(RG, method="half")
An object of class "RGList"
$R
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

$G
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

> backgroundCorrect(RG, method="minimum")
An object of class "RGList"
$R
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

$G
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

> backgroundCorrect(RG, offset=5)
An object of class "RGList"
$R
     [,1]
[1,]    4
[2,]    5
[3,]    6
[4,]    7

$G
     [,1]
[1,]    4
[2,]    5
[3,]    6
[4,]    7

> 
> ### loessFit
> 
> x <- 1:100
> y <- rnorm(100)
> out <- loessFit(y,x)
> f1 <- quantile(out$fitted)
> r1 <- quantile(out$residual)
> w <- rep(1,100)
> w[1:50] <- 0.5
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f2 <- quantile(out$fitted)
> r2 <- quantile(out$residual)
> out <- loessFit(y,x,weights=w,method="locfit")
> f3 <- quantile(out$fitted)
> r3 <- quantile(out$residual)
> out <- loessFit(y,x,weights=w,method="loess")
> f4 <- quantile(out$fitted)
> r4 <- quantile(out$residual)
> w <- rep(1,100)
> w[2*(1:50)] <- 0
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f5 <- quantile(out$fitted)
> r5 <- quantile(out$residual)
> data.frame(f1,f2,f3,f4,f5)
              f1           f2          f3          f4          f5
0%   -0.78835384 -0.687432210 -0.78957137 -0.75758558 -0.63778292
25%  -0.18340154 -0.179683572 -0.18979269 -0.16363329 -0.38064318
50%  -0.11492924 -0.114796040 -0.12087983 -0.07318718 -0.15971879
75%   0.01507921 -0.008145125 -0.01857508  0.03656491  0.07839396
100%  0.21653837  0.145106033  0.19214597  0.23710498  0.51836274
> data.frame(r1,r2,r3,r4,r5)
              r1          r2          r3          r4          r5
0%   -2.04434053 -2.05132680 -2.02404318 -2.09602100 -2.22280633
25%  -0.59321065 -0.57200209 -0.58975649 -0.58142533 -0.71037756
50%   0.05874864  0.04514326  0.08335198 -0.01037007  0.06785517
75%   0.56010750  0.55124530  0.57618740  0.55704748  0.65383830
100%  2.57936026  2.64549799  2.57549257  2.38180448  2.28648835
> 
> ### normalizeWithinArrays
> 
> RG <- new("RGList",list())
> RG$R <- matrix(rexp(100*2),100,2)
> RG$G <- matrix(rexp(100*2),100,2)
> RG$Rb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RG$Gb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="saddle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
       V1                V2                V3               V4        
 Min.   :0.01626   Min.   :0.01213   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.35497   1st Qu.:0.29133   1st Qu.:0.2745   1st Qu.:0.3953  
 Median :0.71793   Median :0.70294   Median :0.6339   Median :0.8223  
 Mean   :0.90184   Mean   :1.00122   Mean   :0.9454   Mean   :1.1324  
 3rd Qu.:1.16891   3rd Qu.:1.33139   3rd Qu.:1.4059   3rd Qu.:1.4221  
 Max.   :4.56267   Max.   :6.37947   Max.   :5.0486   Max.   :6.6295  
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="mle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
       V1                V2                V3               V4        
 Min.   :0.01701   Min.   :0.01255   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.35423   1st Qu.:0.29118   1st Qu.:0.2745   1st Qu.:0.3953  
 Median :0.71719   Median :0.70280   Median :0.6339   Median :0.8223  
 Mean   :0.90118   Mean   :1.00110   Mean   :0.9454   Mean   :1.1324  
 3rd Qu.:1.16817   3rd Qu.:1.33124   3rd Qu.:1.4059   3rd Qu.:1.4221  
 Max.   :4.56193   Max.   :6.37932   Max.   :5.0486   Max.   :6.6295  
> MA <- normalizeWithinArrays(RGb,method="loess")
> summary(MA$M)
       V1                 V2          
 Min.   :-5.82498   Min.   :-5.69877  
 1st Qu.:-1.19140   1st Qu.:-1.55421  
 Median :-0.19318   Median : 0.06267  
 Mean   : 0.08691   Mean   :-0.05369  
 3rd Qu.: 1.48646   3rd Qu.: 1.41900  
 Max.   : 7.16195   Max.   : 6.28902  
> #MA <- normalizeWithinArrays(RG[,1:2], mouse.setup, method="robustspline")
> #MA$M[1:5,]
> #MA <- normalizeWithinArrays(mouse.data, mouse.setup)
> #MA$M[1:5,]
> 
> ### normalizeBetweenArrays
> 
> MA2 <- normalizeBetweenArrays(MA,method="scale")
> MA$M[1:5,]
           [,1]       [,2]
[1,] -1.0618269  4.5343276
[2,]  0.8507603  0.3495635
[3,]  2.7703696  1.4459533
[4,] -1.8511286  0.4894799
[5,]  1.9180276 -5.5363732
> MA$A[1:5,]
            [,1]       [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200  0.2071043
[4,]  0.09281027 -1.3880965
[5,]  0.22385828 -3.0855818
> MA2 <- normalizeBetweenArrays(MA,method="quantile")
> MA$M[1:5,]
           [,1]       [,2]
[1,] -1.0618269  4.5343276
[2,]  0.8507603  0.3495635
[3,]  2.7703696  1.4459533
[4,] -1.8511286  0.4894799
[5,]  1.9180276 -5.5363732
> MA$A[1:5,]
            [,1]       [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200  0.2071043
[4,]  0.09281027 -1.3880965
[5,]  0.22385828 -3.0855818
> 
> ### unwrapdups
> 
> M <- matrix(1:12,6,2)
> unwrapdups(M,ndups=1)
     [,1] [,2]
[1,]    1    7
[2,]    2    8
[3,]    3    9
[4,]    4   10
[5,]    5   11
[6,]    6   12
> unwrapdups(M,ndups=2)
     [,1] [,2] [,3] [,4]
[1,]    1    2    7    8
[2,]    3    4    9   10
[3,]    5    6   11   12
> unwrapdups(M,ndups=3)
     [,1] [,2] [,3] [,4] [,5] [,6]
[1,]    1    2    3    7    8    9
[2,]    4    5    6   10   11   12
> unwrapdups(M,ndups=2,spacing=3)
     [,1] [,2] [,3] [,4]
[1,]    1    4    7   10
[2,]    2    5    8   11
[3,]    3    6    9   12
> 
> ### trigammaInverse
> 
> trigammaInverse(c(1e-6,NA,5,1e6))
[1] 1.000000e+06           NA 4.961687e-01 1.000001e-03
> 
> ### lmFit, eBayes, topTable
> 
> M <- matrix(rnorm(10*6,sd=0.3),10,6)
> rownames(M) <- LETTERS[1:10]
> M[1,1:3] <- M[1,1:3] + 2
> design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
> contrast.matrix <- cbind(First3=c(1,0),Last3=c(0,1),"Last3-First3"=c(-1,1))
> fit <- lmFit(M,design)
> fit2 <- eBayes(contrasts.fit(fit,contrasts=contrast.matrix))
> topTable(fit2)
       First3       Last3 Last3.First3      AveExpr           F      P.Value
A  1.77602021  0.06025114  -1.71576906  0.918135675 50.91471061 7.727200e-23
D -0.05454069  0.39127869   0.44581938  0.168369004  2.51638838 8.075072e-02
F -0.16249607 -0.33009728  -0.16760121 -0.246296671  2.18256779 1.127516e-01
G  0.30852468 -0.06873462  -0.37725930  0.119895035  1.61088775 1.997102e-01
H -0.16942269  0.20578118   0.37520387  0.018179245  1.14554368 3.180510e-01
J  0.21417623  0.07074940  -0.14342683  0.142462814  0.82029274 4.403027e-01
C -0.12236781  0.15095948   0.27332729  0.014295836  0.60885003 5.439761e-01
B -0.11982833  0.13529287   0.25512120  0.007732271  0.52662792 5.905931e-01
E  0.01897934  0.10434934   0.08536999  0.061664340  0.18136849 8.341279e-01
I -0.04720963  0.03996397   0.08717360 -0.003622829  0.06168476 9.401792e-01
     adj.P.Val
A 7.727200e-22
D 3.758388e-01
F 3.758388e-01
G 4.992756e-01
H 6.361019e-01
J 7.338379e-01
C 7.382414e-01
B 7.382414e-01
E 9.268088e-01
I 9.401792e-01
> topTable(fit2,coef=3,resort.by="logFC")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="p")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,sort="logFC",resort.by="t")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="B")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,lfc=1)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p=0.2)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p=0.2,lfc=0.5)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p=0.2,lfc=0.5,sort="none")
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> 
> designlist <- list(Null=matrix(1,6,1),Two=design,Three=cbind(1,c(0,0,1,1,0,0),c(0,0,0,0,1,1)))
> out <- selectModel(M,designlist)
> table(out$pref)

 Null   Two Three 
    5     3     2 
> 
> ### marray object
> 
> #suppressMessages(suppressWarnings(gotmarray <- require(marray,quietly=TRUE)))
> #if(gotmarray) {
> #	data(swirl)
> #	snorm = maNorm(swirl)
> #	fit <- lmFit(snorm, design = c(1,-1,-1,1))
> #	fit <- eBayes(fit)
> #	topTable(fit,resort.by="AveExpr")
> #}
> 
> ### duplicateCorrelation
> 
> cor.out <- duplicateCorrelation(M)
> cor.out$consensus.correlation
[1] -0.09290714
> cor.out$atanh.correlations
[1] -0.4419130  0.4088967 -0.1964978 -0.6093769  0.3730118
> 
> ### gls.series
> 
> fit <- gls.series(M,design,correlation=cor.out$cor)
> fit$coefficients
     First3Arrays Last3Arrays
[1,]   0.82809594  0.09777201
[2,]  -0.08845425  0.27111909
[3,]  -0.07175836 -0.11287397
[4,]   0.06955100  0.06852328
[5,]   0.08348330  0.05535668
> fit$stdev.unscaled
     First3Arrays Last3Arrays
[1,]    0.3888215   0.3888215
[2,]    0.3888215   0.3888215
[3,]    0.3888215   0.3888215
[4,]    0.3888215   0.3888215
[5,]    0.3888215   0.3888215
> fit$sigma
[1] 0.7630059 0.2152728 0.3350370 0.3227781 0.3405473
> fit$df.residual
[1] 10 10 10 10 10
> 
> ### mrlm
> 
> fit <- mrlm(M,design)
Warning message:
In rlm.default(x = X, y = y, weights = w, ...) :
  'rlm' failed to converge in 20 steps
> fit$coef
  First3Arrays Last3Arrays
A   1.75138894  0.06025114
B  -0.11982833  0.10322039
C  -0.09302502  0.15095948
D  -0.05454069  0.33700045
E   0.07927938  0.10434934
F  -0.16249607 -0.34010852
G   0.30852468 -0.06873462
H  -0.16942269  0.24392984
I  -0.04720963  0.03996397
J   0.21417623 -0.05679272
> fit$stdev.unscaled
  First3Arrays Last3Arrays
A    0.5933418   0.5773503
B    0.5773503   0.6096497
C    0.6017444   0.5773503
D    0.5773503   0.6266021
E    0.6307703   0.5773503
F    0.5773503   0.5846707
G    0.5773503   0.5773503
H    0.5773503   0.6544564
I    0.5773503   0.5773503
J    0.5773503   0.6689776
> fit$sigma
 [1] 0.2894294 0.2679396 0.2090236 0.1461395 0.2309018 0.2827476 0.2285945
 [8] 0.2267556 0.3537469 0.2172409
> fit$df.residual
 [1] 4 4 4 4 4 4 4 4 4 4
> 
> # Similar to Mette Langaas 19 May 2004
> set.seed(123)
> narrays <- 9
> ngenes <- 5
> mu <- 0
> alpha <- 2
> beta <- -2
> epsilon <- matrix(rnorm(narrays*ngenes,0,1),ncol=narrays)
> X <- cbind(rep(1,9),c(0,0,0,1,1,1,0,0,0),c(0,0,0,0,0,0,1,1,1))
> dimnames(X) <- list(1:9,c("mu","alpha","beta"))
> yvec <- mu*X[,1]+alpha*X[,2]+beta*X[,3]
> ymat <- matrix(rep(yvec,ngenes),ncol=narrays,byrow=T)+epsilon
> ymat[5,1:2] <- NA
> fit <- lmFit(ymat,design=X)
> test.contr <- cbind(c(0,1,-1),c(1,1,0),c(1,0,1))
> dimnames(test.contr) <- list(c("mu","alpha","beta"),c("alpha-beta","mu+alpha","mu+beta"))
> fit2 <- contrasts.fit(fit,contrasts=test.contr)
> eBayes(fit2)
An object of class "MArrayLM"
$coefficients
     alpha-beta mu+alpha   mu+beta
[1,]   3.537333 1.677465 -1.859868
[2,]   4.355578 2.372554 -1.983024
[3,]   3.197645 1.053584 -2.144061
[4,]   2.697734 1.611443 -1.086291
[5,]   3.502304 2.051995 -1.450309

