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After I got dds from my count matrix by DESeq(), I use results() method to get Padj and log2 fold changes for volcano plotting, then I found some gene's Padj values are infinite and the P-value is very close to 0; this makes those dots on my volcano plotting located very top to Y-aix and also confused me:

  1. is this normal or not, such a p-value (very close to 0) and -logPadj is infinite;
  2. how should I treat those data: are they very different expressed between samples and control or just abnormal data which should be taken off?

Here follows the table: enter image description here

table 1 is dds results, -log10(padj) show inf; table 2 is count matrix from featureCount table 3 is size factor of samples

Please help me !

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  • $\begingroup$ It is not normal to get p-value close to zero, especially if you have not a lot of samples. If I look at your table 2, the mean counts don't tally with the genes in table 1... so can you sort this out $\endgroup$
    – StupidWolf
    Nov 27 '20 at 10:26
  • $\begingroup$ there are some instances, for example you knock out or over-express a gene, then of course you find this insanely differentially expressed.. $\endgroup$
    – StupidWolf
    Nov 27 '20 at 10:28
  • $\begingroup$ The SE are really low in this case though, rather than huge LFC. Very consistent gene expression? $\endgroup$
    – Greg
    Nov 27 '20 at 13:41
  • $\begingroup$ For log(0) error add an arbitrarily small number to the 0 p.adj values (e.g.10^-99) $\endgroup$
    – Greg
    Nov 27 '20 at 13:41
  • $\begingroup$ @StupidWolf Thank you StupidWolf and Greg for answer my question. I didn't list all the samples out, and one of the compounds has the opposite effect from the others, like: d has a positive effect on empty control, and a b c has a negative effect on empty control; According to the answers, I think this may cause the so much differentially expressed(but i'm not so sure). $\endgroup$
    – Chu
    Dec 4 '20 at 9:45
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  1. The fold changes for those are huge (on the order of 32x for the largest), so it's unsurprising for the adjusted p-values to be absurdly small.
  2. There's no reason to treat them differently.
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    $\begingroup$ Agreed. If you want to get rid of Inf you can add a tiny number to the pvalues before the conversion, e.g. p + .Machine$double.xmin. $\endgroup$
    – ATpoint
    Nov 27 '20 at 17:34

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