TY - JOUR T1 - Transcription factor enrichment analysis (TFEA): Quantifying the activity of hundreds of transcription factors from a single experiment JF - bioRxiv DO - 10.1101/2020.01.25.919738 SP - 2020.01.25.919738 AU - Jonathan D. Rubin AU - Jacob T. Stanley AU - Rutendo F. Sigauke AU - Cecilia B. Levandowski AU - Zachary L. Maas AU - Jessica Westfall AU - Dylan J. Taatjes AU - Robin D. Dowell Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/02/06/2020.01.25.919738.abstract N2 - Detecting differential activation of transcription factors (TFs) in response to perturbation provides insight into cellular processes. Transcription Factor Enrichment Analysis (TFEA) is a robust and reliable computational method that detects differential activity of hundreds of TFs given any set of perturbation data. TFEA draws inspiration from GSEA and detects positional motif enrichment within a list of ranked regions of interest (ROIs). As ROIs are typically inferred from the data, we also introduce muMerge, a statistically principled method of generating a consensus list of ROIs from multiple replicates and conditions. TFEA is broadly applicable to data that informs on transcriptional regulation including nascent (eg. PRO-Seq), CAGE, ChIP-Seq, and accessibility (e.g. ATAC-Seq). TFEA not only identifies the key regulators responding to a perturbation, but also temporally unravels regulatory networks with time series data. Consequently, TFEA serves as a hypothesis-generating tool that provides an easy, rigorous, and cost-effective means to broadly assess TF activity yielding new biological insights. ER -