Why the p value and logFC calculated by limma is so small
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xingxd16 ▴ 20
@xingxd16-20156
Last seen 2.7 years ago

Hi all

• I use limma to do the differential expression analysis, I want to do the volcano plot, x-axis is logFC and y-axis is -1*log10(adjust p). But I found the the adjust p output by limma is so small and after -log10 transfor is up to 300, thats mean the p value gived by limma is 1*E-300 unit. But in other paper , the adjust p value is about 1*E-30 unit, and -log10 is about 30. Why the p value is so small in limma? How can I change p value to 1*E-30 unit.
• The logFC is small too. I try to calculate the mean expression for each genes in disease samples and mean value in normal value, then get the logFC, its larger than what limma output . And if I use log2(2) threshold , there is nearly no genes significant express. I have to use log2(1.5) even log2(1.2). Why there are so many genes are so signaficant (with very low p value) but also with a very small logFC at the same times? Can I still consider this genes are differentrial expression(depend on adjust p) even they with small logFC ?

Best

limma volcanoplot pvalue logFC • 863 views
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@gordon-smyth
Last seen 3 hours ago
WEHI, Melbourne, Australia

You haven't told us anything about your data or analysis, so it's impossible to say why you get the results that you do.

If your data has a huge number of replicates, then it would be natural to get small p-values even when the fold changes are relatively modest.

The limma volcanoplot function uses -log10(p-value) for the y-axis, not -log10(adjust p).

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Yes , you are right ! I use limma to do my single cell analysis. The cells in one condition is about 2000+ , the cells in another condition is aboult 3500+ . I just use limma to compare this two conditions follow the "lmFit" and "eBayes" steps .

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Yes, that is a huge number of replicates from limma's point of view. It is to be expected that some of the p-values will be very small.

If you use limma for scRNA-seq, it is very important that you only keep genes in the analysis that are detected in a reasonable number of cells. In your case, that might be a few hundred cells.

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Good advice ! I should fitler the genes that only express in little percentage of the cells first ! So , you mean that p value and logFC are too samll are reasonable with huge number of replicates ? And can I lower the logFC threshold to identify significant expression genes , such as from log2(2) to log2(1.2) ?

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Hi ： I posted another question about limma here , could you help me to answer ! Thanks !