$stdev.unscaled
     alpha-beta  mu+alpha   mu+beta
[1,]  0.8164966 0.5773503 0.5773503
[2,]  0.8164966 0.5773503 0.5773503
[3,]  0.8164966 0.5773503 0.5773503
[4,]  0.8164966 0.5773503 0.5773503
[5,]  1.1547005 0.8368633 0.8368633

$sigma
[1] 1.3425032 0.4647155 1.1993444 0.9428569 0.9421509

$df.residual
[1] 6 6 6 6 4

$cov.coefficients
           alpha-beta      mu+alpha       mu+beta
alpha-beta  0.6666667  3.333333e-01 -3.333333e-01
mu+alpha    0.3333333  3.333333e-01 -1.464215e-16
mu+beta    -0.3333333 -1.464215e-16  3.333333e-01

$rank
[1] 3

$Amean
[1]  0.2034961  0.1954604 -0.2863347  0.1188659  0.1784593

$method
[1] "ls"

$design
  mu alpha beta
1  1     0    0
2  1     0    0
3  1     0    0
4  1     1    0
5  1     1    0
6  1     1    0
7  1     0    1
8  1     0    1
9  1     0    1

$contrasts
      alpha-beta mu+alpha mu+beta
mu             0        1       1
alpha          1        1       0
beta          -1        0       1

$df.prior
[1] 9.306153

$s2.prior
[1] 0.923179

$var.prior
[1] 17.33142 17.33142 12.26855

$proportion
[1] 0.01

$s2.post
[1] 1.2677996 0.6459499 1.1251558 0.9097727 0.9124980

$t
     alpha-beta mu+alpha   mu+beta
[1,]   3.847656 2.580411 -2.860996
[2,]   6.637308 5.113018 -4.273553
[3,]   3.692066 1.720376 -3.500994
[4,]   3.464003 2.926234 -1.972606
[5,]   3.175181 2.566881 -1.814221

$df.total
[1] 15.30615 15.30615 15.30615 15.30615 13.30615

$p.value
       alpha-beta     mu+alpha      mu+beta
[1,] 1.529450e-03 0.0206493481 0.0117123495
[2,] 7.144893e-06 0.0001195844 0.0006385076
[3,] 2.109270e-03 0.1055117477 0.0031325769
[4,] 3.381970e-03 0.0102514264 0.0668844448
[5,] 7.124839e-03 0.0230888584 0.0922478630

$lods
     alpha-beta  mu+alpha    mu+beta
[1,]  -1.013417 -3.702133 -3.0332393
[2,]   3.981496  1.283349 -0.2615911
[3,]  -1.315036 -5.168621 -1.7864101
[4,]  -1.757103 -3.043209 -4.6191869
[5,]  -2.257358 -3.478267 -4.5683738

$F
[1]  7.421911 22.203107  7.608327  6.227010  5.060579

$F.p.value
[1] 5.581800e-03 2.988923e-05 5.080726e-03 1.050148e-02 2.320274e-02

> 
> ### uniquegenelist
> 
> uniquegenelist(letters[1:8],ndups=2)
[1] "a" "c" "e" "g"
> uniquegenelist(letters[1:8],ndups=2,spacing=2)
[1] "a" "b" "e" "f"
> 
> ### classifyTests
> 
> tstat <- matrix(c(0,5,0, 0,2.5,0, -2,-2,2, 1,1,1), 4, 3, byrow=TRUE)
> classifyTestsF(tstat)
TestResults matrix
     [,1] [,2] [,3]
[1,]    0    1    0
[2,]    0    0    0
[3,]   -1   -1    1
[4,]    0    0    0
> classifyTestsF(tstat,fstat.only=TRUE)
[1] 8.333333 2.083333 4.000000 1.000000
attr(,"df1")
[1] 3
attr(,"df2")
[1] Inf
> limma:::.classifyTestsP(tstat)
TestResults matrix
     [,1] [,2] [,3]
[1,]    0    1    0
[2,]    0    1    0
[3,]    0    0    0
[4,]    0    0    0
> 
> ### avereps
> 
> x <- matrix(rnorm(8*3),8,3)
> colnames(x) <- c("S1","S2","S3")
> rownames(x) <- c("b","a","a","c","c","b","b","b")
> avereps(x)
          S1         S2         S3
b -0.2353018  0.5220094  0.2302895
a -0.4347701  0.6453498 -0.6758914
c  0.3482980 -0.4820695 -0.3841313
> 
> ### roast
> 
> y <- matrix(rnorm(100*4),100,4)
> sigma <- sqrt(2/rchisq(100,df=7))
> y <- y*sigma
> design <- cbind(Intercept=1,Group=c(0,0,1,1))
> iset1 <- 1:5
> y[iset1,3:4] <- y[iset1,3:4]+3
> iset2 <- 6:10
> roast(y=y,iset1,design,contrast=2)
         Active.Prop     P.Value
Down               0 0.996498249
Up                 1 0.004002001
UpOrDown           1 0.008000000
Mixed              1 0.008000000
> roast(y=y,iset1,design,contrast=2,array.weights=c(0.5,1,0.5,1))
         Active.Prop    P.Value
Down               0 0.99899950
Up                 1 0.00150075
UpOrDown           1 0.00300000
Mixed              1 0.00300000
> w <- matrix(runif(100*4),100,4)
> roast(y=y,iset1,design,contrast=2,weights=w)
         Active.Prop   P.Value
Down               0 0.9994997
Up                 1 0.0010005
UpOrDown           1 0.0020000
Mixed              1 0.0020000
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,gene.weights=runif(100))
     NGenes PropDown PropUp Direction PValue   FDR PValue.Mixed FDR.Mixed
set1      5        0      1        Up  0.008 0.015        0.008     0.015
set2      5        0      0        Up  0.959 0.959        0.687     0.687
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,array.weights=c(0.5,1,0.5,1))
     NGenes PropDown PropUp Direction PValue   FDR PValue.Mixed FDR.Mixed
set1      5        0      1        Up  0.004 0.007        0.004     0.007
set2      5        0      0        Up  0.679 0.679        0.658     0.658
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w)
     NGenes PropDown PropUp Direction PValue   FDR PValue.Mixed FDR.Mixed
set1      5      0.0      1        Up  0.003 0.005        0.003     0.005
set2      5      0.2      0      Down  0.950 0.950        0.250     0.250
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes PropDown PropUp Direction PValue   FDR PValue.Mixed FDR.Mixed
set1      5        0      1        Up  0.001 0.001        0.001     0.001
set2      5        0      0      Down  0.791 0.791        0.146     0.146
> fry(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes Direction       PValue         FDR PValue.Mixed    FDR.Mixed
set1      5        Up 0.0007432594 0.001486519 1.820548e-05 3.641096e-05
set2      5      Down 0.8208140511 0.820814051 2.211837e-01 2.211837e-01
> rownames(y) <- paste0("Gene",1:100)
> iset1A <- rownames(y)[1:5]
> fry(y=y,index=iset1A,design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes Direction       PValue PValue.Mixed
set1      5        Up 0.0007432594 1.820548e-05
> 
> ### camera
> 
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1),allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction      PValue
set1      5  -0.2481655        Up 0.001050253
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction       PValue        FDR
set1      5  -0.2481655        Up 0.0009047749 0.00180955
set2      5   0.1719094      Down 0.9068364378 0.90683644
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1))
     NGenes Direction       PValue
set1      5        Up 1.105329e-10
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2)
     NGenes Direction       PValue          FDR
set1      5        Up 7.334400e-12 1.466880e-11
set2      5      Down 8.677115e-01 8.677115e-01
> camera(y=y,iset1A,design,contrast=2)
     NGenes Direction     PValue
set1      5        Up 7.3344e-12
> 
> ### with EList arg
> 
> y <- new("EList",list(E=y))
> roast(y=y,iset1,design,contrast=2)
         Active.Prop     P.Value
Down               0 0.997498749
Up                 1 0.003001501
UpOrDown           1 0.006000000
Mixed              1 0.006000000
> camera(y=y,iset1,design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction       PValue
set1      5  -0.2481655        Up 0.0009047749
> camera(y=y,iset1,design,contrast=2)
     NGenes Direction     PValue
set1      5        Up 7.3344e-12
> 
> ### eBayes with trend
> 
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
           logFC     AveExpr         t      P.Value  adj.P.Val          B
Gene2   3.729512  1.73488969  4.865697 0.0004854886 0.02902331  0.1596831
Gene3   3.488703  1.03931081  4.754954 0.0005804663 0.02902331 -0.0144071
Gene4   2.696676  1.74060725  3.356468 0.0063282637 0.21094212 -2.3434702
Gene1   2.391846  1.72305203  3.107124 0.0098781268 0.24695317 -2.7738874
Gene33 -1.492317 -0.07525287 -2.783817 0.0176475742 0.29965463 -3.3300835
Gene5   2.387967  1.63066783  2.773444 0.0179792778 0.29965463 -3.3478204
Gene80 -1.839760 -0.32802306 -2.503584 0.0291489863 0.37972679 -3.8049642
Gene39  1.366141 -0.27360750  2.451133 0.0320042242 0.37972679 -3.8925860
Gene95 -1.907074  1.26297763 -2.414217 0.0341754107 0.37972679 -3.9539571
Gene50  1.034777  0.01608433  2.054690 0.0642289403 0.59978803 -4.5350317
> fit$df.prior
[1] 9.098442
> fit$s2.prior
    Gene1     Gene2     Gene3     Gene4     Gene5     Gene6     Gene7     Gene8 
0.6901845 0.6977354 0.3860494 0.7014122 0.6341068 0.2926337 0.3077620 0.3058098 
    Gene9    Gene10    Gene11    Gene12    Gene13    Gene14    Gene15    Gene16 
0.2985145 0.2832520 0.3232434 0.3279710 0.2816081 0.2943502 0.3127994 0.2894802 
   Gene17    Gene18    Gene19    Gene20    Gene21    Gene22    Gene23    Gene24 
0.2812758 0.2840051 0.2839124 0.2954261 0.2838592 0.2812704 0.3157029 0.2844541 
   Gene25    Gene26    Gene27    Gene28    Gene29    Gene30    Gene31    Gene32 
0.4778832 0.2818242 0.2930360 0.2940957 0.2941862 0.3234399 0.3164779 0.2853510 
   Gene33    Gene34    Gene35    Gene36    Gene37    Gene38    Gene39    Gene40 
0.2988244 0.3450090 0.3048596 0.3089086 0.3104534 0.4551549 0.3220008 0.2813286 
   Gene41    Gene42    Gene43    Gene44    Gene45    Gene46    Gene47    Gene48 
0.2826027 0.2822504 0.2823330 0.3170673 0.3146173 0.3146793 0.2916540 0.2975003 
   Gene49    Gene50    Gene51    Gene52    Gene53    Gene54    Gene55    Gene56 
0.3538946 0.2907240 0.3199596 0.2816641 0.2814293 0.2996822 0.2812885 0.2896157 
   Gene57    Gene58    Gene59    Gene60    Gene61    Gene62    Gene63    Gene64 
0.2955317 0.2815907 0.2919420 0.2849675 0.3540805 0.3491713 0.2975019 0.2939325 
   Gene65    Gene66    Gene67    Gene68    Gene69    Gene70    Gene71    Gene72 
0.2986943 0.3265466 0.3402343 0.3394927 0.2813283 0.2814440 0.3089669 0.3030850 
   Gene73    Gene74    Gene75    Gene76    Gene77    Gene78    Gene79    Gene80 
0.2859286 0.2813216 0.3475231 0.3334419 0.2949550 0.3108702 0.2959688 0.3295294 
   Gene81    Gene82    Gene83    Gene84    Gene85    Gene86    Gene87    Gene88 
0.3413700 0.2946268 0.3029565 0.2920284 0.2926205 0.2818046 0.3425116 0.2882936 
   Gene89    Gene90    Gene91    Gene92    Gene93    Gene94    Gene95    Gene96 
0.2945459 0.3077919 0.2892134 0.2823787 0.3048049 0.2961408 0.4590012 0.2812784 
   Gene97    Gene98    Gene99   Gene100 
0.2846345 0.2819651 0.3137551 0.2856081 
> summary(fit$s2.post)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.2335  0.2603  0.2997  0.3375  0.3655  0.7812 
> 
> y$E[1,1] <- NA
> y$E[1,3] <- NA
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
           logFC     AveExpr         t      P.Value  adj.P.Val          B
Gene3   3.488703  1.03931081  4.604490 0.0007644061 0.07644061 -0.2333915
Gene2   3.729512  1.73488969  4.158038 0.0016033158 0.08016579 -0.9438583
Gene4   2.696676  1.74060725  2.898102 0.0145292666 0.44537707 -3.0530813
Gene33 -1.492317 -0.07525287 -2.784004 0.0178150826 0.44537707 -3.2456324
Gene5   2.387967  1.63066783  2.495395 0.0297982959 0.46902627 -3.7272957
Gene80 -1.839760 -0.32802306 -2.491115 0.0300256116 0.46902627 -3.7343584
Gene39  1.366141 -0.27360750  2.440729 0.0328318388 0.46902627 -3.8172597
Gene1   2.638272  1.47993643  2.227507 0.0530016060 0.58890673 -3.9537576
Gene95 -1.907074  1.26297763 -2.288870 0.0429197808 0.53649726 -4.0642439
Gene50  1.034777  0.01608433  2.063663 0.0635275235 0.60439978 -4.4204731
> fit$df.residual[1]
[1] 0
> fit$df.prior
[1] 8.971891
> fit$s2.prior
  [1] 0.7014084 0.9646561 0.4276287 0.9716476 0.8458852 0.2910492 0.3097052
  [8] 0.3074225 0.2985517 0.2786374 0.3267121 0.3316013 0.2766404 0.2932679
 [15] 0.3154347 0.2869186 0.2761395 0.2799884 0.2795119 0.2946468 0.2794412
 [22] 0.2761282 0.3186442 0.2806092 0.4596465 0.2767847 0.2924541 0.2939204
 [29] 0.2930568 0.3269177 0.3194905 0.2814293 0.2989389 0.3483845 0.3062977
 [36] 0.3110287 0.3127934 0.4418052 0.3254067 0.2761732 0.2780422 0.2773311
 [43] 0.2776653 0.3201314 0.3174515 0.3175199 0.2897731 0.2972785 0.3567262
 [50] 0.2885556 0.3232426 0.2767207 0.2762915 0.3000062 0.2761306 0.2870975
 [57] 0.2947817 0.2766152 0.2901489 0.2813183 0.3568982 0.3724440 0.2972804
 [64] 0.2927300 0.2987764 0.3301406 0.3437962 0.3430762 0.2761729 0.2763094
 [71] 0.3110958 0.3041715 0.2822004 0.2761654 0.3507694 0.3371214 0.2940441
 [78] 0.3132660 0.2953388 0.3331880 0.3448949 0.2946558 0.3040162 0.2902616
 [85] 0.2910320 0.2769211 0.3459946 0.2859057 0.2935193 0.3097398 0.2865663
 [92] 0.2774968 0.3062327 0.2955576 0.5425422 0.2761214 0.2808585 0.2771484
 [99] 0.3164981 0.2817725
> summary(fit$s2.post)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.2296  0.2581  0.3003  0.3453  0.3652  0.9158 
> 
> ### voom
> 
> y <- matrix(rpois(100*4,lambda=20),100,4)
> design <- cbind(Int=1,x=c(0,0,1,1))
> v <- voom(y,design)
Warning message:
In regularize.values(x, y, ties, missing(ties)) :
  collapsing to unique 'x' values
> names(v)
[1] "E"       "weights" "design"  "targets"
> summary(v$E)
       V1              V2              V3              V4       
 Min.   :12.25   Min.   :12.58   Min.   :12.19   Min.   :12.24  
 1st Qu.:13.13   1st Qu.:13.07   1st Qu.:13.15   1st Qu.:13.03  
 Median :13.29   Median :13.30   Median :13.30   Median :13.27  
 Mean   :13.28   Mean   :13.29   Mean   :13.29   Mean   :13.28  
 3rd Qu.:13.49   3rd Qu.:13.51   3rd Qu.:13.50   3rd Qu.:13.50  
 Max.   :14.23   Max.   :14.28   Max.   :13.97   Max.   :13.96  
> summary(v$weights)
       V1               V2               V3               V4        
 Min.   : 5.935   Min.   : 5.935   Min.   : 5.935   Min.   : 5.935  
 1st Qu.: 6.788   1st Qu.: 7.049   1st Qu.: 7.207   1st Qu.: 6.825  
 Median :11.066   Median :10.443   Median :10.606   Median :10.414  
 Mean   :10.421   Mean   :10.485   Mean   :10.571   Mean   :10.532  
 3rd Qu.:13.485   3rd Qu.:14.155   3rd Qu.:13.859   3rd Qu.:14.121  
 Max.   :15.083   Max.   :15.101   Max.   :15.095   Max.   :15.063  
> 
> ### goana
> 
> EB <- c("133746","1339","134","1340","134083","134111","134147","134187","134218","134266",
+ "134353","134359","134391","134429","134430","1345","134510","134526","134549","1346",
+ "134637","1347","134701","134728","1348","134829","134860","134864","1349","134957",
+ "135","1350","1351","135112","135114","135138","135152","135154","1352","135228",
+ "135250","135293","135295","1353","135458","1355","1356","135644","135656","1357",
+ "1358","135892","1359","135924","135935","135941","135946","135948","136","1360",
+ "136051","1361","1362","136227","136242","136259","1363","136306","136319","136332",
+ "136371","1364","1365","136541","1366","136647","1368","136853","1369","136991",
+ "1370","137075","1371","137209","1373","137362","1374","137492","1375","1376",
+ "137682","137695","137735","1378","137814","137868","137872","137886","137902","137964")
> go <- goana(fit,FDR=0.8,geneid=EB)
> topGO(go,n=10,truncate.term=30)
                                     Term Ont  N Up Down        P.Up
GO:0070062          extracellular exosome  CC  8  0    4 1.000000000
GO:0043230        extracellular organelle  CC  8  0    4 1.000000000
GO:1903561          extracellular vesicle  CC  8  0    4 1.000000000
GO:0072359 circulatory system developm...  BP  2  0    2 1.000000000
GO:0007507              heart development  BP  2  0    2 1.000000000
GO:0032501 multicellular organismal pr...  BP 31  6    7 0.796992878
GO:0098609             cell-cell adhesion  BP  5  4    0 0.009503355
GO:0097190    apoptotic signaling pathway  BP  3  3    0 0.010952381
GO:0031252              cell leading edge  CC  3  3    0 0.010952381
GO:0006897                    endocytosis  BP  3  3    0 0.010952381
                P.Down
GO:0070062 0.003047199
GO:0043230 0.003047199
GO:1903561 0.003047199
GO:0072359 0.009090909
GO:0007507 0.009090909
GO:0032501 0.009111120
GO:0098609 1.000000000
GO:0097190 1.000000000
GO:0031252 1.000000000
GO:0006897 1.000000000
> topGO(go,n=10,truncate.term=30,sort="down")
                                     Term Ont  N Up Down      P.Up      P.Down
GO:0070062          extracellular exosome  CC  8  0    4 1.0000000 0.003047199
GO:0043230        extracellular organelle  CC  8  0    4 1.0000000 0.003047199
GO:1903561          extracellular vesicle  CC  8  0    4 1.0000000 0.003047199
GO:0072359 circulatory system developm...  BP  2  0    2 1.0000000 0.009090909
GO:0007507              heart development  BP  2  0    2 1.0000000 0.009090909
GO:0032501 multicellular organismal pr...  BP 31  6    7 0.7969929 0.009111120
GO:0032502          developmental process  BP 25  4    6 0.8946593 0.014492712
GO:0031982                        vesicle  CC 18  1    5 0.9946677 0.015552466
GO:0009887     animal organ morphogenesis  BP  3  0    2 1.0000000 0.025788497
GO:0055082 cellular chemical homeostas...  BP  3  1    2 0.5476190 0.025788497
> 
> proc.time()
   user  system elapsed 
   4.12    0.26    4.37 

