Bioconductor for Genomic Analysis
Martin Morgan
November 3, 2016
Introduction to R and Bioconductor
Vectors, classes, methods, and packages
R
- Statistical
Vectorized
x <- rnorm(100) y <- x + rnorm(100)Large data per se is not a problem
system.time(rnorm(1e7))## user system elapsed ## 0.928 0.000 0.929Classes structure data (nouns)
df <- data.frame(X=x, Y=y) fit <- lm(Y ~ X, df)Methods transform objects (verbs)
anova(fit)## Analysis of Variance Table ## ## Response: Y ## Df Sum Sq Mean Sq F value Pr(>F) ## X 1 91.006 91.006 85.776 4.851e-15 *** ## Residuals 98 103.976 1.061 ## --- ## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1R provides introspection
class(df)## [1] "data.frame"dim(df)## [1] 100 2class(lm)## [1] "function"methods(class=class(lm))## [1] as.data.frame as.list coerce ## [4] coerce<- dispersionFunction<- fortify ## [7] head plot print ## [10] tail ## see '?methods' for accessing help and source code
Packages
Extend base functionality
library(ggplot2) ggplot(df, aes(x=X, y=Y)) + geom_point() + stat_smooth(method="lm")## Warning in grid.Call.graphics(L_polygon, x$x, x$y, index): semi- ## transparency is not supported on this device: reported only once per page
Bioconductor – key software and annotation resources
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library(GenomicRanges) library(TxDb.Hsapiens.UCSC.hg38.knownGene) ex <- exons(TxDb.Hsapiens.UCSC.hg38.knownGene) ex## GRanges object with 581036 ranges and 1 metadata column: ## seqnames ranges strand | exon_id ## <Rle> <IRanges> <Rle> | <integer> ## [1] chr1 [29554, 30039] + | 1 ## [2] chr1 [30267, 30667] + | 2 ## [3] chr1 [30366, 30503] + | 3 ## [4] chr1 [30564, 30667] + | 4 ## [5] chr1 [30976, 31097] + | 5 ## ... ... ... ... . ... ## [581032] chrUn_KI270750v1 [148668, 148843] + | 581032 ## [581033] chrUn_KI270752v1 [ 144, 268] + | 581033 ## [581034] chrUn_KI270752v1 [ 21813, 21944] + | 581034 ## [581035] chrUn_KI270752v1 [ 3497, 3623] - | 581035 ## [581036] chrUn_KI270752v1 [ 9943, 10067] - | 581036 ## ------- ## seqinfo: 455 sequences (1 circular) from hg38 genomekeepStandardChromosomes(ex)## GRanges object with 538594 ranges and 1 metadata column: ## seqnames ranges strand | exon_id ## <Rle> <IRanges> <Rle> | <integer> ## [1] chr1 [29554, 30039] + | 1 ## [2] chr1 [30267, 30667] + | 2 ## [3] chr1 [30366, 30503] + | 3 ## [4] chr1 [30564, 30667] + | 4 ## [5] chr1 [30976, 31097] + | 5 ## ... ... ... ... . ... ## [538590] chrM [ 5826, 5891] - | 538590 ## [538591] chrM [ 7446, 7514] - | 538591 ## [538592] chrM [14149, 14673] - | 538592 ## [538593] chrM [14674, 14742] - | 538593 ## [538594] chrM [15956, 16023] - | 538594 ## ------- ## seqinfo: 25 sequences (1 circular) from hg38 genomehist(log10(width(ex)))
introns <- intronsByTranscript(TxDb.Hsapiens.UCSC.hg38.knownGene, use.names=TRUE) hist(log10(lengths(introns)))
introns[ which.max(lengths(introns)) ]## GRangesList object of length 1: ## $uc031rqc.3 ## GRanges object with 362 ranges and 0 metadata columns: ## seqnames ranges strand ## <Rle> <IRanges> <Rle> ## [1] chr2 [178527308, 178527445] - ## [2] chr2 [178527749, 178528273] - ## [3] chr2 [178528428, 178528527] - ## [4] chr2 [178529220, 178529959] - ## [5] chr2 [178530117, 178530240] - ## ... ... ... ... ## [358] chr2 [178799732, 178799824] - ## [359] chr2 [178799911, 178800394] - ## [360] chr2 [178800683, 178802137] - ## [361] chr2 [178802342, 178804551] - ## [362] chr2 [178804656, 178807211] - ## ## ------- ## seqinfo: 455 sequences (1 circular) from hg38 genomelibrary(TxDb.Hsapiens.UCSC.hg19.knownGene) library(RNAseqData.HNRNPC.bam.chr14) library(GenomicAlignments) fname <- RNAseqData.HNRNPC.bam.chr14_BAMFILES[1] aln <- readGAlignments(fname) genes <- genes(TxDb.Hsapiens.UCSC.hg19.knownGene) chr14 <- genes[seqnames(genes) == "chr14"] hist(log10(1 + countOverlaps(chr14, aln)))
