Mercurial > repos > nikhil-joshi > deseq_and_sam2counts

comparison deseq/deseq.R @ 0:d7f27b43b8ff draft

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author nikhil-joshi
date Thu, 05 Jul 2012 21:02:43 -0400
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-1:000000000000 0:d7f27b43b8ff
1 ## Run DESeq (oversimplified) in Galaxy.
2 ## Format: Rscript deseq3.R tab-delimited-counts.txt comma,delimited,col,names,except,first,gene,id,col comparison,here outputfile
3 ##
4 ## The incoming data must have the first column be the gene names, and
5 ## the rest raw counts. The next argument is the list of groups
6 ## (i.e. liver,liver,kidney,kidney), and the final argument is the
7 ## comparison, i.e. kidney,liver. This produces a top-table called
8 ## top-table.txt ordered by p-value.
9
10 cargs <- commandArgs()
11 cargs <- cargs[(which(cargs == "--args")+1):length(cargs)]
12
13 countstable <- cargs[1]
14 conds <- unlist(strsplit(cargs[2], ','))
15 comparison <- unlist(strsplit(cargs[3], ','))
16 outputfile <- cargs[4]
17 diag.html = cargs[5]
18 temp.files.path = cargs[6]
19 counts.name = cargs[7]
20
21 # the comparison must only have two values and the conds must
22 # be a vector from those values, at least one of each.
23 if (length(unique(conds)) != 2) {
24 stop("You can only have two columns types: ", cargs[2])
25 }
26
27 if (length(comparison) != 2) {
28 stop("Comparison type must be a tuple: ", cargs[3])
29 }
30
31 if (!identical(sort(comparison), sort(unique(conds)))) {
32 stop("Column types must use the two names from Comparison type, and vice versa. Must have at least one of each in the Column types.\nColumn types: ", cargs[2], "\n", "Comparison type: ", cargs[3])
33 }
34
35 sink("/dev/null")
36 dir.create(temp.files.path, recursive=TRUE)
37 library(DESeq)
38
39 d <- read.table(countstable, sep="\t", header=TRUE, row.names=1)
40
41 if (length(d) != length(conds)) {
42 stop("Number of total sample columns in counts file must correspond to the columns types field. E.g. if column types is 'kidney,kidney,liver,liver' then number of sample columns in counts file must be 4 as well.")
43 }
44
45 cds <- newCountDataSet(d, conds)
46 cds <- estimateSizeFactors(cds)
47
48 cdsBlind <- estimateDispersions( cds, method="blind" )
49
50 if (length(conds) != 2) {
51 cds <- estimateDispersions( cds )
52 norep = FALSE
53 }
54
55 if (length(conds) == 2) {
56 cds <- estimateDispersions( cds, method="blind", sharingMode="fit-only" )
57 norep = TRUE
58 }
59
60 plotDispEsts <- function( cds )
61 {
62 plot(
63 rowMeans( counts( cds, normalized=TRUE ) ),
64 fitInfo(cds)$perGeneDispEsts,
65 pch = '.', log="xy" )
66 xg <- 10^seq( -.5, 5, length.out=300 )
67 lines( xg, fitInfo(cds)$dispFun( xg ), col="red" )
68 }
69
70 temp.disp.est.plot = file.path( temp.files.path, "DispersionEstimatePlot.png" )
71 png( temp.disp.est.plot, width=500, height=500 )
72 plotDispEsts( cds )
73 dev.off()
74
75 res <- nbinomTest(cds, comparison[1], comparison[2])
76
77 write.table(res[order(res$padj), ], file=outputfile, quote=FALSE, row.names=FALSE, sep="\t")
78
79
80 plotDE <- function( res )
81 plot(
82 res$baseMean,
83 res$log2FoldChange,
84 log="x", pch=20, cex=.3,
85 col = ifelse( res$padj < .1, "red", "black" ) )
86
87 temp.de.plot = file.path( temp.files.path, "DiffExpPlot.png")
88 png( temp.de.plot, width=500, height=500 )
89 plotDE( res )
90 dev.off()
91
92
93 temp.pval.plot = file.path( temp.files.path, "PvalHist.png")
94 png( temp.pval.plot, width=500, height=500 )
95 hist(res$pval, breaks=100, col="skyblue", border="slateblue", main="")
96 dev.off()
97
98 vsd <- getVarianceStabilizedData( cdsBlind )
99
100 temp.heatmap.plot = file.path( temp.files.path, "heatmap.png" )
