Hi Guys,
I am doing some differential expression analysis of rna seq data using deseq2 . I have 12 different samples and i am using the raw count data and then inputting the matrix in deseq2.
my question is that if i wanted to compare a correlation of Gene A and Gene B within samples (not between samples - as they are co-expressed): do I do this on the raw counts or normalized counts.
so I have 12 values for Gene A across 12 samples
and 12 values for Gene B across 12 samples
doing a raw count correlation gives me around rho 0.8 something
however normalizing using the method in DESeq2 will scale each sample differently by size factors and the rho goes down to 0.5
anyway i am not sure how i should be doing the correlation (raw or normalized), if normalized then which method is preferable for within sample comparisons for 2 different genes.
Thank you for taking the time to read this and hope someone can give me some advice.
I am doing some differential expression analysis of rna seq data using deseq2 . I have 12 different samples and i am using the raw count data and then inputting the matrix in deseq2.
my question is that if i wanted to compare a correlation of Gene A and Gene B within samples (not between samples - as they are co-expressed): do I do this on the raw counts or normalized counts.
so I have 12 values for Gene A across 12 samples
and 12 values for Gene B across 12 samples
doing a raw count correlation gives me around rho 0.8 something
however normalizing using the method in DESeq2 will scale each sample differently by size factors and the rho goes down to 0.5
anyway i am not sure how i should be doing the correlation (raw or normalized), if normalized then which method is preferable for within sample comparisons for 2 different genes.
Thank you for taking the time to read this and hope someone can give me some advice.
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