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library(Biostrings) library(BSgenome.Hsapiens.UCSC.hg38) ex <- exons(TxDb.Hsapiens.UCSC.hg38.knownGene) ex <- keepStandardChromosomes(ex) dna <- getSeq(BSgenome.Hsapiens.UCSC.hg38, ex) dna## A DNAStringSet instance of length 538594 ## width seq ## [1] 486 GTGCACACGGCTCCCATGCGTTGTCTTC...GCGGAGTTGGGCAGCTCGTGTTCAATGG ## [2] 401 TCATCAGTCCAAAGTCCAGCAGTTGTCC...GCTGCAGCGTGTGGTCCCCTTACCAGAG ## [3] 138 GGATGCCCAGCTAGTTTGAATTTTAGAT...TATCTGAGATTCAGAATTAAGCATTTTA ## [4] 104 TTTTGTCCGCCTTCCCTGCCTCCTCTTC...GCTGCAGCGTGTGGTCCCCTTACCAGAG ## [5] 122 TGAGGATGCGAAGAGAAGGTGGCTGTCT...CCTCCAGAACTGTGAGGGATAAATGTAT ## ... ... ... ## [538590] 66 GGTAAAATGGCTGAGTGAAGCATTGGAC...AAGACAGGGGTTAGGCCTCTTTTTACCA ## [538591] 69 GAAAAAGTCATGGAGGCCATGGGGTTGG...TTGGGGGGTTCGATTCCTTCCTTTTTTG ## [538592] 525 ATGATGTATGCTTTGTTTCTGTTGAGTG...TGTAATTGAGATTGCTCGGGGGAATAGG ## [538593] 69 GTTCTTGTAGTTGAAATACAACGATGGT...GGTCGTGGTTGTAGTCCGTGCGAGAATA ## [538594] 68 CAGAGAATAGTTTAAATTAGAATCTTAG...ATGGTGGAGTTAAAGACTTTTTCTCTGAgc <- letterFrequency(dna, "GC", as.prob=TRUE) head(gc)## G|C ## [1,] 0.6028807 ## [2,] 0.4513716 ## [3,] 0.3115942 ## [4,] 0.5576923 ## [5,] 0.5409836 ## [6,] 0.5149254plot(density(gc))
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library(SummarizedExperiment) library(airway) data(airway) airway## class: RangedSummarizedExperiment ## dim: 64102 8 ## metadata(1): '' ## assays(1): counts ## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99 ## rowData names(0): ## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521 ## colData names(9): SampleName cell ... Sample BioSamplehead(assay(airway))## SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516 ## ENSG00000000003 679 448 873 408 1138 ## ENSG00000000005 0 0 0 0 0 ## ENSG00000000419 467 515 621 365 587 ## ENSG00000000457 260 211 263 164 245 ## ENSG00000000460 60 55 40 35 78 ## ENSG00000000938 0 0 2 0 1 ## SRR1039517 SRR1039520 SRR1039521 ## ENSG00000000003 1047 770 572 ## ENSG00000000005 0 0 0 ## ENSG00000000419 799 417 508 ## ENSG00000000457 331 233 229 ## ENSG00000000460 63 76 60 ## ENSG00000000938 0 0 0colData(airway)## DataFrame with 8 rows and 9 columns ## SampleName cell dex albut Run avgLength ## <factor> <factor> <factor> <factor> <factor> <integer> ## SRR1039508 GSM1275862 N61311 untrt untrt SRR1039508 126 ## SRR1039509 GSM1275863 N61311 trt untrt SRR1039509 126 ## SRR1039512 GSM1275866 N052611 untrt untrt SRR1039512 126 ## SRR1039513 GSM1275867 N052611 trt untrt SRR1039513 87 ## SRR1039516 GSM1275870 N080611 untrt untrt SRR1039516 120 ## SRR1039517 GSM1275871 N080611 trt untrt SRR1039517 126 ## SRR1039520 GSM1275874 N061011 untrt untrt SRR1039520 101 ## SRR1039521 GSM1275875 N061011 trt untrt SRR1039521 98 ## Experiment Sample BioSample ## <factor> <factor> <factor> ## SRR1039508 SRX384345 SRS508568 SAMN02422669 ## SRR1039509 SRX384346 SRS508567 SAMN02422675 ## SRR1039512 SRX384349 SRS508571 SAMN02422678 ## SRR1039513 SRX384350 SRS508572 SAMN02422670 ## SRR1039516 SRX384353 SRS508575 SAMN02422682 ## SRR1039517 SRX384354 SRS508576 SAMN02422673 ## SRR1039520 SRX384357 SRS508579 SAMN02422683 ## SRR1039521 SRX384358 SRS508580 SAMN02422677rowRanges(airway)## GRangesList object of length 64102: ## $ENSG00000000003 ## GRanges object with 17 ranges and 2 metadata columns: ## seqnames ranges strand | exon_id exon_name ## <Rle> <IRanges> <Rle> | <integer> <character> ## [1] X [99883667, 99884983] - | 667145 ENSE00001459322 ## [2] X [99885756, 99885863] - | 667146 ENSE00000868868 ## [3] X [99887482, 99887565] - | 667147 ENSE00000401072 ## [4] X [99887538, 99887565] - | 667148 ENSE00001849132 ## [5] X [99888402, 99888536] - | 667149 ENSE00003554016 ## ... ... ... ... . ... ... ## [13] X [99890555, 99890743] - | 667156 ENSE00003512331 ## [14] X [99891188, 99891686] - | 667158 ENSE00001886883 ## [15] X [99891605, 99891803] - | 667159 ENSE00001855382 ## [16] X [99891790, 99892101] - | 667160 ENSE00001863395 ## [17] X [99894942, 99894988] - | 667161 ENSE00001828996 ## ## ... ## <64101 more elements> ## ------- ## seqinfo: 722 sequences (1 circular) from an unspecified genomeairway[, airway$dex == "trt"]## class: RangedSummarizedExperiment ## dim: 64102 4 ## metadata(1): '' ## assays(1): counts ## rownames(64102): ENSG00000000003 ENSG00000000005 ... LRG_98 LRG_99 ## rowData names(0): ## colnames(4): SRR1039509 SRR1039513 SRR1039517 SRR1039521 ## colData names(9): SampleName cell ... Sample BioSample The Bioconductor sequencing ecosystem
Help!