101 png( temp.heatmap.plot, width=500, height=500 )
102 select <- order(res$pval)[1:100]
103 colors <- colorRampPalette(c("white","darkblue"))(100)
104 heatmap( vsd[select,], col = colors, scale = "none", Rowv=NULL, main="")
105 dev.off()
106
107
108 mod_lfc <- (rowMeans( vsd[, conditions(cds)==comparison[2], drop=FALSE] ) - rowMeans( vsd[, conditions(cds)==comparison[1], drop=FALSE] ))
109 lfc <- res$log2FoldChange
110 finite <- is.finite(lfc)
111 table(as.character(lfc[!finite]), useNA="always")
112 largeNumber <- 10
113 lfc <- ifelse(finite, lfc, sign(lfc) * largeNumber)
114
115 temp.modlfc.plot = file.path( temp.files.path, "modlfc.png" )
116 png( temp.modlfc.plot, width=500, height=500 )
117 plot( lfc, mod_lfc, pch=20, cex=.3, col = ifelse( finite, "#80808040", "red" ) )
118 abline( a=0, b=1, col="#40404040" )
119 dev.off()
120
121 temp.sampclust.plot = file.path( temp.files.path, "sampclust.png" )
122 png( temp.sampclust.plot, width=500, height=500 )
123 dists <- dist( t( vsd ) )
124 heatmap( as.matrix( dists ),
125 symm=TRUE, scale="none", margins=c(10,10),
126 col = colorRampPalette(c("darkblue","white"))(100),
127 labRow = paste( pData(cdsBlind)$condition, pData(cdsBlind)$type ) )
128 dev.off()
129
130
131 library(aroma.light)
132 library(lattice)
133
134 mdsPlot <- function(x, conds=NULL, cex=1, ...) {
135 d <- dist(x)
136
137 mds.fit <- cmdscale(d, eig=TRUE, k=2)
138
139 mds.d <- data.frame(x1=mds.fit$points[, 1],
140 x2=mds.fit$points[, 2],
141 labels=rownames(x))
142 if (!is.null(conds))
143 mds.d$treatment <- as.factor(conds)
144
145 if (!is.null(conds)) {
146 p <- xyplot(x2 ~ x1, group=treatment, data=mds.d, panel=function(x, y, ..., groups, subscripts) {
147 panel.text(x, y, mds.d$labels[subscripts], cex=cex, col=trellis.par.get()$superpose.line$col[groups])
148 }, ...)
149 } else {
150 p <- xyplot(x2 ~ x1, data=mds.d, panel=function(x, y, ..., groups, subscripts) {
151 panel.text(x, y, mds.d$labels[subscripts], cex=cex)
152 }, ...)
153 }
154 return(p)
155 }
156
157 if (!norep) {
158 dn = normalizeQuantileRank(as.matrix(d))
159 p = mdsPlot(t(log10(dn+1)), conds=conds, xlab="dimension 1", ylab="dimension 2", main="")
160
161 temp.mds.plot = file.path( temp.files.path, "mds.png" )
162 png( temp.mds.plot, width=500, height=500 )
163 print(p)
164 dev.off()
165 }
166
167 file.conn = file(diag.html, open="w")
168 writeLines( c("<html><body bgcolor='lightgray'>"), file.conn)
169 writeLines( c("<h2><u>Diagnostic Plots for ", counts.name, "</u></h2>"), file.conn)
170 writeLines( c("<h3>Dispersion Estimates</h3>"), file.conn)
171 writeLines( c("<img src='DispersionEstimatePlot.png'><br/><br/>"), file.conn)
172 writeLines( c("<h3>Differential Expression - Base Mean vs. Log2 Fold Change</h3>"), file.conn)
173 writeLines( c("<img src='DiffExpPlot.png'><br/><br/>"), file.conn)
174 writeLines( c("<h3>P-value histogram</h3>"), file.conn)
175 writeLines( c("<img src='PvalHist.png'><br/><br/>"), file.conn)
176 writeLines( c("<h3>Top 100 Genes/Transcripts by P-value</h3>"), file.conn)
177 writeLines( c("<img src='heatmap.png'><br/><br/>"), file.conn)
178 writeLines( c("<h3>Moderated LFC vs. LFC</h3>"), file.conn)
179 writeLines( c("<img src='modlfc.png'><br/><br/>"), file.conn)
180 writeLines( c("<h3>VST Sample Clustering</h3>"), file.conn)
181 writeLines( c("<img src='sampclust.png'><br/><br/>"), file.conn)
182 writeLines( c("<h3>MDS Plot</h3>"), file.conn)
183
184 if (!norep) {
185 writeLines( c("<img src='mds.png'><br/><br/>"), file.conn)
186 }
187
188 if (norep) {
189 writeLines( c("<h4>MDS plot not produced for unreplicated data</h4>"), file.conn)
190 }
191
192 writeLines( c("</body></html>"), file.conn)
193 close(file.conn)
194
195 sink(NULL)