DESeq2 doesn't compare the right ctrl vs treatment?

Question: DESeq2 doesn't compare the right ctrl vs treatment?

5.9 years ago by

madkitty • 600

Canada

madkitty • 600 wrote:

We have a very simple experiment, 1 control (C) and 3 independent treatments (T, B, A). So technically I wrote the following script thinking it would compare C to T, then C to B and finally C to A.

Though at the bottom of my pipeline, it seems that DESeq2 is comparing treatments T vs A. Is there a better way to write the condition so it would compare independentely control to each treatment ? (After that I should cluster genes and output a heatmap)

# DESeq1 libraries library( "DESeq2" ) library("Biobase") # Heatmap libraries library( "genefilter" ) library(gplots)

r2 = read.table("matrix.txt", header=TRUE, row.names=1) head(r2) samples <- data.frame(row.names=c("C1", "T1", "B1", "A1"), condition=as.factor(c("C1", "T1", "B1", "A1"))) dds <- DESeqDataSetFromMatrix(countData = as.matrix(r2), colData=samples, design=~condition)

# Run DESeq2 dds <- DESeq(dds)

# DESeq2 Results res <- results(dds) head (res)

********** mcols(res, use.names=TRUE) > mcols(res, use.names=TRUE) DataFrame with 6 rows and 2 columns type description <character> <character> baseMean intermediate the base mean over all rows log2FoldChange results log2 fold change (MAP): condition T1 vs A1 lfcSE results standard error: condition T1 vs A1 stat results Wald statistic: condition T1 vs A1 pvalue results Wald test p-value: condition T1 vs A1 padj results BH adjusted p-values

rna-seq deseq2 • 8.6k views

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modified 5.9 years ago by Devon Ryan ♦ 95k • written 5.9 years ago by madkitty • 600

5.9 years ago by

Devon Ryan ♦ 95k

Freiburg, Germany

Devon Ryan ♦ 95k wrote:

This has nothing to do with DESeq2. It can't read your mind that you want C1 to be the base level. You just need to samples$condition <- relevel(samples$condition, "C1") before making dds.

ADD COMMENT • link written 5.9 years ago by Devon Ryan ♦ 95k

Oh Thanks, I see where my mistake was. I re-wrote the script, can you confirm this is the proper way to use it?

samples <- data.frame(row.names=c("C1", "T1", "B1", "A1"), condition=as.factor(c("C1", "T1", "B1", "A1")))

samples$condition <- relevel(samples$condition, "C1") dds <- DESeqDataSetFromMatrix(countData = as.matrix(r2), colData=samples$condition, design=~condition)

ADD REPLY • link written 5.9 years ago by madkitty • 600

That should be correct.

When you make a factor, the levels are ordered lexicographically and most programs will just assume that the first level in the list of levels (i.e., from levels(samples$condition), not the first value of samples$condition) is the base level for comparisons. To do otherwise would open a very large bag of worms (e.g., if you assume that some factors starting with C are control, then you'd piss off people working on cancer:normal comparisons).

ADD REPLY • link written 5.9 years ago by Devon Ryan ♦ 95k

Unfortunately, it returns an error message.

I have a first column with gene names (before C1), which should be row.names. Am I using data.frame properly? Should I adapt row.names?

> samples <- data.frame(row.names=c("C1", "T1", "B1", "A1"), condition=as.factor(c("C1", "T1", "B1", "A1"))) > samples$condition <- relevel(samples$condition, "C1")

> dds <- DESeqDataSetFromMatrix(countData = as.matrix(r2), colData=samples$condition, design=~condition) Error in validObject(.Object) : invalid class “SummarizedExperiment” object: invalid object for slot "colData" in class "SummarizedExperiment": got class "factor", should be or extend class "DataFrame"

ADD REPLY • link written 5.9 years ago by madkitty • 600

dds <- DESeqDataSetFromMatrix(countData = as.matrix(r2), colData=samples, design=~condition)

ADD REPLY • link written 5.9 years ago by Devon Ryan ♦ 95k

Thanks! it seems to be working well now. Though when I run mcols, it says T1 vs C1. What happened to treatment A and B? Is it still comparing them separately versus the control C1?

> mcols(res, use.names=TRUE) DataFrame with 6 rows and 2 columns type description <character> <character> baseMean intermediate the base mean over all rows log2FoldChange results log2 fold change (MAP): condition T1 vs C1 lfcSE results standard error: condition T1 vs C1 stat results Wald statistic: condition T1 vs C1 pvalue results Wald test p-value: condition T1 vs C1 padj results BH adjusted p-values

ADD REPLY • link modified 5.9 years ago • written 5.9 years ago by madkitty • 600

The results() function has a 'coef' option (or something like that) that you can use to extract the results for the other coefficients.

ADD REPLY • link written 5.9 years ago by Devon Ryan ♦ 95k

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