R Under development (unstable) (2019-03-09 r76216) -- "Unsuffered Consequences"
Copyright (C) 2019 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(limma)
> 
> set.seed(0); u <- runif(100)
> 
> ### strsplit2
> 
> x <- c("ab;cd;efg","abc;def","z","")
> strsplit2(x,split=";")
     [,1]  [,2]  [,3] 
[1,] "ab"  "cd"  "efg"
[2,] "abc" "def" ""   
[3,] "z"   ""    ""   
[4,] ""    ""    ""   
> 
> ### removeext
> 
> removeExt(c("slide1.spot","slide.2.spot"))
[1] "slide1"  "slide.2"
> removeExt(c("slide1.spot","slide"))
[1] "slide1.spot" "slide"      
> 
> ### printorder
> 
> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6),ndups=2,start="topright",npins=4)
$printorder
  [1]   6   5   4   3   2   1  12  11  10   9   8   7  18  17  16  15  14  13
 [19]  24  23  22  21  20  19  30  29  28  27  26  25  36  35  34  33  32  31
 [37]  42  41  40  39  38  37  48  47  46  45  44  43   6   5   4   3   2   1
 [55]  12  11  10   9   8   7  18  17  16  15  14  13  24  23  22  21  20  19
 [73]  30  29  28  27  26  25  36  35  34  33  32  31  42  41  40  39  38  37
 [91]  48  47  46  45  44  43   6   5   4   3   2   1  12  11  10   9   8   7
[109]  18  17  16  15  14  13  24  23  22  21  20  19  30  29  28  27  26  25
[127]  36  35  34  33  32  31  42  41  40  39  38  37  48  47  46  45  44  43
[145]   6   5   4   3   2   1  12  11  10   9   8   7  18  17  16  15  14  13
[163]  24  23  22  21  20  19  30  29  28  27  26  25  36  35  34  33  32  31
[181]  42  41  40  39  38  37  48  47  46  45  44  43  54  53  52  51  50  49
[199]  60  59  58  57  56  55  66  65  64  63  62  61  72  71  70  69  68  67
[217]  78  77  76  75  74  73  84  83  82  81  80  79  90  89  88  87  86  85
[235]  96  95  94  93  92  91  54  53  52  51  50  49  60  59  58  57  56  55
[253]  66  65  64  63  62  61  72  71  70  69  68  67  78  77  76  75  74  73
[271]  84  83  82  81  80  79  90  89  88  87  86  85  96  95  94  93  92  91
[289]  54  53  52  51  50  49  60  59  58  57  56  55  66  65  64  63  62  61
[307]  72  71  70  69  68  67  78  77  76  75  74  73  84  83  82  81  80  79
[325]  90  89  88  87  86  85  96  95  94  93  92  91  54  53  52  51  50  49
[343]  60  59  58  57  56  55  66  65  64  63  62  61  72  71  70  69  68  67
[361]  78  77  76  75  74  73  84  83  82  81  80  79  90  89  88  87  86  85
[379]  96  95  94  93  92  91 102 101 100  99  98  97 108 107 106 105 104 103
[397] 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121
[415] 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139
[433] 102 101 100  99  98  97 108 107 106 105 104 103 114 113 112 111 110 109
[451] 120 119 118 117 116 115 126 125 124 123 122 121 132 131 130 129 128 127
[469] 138 137 136 135 134 133 144 143 142 141 140 139 102 101 100  99  98  97
[487] 108 107 106 105 104 103 114 113 112 111 110 109 120 119 118 117 116 115
[505] 126 125 124 123 122 121 132 131 130 129 128 127 138 137 136 135 134 133
[523] 144 143 142 141 140 139 102 101 100  99  98  97 108 107 106 105 104 103
[541] 114 113 112 111 110 109 120 119 118 117 116 115 126 125 124 123 122 121
[559] 132 131 130 129 128 127 138 137 136 135 134 133 144 143 142 141 140 139
[577] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157
[595] 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[613] 186 185 184 183 182 181 192 191 190 189 188 187 150 149 148 147 146 145
[631] 156 155 154 153 152 151 162 161 160 159 158 157 168 167 166 165 164 163
[649] 174 173 172 171 170 169 180 179 178 177 176 175 186 185 184 183 182 181
[667] 192 191 190 189 188 187 150 149 148 147 146 145 156 155 154 153 152 151
[685] 162 161 160 159 158 157 168 167 166 165 164 163 174 173 172 171 170 169
[703] 180 179 178 177 176 175 186 185 184 183 182 181 192 191 190 189 188 187
[721] 150 149 148 147 146 145 156 155 154 153 152 151 162 161 160 159 158 157
[739] 168 167 166 165 164 163 174 173 172 171 170 169 180 179 178 177 176 175
[757] 186 185 184 183 182 181 192 191 190 189 188 187

$plate
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[186] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[519] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[556] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[667] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[741] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

$plate.r
  [1]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4
 [26]  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  4  3  3
 [51]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3
 [76]  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  3  2  2  2  2
[101]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2
[126]  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  2  1  1  1  1  1  1
[151]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1
[176]  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  8  8  8  8  8  8  8  8
[201]  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8
[226]  8  8  8  8  8  8  8  8  8  8  8  8  8  8  8  7  7  7  7  7  7  7  7  7  7
[251]  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7  7
[276]  7  7  7  7  7  7  7  7  7  7  7  7  7  6  6  6  6  6  6  6  6  6  6  6  6
[301]  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6  6
[326]  6  6  6  6  6  6  6  6  6  6  6  5  5  5  5  5  5  5  5  5  5  5  5  5  5
[351]  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5  5
[376]  5  5  5  5  5  5  5  5  5 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[401] 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12
[426] 12 12 12 12 12 12 12 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[451] 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11
[476] 11 11 11 11 11 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[501] 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
[526] 10 10 10  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
[551]  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9  9
[576]  9 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16
[601] 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 15
[626] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15
[651] 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 14 14 14
[676] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14
[701] 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 13 13 13 13 13
[726] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13
[751] 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13

