Abstract
The integration of multi-omics data can greatly facilitate the advancement of research in Life Sciences by highlighting new interactions. However, there is currently no widespread procedure for meaningful multi-omics data integration. Here, we present a robust framework, called InterTADs, for integrating multi-omics data derived from the same sample, and considering the chromatin configuration of the genome, i.e. the topologically associating domains (TADs). Following the integration process, statistical analysis highlights the differences between the groups of interest (normal versus cancer cells) relating to (i) independent and (ii) integrated events through TADs. Finally, enrichment analysis using KEGG database, Gene Ontology and transcription factor binding sites and visualization approaches are available. We applied InterTADs to multi-omics datasets from 135 patients with chronic lymphocytic leukemia (CLL) and found that the integration through TADs resulted in a dramatic reduction of heterogeneity compared to individual events. Significant differences for individual events and on TADs level were identified between patients differing in the somatic hypermutation status of the clonotypic immunoglobulin genes, the core biological stratifier in CLL, attesting to the biomedical relevance of InterTADs. In conclusion, our approach suggests a new perspective towards analyzing multi-omics data, by offering reasonable execution time, biological benchmarking and potentially contributing to pattern discovery through TADs.