based on the results of a previous post (C: Possible ways of performing differential gene expression and analysis of RNA-Seq), regarding the analysis of a TCGA RNA-Seq data, i ended up with a list of DE genes (the analysis was performed on log2(estimated counts +1) values, with the pipeline of microarrays:limma-eBayes). Then, from a simple Venn diagram, i compared the ~5000 DE gene symbols from the RNA-Seq, with another small gene signature(94 DE genes), which i have acquired from a independent microarray experiment with similar experimental condition (same cancer, very similar comparison in limma,etc). Based on Venn diagram, the 89 gene symbols are common-and also they have the same alteration of gene expression (based on the log2FC).
Thus, the most appropriate/unbiased way of interpreting the results would be that these 89 gene symbols are more genuine DE ? Found in two independent datasets ? Or an even more "advanced approach" could be utilized, to also take the log2FCs into account ? Like a small-approach/kind of meta-analysis ? Despite the different high-throughput technologies ? As also another (might) drawback, regarding the annotation process ? That is, the microarrays were analyzed with customCDF arrays (affymetrix), whereas the RNA-Seq loaded from a specific R package has been already annotated to unique gene symbols.
[*I understand that this question might be a little more general for the purpose of this group, but there might be R packages or approaches for this purpose which i'm not familiar with.]
Any opinion or feedback is welcome !!