I've a dataset of gene expressions of 102 patients and 9 healthy controls. I downloaded this dataset from GEO, I applied several preprocessing steps (normalization, batch correction based on date, etc), and I was finally able to generate a table containing:
the individuals on the rows
the genes on the columns
- each entry ij containing a real value that indicates the expression of the gene_i in the individual_j
This first preprocessing phase was a lot of effort. Now I would like to perform a differential gene expression analysis, to see how the genes expressions differ between the patients and the healthy controls.
I checked some packages online (such as DESeq2), and I noticed they all have specific requirements for input files, that need to contain raw counts. Unfortunately, I don't have raw counts.
I would like to perform a differential gene expression analysis by myself, by taking advantage of biostatistics R functions applied on my preprocessed tables.
How can I do it? Any suggestion?