RNASeq differential expression: How to deal with few genes with extremely high expression levels

Question

[komal.rathi]

Hi everyone,

I have a set of 4 KO and 4 Control samples from mice and I am performing a differential expression on it. My pipeline is STAR alignment to mm10 followed by RSEM. I then use the expected counts from RSEM and normalize them using Voom because I want to perform a differential gene expression using limma.

Here are some QC plots:

Boxplot of Samples before and after normalization: https://ibb.co/d1czbF

PCA of Samples before and after normalization: https://ibb.co/bE85GF

Reads distribution: https://ibb.co/j6R33v

First my controls and KOs do not group as I would expect. But my main concern is the gene expression - you can see in the read distribution plots that there are a few genes with extremely high expression levels. Top 8/20 highest expressing genes belong to chromosome M. I am normalizing the expression levels using Voom (limma) but I wanted to know if this distribution will affect any downstream differential expression results and if yes, how can I fix it?

Thanks!

Answer 1

[Carlo Yague]

But my main concern is the gene expression - you can see in the read distribution plots that there are a few genes with extremely high expression levels.

This is very typical of RNA-seq experiments. You should take the log if you want to see something in the distribution of the un-normalized counts.

First my controls and KOs do not group as I would expect.

If you look at the PCA after normalization, you can see that the sample 1142 HP is a clear outlier that totally dominate the PC1. But on the PC2, the control and KOs samples group relatively well.

I wanted to know if this distribution will affect any downstream differential expression results and if yes, how can I fix it?

Voom normalization is robust to highly expressed genes so it should be ok.

[komal.rathi]

Thanks for the answer - that was very helpful. The reason I was just wondering if my downstream analysis for finding differentially expressed genes got affected because I only found 8 genes differentially expressed and I am quite used to seeing hundreds of genes popping up as differentially expressed.