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displays pvalues for all the covariates in the model #21

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displays pvalues for all the covariates in the model #21

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b8bcae5
displays pvalues for all the covariates in the model
jazberna Jul 28, 2014
aea2b6c
reports LTR against intercept only nmodel
jazberna Aug 6, 2014
35457a4
Merge remote-tracking branch 'upstream/master'
jazberna Aug 6, 2014
17c554e
added LTR test
jazberna Aug 6, 2014
0e7557c
had to resolve conflicts
jazberna Aug 6, 2014
7fba6d0
added interactions
jazberna Aug 6, 2014
04c62b2
call to resultsNames(dds) was in wrong place
jazberna Aug 7, 2014
e9ea85c
fixed bug in the selection of the main condition term
jazberna Aug 7, 2014
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35 changes: 28 additions & 7 deletions script/run_deseq.R
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,7 @@ sizeFactorsFile <- Args[7]
qcPdfFile <- Args[8]
filterPercentile <- as.numeric( Args[9] )


# Get data and samples
countData <- read.table( countFile, header=TRUE, row.names=1 )
samples <- read.table( samplesFile, header=TRUE, row.names=1 )
Expand Down Expand Up @@ -42,7 +43,7 @@ if (filterPercentile) {
# Create DESeqDataSet (with design according to number of factors)
dds <- DESeqDataSetFromMatrix(countData, samples, design = ~ condition)
if (numFactors == 2) {
design(dds) <- formula(~ group + condition)
design(dds) <- formula(~ group * condition)
}

# Ensure control level (usually "sibling") is first level (i.e. before "mutant")
Expand All @@ -51,14 +52,34 @@ colData(dds)$condition <- factor(colData(dds)$condition,

# Differential expression analysis
dds <- DESeq(dds)
res <- results(dds)

# Find the condition term (does not contain '.' and constains 'condition')
results_names=resultsNames(dds)
non_interaction_terms = which(grepl("\\.",results_names,perl =T) == FALSE)
all_condition_terms_index = which(grepl("condition",results_names) == TRUE)
condition_terms_index = intersect(non_interaction_terms,all_condition_terms_index)

# create matrix with pvalues of all terms in the model
pvalues_matrix = matrix(ncol=0,nrow=nrow(dds))
col_names<-c();
for (i in 1:length(results_names) )
{
equation_term = results_names[i];
results = results(dds, name= equation_term)
palues = as.matrix(results$pvalue)
padj = as.matrix(results$padj)
pvalues_matrix = cbind(pvalues_matrix,palues)
pvalues_matrix = cbind(pvalues_matrix,padj)
col_names<-c(col_names,paste(equation_term,"_pvalue",sep=""),paste(equation_term,"_pvalue_adjusted",sep=""))
}
colnames(pvalues_matrix)=col_names;



# Write output
out <- data.frame(pvalue=res$pvalue, padj=res$padj, row.names=rownames(res))
write.table( out, file=outputFile, col.names=FALSE, row.names=TRUE,
quote=FALSE, sep="\t" )
write.table( sizeFactors( dds ), file=sizeFactorsFile, col.names=FALSE,
row.names=FALSE, quote=FALSE, sep="\t" )
out <- data.frame(pvalues_matrix, row.names=rownames(res))
write.table( out, file=outputFile, col.names=TRUE, row.names=TRUE, quote=FALSE, sep="\t" )
write.table( sizeFactors( dds ), file=sizeFactorsFile, col.names=FALSE,row.names=FALSE, quote=FALSE, sep="\t" )

# Data transformations for QC
rld <- rlogTransformation(dds, blind=TRUE)
Expand Down