What are the reasons to find so few Differentially expressed genes (DEGs)?

Question

[sunnykevin97]

HI,

After the differential gene expression analysis, I had got only 15 genes with logFC < 1.5. Is it because of the Transcriptome reference annotation, expression quantification method, and DEG detection methods which are affecting the optimal filtering threshold ?

Comments.

[ATpoint]

Hard to comment without any details, can be anything including no real DEGs, poor data quality, inappropriate use of any tool along the processing pipeline, underpowered data, strong batch effects, you name it.

[Manuel Sokolov Ravasqueira]

When you plot the distribution of logfc values over patients what is the mean + std and mean - std values? Perhaps the threshold of logfc values is high with 1.5 and could be for example 1 or even 0.5. Additionally, what is the p value the you are considering 0.01 or 0.05?

Answer 1

[Antonio R. Franco]

Sometimes that is real, sometimes it could be a problem with your data

I explain an actual case. I compared two sets of 3 replicates under two conditions and ended with a similar result. I found almost no DEGs.

Thus I run a PCA analysis that allow me to check that the sequencing company had messed with my samples. One treated experiment was named as control and viceversa. And that was enough to hide the DEGs. A rename of the samples did the magic and I ended with thousand of DEG as expected. PCA analysis actually showed me that samples were separated

So I recommend you to run the same PCA analysis. It will tell you how separare or different are your samples, and also will reveal any failure