Question: How to choose threshold for filtering low counts before Voom transformation?
gravatar for ihc.europa
7 weeks ago by
Spain/Valencia/IVI Foundation
ihc.europa20 wrote:


I'm currently performing a differential expression experiment to which I'm trying to apply Voom transformation using limma package.

I have the experimental design, the count matrix and dge object prepared. I'm having trouble choosing the method to filter low counts reads.

The first method I am employing is filterByExpr from edgeR package:

keep <- filterByExpr(dge, design = design)
dge <- dge[keep, , keep.lib.sizes = F]

After calculating normalization factors using calcNormFactors and performing voom with v <- voom(dge, design, plot = T, normalize.method = "quantile") I got this plot:

Which from what I'm reading it still needs some filtering to do given the trend of the variance.

The alternative method I used consisted on applying:

cutoff <- 1
drop <- which(apply(cpm(dge), 1, max) < cutoff)
d <- dge[-drop,] 

But while the number of genes trimmed is different, the plot is mostly the same.

How should I choose a threshold to filter low counts? Is there another way?

Thank you very much,

Kind regards

voom rna-seq limma R • 157 views
ADD COMMENTlink modified 26 days ago by h.mon31k • written 7 weeks ago by ihc.europa20
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