DESeq2: multiple conditions design -- How to select subset comparisons from the DESeq object for PCA, ...
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@martinhoelzer-8847
Last seen 5.5 years ago
Germany

Hello, 

I already found out how to select specific comparisons from the object resulting from the DESeq() function

dds <- DESeq(ddsHTSeq)

using the 'contrast' variable in the 'results' function like this:

deseq2.res <- results(dds, contrast=c("condition","treated_A","untreated_A"))

With this 'deseq2.res' object, I can now do my further analyses, also including MA plot, ...

But now, lets say I also want to plot a PCA only for this specific comparison between 'treated_A' and 'untreated_A' using the rld transformed values:

rld <- rlog(dds)
plotPCA(rld, intgroup=c("condition", "type"), returnData=TRUE)

Is there a way, to build from the full 'dds' object with all conditions and values a subset only including those for the comparisons 'treated_A' and 'untreated_A'? 

It would be nice, to have such an object to continue with downstream analyses like PCA, heatmaps, etc... but still have the normalization and estimations over all samples and conditions from the full 'dds' object.

Is there such a way?

Thanks!

Martin

 

 

deseq2 • 9.0k views
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@mikelove
Last seen 7 hours ago
United States

Sure:

rld.sub <- rld[ , rld$condition %in% c("treated_A", "untreated_A") ]
​plotPCA(rld.sub, "condition")
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@ruy204github-12257
Last seen 4.6 years ago
rld.sub<-rld[,rld@colData@listData$condition %in% c("treated_A","untreated_A")]

You could find the detailed rld information by str(rld) and then find the corresponding condition location for subsetting.

Hope this helps!

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You can access a column of the colData of a SummarizedExperiment with the $:

dds$condition
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