?
?"findOverlaps<tab>"?GenomicRanges::findOverlaps
biocViews and landing pages
Vigettes, workflows & training material
RNA-seq
This part of the workshop uses data from a gene-level RNA-seq experiment involving airway smooth muscle cells; details are provided in the vignette accompanying the airway package. The original data is from Himes et al., “RNA-Seq Transcriptome Profiling Identifies CRISPLD2 as a Glucocorticoid Responsive Gene that Modulates Cytokine Function in Airway Smooth Muscle Cells.” PLoS One. 2014 Jun 13;9(6):e99625. PMID: 24926665 GEO: GSE52778. From the Abstract: “Using RNA-Seq […] we characterized transcriptomic changes in four primary human ASM cell lines that were treated with dexamethasone - a potent synthetic glucocorticoid (1 micromolar for 18 hours).”
We join the analysis after the sequencing, read aligment, and summary of aligned reads to a table of counts of reads overlapping regions of interest (genes) in each sample. We focus on a subset of the experiment, with 4 cell lines each treated with dexamethasone or a control.
More extensive work flows are available for use of DESeq2 and edgeR / limma voom.
Setup (not necessary during the workshop)
We’ll use two packages from Bioconductor. These depend in turn on several other packages. The packages and their dependencies are already installed on the Amazon Machine Instance (AMI) used in the course. For your own computers after the course, install packages with
source("https://bioconductor.org/biocLite.R")
biocLite(c("DESeq2", "org.Hs.eg.db"))Installation needs to be performed once per computer, not every time the packages are used.
We use two data files in the analysis. The data files are in the Brisbane2016 github repository. Install the github repository with
biocLite("Bioconductor/Brisbane2016")Once the package is installed, the location of the files (to be used in file.choose(), below) is given by
system.file(package="Brisbane2016", "extdata")Sequence analysis work flows
Research question
- Designed experiment
- Gene-level differential expression
RNA-seq data (http://bio.lundberg.gu.se/courses/vt13/rnaseq.html)
Data processing steps
- Quality assessment: FASTQ files; outside R
- Alignment and summary to count table
- Assessment of differential expression
- Results placed in context, e.g., gene set enrichment
Alignment & summary (traditional)
- Full alignment to BAM files, summarizing gene or transcript abundance, e.g., Bowtie / tophat / cufflinks; RSEM; Rsubread
- Summarize to gene-level count tables or estimates of abundance
- Counts are important: information about statistical uncertainty of estimate
Alignment & summary (contemporary)
Exploring count tables
We start with the ‘phenotypic’ data, describing the samples used in the experiment. The data is a simple table of 8 rows and several columns; it could be created in Excel and exported as a tab-delimited file. Find the location of the file on your Amazon machine instance
colDataFile <- file.chooose() # find 'airway-colData.tab'and read the data in to R using the read.table() function. The data is small enough to be viewed in the R session by typing the name of the variable) or in RStudio (by using View() or double-clicking on the variable in the ‘Environment’ tab).