$plate.c
  [1]  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15
 [26] 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3
 [51]  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14
 [76] 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2
[101]  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13
[126] 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1
[151]  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18
[176] 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6
[201]  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17
[226] 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5
[251]  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16
[276] 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4
[301]  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21
[326] 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9
[351]  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20
[376] 20 19 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8
[401]  7  7 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19
[426] 19 24 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7
[451] 12 12 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24
[476] 24 23 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12
[501] 11 11 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23
[526] 23 22 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11
[551] 10 10 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22
[576] 22  3  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10
[601] 15 15 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3
[626]  3  2  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15
[651] 14 14 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2
[676]  2  1  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14
[701] 13 13 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22  3  3  2  2  1
[726]  1  6  6  5  5  4  4  9  9  8  8  7  7 12 12 11 11 10 10 15 15 14 14 13 13
[751] 18 18 17 17 16 16 21 21 20 20 19 19 24 24 23 23 22 22

$plateposition
  [1] "p1D03" "p1D03" "p1D02" "p1D02" "p1D01" "p1D01" "p1D06" "p1D06" "p1D05"
 [10] "p1D05" "p1D04" "p1D04" "p1D09" "p1D09" "p1D08" "p1D08" "p1D07" "p1D07"
 [19] "p1D12" "p1D12" "p1D11" "p1D11" "p1D10" "p1D10" "p1D15" "p1D15" "p1D14"
 [28] "p1D14" "p1D13" "p1D13" "p1D18" "p1D18" "p1D17" "p1D17" "p1D16" "p1D16"
 [37] "p1D21" "p1D21" "p1D20" "p1D20" "p1D19" "p1D19" "p1D24" "p1D24" "p1D23"
 [46] "p1D23" "p1D22" "p1D22" "p1C03" "p1C03" "p1C02" "p1C02" "p1C01" "p1C01"
 [55] "p1C06" "p1C06" "p1C05" "p1C05" "p1C04" "p1C04" "p1C09" "p1C09" "p1C08"
 [64] "p1C08" "p1C07" "p1C07" "p1C12" "p1C12" "p1C11" "p1C11" "p1C10" "p1C10"
 [73] "p1C15" "p1C15" "p1C14" "p1C14" "p1C13" "p1C13" "p1C18" "p1C18" "p1C17"
 [82] "p1C17" "p1C16" "p1C16" "p1C21" "p1C21" "p1C20" "p1C20" "p1C19" "p1C19"
 [91] "p1C24" "p1C24" "p1C23" "p1C23" "p1C22" "p1C22" "p1B03" "p1B03" "p1B02"
[100] "p1B02" "p1B01" "p1B01" "p1B06" "p1B06" "p1B05" "p1B05" "p1B04" "p1B04"
[109] "p1B09" "p1B09" "p1B08" "p1B08" "p1B07" "p1B07" "p1B12" "p1B12" "p1B11"
[118] "p1B11" "p1B10" "p1B10" "p1B15" "p1B15" "p1B14" "p1B14" "p1B13" "p1B13"
[127] "p1B18" "p1B18" "p1B17" "p1B17" "p1B16" "p1B16" "p1B21" "p1B21" "p1B20"
[136] "p1B20" "p1B19" "p1B19" "p1B24" "p1B24" "p1B23" "p1B23" "p1B22" "p1B22"
[145] "p1A03" "p1A03" "p1A02" "p1A02" "p1A01" "p1A01" "p1A06" "p1A06" "p1A05"
[154] "p1A05" "p1A04" "p1A04" "p1A09" "p1A09" "p1A08" "p1A08" "p1A07" "p1A07"
[163] "p1A12" "p1A12" "p1A11" "p1A11" "p1A10" "p1A10" "p1A15" "p1A15" "p1A14"
[172] "p1A14" "p1A13" "p1A13" "p1A18" "p1A18" "p1A17" "p1A17" "p1A16" "p1A16"
[181] "p1A21" "p1A21" "p1A20" "p1A20" "p1A19" "p1A19" "p1A24" "p1A24" "p1A23"
[190] "p1A23" "p1A22" "p1A22" "p1H03" "p1H03" "p1H02" "p1H02" "p1H01" "p1H01"
[199] "p1H06" "p1H06" "p1H05" "p1H05" "p1H04" "p1H04" "p1H09" "p1H09" "p1H08"
[208] "p1H08" "p1H07" "p1H07" "p1H12" "p1H12" "p1H11" "p1H11" "p1H10" "p1H10"
[217] "p1H15" "p1H15" "p1H14" "p1H14" "p1H13" "p1H13" "p1H18" "p1H18" "p1H17"
[226] "p1H17" "p1H16" "p1H16" "p1H21" "p1H21" "p1H20" "p1H20" "p1H19" "p1H19"
[235] "p1H24" "p1H24" "p1H23" "p1H23" "p1H22" "p1H22" "p1G03" "p1G03" "p1G02"
[244] "p1G02" "p1G01" "p1G01" "p1G06" "p1G06" "p1G05" "p1G05" "p1G04" "p1G04"
[253] "p1G09" "p1G09" "p1G08" "p1G08" "p1G07" "p1G07" "p1G12" "p1G12" "p1G11"
[262] "p1G11" "p1G10" "p1G10" "p1G15" "p1G15" "p1G14" "p1G14" "p1G13" "p1G13"
[271] "p1G18" "p1G18" "p1G17" "p1G17" "p1G16" "p1G16" "p1G21" "p1G21" "p1G20"
[280] "p1G20" "p1G19" "p1G19" "p1G24" "p1G24" "p1G23" "p1G23" "p1G22" "p1G22"
[289] "p1F03" "p1F03" "p1F02" "p1F02" "p1F01" "p1F01" "p1F06" "p1F06" "p1F05"
[298] "p1F05" "p1F04" "p1F04" "p1F09" "p1F09" "p1F08" "p1F08" "p1F07" "p1F07"
[307] "p1F12" "p1F12" "p1F11" "p1F11" "p1F10" "p1F10" "p1F15" "p1F15" "p1F14"
[316] "p1F14" "p1F13" "p1F13" "p1F18" "p1F18" "p1F17" "p1F17" "p1F16" "p1F16"
[325] "p1F21" "p1F21" "p1F20" "p1F20" "p1F19" "p1F19" "p1F24" "p1F24" "p1F23"
[334] "p1F23" "p1F22" "p1F22" "p1E03" "p1E03" "p1E02" "p1E02" "p1E01" "p1E01"
[343] "p1E06" "p1E06" "p1E05" "p1E05" "p1E04" "p1E04" "p1E09" "p1E09" "p1E08"
[352] "p1E08" "p1E07" "p1E07" "p1E12" "p1E12" "p1E11" "p1E11" "p1E10" "p1E10"
[361] "p1E15" "p1E15" "p1E14" "p1E14" "p1E13" "p1E13" "p1E18" "p1E18" "p1E17"
[370] "p1E17" "p1E16" "p1E16" "p1E21" "p1E21" "p1E20" "p1E20" "p1E19" "p1E19"
[379] "p1E24" "p1E24" "p1E23" "p1E23" "p1E22" "p1E22" "p1L03" "p1L03" "p1L02"
[388] "p1L02" "p1L01" "p1L01" "p1L06" "p1L06" "p1L05" "p1L05" "p1L04" "p1L04"
[397] "p1L09" "p1L09" "p1L08" "p1L08" "p1L07" "p1L07" "p1L12" "p1L12" "p1L11"
[406] "p1L11" "p1L10" "p1L10" "p1L15" "p1L15" "p1L14" "p1L14" "p1L13" "p1L13"
[415] "p1L18" "p1L18" "p1L17" "p1L17" "p1L16" "p1L16" "p1L21" "p1L21" "p1L20"
[424] "p1L20" "p1L19" "p1L19" "p1L24" "p1L24" "p1L23" "p1L23" "p1L22" "p1L22"
[433] "p1K03" "p1K03" "p1K02" "p1K02" "p1K01" "p1K01" "p1K06" "p1K06" "p1K05"
[442] "p1K05" "p1K04" "p1K04" "p1K09" "p1K09" "p1K08" "p1K08" "p1K07" "p1K07"
[451] "p1K12" "p1K12" "p1K11" "p1K11" "p1K10" "p1K10" "p1K15" "p1K15" "p1K14"
[460] "p1K14" "p1K13" "p1K13" "p1K18" "p1K18" "p1K17" "p1K17" "p1K16" "p1K16"
[469] "p1K21" "p1K21" "p1K20" "p1K20" "p1K19" "p1K19" "p1K24" "p1K24" "p1K23"
[478] "p1K23" "p1K22" "p1K22" "p1J03" "p1J03" "p1J02" "p1J02" "p1J01" "p1J01"
[487] "p1J06" "p1J06" "p1J05" "p1J05" "p1J04" "p1J04" "p1J09" "p1J09" "p1J08"
[496] "p1J08" "p1J07" "p1J07" "p1J12" "p1J12" "p1J11" "p1J11" "p1J10" "p1J10"
[505] "p1J15" "p1J15" "p1J14" "p1J14" "p1J13" "p1J13" "p1J18" "p1J18" "p1J17"
[514] "p1J17" "p1J16" "p1J16" "p1J21" "p1J21" "p1J20" "p1J20" "p1J19" "p1J19"
[523] "p1J24" "p1J24" "p1J23" "p1J23" "p1J22" "p1J22" "p1I03" "p1I03" "p1I02"
[532] "p1I02" "p1I01" "p1I01" "p1I06" "p1I06" "p1I05" "p1I05" "p1I04" "p1I04"
[541] "p1I09" "p1I09" "p1I08" "p1I08" "p1I07" "p1I07" "p1I12" "p1I12" "p1I11"
[550] "p1I11" "p1I10" "p1I10" "p1I15" "p1I15" "p1I14" "p1I14" "p1I13" "p1I13"
[559] "p1I18" "p1I18" "p1I17" "p1I17" "p1I16" "p1I16" "p1I21" "p1I21" "p1I20"
[568] "p1I20" "p1I19" "p1I19" "p1I24" "p1I24" "p1I23" "p1I23" "p1I22" "p1I22"
[577] "p1P03" "p1P03" "p1P02" "p1P02" "p1P01" "p1P01" "p1P06" "p1P06" "p1P05"
[586] "p1P05" "p1P04" "p1P04" "p1P09" "p1P09" "p1P08" "p1P08" "p1P07" "p1P07"
[595] "p1P12" "p1P12" "p1P11" "p1P11" "p1P10" "p1P10" "p1P15" "p1P15" "p1P14"
[604] "p1P14" "p1P13" "p1P13" "p1P18" "p1P18" "p1P17" "p1P17" "p1P16" "p1P16"
[613] "p1P21" "p1P21" "p1P20" "p1P20" "p1P19" "p1P19" "p1P24" "p1P24" "p1P23"
[622] "p1P23" "p1P22" "p1P22" "p1O03" "p1O03" "p1O02" "p1O02" "p1O01" "p1O01"
[631] "p1O06" "p1O06" "p1O05" "p1O05" "p1O04" "p1O04" "p1O09" "p1O09" "p1O08"
[640] "p1O08" "p1O07" "p1O07" "p1O12" "p1O12" "p1O11" "p1O11" "p1O10" "p1O10"
[649] "p1O15" "p1O15" "p1O14" "p1O14" "p1O13" "p1O13" "p1O18" "p1O18" "p1O17"
[658] "p1O17" "p1O16" "p1O16" "p1O21" "p1O21" "p1O20" "p1O20" "p1O19" "p1O19"
[667] "p1O24" "p1O24" "p1O23" "p1O23" "p1O22" "p1O22" "p1N03" "p1N03" "p1N02"
[676] "p1N02" "p1N01" "p1N01" "p1N06" "p1N06" "p1N05" "p1N05" "p1N04" "p1N04"
[685] "p1N09" "p1N09" "p1N08" "p1N08" "p1N07" "p1N07" "p1N12" "p1N12" "p1N11"
[694] "p1N11" "p1N10" "p1N10" "p1N15" "p1N15" "p1N14" "p1N14" "p1N13" "p1N13"
[703] "p1N18" "p1N18" "p1N17" "p1N17" "p1N16" "p1N16" "p1N21" "p1N21" "p1N20"
[712] "p1N20" "p1N19" "p1N19" "p1N24" "p1N24" "p1N23" "p1N23" "p1N22" "p1N22"
[721] "p1M03" "p1M03" "p1M02" "p1M02" "p1M01" "p1M01" "p1M06" "p1M06" "p1M05"
[730] "p1M05" "p1M04" "p1M04" "p1M09" "p1M09" "p1M08" "p1M08" "p1M07" "p1M07"
[739] "p1M12" "p1M12" "p1M11" "p1M11" "p1M10" "p1M10" "p1M15" "p1M15" "p1M14"
[748] "p1M14" "p1M13" "p1M13" "p1M18" "p1M18" "p1M17" "p1M17" "p1M16" "p1M16"
[757] "p1M21" "p1M21" "p1M20" "p1M20" "p1M19" "p1M19" "p1M24" "p1M24" "p1M23"
[766] "p1M23" "p1M22" "p1M22"

> printorder(list(ngrid.r=4,ngrid.c=4,nspot.r=8,nspot.c=6))
$printorder
  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
 [26] 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2
 [51]  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
 [76] 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4
[101]  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
[126] 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6
[151]  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
[176] 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8
[201]  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33
[226] 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10
[251] 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
[276] 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12
[301] 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
[326] 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14
[351] 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39
[376] 40 41 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16
[401] 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
[426] 42 43 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18
[451] 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
[476] 44 45 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
[501] 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45
[526] 46 47 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22
[551] 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47
[576] 48  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
[601] 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1
[626]  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
[651] 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3
[676]  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
[701] 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48  1  2  3  4  5
[726]  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
[751] 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

