Limma Differential Analysis on Proteomics data
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@3c9b3fdc
Last seen 4 months ago
United States

Hi, I have a proteomics data set and I am doing the differential analysis on that. I used the Limma package to do that. I first removed the negative counts and did the analysis but I am getting all upregulated ones and none of the ones are down .

groups<-interaction(final_val_C144$Qp_Group,final_val_C144$Day)
design = model.matrix(~0+groups)

colnames(design) = gsub("groups","",colnames(design))

d0 <- DGEList(proteomeRaw_c144)
d0 <- calcNormFactors(d0)
y<-voom(d0,design,plot=T)
fit <- lmFit(y, design)
fit1 <- eBayes(fit)

top.table <- topTable(fit1, sort.by = "F", n = Inf)


On running the below code, I get the following output

summary(decideTests(fit1))

Down         0      0      0     0      0     0      0     0
NotSig       0      0      0     0      0     0      0     0
Up        8106   8106   8106  8106   8106  8106   8106  8106


Any help .

Proteomics limma Limma DifferentialExpression • 321 views
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Cross posted to Biostars

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@gordon-smyth
Last seen 38 minutes ago
WEHI, Melbourne, Australia

The crazy DE result is because you haven't formed any contrasts to compare any of the groups. If you form a contrast then things will be normal again.

But I wonder about the nature of the data. Proteomics data shouldn't have any negative values and how did you remove them? voom() is only for sequencing data, not for proteomics. Are you analysing spectral counts? It would be better to convert to logs and use limma-trend.