colData <- read.table(colDataFile)
colData## SampleName cell dex albut Run avgLength Experiment
## SRR1039508 GSM1275862 N61311 untrt untrt SRR1039508 126 SRX384345
## SRR1039509 GSM1275863 N61311 trt untrt SRR1039509 126 SRX384346
## SRR1039512 GSM1275866 N052611 untrt untrt SRR1039512 126 SRX384349
## SRR1039513 GSM1275867 N052611 trt untrt SRR1039513 87 SRX384350
## SRR1039516 GSM1275870 N080611 untrt untrt SRR1039516 120 SRX384353
## SRR1039517 GSM1275871 N080611 trt untrt SRR1039517 126 SRX384354
## SRR1039520 GSM1275874 N061011 untrt untrt SRR1039520 101 SRX384357
## SRR1039521 GSM1275875 N061011 trt untrt SRR1039521 98 SRX384358
## Sample BioSample
## SRR1039508 SRS508568 SAMN02422669
## SRR1039509 SRS508567 SAMN02422675
## SRR1039512 SRS508571 SAMN02422678
## SRR1039513 SRS508572 SAMN02422670
## SRR1039516 SRS508575 SAMN02422682
## SRR1039517 SRS508576 SAMN02422673
## SRR1039520 SRS508579 SAMN02422683
## SRR1039521 SRS508580 SAMN02422677This should go smoothly; with real data one often needs to spend considerable time adjusting arguments to read.table() to account for the presence of a header, row names, comments, etc.
The next challenge is to input the expression estimates. This is a matrix of rows representing regions of interest (genes) and columns representing samples. Entries in the matrix are the number of reads overlapping each region in each sample. It is important that the values are raw counts, rather than scaled measures such as FPKM. Find the file
assayFile <- file.chooose() # find 'airway-assay.tab'Input the data and use head() to view the first few rows of the data.
assay <- read.table(assayFile)
head(assay)## SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516
## ENSG00000000003 679 448 873 408 1138
## ENSG00000000419 467 515 621 365 587
## ENSG00000000457 260 211 263 164 245
## ENSG00000000460 60 55 40 35 78
## ENSG00000000938 0 0 2 0 1
## ENSG00000000971 3251 3679 6177 4252 6721
## SRR1039517 SRR1039520 SRR1039521
## ENSG00000000003 1047 770 572
## ENSG00000000419 799 417 508
## ENSG00000000457 331 233 229
## ENSG00000000460 63 76 60
## ENSG00000000938 0 0 0
## ENSG00000000971 11027 5176 7995NOTE Most modern work flows will start with data generated from kallisto, salmon, etc.; see the tximport package and vignette for importing data from these software tools into R.
Calculate the ‘library size’ (total number of mapped reads) of each sample using colSums()
colSums(assay)## SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516 SRR1039517
## 20637971 18809481 25348649 15163415 24448408 30818215
## SRR1039520 SRR1039521
## 19126151 21164133Create a density plot of the average asinh-transformed (asinh is log-like, except near zero) read counts of each gene using the following series of commands.
plot(density(rowMeans(asinh(assay))))
Multi-dimensional scaling (MDS) is a dimensionality reduction method that takes vectors in n-space and projects them into two (or more) dimensions. Use the dist() function to calculate the (Euclidean) distance bewteen each sample, and the base R function cmdscale() to perform MDS on the distance matrix. We can use plot() to visualize the results and see the approximate location of each of the 8 samples. Use the argument col to color the points based on cell line (colData$cell) or experimental treatment colData$dex.
d <- dist(t(asinh(assay)))
plot(cmdscale(d), pch=19, cex=2)
plot(cmdscale(d), pch=19, cex=2, col=colData$cell)
plot(cmdscale(d), pch=19, cex=2, col=colData$dex)
Note that cell lines are relatively similar to one another. This suggests that cell line should be used as a covariate in subsequent analysis.
Differential expression
We will use the DESeq2 package for differential expression analysis; other choices are possible, notably edgeR and limma.
The analysis starts by providing the expression count data, a description of the experiment, and a ‘model’ that describes the statistical relationship we’d like to estimate. For our model and based in part on the exploratory analysis of the previous section, we suppose that count is determined cell line and dexamethasone treatment. We include cell line primarily as a covariate; our primary interest is in dexamethasone.
library(DESeq2)
dds <- DESeqDataSetFromMatrix(assay, colData, ~ cell + dex)The analysis is extremely straight-forward to invoke, but the calculations involve a number of sophisticated statistical issues, including:
- Robust estimate of library size
- Use of a negative binomial model to describe the relationship between experimental design and counts
- Use of moderated test statistics that balance gene-level parameter estimates with experiment wide estimates
- Data-driven approaches to outlier detection
- Filtering and control for multiple testing
The code is invoked as:
dds <- DESeq(dds)## estimating size factors## estimating dispersions## gene-wise dispersion estimates## mean-dispersion relationship## final dispersion estimates## fitting model and testingdds## class: DESeqDataSet
## dim: 33469 8
## metadata(1): version
## assays(3): counts mu cooks
## rownames(33469): ENSG00000000003 ENSG00000000419 ...
## ENSG00000273492 ENSG00000273493
## rowData names(46): baseMean baseVar ... deviance maxCooks
## colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
## colData names(10): SampleName cell ... BioSample sizeFactorThe DESeq() function returns an object that can be used as a starting point for further analysis, for instance generating a ‘top table’ of differentially expressed genes, orderd by adjusted (for multiple comparison) P values.
