Activity of transcriptional regulators are crucial in elucidating the mechanism of phenotypes. However regulatory activity hypotheses are difficult to experimentally test. Therefore we need accurate and reliable computational methods for regulator activity inference.
There is extensive work in this area, however, current methods have difficulty with one or more of the following: resolving activity of TFs with overlapping regulons, reflecting known regulatory relationships, or flexible modeling of TF activity over the regulon.
We present EPEE (Effector and Perturbation Estimation Engine), a method for differential analysis of transcription factor (TF) activity from gene expression data.
EPEE addresses each of these principal challenges in the field. Firstly, EPEE collectively models all TF activity in a single multivariate model, thereby accounting for the intrinsic coupling among TFs that share targets, which is highly frequent. Secondly, EPEE incorporates context-specific TF-gene regulatory networks and therefore adapts the analysis to each biological context. Finally, EPEE can flexibly reflect different regulatory activity of a single TF among its potential targets.
This allows the flexibility to implicitly recover other regulatory influences such as co-activators or repressors. We comparatively validated EPEE in fifteen datasets from three well-studied contexts, namely immunology, cancer, and hematopoiesis. We show that addressing the aforementioned challenges enable EPEE to outperform alternative methods and reliably produce accurate results.https://github.com/Cobanoglu-Lab/EPEE.Supplementary data are available at Bioinformatics online.
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