I have an RNASeq experiment, and I am using DESeq2. After I get the results, I plot the MA plot. This is the output of plotMA:
And this is my attempt at the MA plot:
res$significant = (res$padj < .05) res$significant = as.factor(res$significant) res$significant[is.na(res$significant)] = F ggplot(as.data.table(res), aes(x=log2(baseMean), y=log2FoldChange, color=significant)) + geom_point() + geom_hline(color = "blue3", yintercept = 0) + stat_smooth(se = FALSE, method = "loess", color = "red3") + scale_color_manual(values=c("Black","Red"))
- There is a slight bias at the end, so genes with a high A, tend to have a high M, and we are detecting more up-regulation than down. Is this a problem? What might be causing this, and more importantly, is there something we can do to fix it?
- Even if the slight effect is too little to be a problem, what causes problems like this? Imbalanced sampling depth at the two conditions? Why doesn't normalization (sample size factors) fix this?
Also, is there a reason why DESeq2::plotMA doesn't plot the best fit line?
Disclaimer: Cross posted to Biconductor questions.