Transcription factor enrichment analysis (TFEA): Quantifying the activity of hundreds of TFs from a single experiment

Detecting differential activation of transcription factors (TFs) in response to perturbation provides insight into global cellular processes. We present here Transcription Factor Enrichment Analysis (TFEA), a robust and reliable computational method that can detect differential activity of hundreds of TFs given any set of perturbation data. TFEA draws inspiration from GSEA and detects positional motif enrichment within an ordered list of regions of interest (ROI). As ROI are typically directly inferred from the data, we also introduce muMerge, a statistically principled method that generates a consensus list of ROIs from multiple replicates and conditions. TFEA is broadly applicable to data types that inform on transcriptional regulation, including CAGE data, ChIP-Seq, and accessibility data (e.g. ATAC-Seq). We demonstrate that TFEA can not only identify key TFs that respond to a perturbation, but also temporally unravel complex regulatory networks with time series data. Consequently, TFEA serves as a “hypothesis-generating engine” that provides an easy, rigorous, and cost-effective means to broadly assess TF activity to yield new biological insights about basic cellular processes. CRISPR/Cas9 edited MCF10A cells (full length wild type p53 cDNA was inserted at transcriptional start site in exon 2) were treated with 10uM nutlin3a and 0.01% DMSO (control) for 3 hours with a total of 2 biological replicates. Nuclei were isolated and PRO-seq libraries were constructed and sequenced.