AbstractChanges in a few key transcriptional regulators can lead to different biological states, including cell activation and differentiation, and diseases. Extracting the key gene regulators governing a biological state allows us to gain mechanistic insights and can further help in translational research. Most current tools perform pathway/GO enrichment analysis to identify key genes and regulators but tend to overlook the regulatory interactions between genes and proteins. Here we present RegEnrich, an open-source Bioconductor R package, which combines differential expression analysis, data-driven gene regulatory network inference, enrichment analysis, and gene regulator ranking to identify key regulators using gene/protein expression profiling data. By benchmarking using multiple gene expression datasets of gene silencing studies, we found that RegEnrich using the GSEA method to rank the regulators performed the best to retrieve the key regulators. Further, RegEnrich was applied to 21 publicly available datasets on in vitro interferon-stimulation of different cell types. We found that not only IRF and STAT transcription factor families played an important role in cells responding to IFN, but also several ETS transcription factor family members, such as ELF1 and ETV7, are highly associated with IFN stimulations. Collectively, RegEnrich can accurately identify key gene regulators from the cells under different biological states in a data-driven manner, which can be valuable in mechanistically studying cell differentiation, cell response to drug stimulation, disease development, and ultimately drug development.
Comparison of MAPK specificity across the ETS transcription factor family identifies a high-affinity ERK interaction required for ERG function in prostate cells