Towards an integrative multi-omics workflow

The advent of high-throughput techniques has greatly enhanced biological discovery. Last years, analysis of multi-omics data has taken the front seat to improve physiological understanding. Handling functional enrichment results from various biological data raises practical questions. We propose an integrative workflow to better interpret biological process insights in a multi-omics approach applied to breast cancer data from The Cancer Genome Atlas (TCGA) related to Invasive Ductal Carcinoma (IDC) and Invasive Lobular Carcinoma (ILC). Pathway enrichment by Over Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) has been conducted with both features information from differential expression analysis or selected features from multi-block sPLS-DA methods. Then, comprehensive comparisons of enrichment results have been carried out by looking at classical enrichment analysis, probabilities pooling by Stouffers Z scores method and pathways clustering in biological themes. Our work shows that ORA enrichment with selected sPLS-DA features and pathways probabilities pooling by Stouffers method lead to enrichment maps highly associated to physiological knowledge of IDC or ILC phenotypes, better than ORA and GSEA with differential expression driven features.