Identifying causal regulatory modules in large cancer datasets

Exploring variation in cancer patients samples and correlating them with regulatory differences which effect disease outcomes. We analyse large cancer transcriptome datasets such as The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) to identify correlated sets of genes and their regulatory modules. If these regulatory modules are causal, then they are also studied in Indian population samples to understand their role in disease outcomes in Indian patients.