We are two biologists (so very new in bioinformatic field...) working with RNAseq data and having little "troubles" with pathways analysis. We performed mRNA sequencing on 4 distinct cell populations to compare their transcriptional profile (platform Illumina HiSeq 2000). Row reads were mapped using TopHat and differential analysis was performed with edgeR+voom+limma packages. Our final output is a table (.txt file) for each contrast containing our 16058 expressed genes with respective log fold change, expression values (normalized) and adjusted p-values. We wish to perform pathway enrichment analysis (first GO for a global level and KEGG for a more precise analysis) to determine which pathways are enriched/depleted in specific cell population compared to the others in order to infer cell-type specific functional signatures. However, we have difficulties to find an optimal method to do this. We tried several packages (e.g gage, goseq) and web-based softwares (e.g GeneGO, AmiGO) and found different outputs (sometimes opposite results). What could be the more "validated" method/package for these analysis? In addition, we found differences considering the input data (raw reads, log FC, a list of differentially expressed genes?) and finally we don't understand what should be the input data for the analysis (we think that it is dependent of the package/method used?). So, does anyone of you experiences with GO/KEGG for RNAseq and maybe help us to use a good quantification method please?
Thank you very much for your help.