Question

DESEq2 VST error

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anpham ▴ 60

@anpham-7402

Last seen 7.2 years ago

I'd like to perform variance stabilization transformation in DESeq2 but got this error message. Any input would be much appreciated. Thank you.

vsd <- vst(dds, blind = TRUE)
Error in vst(dds, blind = TRUE) : less than 'nsub' rows,
it is recommended to use varianceStabilizingTransformation directly

deseq2 variancestabilizingtransformation • 15k views

ADD COMMENT • link updated 2.4 years ago by Muhammad • 0 • written 7.4 years ago by anpham ▴ 60

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how many rows are in your dds? ie. what is the output of nrow(dds)?

ADD REPLY • link 7.4 years ago Steve Lianoglou ★ 13k

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Thank you, you're right. This is a miRNA dataset so there are 767 rows. Would I have another option to do VST with this number of rows?

ADD REPLY • link 7.4 years ago anpham ▴ 60

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The default number of rows for vst() is nsub=1000. You can just set nsub=nrow(dds), or you can use the function that is recommended by name in the error message.

ADD REPLY • link 7.4 years ago Michael Love 43k

score 2 · Answer 1 · 2022-04-23

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Angli Xue ▴ 20

@b01f5b5a

Last seen 16 months ago

Australia

I found that the new error message is slightly different:

Error in vst(dds, blind = TRUE, nsub = nrow(dds)) : less than 'nsub' rows with mean normalized count > 5, it is recommended to use varianceStabilizingTransformation directly

In some cases, setting nsub=nrow(dds) still didn't solve the problem because the rows with mean normalized count are less than 1000.

So it's better to check the row number by

sum( rowMeans( counts(dds, normalized=TRUE)) > 5 )

And assign this number to nsub.

ADD COMMENT • link 2.7 years ago Angli Xue ▴ 20

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thank you. this worked in my case.

ADD REPLY • link 2.4 years ago Muhammad • 0

score 1 · Answer 2 · 2017-07-28

To put more meat on my comment above, take a look at the code in the vst function (look at ?vst and just type vst at the console and hit return, the code will reveal itself)

You'll find that the vst function does its magic on a subset of rows (ie. the size of the subset is equal to nsub) then can apply it to the entire dataset.

If you call varianceStabilizingTransform directly on your expression object dds, it doesn't try to work on a subset of the genes in your expression matrix, and just does its thing on the whole thing.

Your problem is that your expression matrix doesn't have more than 1000 genes (rows) in it.