Dear all,
I run DESeq2 program on 52 tumor Vs only 3 normal samples.
Applying >=0.05 cutoff on adjusted p values and |logFC>=1| was resulted to 1450 up-regulated Vs 440 down-regulated genes.
Now my question is:" is this large numbers of de-regulated genes has been caused by the very small number of normal samples?"
How can I tackle the problem of the small size of normal samples? Is it reasonable to apply more stringent cutoff on logFC or adjusted p-values?
Is it acceptable if I work on these large number of deregulated genes and report them?
I am looking forward to your comments
Nazanin
Hi Kevin,
Yes, I have run lfcShrink function. However, when I checked the results I strangely saw no down-regulated genes were detected.
So I preferred to use the results of dds <- DESeq(dds) instead. As you suggested to me I used |log2FC|>=2 and adjusted P<=0.01, however, no down-regulated genes was detected.
I have another question. I am running DESEq2 on different races. The sample size of tumor Vs normal is very different in distinct races. Should I use the same cutoff (adj p value and logFC) for all races? I want to compare the results between different races.
Thank you so much
Nazanin
You only have 3 normals, though? How many races are in your dataset?
Note that I published recently on this topic: Racial differences in endometrial cancer molecular portraits in The Cancer Genome Atlas.
I am trying to analyze 3 races: Asian(50T, 3N), white(330T,50N) and black\african-american( 28T, 4N).
However, I have analyzed not reported groups, but I am not sure if I include them in the analysis.
And thank u for the paper. I will read it as soon as possible.
If you want to explore differences between the different races, then you could normalise all samples together and include
race
+tissue
in your design formula.As you suggested to me, at first I wanted to normalize all samples together, however, I had to run the analysis on my laptop I run the analysis separately for each race. I will try to repeat the analysis by normalizing all samples together and compare the results.