## ----style-knitr, eval=TRUE, echo=FALSE, results="asis"--------------------
BiocStyle::latex(relative.path = TRUE)

## ----knitr, echo=FALSE, results="hide"-------------------------------------
library("knitr")
opts_chunk$set(
  tidy=FALSE,
  dev="png",
  fig.show="hide",
#  fig.width=4, fig.height=4.5,
  fig.width=10, fig.height=8,
  fig.pos="tbh",
  cache=TRUE,
  message=FALSE)

## ----chu_1-----------------------------------------------------------------
library(DaMiRseq)
## only for example:
# rawdata.path <- system.file(package = "DaMiRseq","extdata")
# setwd(rawdata.path)
# filecounts <- list.files(rawdata.path, full.names = TRUE)[1]
# filecovariates <- list.files(rawdata.path, full.names = TRUE)[2]
# count_data <- read.delim(filecounts)
# covariate_data <- read.delim(filecovariates)
# SE<-DaMiR.makeSE(count_data, covariate_data)

## ----chu_2-----------------------------------------------------------------
data(SE)
assay(SE)[1:5, c(1:5, 21:25)]
colData(SE)

## ----chu_4-----------------------------------------------------------------
data_norm <- DaMiR.normalization(SE, minCounts=10, fSample=0.7,
                                 hyper = "no")


## ----chu_5-----------------------------------------------------------------
data_norm <- DaMiR.normalization(SE, minCounts=10, fSample=0.7,
                                 hyper = "yes", th.cv=3)
print(data_norm)
assay(data_norm)[c(1:5), c(1:5, 21:25)]

## ----chu_6, eval=FALSE-----------------------------------------------------
#  # Time Difference, using VST or rlog for normalization:
#  #
#  #data_norm <- DaMiR.normalization(dds, minCounts=10, fSample=0.7, th.cv=3)
#  # VST: about 80 seconds
#  #
#  #data_norm <- DaMiR.normalization(dds, minCounts=10, fSample=0.7, th.cv=3,
#  #                                 type="rlog")
#  # rlog: about 8890 seconds (i.e. 2 hours and 28 minutes!)

## ----chu_7-----------------------------------------------------------------
data_filt <- DaMiR.sampleFilt(data_norm, th.corr=0.9)
dim(data_filt)

## ----chu_8, dev="pdf"------------------------------------------------------
 sv <- DaMiR.SV(data_filt)

## ----chu_9, dev="pdf"------------------------------------------------------
 DaMiR.corrplot(sv, colData(data_filt), sig.level = 0.01)

## ----chu_10, dev="pdf"-----------------------------------------------------
data_adjust<-DaMiR.SVadjust(data_filt, sv, n.sv=4)
assay(data_adjust[c(1:5), c(1:5, 21:25)])

## ----chu_11, dev="pdf"-----------------------------------------------------
# After gene filtering and normalization
DaMiR.Allplot(data_filt, colData(data_filt))

## ----chu_12, dev="pdf"-----------------------------------------------------
# After sample filtering and sv adjusting
DaMiR.Allplot(data_adjust, colData(data_adjust))

## ----chu_13----------------------------------------------------------------
set.seed(12345)
data_clean<-DaMiR.transpose(assay(data_adjust))
df<-colData(data_adjust)
data_reduced <- DaMiR.FSelect(data_clean, df, th.corr=0.4)

## ----chu_14, dev="pdf"-----------------------------------------------------
data_reduced <- DaMiR.FReduct(data_reduced$data)
DaMiR.MDSplot(data_reduced, df)

## ----chu_15, dev="pdf"-----------------------------------------------------
  # Rank genes by importance:
  df.importance <- DaMiR.FSort(data_reduced, df)
  head(df.importance)

## ----chu_16, dev="pdf"-----------------------------------------------------
  # Select Best Predictors:
  selected_features <- DaMiR.FBest(data_reduced, ranking=df.importance,
                                   n.pred = 5)
  selected_features$predictors
  # Dendrogram and heatmap:
  DaMiR.Clustplot(selected_features$data, df)

## ----chu_17, dev="pdf"-----------------------------------------------------

  Classification_res <- DaMiR.EnsembleLearning(selected_features$data,
                          classes=df$class, fSample.tr = 0.5,
                          fSample.tr.w = 0.5, iter = 30)

## ----chu_export, dev="pdf", eval=FALSE-------------------------------------
#  outputfile <- "DataNormalized.txt"
#  write.table(data_norm, file = outputfile_norm, quote = FALSE, sep = "\t")

## ----chu_18, dev="pdf"-----------------------------------------------------
## Feature Selection
set.seed(12345)
data_clean_2<-DaMiR.transpose(assay(data_filt))
df_2<-colData(data_filt)

data_reduced_2 <- DaMiR.FSelect(data_clean_2, df_2, th.corr=0.4)
data_reduced_2 <- DaMiR.FReduct(data_reduced_2$data)
df.importance_2 <- DaMiR.FSort(data_reduced_2, df_2)
head(df.importance_2)

selected_features_2 <- DaMiR.FBest(data_reduced_2, ranking=df.importance_2,
                                   n.pred=5)
selected_features_2$predictors

## Classification
Classification_res_2 <- DaMiR.EnsembleLearning(selected_features_2$data,
                                             classes=df_2$class,
                                             fSample.tr = 0.5,
                                             fSample.tr.w = 0.5,
                                             iter = 30)



## ----sessInfo, results="asis", echo=FALSE----------------------------------
toLatex(sessionInfo())