Hi, I am trying to check gene expression levels from RNA-seq data, I used DESeq2 without a design just an intercept design = ~1 because I just want to check the general gene expression in my samples, I do not have two different conditions to compare. featureCounts generated the matrix with almost 24000 genes for 954 samples. the problem is that the counts are not well distributed, some samples barely have genes and others have a very high count for a some genes. I would like to plot a heatmap, unfortunately with the huge number of samples and after limiting the genes to 70 (those that have a baseMean >= 50) the heatmap is vey ugly.
i will put the gene dispersion plot from DESeq2, i am not sure if the whole analysis should be taken into consideration
What is the analysis goal? Expression values between genes are wildly different, that is expected. I do not see the point here.
I am just trying to identify 60 - 70 most expressed genes and get their GO terms. samples originate from COVID nasopharyngeal swabs
Expression levels in RNA-seq do not correlate with biological meaning.
my main question is can i use this data to build up on. and how is possible to visualize it