Classification and Regression Trees to predict Transcription Factor Combinatorial Interaction in scRNA-seq data

Abstract

AbstractUnderstanding the regulatory mechanisms that govern gene expression is crucial for deciphering cellular functions. Transcription factors (TFs) play a key role in regulating gene expression. In particular TF combinatorial interactions (TFCI) are now thought to largely shape genomic transcriptional responses, but predicting TFCIper seis still a difficult task. Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool providing a whole new readout of gene regulatory effects. In this study, we propose a machine learning approach utilizing Classification and Regression Trees (CART) for predicting TFCI in >110k scRNA-seq data points yielded fromArabidopsis thalianaroot. The proposed methodology provides a valuable tool for pointing to new TFCI mechanisms and could advance our understanding of Gene Regulatory Networks’ functioning.