$plate
  [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2
 [38] 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2
 [75] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[112] 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1
[149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[186] 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2
[223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[260] 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1
[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[334] 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2
[371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[408] 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1
[445] 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1
[482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[519] 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2
[556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[593] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1
[630] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
[667] 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2
[704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
[741] 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

$plate.r
  [1]  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  4
 [26]  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3  3
 [51]  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3  3
 [76]  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2  2
[101]  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2  2
[126]  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1  1
[151]  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1  5
[176]  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  4  4  4  4  4  4  8  8
[201]  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  4  4  4  4  4  4  8  8  8
[226]  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3  3  3  3  3  3  7  7  7  7
[251]  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3  3  3  3  3  7  7  7  7  7
[276]  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2  2  2  2  6  6  6  6  6  6
[301] 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2  2  2  6  6  6  6  6  6 10
[326] 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1  1  5  5  5  5  5  5  9  9
[351]  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9
[376]  9  9  9 13 13 13 13 13 13  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12
[401] 12 12 16 16 16 16 16 16  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12
[426] 12 16 16 16 16 16 16  3  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11
[451] 15 15 15 15 15 15  3  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15
[476] 15 15 15 15 15  2  2  2  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14
[501] 14 14 14 14  2  2  2  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14
[526] 14 14 14  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13
[551] 13 13  1  1  1  1  1  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13
[576] 13  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16
[601]  4  4  4  4  4  4  8  8  8  8  8  8 12 12 12 12 12 12 16 16 16 16 16 16  3
[626]  3  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  3  3
[651]  3  3  3  3  7  7  7  7  7  7 11 11 11 11 11 11 15 15 15 15 15 15  2  2  2
[676]  2  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  2  2  2  2
[701]  2  2  6  6  6  6  6  6 10 10 10 10 10 10 14 14 14 14 14 14  1  1  1  1  1
[726]  1  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13  1  1  1  1  1  1
[751]  5  5  5  5  5  5  9  9  9  9  9  9 13 13 13 13 13 13

$plate.c
  [1]  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1
 [26]  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5
 [51]  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9
 [76] 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13
[101] 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17
[126] 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21
[151]  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  1
[176]  5  9 13 17 21  1  5  9 13 17 21  1  5  9 13 17 21  2  6 10 14 18 22  2  6
[201] 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10
[226] 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14
[251] 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18
[276] 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22
[301]  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2
[326]  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6
[351] 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10 14 18 22  2  6 10
[376] 14 18 22  2  6 10 14 18 22  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15
[401] 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19
[426] 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23
[451]  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3
[476]  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7
[501] 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11
[526] 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15
[551] 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19 23  3  7 11 15 19
[576] 23  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24
[601]  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4
[626]  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8
[651] 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12
[676] 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16
[701] 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20
[726] 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24
[751]  4  8 12 16 20 24  4  8 12 16 20 24  4  8 12 16 20 24

$plateposition
  [1] "p1D01" "p1D05" "p1D09" "p1D13" "p1D17" "p1D21" "p1H01" "p1H05" "p1H09"
 [10] "p1H13" "p1H17" "p1H21" "p1L01" "p1L05" "p1L09" "p1L13" "p1L17" "p1L21"
 [19] "p1P01" "p1P05" "p1P09" "p1P13" "p1P17" "p1P21" "p2D01" "p2D05" "p2D09"
 [28] "p2D13" "p2D17" "p2D21" "p2H01" "p2H05" "p2H09" "p2H13" "p2H17" "p2H21"
 [37] "p2L01" "p2L05" "p2L09" "p2L13" "p2L17" "p2L21" "p2P01" "p2P05" "p2P09"
 [46] "p2P13" "p2P17" "p2P21" "p1C01" "p1C05" "p1C09" "p1C13" "p1C17" "p1C21"
 [55] "p1G01" "p1G05" "p1G09" "p1G13" "p1G17" "p1G21" "p1K01" "p1K05" "p1K09"
 [64] "p1K13" "p1K17" "p1K21" "p1O01" "p1O05" "p1O09" "p1O13" "p1O17" "p1O21"
 [73] "p2C01" "p2C05" "p2C09" "p2C13" "p2C17" "p2C21" "p2G01" "p2G05" "p2G09"
 [82] "p2G13" "p2G17" "p2G21" "p2K01" "p2K05" "p2K09" "p2K13" "p2K17" "p2K21"
 [91] "p2O01" "p2O05" "p2O09" "p2O13" "p2O17" "p2O21" "p1B01" "p1B05" "p1B09"
[100] "p1B13" "p1B17" "p1B21" "p1F01" "p1F05" "p1F09" "p1F13" "p1F17" "p1F21"
[109] "p1J01" "p1J05" "p1J09" "p1J13" "p1J17" "p1J21" "p1N01" "p1N05" "p1N09"
[118] "p1N13" "p1N17" "p1N21" "p2B01" "p2B05" "p2B09" "p2B13" "p2B17" "p2B21"
[127] "p2F01" "p2F05" "p2F09" "p2F13" "p2F17" "p2F21" "p2J01" "p2J05" "p2J09"
[136] "p2J13" "p2J17" "p2J21" "p2N01" "p2N05" "p2N09" "p2N13" "p2N17" "p2N21"
[145] "p1A01" "p1A05" "p1A09" "p1A13" "p1A17" "p1A21" "p1E01" "p1E05" "p1E09"
[154] "p1E13" "p1E17" "p1E21" "p1I01" "p1I05" "p1I09" "p1I13" "p1I17" "p1I21"
[163] "p1M01" "p1M05" "p1M09" "p1M13" "p1M17" "p1M21" "p2A01" "p2A05" "p2A09"
[172] "p2A13" "p2A17" "p2A21" "p2E01" "p2E05" "p2E09" "p2E13" "p2E17" "p2E21"
[181] "p2I01" "p2I05" "p2I09" "p2I13" "p2I17" "p2I21" "p2M01" "p2M05" "p2M09"
[190] "p2M13" "p2M17" "p2M21" "p1D02" "p1D06" "p1D10" "p1D14" "p1D18" "p1D22"
[199] "p1H02" "p1H06" "p1H10" "p1H14" "p1H18" "p1H22" "p1L02" "p1L06" "p1L10"
[208] "p1L14" "p1L18" "p1L22" "p1P02" "p1P06" "p1P10" "p1P14" "p1P18" "p1P22"
[217] "p2D02" "p2D06" "p2D10" "p2D14" "p2D18" "p2D22" "p2H02" "p2H06" "p2H10"
[226] "p2H14" "p2H18" "p2H22" "p2L02" "p2L06" "p2L10" "p2L14" "p2L18" "p2L22"
[235] "p2P02" "p2P06" "p2P10" "p2P14" "p2P18" "p2P22" "p1C02" "p1C06" "p1C10"
[244] "p1C14" "p1C18" "p1C22" "p1G02" "p1G06" "p1G10" "p1G14" "p1G18" "p1G22"
[253] "p1K02" "p1K06" "p1K10" "p1K14" "p1K18" "p1K22" "p1O02" "p1O06" "p1O10"
[262] "p1O14" "p1O18" "p1O22" "p2C02" "p2C06" "p2C10" "p2C14" "p2C18" "p2C22"
[271] "p2G02" "p2G06" "p2G10" "p2G14" "p2G18" "p2G22" "p2K02" "p2K06" "p2K10"
[280] "p2K14" "p2K18" "p2K22" "p2O02" "p2O06" "p2O10" "p2O14" "p2O18" "p2O22"
[289] "p1B02" "p1B06" "p1B10" "p1B14" "p1B18" "p1B22" "p1F02" "p1F06" "p1F10"
[298] "p1F14" "p1F18" "p1F22" "p1J02" "p1J06" "p1J10" "p1J14" "p1J18" "p1J22"
[307] "p1N02" "p1N06" "p1N10" "p1N14" "p1N18" "p1N22" "p2B02" "p2B06" "p2B10"
[316] "p2B14" "p2B18" "p2B22" "p2F02" "p2F06" "p2F10" "p2F14" "p2F18" "p2F22"
[325] "p2J02" "p2J06" "p2J10" "p2J14" "p2J18" "p2J22" "p2N02" "p2N06" "p2N10"
[334] "p2N14" "p2N18" "p2N22" "p1A02" "p1A06" "p1A10" "p1A14" "p1A18" "p1A22"
[343] "p1E02" "p1E06" "p1E10" "p1E14" "p1E18" "p1E22" "p1I02" "p1I06" "p1I10"
[352] "p1I14" "p1I18" "p1I22" "p1M02" "p1M06" "p1M10" "p1M14" "p1M18" "p1M22"
[361] "p2A02" "p2A06" "p2A10" "p2A14" "p2A18" "p2A22" "p2E02" "p2E06" "p2E10"
[370] "p2E14" "p2E18" "p2E22" "p2I02" "p2I06" "p2I10" "p2I14" "p2I18" "p2I22"
[379] "p2M02" "p2M06" "p2M10" "p2M14" "p2M18" "p2M22" "p1D03" "p1D07" "p1D11"
[388] "p1D15" "p1D19" "p1D23" "p1H03" "p1H07" "p1H11" "p1H15" "p1H19" "p1H23"
[397] "p1L03" "p1L07" "p1L11" "p1L15" "p1L19" "p1L23" "p1P03" "p1P07" "p1P11"
[406] "p1P15" "p1P19" "p1P23" "p2D03" "p2D07" "p2D11" "p2D15" "p2D19" "p2D23"
[415] "p2H03" "p2H07" "p2H11" "p2H15" "p2H19" "p2H23" "p2L03" "p2L07" "p2L11"
[424] "p2L15" "p2L19" "p2L23" "p2P03" "p2P07" "p2P11" "p2P15" "p2P19" "p2P23"
[433] "p1C03" "p1C07" "p1C11" "p1C15" "p1C19" "p1C23" "p1G03" "p1G07" "p1G11"
[442] "p1G15" "p1G19" "p1G23" "p1K03" "p1K07" "p1K11" "p1K15" "p1K19" "p1K23"
[451] "p1O03" "p1O07" "p1O11" "p1O15" "p1O19" "p1O23" "p2C03" "p2C07" "p2C11"
[460] "p2C15" "p2C19" "p2C23" "p2G03" "p2G07" "p2G11" "p2G15" "p2G19" "p2G23"
[469] "p2K03" "p2K07" "p2K11" "p2K15" "p2K19" "p2K23" "p2O03" "p2O07" "p2O11"
[478] "p2O15" "p2O19" "p2O23" "p1B03" "p1B07" "p1B11" "p1B15" "p1B19" "p1B23"
[487] "p1F03" "p1F07" "p1F11" "p1F15" "p1F19" "p1F23" "p1J03" "p1J07" "p1J11"
[496] "p1J15" "p1J19" "p1J23" "p1N03" "p1N07" "p1N11" "p1N15" "p1N19" "p1N23"
[505] "p2B03" "p2B07" "p2B11" "p2B15" "p2B19" "p2B23" "p2F03" "p2F07" "p2F11"
[514] "p2F15" "p2F19" "p2F23" "p2J03" "p2J07" "p2J11" "p2J15" "p2J19" "p2J23"
[523] "p2N03" "p2N07" "p2N11" "p2N15" "p2N19" "p2N23" "p1A03" "p1A07" "p1A11"
[532] "p1A15" "p1A19" "p1A23" "p1E03" "p1E07" "p1E11" "p1E15" "p1E19" "p1E23"
[541] "p1I03" "p1I07" "p1I11" "p1I15" "p1I19" "p1I23" "p1M03" "p1M07" "p1M11"
[550] "p1M15" "p1M19" "p1M23" "p2A03" "p2A07" "p2A11" "p2A15" "p2A19" "p2A23"
[559] "p2E03" "p2E07" "p2E11" "p2E15" "p2E19" "p2E23" "p2I03" "p2I07" "p2I11"
[568] "p2I15" "p2I19" "p2I23" "p2M03" "p2M07" "p2M11" "p2M15" "p2M19" "p2M23"
[577] "p1D04" "p1D08" "p1D12" "p1D16" "p1D20" "p1D24" "p1H04" "p1H08" "p1H12"
[586] "p1H16" "p1H20" "p1H24" "p1L04" "p1L08" "p1L12" "p1L16" "p1L20" "p1L24"
[595] "p1P04" "p1P08" "p1P12" "p1P16" "p1P20" "p1P24" "p2D04" "p2D08" "p2D12"
[604] "p2D16" "p2D20" "p2D24" "p2H04" "p2H08" "p2H12" "p2H16" "p2H20" "p2H24"
[613] "p2L04" "p2L08" "p2L12" "p2L16" "p2L20" "p2L24" "p2P04" "p2P08" "p2P12"
[622] "p2P16" "p2P20" "p2P24" "p1C04" "p1C08" "p1C12" "p1C16" "p1C20" "p1C24"
[631] "p1G04" "p1G08" "p1G12" "p1G16" "p1G20" "p1G24" "p1K04" "p1K08" "p1K12"
[640] "p1K16" "p1K20" "p1K24" "p1O04" "p1O08" "p1O12" "p1O16" "p1O20" "p1O24"
[649] "p2C04" "p2C08" "p2C12" "p2C16" "p2C20" "p2C24" "p2G04" "p2G08" "p2G12"
[658] "p2G16" "p2G20" "p2G24" "p2K04" "p2K08" "p2K12" "p2K16" "p2K20" "p2K24"
[667] "p2O04" "p2O08" "p2O12" "p2O16" "p2O20" "p2O24" "p1B04" "p1B08" "p1B12"
[676] "p1B16" "p1B20" "p1B24" "p1F04" "p1F08" "p1F12" "p1F16" "p1F20" "p1F24"
[685] "p1J04" "p1J08" "p1J12" "p1J16" "p1J20" "p1J24" "p1N04" "p1N08" "p1N12"
[694] "p1N16" "p1N20" "p1N24" "p2B04" "p2B08" "p2B12" "p2B16" "p2B20" "p2B24"
[703] "p2F04" "p2F08" "p2F12" "p2F16" "p2F20" "p2F24" "p2J04" "p2J08" "p2J12"
[712] "p2J16" "p2J20" "p2J24" "p2N04" "p2N08" "p2N12" "p2N16" "p2N20" "p2N24"
[721] "p1A04" "p1A08" "p1A12" "p1A16" "p1A20" "p1A24" "p1E04" "p1E08" "p1E12"
[730] "p1E16" "p1E20" "p1E24" "p1I04" "p1I08" "p1I12" "p1I16" "p1I20" "p1I24"
[739] "p1M04" "p1M08" "p1M12" "p1M16" "p1M20" "p1M24" "p2A04" "p2A08" "p2A12"
[748] "p2A16" "p2A20" "p2A24" "p2E04" "p2E08" "p2E12" "p2E16" "p2E20" "p2E24"
[757] "p2I04" "p2I08" "p2I12" "p2I16" "p2I20" "p2I24" "p2M04" "p2M08" "p2M12"
[766] "p2M16" "p2M20" "p2M24"

