alpha in lfcshrink?
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@andrebolerbarros-16788
Last seen 6 months ago
Portugal

Hi everyone,

In DESeq2, we can optimize the independent filtering step in calculating the results by changing alpha. Is there a way to perform that as well for lfcshrink?

Thanks!
deseq2 • 3.3k views
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@mikelove
Last seen 19 hours ago
United States

The way we wrote it, the extra arguments for lfcShrink() are passed to the shrinkage methods, but if you want to specify arguments to results(), you can just pass the results table yourself:

res <- results(dds, ...)
res <- lfcShrink(dds, res=res, ...)

This way you have controlled the generation of the results table.
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Hello Michael Love l! @mikelove I have 3 questions about filtering and lfcShrink.

1-what's the risk of turn off the filtering

res <- results(dds, independentFiltering = F)

2- If I have applied the independentFiltering = F to res, I should not have filtering when lfcShrink, as below,right?

res <- lfcShrink(dds, res=res, ...)

3- I have 2 conditions, and 2 groups per condition, now if I run first for the interaction of cond1 and cond2

res <- results(dds,independentFiltering=FALSE)

without any contrast, and then for the first condition cond1

res_g <- results(dds ,contrast = c("cond1","g1","g2" ),independentFiltering = F)

and I want to apply the lfcShrink function like

resLFC <- lfcShrink(dds, res =res ,coef="con1.cond2", type="apeglm")

I have no problem with the interaction, but then when running

resLFC_g <- lfcShrink(dds, res =res_g, coef =  "cond1_g1_vs_g2" ,type="apeglm")

I have an error, Error in lfcShrink(dds, res = res_g, coef = "cond1_g1_vs_g2" , type = "apeglm") : 'coef' should specify same coefficient as in results 'res',and as apeglm does not work with contrast, and neither I can use coef to generate res_g, I am stocked here, any suggestions how to specify the same coeficients? What I want is to have lfcShrink results for a contrast without filtering out any gene, and having all padj !=NA Thanks!

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Without filtering, you are including genes with low power so the multiple testing burden is higher. See: Bourgon 2010 or Ignatiadis 2016.

This is fine, you can do your own filtering at the top of the script with e.g. a common rule is:

keep <- rowSums(counts(dds) >= 10) >= X

Where you choose X to be the minimum number of samples with a count of 10 or more.

lfcShrink does not have a filtering argument. It does not perform this operation.

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Sorry Michael, I rephrase. Which option passed to lfcShrink function, allow me to not have ANY padj=NA in the shrunken results? using the apeglm algorithm

I apply a filter to keep genes that are present in 80% of the samples of at least one experimental group (n per group =10), no matter number of counts, what do you think? I should apply something similar to what you suggest in addition to mine?

Thanks

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At least one of the 3 options you give in the below post works for me, Thanks Dealing with genes that have Padj=NA

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Are the cases where the padj is NA also where the pvalue is NA? In that case see the vignette section that explains the NA values.

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Only padj is NA, I don't have any p value=NA, so I solved adjusting manually as res$padj <- p.adjust(res$pvalue, method="BH")

Thanks Michael, problem solved!

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I solved question 3 using the shrink type="ashr" algorithm and giving only res and no coef or contrast

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