result <- results(dds)
result## log2 fold change (MAP): dex untrt vs trt
## Wald test p-value: dex untrt vs trt
## DataFrame with 33469 rows and 6 columns
## baseMean log2FoldChange lfcSE stat
## <numeric> <numeric> <numeric> <numeric>
## ENSG00000000003 708.6021697 0.37415246 0.09884435 3.7852692
## ENSG00000000419 520.2979006 -0.20206175 0.10974241 -1.8412367
## ENSG00000000457 237.1630368 -0.03616686 0.13834540 -0.2614244
## ENSG00000000460 57.9326331 0.08445399 0.24990709 0.3379415
## ENSG00000000938 0.3180984 0.08413901 0.15133424 0.5559813
## ... ... ... ... ...
## ENSG00000273487 8.1632350 -0.55007238 0.3725061 -1.4766803
## ENSG00000273488 8.5844790 -0.10513006 0.3683837 -0.2853820
## ENSG00000273489 0.2758994 -0.06947899 0.1512520 -0.4593591
## ENSG00000273492 0.1059784 0.02314357 0.1512520 0.1530133
## ENSG00000273493 0.1061417 0.02314357 0.1512520 0.1530133
## pvalue padj
## <numeric> <numeric>
## ENSG00000000003 0.0001535423 0.001279829
## ENSG00000000419 0.0655868795 0.196015831
## ENSG00000000457 0.7937652416 0.912936622
## ENSG00000000460 0.7354072415 0.883203048
## ENSG00000000938 0.5782236287 NA
## ... ... ...
## ENSG00000273487 0.1397614 0.3376252
## ENSG00000273488 0.7753515 0.9032267
## ENSG00000273489 0.6459763 NA
## ENSG00000273492 0.8783878 NA
## ENSG00000273493 0.8783878 NAridx <- head(order(result$padj), 10)
top = result[ridx,]
top## log2 fold change (MAP): dex untrt vs trt
## Wald test p-value: dex untrt vs trt
## DataFrame with 10 rows and 6 columns
## baseMean log2FoldChange lfcSE stat
## <numeric> <numeric> <numeric> <numeric>
## ENSG00000152583 997.4398 -4.313962 0.17213733 -25.06116
## ENSG00000165995 495.0929 -3.186823 0.12815654 -24.86664
## ENSG00000101347 12703.3871 -3.618734 0.14894336 -24.29604
## ENSG00000120129 3409.0294 -2.871488 0.11824908 -24.28338
## ENSG00000189221 2341.7673 -3.230395 0.13667447 -23.63569
## ENSG00000211445 12285.6151 -3.553360 0.15798211 -22.49217
## ENSG00000157214 3009.2632 -1.948723 0.08867432 -21.97618
## ENSG00000162614 5393.1017 -2.003487 0.09269629 -21.61345
## ENSG00000125148 3656.2528 -2.167122 0.10354724 -20.92882
## ENSG00000154734 30315.1355 -2.286778 0.11308412 -20.22192
## pvalue padj
## <numeric> <numeric>
## ENSG00000152583 1.319237e-138 2.360906e-134
## ENSG00000165995 1.708565e-136 1.528824e-132
## ENSG00000101347 2.158637e-130 1.287699e-126
## ENSG00000120129 2.937247e-130 1.314124e-126
## ENSG00000189221 1.656535e-123 5.929070e-120
## ENSG00000211445 4.952260e-112 1.477094e-108
## ENSG00000157214 4.867315e-107 1.244364e-103
## ENSG00000162614 1.342345e-103 3.002826e-100
## ENSG00000125148 2.926277e-97 5.818739e-94
## ENSG00000154734 6.278709e-91 1.123638e-87Two common ways of visualizing results are as “MA” plots that summarize the relationship between count and statistical sigificance
plotMA(result)## Warning in int_abline(a = a, b = b, h = h, v = v, untf = untf, ...): semi-
## transparency is not supported on this device: reported only once per page
and volcano plots where the X-axis corresponds to ‘biology’ (size of change) and Y-axis to ‘statistics’ (significance of observed change).
plot(-log10(pvalue) ~ log2FoldChange, result)
A fun sanity check is the histogram of raw P-values, which should be uniform under the null; deviations often suggest batch effects or other artifacts that should be addressed in upstream analysis.
hist(result$pvalue)
Annotation and variant annotation
Simple annotation of statistical results
There are many opportunities to place the statistical results into biological context. An initial step is to map the cryptic Ensembl gene identifiers used to label regions of interest to more famliar HGNC gene symbols. For this we use the org.Hs.eg.db package, an example of a Bioconductor ‘annotation’ package containing curated data derived from public-domain resources and updated semi-annually. The mapId() function maps between identifier types, in our case to SYMBOL gene ids from ENSEMBL ids. We add these to the top table results so that they can be processed together with the statistical results.