> 
> ### merge.rglist
> 
> R <- G <- matrix(11:14,4,2)
> rownames(R) <- rownames(G) <- c("a","a","b","c")
> RG1 <- new("RGList",list(R=R,G=G))
> R <- G <- matrix(21:24,4,2)
> rownames(R) <- rownames(G) <- c("b","a","a","c")
> RG2 <- new("RGList",list(R=R,G=G))
> merge(RG1,RG2)
An object of class "RGList"
$R
  [,1] [,2] [,3] [,4]
a   11   11   22   22
a   12   12   23   23
b   13   13   21   21
c   14   14   24   24

$G
  [,1] [,2] [,3] [,4]
a   11   11   22   22
a   12   12   23   23
b   13   13   21   21
c   14   14   24   24

> merge(RG2,RG1)
An object of class "RGList"
$R
  [,1] [,2] [,3] [,4]
b   21   21   13   13
a   22   22   11   11
a   23   23   12   12
c   24   24   14   14

$G
  [,1] [,2] [,3] [,4]
b   21   21   13   13
a   22   22   11   11
a   23   23   12   12
c   24   24   14   14

> 
> ### background correction
> 
> RG <- new("RGList", list(R=c(1,2,3,4),G=c(1,2,3,4),Rb=c(2,2,2,2),Gb=c(2,2,2,2)))
> backgroundCorrect(RG)
An object of class "RGList"
$R
     [,1]
[1,]   -1
[2,]    0
[3,]    1
[4,]    2

$G
     [,1]
[1,]   -1
[2,]    0
[3,]    1
[4,]    2

> backgroundCorrect(RG, method="half")
An object of class "RGList"
$R
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

$G
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

> backgroundCorrect(RG, method="minimum")
An object of class "RGList"
$R
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

$G
     [,1]
[1,]  0.5
[2,]  0.5
[3,]  1.0
[4,]  2.0

> backgroundCorrect(RG, offset=5)
An object of class "RGList"
$R
     [,1]
[1,]    4
[2,]    5
[3,]    6
[4,]    7

$G
     [,1]
[1,]    4
[2,]    5
[3,]    6
[4,]    7

> 
> ### loessFit
> 
> x <- 1:100
> y <- rnorm(100)
> out <- loessFit(y,x)
> f1 <- quantile(out$fitted)
> r1 <- quantile(out$residual)
> w <- rep(1,100)
> w[1:50] <- 0.5
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f2 <- quantile(out$fitted)
> r2 <- quantile(out$residual)
> out <- loessFit(y,x,weights=w,method="locfit")
> f3 <- quantile(out$fitted)
> r3 <- quantile(out$residual)
> out <- loessFit(y,x,weights=w,method="loess")
> f4 <- quantile(out$fitted)
> r4 <- quantile(out$residual)
> w <- rep(1,100)
> w[2*(1:50)] <- 0
> out <- loessFit(y,x,weights=w,method="weightedLowess")
> f5 <- quantile(out$fitted)
> r5 <- quantile(out$residual)
> data.frame(f1,f2,f3,f4,f5)
              f1           f2          f3          f4          f5
0%   -0.78835384 -0.687432210 -0.78957137 -0.76756060 -0.63778292
25%  -0.18340154 -0.179683572 -0.18979269 -0.16773223 -0.38064318
50%  -0.11492924 -0.114796040 -0.12087983 -0.07185314 -0.15971879
75%   0.01507921 -0.008145125 -0.01857508  0.04030634  0.07839396
100%  0.21653837  0.145106033  0.19214597  0.21417361  0.51836274
> data.frame(r1,r2,r3,r4,r5)
              r1          r2          r3           r4          r5
0%   -2.04434053 -2.05132680 -2.02404318 -2.101242874 -2.22280633
25%  -0.59321065 -0.57200209 -0.58975649 -0.577887481 -0.71037756
50%   0.05874864  0.04514326  0.08335198 -0.001769806  0.06785517
75%   0.56010750  0.55124530  0.57618740  0.561454370  0.65383830
100%  2.57936026  2.64549799  2.57549257  2.402324533  2.28648835
> 
> ### normalizeWithinArrays
> 
> RG <- new("RGList",list())
> RG$R <- matrix(rexp(100*2),100,2)
> RG$G <- matrix(rexp(100*2),100,2)
> RG$Rb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RG$Gb <- matrix(rnorm(100*2,sd=0.02),100,2)
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="saddle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
       V1                V2                V3               V4        
 Min.   :0.01626   Min.   :0.01213   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.35497   1st Qu.:0.29133   1st Qu.:0.2745   1st Qu.:0.3953  
 Median :0.71793   Median :0.70294   Median :0.6339   Median :0.8223  
 Mean   :0.90184   Mean   :1.00122   Mean   :0.9454   Mean   :1.1324  
 3rd Qu.:1.16891   3rd Qu.:1.33139   3rd Qu.:1.4059   3rd Qu.:1.4221  
 Max.   :4.56267   Max.   :6.37947   Max.   :5.0486   Max.   :6.6295  
> RGb <- backgroundCorrect(RG,method="normexp",normexp.method="mle")
Array 1 corrected
Array 2 corrected
Array 1 corrected
Array 2 corrected
> summary(cbind(RGb$R,RGb$G))
       V1                V2                V3               V4        
 Min.   :0.01701   Min.   :0.01255   Min.   :0.0000   Min.   :0.0000  
 1st Qu.:0.35423   1st Qu.:0.29118   1st Qu.:0.2745   1st Qu.:0.3953  
 Median :0.71719   Median :0.70280   Median :0.6339   Median :0.8223  
 Mean   :0.90118   Mean   :1.00110   Mean   :0.9454   Mean   :1.1324  
 3rd Qu.:1.16817   3rd Qu.:1.33124   3rd Qu.:1.4059   3rd Qu.:1.4221  
 Max.   :4.56193   Max.   :6.37932   Max.   :5.0486   Max.   :6.6295  
> MA <- normalizeWithinArrays(RGb,method="loess")
> summary(MA$M)
       V1                 V2          
 Min.   :-5.88044   Min.   :-5.66985  
 1st Qu.:-1.18483   1st Qu.:-1.57014  
 Median :-0.21632   Median : 0.04823  
 Mean   : 0.03487   Mean   :-0.05481  
 3rd Qu.: 1.49669   3rd Qu.: 1.45113  
 Max.   : 7.07324   Max.   : 6.19744  
> #MA <- normalizeWithinArrays(RG[,1:2], mouse.setup, method="robustspline")
> #MA$M[1:5,]
> #MA <- normalizeWithinArrays(mouse.data, mouse.setup)
> #MA$M[1:5,]
> 
> ### normalizeBetweenArrays
> 
> MA2 <- normalizeBetweenArrays(MA,method="scale")
> MA$M[1:5,]
           [,1]       [,2]
[1,] -1.1689588  4.5558123
[2,]  0.8971363  0.3296544
[3,]  2.8247439  1.4249960
[4,] -1.8533240  0.4804851
[5,]  1.9158459 -5.5087631
> MA$A[1:5,]
            [,1]       [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200  0.2071043
[4,]  0.09281027 -1.3880965
[5,]  0.22385828 -3.0855818
> MA2 <- normalizeBetweenArrays(MA,method="quantile")
> MA$M[1:5,]
           [,1]       [,2]
[1,] -1.1689588  4.5558123
[2,]  0.8971363  0.3296544
[3,]  2.8247439  1.4249960
[4,] -1.8533240  0.4804851
[5,]  1.9158459 -5.5087631
> MA$A[1:5,]
            [,1]       [,2]
[1,] -2.48465011 -2.4041550
[2,] -0.79230447 -0.9002250
[3,] -0.76237200  0.2071043
[4,]  0.09281027 -1.3880965
[5,]  0.22385828 -3.0855818
> 
> ### unwrapdups
> 
> M <- matrix(1:12,6,2)
> unwrapdups(M,ndups=1)
     [,1] [,2]
[1,]    1    7
[2,]    2    8
[3,]    3    9
[4,]    4   10
[5,]    5   11
[6,]    6   12
> unwrapdups(M,ndups=2)
     [,1] [,2] [,3] [,4]
[1,]    1    2    7    8
[2,]    3    4    9   10
[3,]    5    6   11   12
> unwrapdups(M,ndups=3)
     [,1] [,2] [,3] [,4] [,5] [,6]
[1,]    1    2    3    7    8    9
[2,]    4    5    6   10   11   12
> unwrapdups(M,ndups=2,spacing=3)
     [,1] [,2] [,3] [,4]
[1,]    1    4    7   10
[2,]    2    5    8   11
[3,]    3    6    9   12
> 
> ### trigammaInverse
> 
> trigammaInverse(c(1e-6,NA,5,1e6))
[1] 1.000000e+06           NA 4.961687e-01 1.000001e-03
> 
> ### lmFit, eBayes, topTable
> 
> M <- matrix(rnorm(10*6,sd=0.3),10,6)
> rownames(M) <- LETTERS[1:10]
> M[1,1:3] <- M[1,1:3] + 2
> design <- cbind(First3Arrays=c(1,1,1,0,0,0),Last3Arrays=c(0,0,0,1,1,1))
> contrast.matrix <- cbind(First3=c(1,0),Last3=c(0,1),"Last3-First3"=c(-1,1))
> fit <- lmFit(M,design)
> fit2 <- eBayes(contrasts.fit(fit,contrasts=contrast.matrix))
> topTable(fit2)
       First3       Last3 Last3.First3      AveExpr           F      P.Value
A  1.77602021  0.06025114  -1.71576906  0.918135675 50.91471061 7.727200e-23
D -0.05454069  0.39127869   0.44581938  0.168369004  2.51638838 8.075072e-02
F -0.16249607 -0.33009728  -0.16760121 -0.246296671  2.18256779 1.127516e-01
G  0.30852468 -0.06873462  -0.37725930  0.119895035  1.61088775 1.997102e-01
H -0.16942269  0.20578118   0.37520387  0.018179245  1.14554368 3.180510e-01
J  0.21417623  0.07074940  -0.14342683  0.142462814  0.82029274 4.403027e-01
C -0.12236781  0.15095948   0.27332729  0.014295836  0.60885003 5.439761e-01
B -0.11982833  0.13529287   0.25512120  0.007732271  0.52662792 5.905931e-01
E  0.01897934  0.10434934   0.08536999  0.061664340  0.18136849 8.341279e-01
I -0.04720963  0.03996397   0.08717360 -0.003622829  0.06168476 9.401792e-01
     adj.P.Val
A 7.727200e-22
D 3.758388e-01
F 3.758388e-01
G 4.992756e-01
H 6.361019e-01
J 7.338379e-01
C 7.382414e-01
B 7.382414e-01
E 9.268088e-01
I 9.401792e-01
> topTable(fit2,coef=3,resort.by="logFC")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="p")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,sort="logFC",resort.by="t")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
> topTable(fit2,coef=3,resort.by="B")
        logFC      AveExpr          t      P.Value    adj.P.Val         B
A -1.71576906  0.918135675 -6.8894222 2.674199e-08 2.674199e-07 16.590631
D  0.44581938  0.168369004  1.7901232 8.100587e-02 3.494414e-01 -5.323150
G -0.37725930  0.119895035 -1.5148301 1.376783e-01 3.494414e-01 -5.773625
H  0.37520387  0.018179245  1.5065768 1.397766e-01 3.494414e-01 -5.785971
C  0.27332729  0.014295836  1.0975061 2.789833e-01 5.196681e-01 -6.313399
B  0.25512120  0.007732271  1.0244023 3.118009e-01 5.196681e-01 -6.390202
F -0.16760121 -0.246296671 -0.6729784 5.048308e-01 7.098782e-01 -6.685541
J -0.14342683  0.142462814 -0.5759097 5.679026e-01 7.098782e-01 -6.745563
I  0.08717360 -0.003622829  0.3500330 7.281504e-01 7.335508e-01 -6.849117
E  0.08536999  0.061664340  0.3427908 7.335508e-01 7.335508e-01 -6.851601
> topTable(fit2,coef=3,lfc=1)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p=0.2)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p=0.2,lfc=0.5)
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> topTable(fit2,coef=3,p=0.2,lfc=0.5,sort="none")
      logFC   AveExpr         t      P.Value    adj.P.Val        B
A -1.715769 0.9181357 -6.889422 2.674199e-08 2.674199e-07 16.59063
> 
> designlist <- list(Null=matrix(1,6,1),Two=design,Three=cbind(1,c(0,0,1,1,0,0),c(0,0,0,0,1,1)))
> out <- selectModel(M,designlist)
> table(out$pref)

