Entering edit mode
2.6 years ago
lluc.cabus
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20
Hi,
I'm doing a differential gene expression between samples with two different conditions (with only 6 samples in each group) following the tutorial from edgeR, but I have seen that the p-values follow a strange distribution in the volcano plot. The samples with higher logFC have lower p-values and in general the volcano plots don't seem like the normal plots. Do you know why could this be happening or if this is normal?
The code that I run is this one:
dgList <- DGEList(counts=FL_count_matrix[,1:12], genes=rownames(FL_count_matrix), group = as.factor(FL_metadata_EDTA_SD$Tube_type))
dgList <- calcNormFactors(dgList, method="TMM")
dgList <- estimateCommonDisp(dgList, verbose = T)
dgList <- estimateGLMTrendedDisp(dgList, verbose = T)
dgList <- estimateGLMTagwiseDisp(dgList)
fit <- glmQLFit(dgList)
qlf <- glmQLFTest(fit, coef=2)
Thank you very much, Lluc
Didn't we discuss this already here? Volcano plot with FDR values looks weird How is the question different from the previous one? It looks "odd" because you have many large p-values and no or few DEGs, so the Volcanos are pretty much bunched together vertically rather than nicely separating on the y-axis.
Yes ATpoint , I understand that I have many large p-values and low logFC and in the end I find few differentially expressed genes, but my question was more in the lines of why do the points of the p-values seem to follow a kind of quadratic function (in the first plot). Normally in the volcano plots the p-values are more "randomly" distributed, but they seem to follow a pattern here and I don't know if this is normal. This is one of my first differential gene expression analysis, so I'm not very familiarized with this, sorry if this is too obvious