Discovery of gene-gene co-perturbation through big data

Scientists have learned much about gene expression in the past few years. Integrating large-scale gene-expression data will significantly bolster researchers’ understanding of biology and diseases. While we currently use co-expression models to discover gene-gene associations in RNA seq data, integrating RNA seq data generated from different experiments can have a “batch effect,” which decreases data quality and makes it harder to glean definitive gene relationships from co-expression models. The biological relationship can also be context-dependent and non-linear, which make them difficult to detect using current co-expression approaches. Here, we propose a co-perturbation model that better identifies gene-gene associations in integrated data and reveals non-linear correlation between genes.