 Null   Two Three 
    5     3     2 
> 
> ### marray object
> 
> #suppressMessages(suppressWarnings(gotmarray <- require(marray,quietly=TRUE)))
> #if(gotmarray) {
> #	data(swirl)
> #	snorm = maNorm(swirl)
> #	fit <- lmFit(snorm, design = c(1,-1,-1,1))
> #	fit <- eBayes(fit)
> #	topTable(fit,resort.by="AveExpr")
> #}
> 
> ### duplicateCorrelation
> 
> cor.out <- duplicateCorrelation(M)
> cor.out$consensus.correlation
[1] -0.09290714
> cor.out$atanh.correlations
[1] -0.4419130  0.4088967 -0.1964978 -0.6093769  0.3730118
> 
> ### gls.series
> 
> fit <- gls.series(M,design,correlation=cor.out$cor)
> fit$coefficients
     First3Arrays Last3Arrays
[1,]   0.82809594  0.09777201
[2,]  -0.08845425  0.27111909
[3,]  -0.07175836 -0.11287397
[4,]   0.06955100  0.06852328
[5,]   0.08348330  0.05535668
> fit$stdev.unscaled
     First3Arrays Last3Arrays
[1,]    0.3888215   0.3888215
[2,]    0.3888215   0.3888215
[3,]    0.3888215   0.3888215
[4,]    0.3888215   0.3888215
[5,]    0.3888215   0.3888215
> fit$sigma
[1] 0.7630059 0.2152728 0.3350370 0.3227781 0.3405473
> fit$df.residual
[1] 10 10 10 10 10
> 
> ### mrlm
> 
> fit <- mrlm(M,design)
Warning message:
In rlm.default(x = X, y = y, weights = w, ...) :
  'rlm' failed to converge in 20 steps
> fit$coef
  First3Arrays Last3Arrays
A   1.75138894  0.06025114
B  -0.11982833  0.10322039
C  -0.09302502  0.15095948
D  -0.05454069  0.33700045
E   0.07927938  0.10434934
F  -0.16249607 -0.34010852
G   0.30852468 -0.06873462
H  -0.16942269  0.24392984
I  -0.04720963  0.03996397
J   0.21417623 -0.05679272
> fit$stdev.unscaled
  First3Arrays Last3Arrays
A    0.5933418   0.5773503
B    0.5773503   0.6096497
C    0.6017444   0.5773503
D    0.5773503   0.6266021
E    0.6307703   0.5773503
F    0.5773503   0.5846707
G    0.5773503   0.5773503
H    0.5773503   0.6544564
I    0.5773503   0.5773503
J    0.5773503   0.6689776
> fit$sigma
 [1] 0.2894294 0.2679396 0.2090236 0.1461395 0.2309018 0.2827476 0.2285945
 [8] 0.2267556 0.3537469 0.2172409
> fit$df.residual
 [1] 4 4 4 4 4 4 4 4 4 4
> 
> # Similar to Mette Langaas 19 May 2004
> set.seed(123)
> narrays <- 9
> ngenes <- 5
> mu <- 0
> alpha <- 2
> beta <- -2
> epsilon <- matrix(rnorm(narrays*ngenes,0,1),ncol=narrays)
> X <- cbind(rep(1,9),c(0,0,0,1,1,1,0,0,0),c(0,0,0,0,0,0,1,1,1))
> dimnames(X) <- list(1:9,c("mu","alpha","beta"))
> yvec <- mu*X[,1]+alpha*X[,2]+beta*X[,3]
> ymat <- matrix(rep(yvec,ngenes),ncol=narrays,byrow=T)+epsilon
> ymat[5,1:2] <- NA
> fit <- lmFit(ymat,design=X)
> test.contr <- cbind(c(0,1,-1),c(1,1,0),c(1,0,1))
> dimnames(test.contr) <- list(c("mu","alpha","beta"),c("alpha-beta","mu+alpha","mu+beta"))
> fit2 <- contrasts.fit(fit,contrasts=test.contr)
> eBayes(fit2)
An object of class "MArrayLM"
$coefficients
     alpha-beta mu+alpha   mu+beta
[1,]   3.537333 1.677465 -1.859868
[2,]   4.355578 2.372554 -1.983024
[3,]   3.197645 1.053584 -2.144061
[4,]   2.697734 1.611443 -1.086291
[5,]   3.502304 2.051995 -1.450309

$stdev.unscaled
     alpha-beta  mu+alpha   mu+beta
[1,]  0.8164966 0.5773503 0.5773503
[2,]  0.8164966 0.5773503 0.5773503
[3,]  0.8164966 0.5773503 0.5773503
[4,]  0.8164966 0.5773503 0.5773503
[5,]  1.1547005 0.8368633 0.8368633

$sigma
[1] 1.3425032 0.4647155 1.1993444 0.9428569 0.9421509

$df.residual
[1] 6 6 6 6 4

$cov.coefficients
           alpha-beta     mu+alpha       mu+beta
alpha-beta  0.6666667 3.333333e-01 -3.333333e-01
mu+alpha    0.3333333 3.333333e-01  5.551115e-17
mu+beta    -0.3333333 5.551115e-17  3.333333e-01

$rank
[1] 3

$Amean
[1]  0.2034961  0.1954604 -0.2863347  0.1188659  0.1784593

$method
[1] "ls"

$design
  mu alpha beta
1  1     0    0
2  1     0    0
3  1     0    0
4  1     1    0
5  1     1    0
6  1     1    0
7  1     0    1
8  1     0    1
9  1     0    1

$contrasts
      alpha-beta mu+alpha mu+beta
mu             0        1       1
alpha          1        1       0
beta          -1        0       1

$df.prior
[1] 9.306153

$s2.prior
[1] 0.923179

$var.prior
[1] 17.33142 17.33142 12.26855

$proportion
[1] 0.01

$s2.post
[1] 1.2677996 0.6459499 1.1251558 0.9097727 0.9124980

$t
     alpha-beta mu+alpha   mu+beta
[1,]   3.847656 2.580411 -2.860996
[2,]   6.637308 5.113018 -4.273553
[3,]   3.692066 1.720376 -3.500994
[4,]   3.464003 2.926234 -1.972606
[5,]   3.175181 2.566881 -1.814221

$df.total
[1] 15.30615 15.30615 15.30615 15.30615 13.30615

$p.value
       alpha-beta     mu+alpha      mu+beta
[1,] 1.529450e-03 0.0206493481 0.0117123495
[2,] 7.144893e-06 0.0001195844 0.0006385076
[3,] 2.109270e-03 0.1055117477 0.0031325769
[4,] 3.381970e-03 0.0102514264 0.0668844448
[5,] 7.124839e-03 0.0230888584 0.0922478630

$lods
     alpha-beta  mu+alpha    mu+beta
[1,]  -1.013417 -3.702133 -3.0332393
[2,]   3.981496  1.283349 -0.2615911
[3,]  -1.315036 -5.168621 -1.7864101
[4,]  -1.757103 -3.043209 -4.6191869
[5,]  -2.257358 -3.478267 -4.5683738