library(org.Hs.eg.db)
top$Symbol <- mapIds(org.Hs.eg.db, rownames(top), "SYMBOL", "ENSEMBL")## 'select()' returned 1:1 mapping between keys and columnstop## log2 fold change (MAP): dex untrt vs trt
## Wald test p-value: dex untrt vs trt
## DataFrame with 10 rows and 7 columns
## baseMean log2FoldChange lfcSE stat
## <numeric> <numeric> <numeric> <numeric>
## ENSG00000152583 997.4398 -4.313962 0.17213733 -25.06116
## ENSG00000165995 495.0929 -3.186823 0.12815654 -24.86664
## ENSG00000101347 12703.3871 -3.618734 0.14894336 -24.29604
## ENSG00000120129 3409.0294 -2.871488 0.11824908 -24.28338
## ENSG00000189221 2341.7673 -3.230395 0.13667447 -23.63569
## ENSG00000211445 12285.6151 -3.553360 0.15798211 -22.49217
## ENSG00000157214 3009.2632 -1.948723 0.08867432 -21.97618
## ENSG00000162614 5393.1017 -2.003487 0.09269629 -21.61345
## ENSG00000125148 3656.2528 -2.167122 0.10354724 -20.92882
## ENSG00000154734 30315.1355 -2.286778 0.11308412 -20.22192
## pvalue padj Symbol
## <numeric> <numeric> <character>
## ENSG00000152583 1.319237e-138 2.360906e-134 SPARCL1
## ENSG00000165995 1.708565e-136 1.528824e-132 CACNB2
## ENSG00000101347 2.158637e-130 1.287699e-126 SAMHD1
## ENSG00000120129 2.937247e-130 1.314124e-126 DUSP1
## ENSG00000189221 1.656535e-123 5.929070e-120 MAOA
## ENSG00000211445 4.952260e-112 1.477094e-108 GPX3
## ENSG00000157214 4.867315e-107 1.244364e-103 STEAP2
## ENSG00000162614 1.342345e-103 3.002826e-100 NEXN
## ENSG00000125148 2.926277e-97 5.818739e-94 MT2A
## ENSG00000154734 6.278709e-91 1.123638e-87 ADAMTS1Sneak peak:
biocLite("Bioconductor/Organism.dplyr")library(Organism.dplyr)src <- src_ucsc("Homo sapiens")## using org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGenesrc## src: sqlite 3.8.6 [/home/mtmorgan/R/x86_64-pc-linux-gnu-library/3.4-Bioc-3.5/org.Hs.eg.db/extdata/org.Hs.eg.sqlite]
## tbls: id, id_accession, id_go, id_go_all, id_omim_pm, id_protein,
## id_transcript, ranges_cds, ranges_exon, ranges_gene, ranges_txtbl(src, "id")## Source: query [?? x 6]
## Database: sqlite 3.8.6 [/home/mtmorgan/R/x86_64-pc-linux-gnu-library/3.4-Bioc-3.5/org.Hs.eg.db/extdata/org.Hs.eg.sqlite]
##
## entrez map ensembl symbol genename alias
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 1 19q13.4 ENSG00000121410 A1BG alpha-1-B glycoprotein A1B
## 2 1 19q13.4 ENSG00000121410 A1BG alpha-1-B glycoprotein ABG
## 3 1 19q13.4 ENSG00000121410 A1BG alpha-1-B glycoprotein GAB
## 4 1 19q13.4 ENSG00000121410 A1BG alpha-1-B glycoprotein HYST2477
## 5 1 19q13.4 ENSG00000121410 A1BG alpha-1-B glycoprotein A1BG
## 6 10 8p22 ENSG00000156006 NAT2 N-acetyltransferase 2 AAC2
## 7 10 8p22 ENSG00000156006 NAT2 N-acetyltransferase 2 NAT-2
## 8 10 8p22 ENSG00000156006 NAT2 N-acetyltransferase 2 PNAT
## 9 10 8p22 ENSG00000156006 NAT2 N-acetyltransferase 2 NAT2
## 10 100 20q13.12 ENSG00000196839 ADA adenosine deaminase ADA
## # ... with more rowsids <- head(rownames(top))
tbl(src, "id") %>% filter(ensembl %in% ids) %>%
select(ensembl, symbol, genename)## Source: query [?? x 3]
## Database: sqlite 3.8.6 [/home/mtmorgan/R/x86_64-pc-linux-gnu-library/3.4-Bioc-3.5/org.Hs.eg.db/extdata/org.Hs.eg.sqlite]
##
## ensembl symbol
## <chr> <chr>
## 1 ENSG00000101347 SAMHD1
## 2 ENSG00000120129 DUSP1
## 3 ENSG00000152583 SPARCL1
## 4 ENSG00000165995 CACNB2
## 5 ENSG00000189221 MAOA
## 6 ENSG00000211445 GPX3
## # ... with 1 more variables: genename <chr>id <- "uc031rqc.3" ## transcript with most exons
t1 <- tbl(src, "id") %>% select(entrez, symbol, genename)
t2 <- tbl(src, "ranges_tx") %>% filter(tx_name == id)
inner_join(t1, t2)## Joining, by = "entrez"## Source: query [?? x 10]
