Question

DESeq2 incorrect basemean

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asd • 0

@asd-12785

Last seen 7.7 years ago

I have four genes in four rows in a matrix. The results function of the DESeq2 calculated the baseMean of the four row as

37.6, 2071.5, 1.0, 491.6

But if I calculate the means with rowMeans(counts(dds,normalized=TRUE)), then I get

45.2, 2679.8, 1.0, 491.6

Why is this happen?

And when I calculate the base mean per level with

baseMeanPerLvl <- sapply( levels(dds$condition), function(lvl) rowMeans( counts(dds,normalized=TRUE)[,dds$condition == lvl] ) )

which was posted on this forum by Michael Love, and I calculate the log2 fold change, then I get a different value then it is calculated by the results function.

deseq2 mean • 2.1k views

ADD COMMENT • link updated 7.7 years ago by Michael Love 43k • written 7.7 years ago by asd • 0

score 3 · Accepted Answer · 2017-04-06

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Michael Love 43k

@mikelove

Last seen 2 days ago

United States

The baseMean can be different than the mean of the normalized counts accessed by counts(dds) if extreme count outliers were replaced by DESeq(). The function would have printed a message about this to the console. The counts(dds) are the original counts, but the baseMean is over the counts with replaced outliers, see the Details in ?DESeq.

The baseMeanPerLvl is just that: mean of normalized counts *per level*, so it's a vector of numbers. The baseMean is a single number, the mean of normalized counts in the dds object.

ADD COMMENT • link 7.7 years ago Michael Love 43k

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Thank you for your answer. How can I calculate the base mean per level with replaced extreme count outliers?

ADD REPLY • link 7.7 years ago asd • 0

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In ?DESeq we say:

"the replacement counts used for fitting are kept in assays(dds)[["replaceCounts"]]"

So you would use the same code as above but with this matrix instead:

normMat <- t(t(assays(dds)[["replaceCounts"]])/sizeFactors(dds))
baseMeanPerLvl <- sapply(levels(dds$condition), function(lvl) rowMeans(normMat[,dds$condition == lvl]))

ADD REPLY • link 7.7 years ago Michael Love 43k

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Why does the summary show that I have no outlier?

out of 4 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)     : 0, 0%
LFC < 0 (down)   : 3, 75%
outliers [1]     : 0, 0%
low counts [2]   : 0, 0%
(mean count < 1)

ADD REPLY • link 7.7 years ago asd • 0

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DESeq() if appropriate replaces outliers, and so in the final results table, there are no outliers which would lead to filtering of genes. They are flagged in other ways, documented in the man pages.

ADD REPLY • link 7.7 years ago Michael Love 43k