$F
[1]  7.421911 22.203107  7.608327  6.227010  5.060579

$F.p.value
[1] 5.581800e-03 2.988923e-05 5.080726e-03 1.050148e-02 2.320274e-02

> 
> ### uniquegenelist
> 
> uniquegenelist(letters[1:8],ndups=2)
[1] "a" "c" "e" "g"
> uniquegenelist(letters[1:8],ndups=2,spacing=2)
[1] "a" "b" "e" "f"
> 
> ### classifyTests
> 
> tstat <- matrix(c(0,5,0, 0,2.5,0, -2,-2,2, 1,1,1), 4, 3, byrow=TRUE)
> classifyTestsF(tstat)
TestResults matrix
     [,1] [,2] [,3]
[1,]    0    1    0
[2,]    0    0    0
[3,]   -1   -1    1
[4,]    0    0    0
> classifyTestsF(tstat,fstat.only=TRUE)
[1] 8.333333 2.083333 4.000000 1.000000
attr(,"df1")
[1] 3
attr(,"df2")
[1] Inf
> limma:::.classifyTestsP(tstat)
TestResults matrix
     [,1] [,2] [,3]
[1,]    0    1    0
[2,]    0    1    0
[3,]    0    0    0
[4,]    0    0    0
> 
> ### avereps
> 
> x <- matrix(rnorm(8*3),8,3)
> colnames(x) <- c("S1","S2","S3")
> rownames(x) <- c("b","a","a","c","c","b","b","b")
> avereps(x)
          S1         S2         S3
b -0.2353018  0.5220094  0.2302895
a -0.4347701  0.6453498 -0.6758914
c  0.3482980 -0.4820695 -0.3841313
> 
> ### roast
> 
> y <- matrix(rnorm(100*4),100,4)
> sigma <- sqrt(2/rchisq(100,df=7))
> y <- y*sigma
> design <- cbind(Intercept=1,Group=c(0,0,1,1))
> iset1 <- 1:5
> y[iset1,3:4] <- y[iset1,3:4]+3
> iset2 <- 6:10
> roast(y=y,iset1,design,contrast=2)
         Active.Prop     P.Value
Down               0 0.996498249
Up                 1 0.004002001
UpOrDown           1 0.008000000
Mixed              1 0.008000000
> roast(y=y,iset1,design,contrast=2,array.weights=c(0.5,1,0.5,1))
         Active.Prop    P.Value
Down               0 0.99899950
Up                 1 0.00150075
UpOrDown           1 0.00300000
Mixed              1 0.00300000
> w <- matrix(runif(100*4),100,4)
> roast(y=y,iset1,design,contrast=2,weights=w)
         Active.Prop   P.Value
Down               0 0.9994997
Up                 1 0.0010005
UpOrDown           1 0.0020000
Mixed              1 0.0020000
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,gene.weights=runif(100))
     NGenes PropDown PropUp Direction PValue   FDR PValue.Mixed FDR.Mixed
set1      5        0      1        Up  0.008 0.015        0.008     0.015
set2      5        0      0        Up  0.959 0.959        0.687     0.687
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,array.weights=c(0.5,1,0.5,1))
     NGenes PropDown PropUp Direction PValue   FDR PValue.Mixed FDR.Mixed
set1      5        0      1        Up  0.004 0.007        0.004     0.007
set2      5        0      0        Up  0.679 0.679        0.658     0.658
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w)
     NGenes PropDown PropUp Direction PValue   FDR PValue.Mixed FDR.Mixed
set1      5      0.0      1        Up  0.003 0.005        0.003     0.005
set2      5      0.2      0      Down  0.950 0.950        0.250     0.250
> mroast(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes PropDown PropUp Direction PValue   FDR PValue.Mixed FDR.Mixed
set1      5        0      1        Up  0.001 0.001        0.001     0.001
set2      5        0      0      Down  0.791 0.791        0.146     0.146
> fry(y=y,list(set1=iset1,set2=iset2),design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes Direction       PValue         FDR PValue.Mixed    FDR.Mixed
set1      5        Up 0.0007432594 0.001486519 1.820548e-05 3.641096e-05
set2      5      Down 0.8208140511 0.820814051 2.211837e-01 2.211837e-01
> rownames(y) <- paste0("Gene",1:100)
> iset1A <- rownames(y)[1:5]
> fry(y=y,index=iset1A,design,contrast=2,weights=w,array.weights=c(0.5,1,0.5,1))
     NGenes Direction       PValue PValue.Mixed
set1      5        Up 0.0007432594 1.820548e-05
> 
> ### camera
> 
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1),allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction      PValue
set1      5  -0.2481655        Up 0.001050253
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction       PValue        FDR
set1      5  -0.2481655        Up 0.0009047749 0.00180955
set2      5   0.1719094      Down 0.9068364378 0.90683644
> camera(y=y,iset1,design,contrast=2,weights=c(0.5,1,0.5,1))
     NGenes Direction       PValue
set1      5        Up 1.105329e-10
> camera(y=y,list(set1=iset1,set2=iset2),design,contrast=2)
     NGenes Direction       PValue          FDR
set1      5        Up 7.334400e-12 1.466880e-11
set2      5      Down 8.677115e-01 8.677115e-01
> camera(y=y,iset1A,design,contrast=2)
     NGenes Direction     PValue
set1      5        Up 7.3344e-12
> 
> ### with EList arg
> 
> y <- new("EList",list(E=y))
> roast(y=y,iset1,design,contrast=2)
         Active.Prop     P.Value
Down               0 0.997498749
Up                 1 0.003001501
UpOrDown           1 0.006000000
Mixed              1 0.006000000
> camera(y=y,iset1,design,contrast=2,allow.neg.cor=TRUE,inter.gene.cor=NA)
     NGenes Correlation Direction       PValue
set1      5  -0.2481655        Up 0.0009047749
> camera(y=y,iset1,design,contrast=2)
     NGenes Direction     PValue
set1      5        Up 7.3344e-12
> 
> ### eBayes with trend
> 
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
           logFC     AveExpr         t      P.Value  adj.P.Val          B
Gene2   3.729512  1.73488969  4.865697 0.0004854886 0.02902331  0.1596831
Gene3   3.488703  1.03931081  4.754954 0.0005804663 0.02902331 -0.0144071
Gene4   2.696676  1.74060725  3.356468 0.0063282637 0.21094212 -2.3434702
Gene1   2.391846  1.72305203  3.107124 0.0098781268 0.24695317 -2.7738874
Gene33 -1.492317 -0.07525287 -2.783817 0.0176475742 0.29965463 -3.3300835
Gene5   2.387967  1.63066783  2.773444 0.0179792778 0.29965463 -3.3478204
Gene80 -1.839760 -0.32802306 -2.503584 0.0291489863 0.37972679 -3.8049642
Gene39  1.366141 -0.27360750  2.451133 0.0320042242 0.37972679 -3.8925860
Gene95 -1.907074  1.26297763 -2.414217 0.0341754107 0.37972679 -3.9539571
Gene50  1.034777  0.01608433  2.054690 0.0642289403 0.59978803 -4.5350317
> fit$df.prior
[1] 9.098442
> fit$s2.prior
    Gene1     Gene2     Gene3     Gene4     Gene5     Gene6     Gene7     Gene8 
0.6901845 0.6977354 0.3860494 0.7014122 0.6341068 0.2926337 0.3077620 0.3058098 
    Gene9    Gene10    Gene11    Gene12    Gene13    Gene14    Gene15    Gene16 
0.2985145 0.2832520 0.3232434 0.3279710 0.2816081 0.2943502 0.3127994 0.2894802 
   Gene17    Gene18    Gene19    Gene20    Gene21    Gene22    Gene23    Gene24 
0.2812758 0.2840051 0.2839124 0.2954261 0.2838592 0.2812704 0.3157029 0.2844541 
   Gene25    Gene26    Gene27    Gene28    Gene29    Gene30    Gene31    Gene32 
0.4778832 0.2818242 0.2930360 0.2940957 0.2941862 0.3234399 0.3164779 0.2853510 
   Gene33    Gene34    Gene35    Gene36    Gene37    Gene38    Gene39    Gene40 
0.2988244 0.3450090 0.3048596 0.3089086 0.3104534 0.4551549 0.3220008 0.2813286 
   Gene41    Gene42    Gene43    Gene44    Gene45    Gene46    Gene47    Gene48 
0.2826027 0.2822504 0.2823330 0.3170673 0.3146173 0.3146793 0.2916540 0.2975003 
   Gene49    Gene50    Gene51    Gene52    Gene53    Gene54    Gene55    Gene56 
0.3538946 0.2907240 0.3199596 0.2816641 0.2814293 0.2996822 0.2812885 0.2896157 
   Gene57    Gene58    Gene59    Gene60    Gene61    Gene62    Gene63    Gene64 
0.2955317 0.2815907 0.2919420 0.2849675 0.3540805 0.3491713 0.2975019 0.2939325 
   Gene65    Gene66    Gene67    Gene68    Gene69    Gene70    Gene71    Gene72 
0.2986943 0.3265466 0.3402343 0.3394927 0.2813283 0.2814440 0.3089669 0.3030850 
   Gene73    Gene74    Gene75    Gene76    Gene77    Gene78    Gene79    Gene80 
0.2859286 0.2813216 0.3475231 0.3334419 0.2949550 0.3108702 0.2959688 0.3295294 
   Gene81    Gene82    Gene83    Gene84    Gene85    Gene86    Gene87    Gene88 
0.3413700 0.2946268 0.3029565 0.2920284 0.2926205 0.2818046 0.3425116 0.2882936 
   Gene89    Gene90    Gene91    Gene92    Gene93    Gene94    Gene95    Gene96 
0.2945459 0.3077919 0.2892134 0.2823787 0.3048049 0.2961408 0.4590012 0.2812784 
   Gene97    Gene98    Gene99   Gene100 
0.2846345 0.2819651 0.3137551 0.2856081 
> summary(fit$s2.post)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.2335  0.2603  0.2997  0.3375  0.3655  0.7812 
> 
> y$E[1,1] <- NA
> y$E[1,3] <- NA
> fit <- lmFit(y,design)
> fit <- eBayes(fit,trend=TRUE)
> topTable(fit,coef=2)
           logFC     AveExpr         t      P.Value  adj.P.Val          B
Gene3   3.488703  1.03931081  4.604490 0.0007644061 0.07644061 -0.2333915
Gene2   3.729512  1.73488969  4.158038 0.0016033158 0.08016579 -0.9438583
Gene4   2.696676  1.74060725  2.898102 0.0145292666 0.44537707 -3.0530813
Gene33 -1.492317 -0.07525287 -2.784004 0.0178150826 0.44537707 -3.2456324
Gene5   2.387967  1.63066783  2.495395 0.0297982959 0.46902627 -3.7272957
Gene80 -1.839760 -0.32802306 -2.491115 0.0300256116 0.46902627 -3.7343584
Gene39  1.366141 -0.27360750  2.440729 0.0328318388 0.46902627 -3.8172597
Gene1   2.638272  1.47993643  2.227507 0.0530016060 0.58890673 -3.9537576
Gene95 -1.907074  1.26297763 -2.288870 0.0429197808 0.53649726 -4.0642439
Gene50  1.034777  0.01608433  2.063663 0.0635275235 0.60439978 -4.4204731
> fit$df.residual[1]
[1] 0
> fit$df.prior
[1] 8.971891
> fit$s2.prior
  [1] 0.7014084 0.9646561 0.4276287 0.9716476 0.8458852 0.2910492 0.3097052
  [8] 0.3074225 0.2985517 0.2786374 0.3267121 0.3316013 0.2766404 0.2932679
 [15] 0.3154347 0.2869186 0.2761395 0.2799884 0.2795119 0.2946468 0.2794412
 [22] 0.2761282 0.3186442 0.2806092 0.4596465 0.2767847 0.2924541 0.2939204
 [29] 0.2930568 0.3269177 0.3194905 0.2814293 0.2989389 0.3483845 0.3062977
 [36] 0.3110287 0.3127934 0.4418052 0.3254067 0.2761732 0.2780422 0.2773311
 [43] 0.2776653 0.3201314 0.3174515 0.3175199 0.2897731 0.2972785 0.3567262
 [50] 0.2885556 0.3232426 0.2767207 0.2762915 0.3000062 0.2761306 0.2870975
 [57] 0.2947817 0.2766152 0.2901489 0.2813183 0.3568982 0.3724440 0.2972804
 [64] 0.2927300 0.2987764 0.3301406 0.3437962 0.3430762 0.2761729 0.2763094
 [71] 0.3110958 0.3041715 0.2822004 0.2761654 0.3507694 0.3371214 0.2940441
 [78] 0.3132660 0.2953388 0.3331880 0.3448949 0.2946558 0.3040162 0.2902616
 [85] 0.2910320 0.2769211 0.3459946 0.2859057 0.2935193 0.3097398 0.2865663
 [92] 0.2774968 0.3062327 0.2955576 0.5425422 0.2761214 0.2808585 0.2771484
 [99] 0.3164981 0.2817725
> summary(fit$s2.post)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.2296  0.2581  0.3003  0.3453  0.3652  0.9158 
> 
> ### voom
> 
> y <- matrix(rpois(100*4,lambda=20),100,4)
> design <- cbind(Int=1,x=c(0,0,1,1))
> v <- voom(y,design)
Warning message:
In regularize.values(x, y, ties, missing(ties)) :
  collapsing to unique 'x' values
> names(v)
[1] "E"       "weights" "design"  "targets"
> summary(v$E)
       V1              V2              V3              V4       
 Min.   :12.25   Min.   :12.58   Min.   :12.19   Min.   :12.24  
 1st Qu.:13.13   1st Qu.:13.07   1st Qu.:13.15   1st Qu.:13.03  
 Median :13.29   Median :13.30   Median :13.30   Median :13.27  
 Mean   :13.28   Mean   :13.29   Mean   :13.29   Mean   :13.28  
 3rd Qu.:13.49   3rd Qu.:13.51   3rd Qu.:13.50   3rd Qu.:13.50  
 Max.   :14.23   Max.   :14.28   Max.   :13.97   Max.   :13.96  
> summary(v$weights)
       V1               V2               V3               V4        
 Min.   : 5.935   Min.   : 5.935   Min.   : 5.935   Min.   : 5.935  
 1st Qu.: 6.788   1st Qu.: 7.049   1st Qu.: 7.207   1st Qu.: 6.825  
 Median :11.066   Median :10.443   Median :10.606   Median :10.414  
 Mean   :10.421   Mean   :10.485   Mean   :10.571   Mean   :10.532  
 3rd Qu.:13.485   3rd Qu.:14.155   3rd Qu.:13.859   3rd Qu.:14.121  
 Max.   :15.083   Max.   :15.101   Max.   :15.095   Max.   :15.063  
> 
> ### goana
> 
> EB <- c("133746","1339","134","1340","134083","134111","134147","134187","134218","134266",
+ "134353","134359","134391","134429","134430","1345","134510","134526","134549","1346",
+ "134637","1347","134701","134728","1348","134829","134860","134864","1349","134957",
+ "135","1350","1351","135112","135114","135138","135152","135154","1352","135228",
+ "135250","135293","135295","1353","135458","1355","1356","135644","135656","1357",
+ "1358","135892","1359","135924","135935","135941","135946","135948","136","1360",
+ "136051","1361","1362","136227","136242","136259","1363","136306","136319","136332",
+ "136371","1364","1365","136541","1366","136647","1368","136853","1369","136991",
+ "1370","137075","1371","137209","1373","137362","1374","137492","1375","1376",
+ "137682","137695","137735","1378","137814","137868","137872","137886","137902","137964")
> go <- goana(fit,FDR=0.8,geneid=EB)
> topGO(go,n=10,truncate.term=30)
                                     Term Ont  N Up Down        P.Up
GO:0070062          extracellular exosome  CC  8  0    4 1.000000000
GO:0043230        extracellular organelle  CC  8  0    4 1.000000000
GO:1903561          extracellular vesicle  CC  8  0    4 1.000000000
GO:0072359 circulatory system developm...  BP  2  0    2 1.000000000
GO:0007507              heart development  BP  2  0    2 1.000000000
GO:0032501 multicellular organismal pr...  BP 31  6    7 0.796992878
GO:0098609             cell-cell adhesion  BP  5  4    0 0.009503355
GO:0097190    apoptotic signaling pathway  BP  3  3    0 0.010952381
GO:0031252              cell leading edge  CC  3  3    0 0.010952381
GO:0006897                    endocytosis  BP  3  3    0 0.010952381
                P.Down
GO:0070062 0.003047199
GO:0043230 0.003047199
GO:1903561 0.003047199
GO:0072359 0.009090909
GO:0007507 0.009090909
GO:0032501 0.009111120
GO:0098609 1.000000000
GO:0097190 1.000000000
GO:0031252 1.000000000
GO:0006897 1.000000000
> topGO(go,n=10,truncate.term=30,sort="down")
                                     Term Ont  N Up Down      P.Up      P.Down
GO:0070062          extracellular exosome  CC  8  0    4 1.0000000 0.003047199
GO:0043230        extracellular organelle  CC  8  0    4 1.0000000 0.003047199
GO:1903561          extracellular vesicle  CC  8  0    4 1.0000000 0.003047199
GO:0072359 circulatory system developm...  BP  2  0    2 1.0000000 0.009090909
GO:0007507              heart development  BP  2  0    2 1.0000000 0.009090909
GO:0032501 multicellular organismal pr...  BP 31  6    7 0.7969929 0.009111120
GO:0032502          developmental process  BP 25  4    6 0.8946593 0.014492712
GO:0031982                        vesicle  CC 18  1    5 0.9946677 0.015552466
GO:0009887     animal organ morphogenesis  BP  3  0    2 1.0000000 0.025788497
GO:0055082 cellular chemical homeostas...  BP  3  1    2 0.5476190 0.025788497
> 
> proc.time()
   user  system elapsed 
   3.82    0.18    4.01