## Database: sqlite 3.8.6 [/home/mtmorgan/R/x86_64-pc-linux-gnu-library/3.4-Bioc-3.5/org.Hs.eg.db/extdata/org.Hs.eg.sqlite]
##
## entrez symbol genename tx_chrom tx_strand tx_start tx_end tx_id
## <chr> <chr> <chr> <chr> <chr> <int> <int> <int>
## 1 7273 TTN titin chr2 - 178525989 178807423 27531
## # ... with 2 more variables: tx_name <chr>, tx_type <chr>inner_join(t1, t2) %>% collect %>% GRanges## Joining, by = "entrez"## GRanges object with 1 range and 6 metadata columns:
## seqnames ranges strand | entrez symbol
## <Rle> <IRanges> <Rle> | <character> <character>
## [1] chr2 [178525989, 178807423] - | 7273 TTN
## genename tx_id tx_name tx_type
## <character> <integer> <character> <character>
## [1] titin 27531 uc031rqc.3 <NA>
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsWorking with called variants
library(VariantAnnotation)
CEUvcf <- system.file(package="VariantFiltering", "extdata", "CEUtrio.vcf.bgz")
vcf <- readVcf(CEUvcf)
vcf## class: CollapsedVCF
## dim: 1000 3
## rowRanges(vcf):
## GRanges with 5 metadata columns: paramRangeID, REF, ALT, QUAL, FILTER
## info(vcf):
## DataFrame with 26 columns: AC, AF, AN, BaseQRankSum, CCC, ClippingRan...
## info(header(vcf)):
## Number Type Description
## AC A Integer Allele count in genotypes, for each A...
## AF A Float Allele Frequency, for each ALT allele...
## AN 1 Integer Total number of alleles in called gen...
## BaseQRankSum 1 Float Z-score from Wilcoxon rank sum test o...
## CCC 1 Integer Number of called chromosomes
## ClippingRankSum 1 Float Z-score From Wilcoxon rank sum test o...
## DB 0 Flag dbSNP Membership
## DP 1 Integer Approximate read depth; some reads ma...
## DS 0 Flag Were any of the samples downsampled?
## END 1 Integer Stop position of the interval
## FS 1 Float Phred-scaled p-value using Fisher's e...
## GQ_MEAN 1 Float Mean of all GQ values
## GQ_STDDEV 1 Float Standard deviation of all GQ values
## HWP 1 Float P value from test of Hardy Weinberg E...
## HaplotypeScore 1 Float Consistency of the site with at most ...
## InbreedingCoeff 1 Float Inbreeding coefficient as estimated f...
## MLEAC A Integer Maximum likelihood expectation (MLE) ...
## MLEAF A Float Maximum likelihood expectation (MLE) ...
## MQ 1 Float RMS Mapping Quality
## MQ0 1 Integer Total Mapping Quality Zero Reads
## MQRankSum 1 Float Z-score From Wilcoxon rank sum test o...
## NCC 1 Integer Number of no-called samples
## QD 1 Float Variant Confidence/Quality by Depth
## ReadPosRankSum 1 Float Z-score from Wilcoxon rank sum test o...
## SOR 1 Float Symmetric Odds Ratio of 2x2 contingen...
## set 1 String Source VCF for the merged record in C...
## geno(vcf):
## SimpleList of length 9: GT, AD, DP, GQ, MIN_DP, PGT, PID, PL, SB
## geno(header(vcf)):
## Number Type Description
## GT 1 String Genotype
## AD . Integer Allelic depths for the ref and alt alleles in ...
## DP 1 Integer Approximate read depth (reads with MQ=255 or w...
## GQ 1 Integer Genotype Quality
## MIN_DP 1 Integer Minimum DP observed within the GVCF block
## PGT 1 String Physical phasing haplotype information, descri...
## PID 1 String Physical phasing ID information, where each un...
## PL G Integer Normalized, Phred-scaled likelihoods for genot...
## SB 4 Integer Per-sample component statistics which comprise...library(TxDb.Hsapiens.UCSC.hg19.knownGene)
seqlevelsStyle(vcf) <- seqlevelsStyle(TxDb.Hsapiens.UCSC.hg19.knownGene)
genome(vcf) <- "hg19"
seqlevels(vcf, force=TRUE) <- paste0("chr", 1:22)vars <- locateVariants(vcf, TxDb.Hsapiens.UCSC.hg19.knownGene, AllVariants())
vars## GRanges object with 1856 ranges and 9 metadata columns:
## seqnames ranges strand | LOCATION LOCSTART
## <Rle> <IRanges> <Rle> | <factor> <integer>
## 1 chr7 [107341641, 107341641] + | coding 1803
## 2 chr7 [107341641, 107341641] + | coding 564
## 3 chr7 [107341641, 107341641] + | coding 486
## 4 chr7 [107341641, 107341641] + | coding 510
## chr11 [ 61719380, 61719380] + | intron 1481
## . ... ... ... . ... ...
## chr22 [51042513, 51042513] + | intron 2483
## chr22 [51042513, 51042513] + | spliceSite <NA>
## chr22 [51042513, 51042513] + | intron 250
## chr22 [51042513, 51042513] + | spliceSite <NA>
## chr22 [51042513, 51042513] + | promoter <NA>
## LOCEND QUERYID TXID CDSID GENEID
## <integer> <integer> <character> <IntegerList> <character>
## 1 1803 1 29193 87760 5172
## 2 564 1 29194 87760 5172
## 3 486 1 29195 87760 5172
## 4 510 1 29196 87760 5172
## 1481 2 41898 7439
## . ... ... ... ... ...
## 2483 743 74402 23542
## <NA> 743 74402 23542
## 250 743 74403 23542
## <NA> 743 74403 23542
## <NA> 743 74404 23542
## PRECEDEID FOLLOWID
## <CharacterList> <CharacterList>
## 1
## 2
## 3
## 4
##
## . ... ...
##
##
##
##
##
## -------
## seqinfo: 22 sequences from hg19 genomeReproducibility
The calculations here are made more reproducible by reporting the version of software used in the analysis, as follows:
sessionInfo()## R Under development (unstable) (2016-10-21 r71563)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.1 LTS
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] Organism.dplyr_0.0.6
## [2] dplyr_0.5.0
## [3] GenomicAlignments_1.11.0
## [4] RNAseqData.HNRNPC.bam.chr14_0.13.0
## [5] airway_0.109.0
## [6] org.Hs.eg.db_3.4.0
## [7] BSgenome.Hsapiens.1000genomes.hs37d5_0.99.1
## [8] BSgenome.Hsapiens.UCSC.hg38_1.4.1
## [9] BSgenome_1.43.0
## [10] rtracklayer_1.35.0
## [11] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [12] TxDb.Hsapiens.UCSC.hg38.knownGene_3.4.0
## [13] GenomicFeatures_1.27.0
## [14] AnnotationDbi_1.37.0
## [15] VariantFiltering_1.11.0
## [16] VariantAnnotation_1.21.0
## [17] Rsamtools_1.27.2
## [18] DESeq2_1.15.2
## [19] SummarizedExperiment_1.5.1
## [20] Biobase_2.35.0
## [21] GenomicRanges_1.27.2
## [22] GenomeInfoDb_1.11.0
## [23] Biostrings_2.43.0
## [24] XVector_0.15.0
## [25] IRanges_2.9.0
## [26] S4Vectors_0.13.1
## [27] BiocGenerics_0.21.0
## [28] ggplot2_2.1.0
## [29] BiocInstaller_1.25.2
##
## loaded via a namespace (and not attached):
## [1] httr_1.2.1 AnnotationHub_2.7.0
## [3] splines_3.4.0 Formula_1.2-1
## [5] shiny_0.14.1 assertthat_0.1
## [7] interactiveDisplayBase_1.13.0 latticeExtra_0.6-28
## [9] RBGL_1.51.0 yaml_2.1.13
## [11] RSQLite_1.0.0 lattice_0.20-34
## [13] biovizBase_1.23.1 chron_2.3-47
## [15] digest_0.6.10 RColorBrewer_1.1-2
## [17] colorspace_1.2-7 htmltools_0.3.5
## [19] httpuv_1.3.3 Matrix_1.2-7.1
## [21] plyr_1.8.4 XML_3.98-1.4
## [23] biomaRt_2.31.1 genefilter_1.57.0
## [25] zlibbioc_1.21.0 xtable_1.8-2
## [27] scales_0.4.0 BiocParallel_1.9.0
## [29] tibble_1.2 annotate_1.53.0
## [31] lazyeval_0.2.0 nnet_7.3-12
## [33] Gviz_1.19.0 survival_2.39-5
## [35] magrittr_1.5 mime_0.5
## [37] evaluate_0.10 foreign_0.8-67
## [39] graph_1.53.0 tools_3.4.0
## [41] data.table_1.9.6 formatR_1.4
## [43] matrixStats_0.51.0 stringr_1.1.0
## [45] munsell_0.4.3 locfit_1.5-9.1
## [47] cluster_2.0.5 ensembldb_1.99.0
## [49] grid_3.4.0 RCurl_1.95-4.8
## [51] dichromat_2.0-0 bitops_1.0-6
## [53] rmarkdown_1.1 codetools_0.2-15
## [55] gtable_0.2.0 DBI_0.5-1
## [57] R6_2.2.0 gridExtra_2.2.1
## [59] knitr_1.14 Hmisc_3.17-4
## [61] ProtGenerics_1.7.0 stringi_1.1.2
## [63] Rcpp_0.12.7 geneplotter_1.53.0
## [65] rpart_4.1-10 acepack_1.4.0