DEG analysis by limma
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Last seen 4.8 years ago


I have analyzed DEG between control vs sensitive to drug X, control vs insensitive to drug X, and sensitive vs insensitive. I used limma to find related DEGs, here is my codes


fit <- lmFit(data, design)
keep <- fit$Amean > median(fit$Amean)
ebayes <- eBayes(fit[keep,], robust=TRUE, trend=TRUE)
tab <- topTable(ebayes, coef=2, adjust="BH",n=100)


[1] "(Intercept)"                                            "factor(sensitivity$chemosensitivity)Rx Sensitive"

Now from limma output, I found genes with negative log fold change that are express higher in the insensitive samples and genes with positive log fold changes that are express higher in the sensitive samples, but, what I want to know is how can I find down regulated genes in each phenotype?

DEG Limma • 895 views
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Your analysis doesn't quite make sense because you seem to have three treatment groups but you have fitted a model with only two groups. What has happened to the control samples in your data analysis? You actually got a lot of help for this same analysis before: DEG analysis of chemo-sensitive vs resistance by limma

Given that it's such a straightforward analysis, I'm not quite sure what you are asking. Down regulated genes are simply ones with negative log fold changes.

If you compare sensitive to insensitive, then the genes with positive logFC are up in "sensitive" and down in "insensitive". The genes with negative logFC are down in "sensitive" and up in "insensitive". This is what up and down means! Any gene that is up in one of the conditions being compared must be, by definition, down in the other condition -- it's all relative.

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Aaron Lun ★ 27k
Last seen 11 hours ago
The city by the bay

What do you mean by "down regulated genes in each phenotype"? By its nature, a DE analysis looks for changes in expression between two or more conditions. In your case, this necessarily involves comparing phenotypes against each other. I don't know what else you're expecting to get from limma.

Also, your filter doesn't seem to make much sense. You're always throwing away half of your probes, regardless of how highly or lowly expressed they might be. You should get a better idea of the filter threshold from your control probes - have a look at Section 17.4 of the user's guide, for example.


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