GSCA: Gene Set Context Analysis

Although techniques of chromatin immunoprecipitation coupled with high throughput sequencing (ChIP-seq) or tiling array hybridization (ChIP-chip) have came into being for a long time, it still remains difficult to generate a quality ChIPx (i.e., ChIP-seq or ChIP-chip) data set due to the tremendous amount of effort required to develop effective antibodies and efficient protocols. Especially with recent cuts in research fundings, most labs are unable to easily obtain ChIPx data in more than a handful of biological contexts. Thus, standard ChIPx analyses primarily focus on analyzing data from one experiment, and the discoveries are restricted to a specific biological context. We propose to enrich this existing data analysis paradigm by developing a novel approach, GSCA, which superimposes ChIPx data on large amounts of publicly available human and mouse gene expression data containing a diverse collection of cell types, tissues, and disease conditions to discover new biological contexts with potential geneset activity patterns. GSCA could also serve as an informative guide for biologists to prescreen interested biological contexts when designing their experiments.