Classification and Regression Trees to predict Transcription Factor Combinatorial Interaction in scRNA-seq data - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Access content directly
Preprints, Working Papers, ... Year : 2024

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

Abstract

Abstract Understanding 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 TFCI per se is 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 from Arabidopsis thaliana root. The proposed methodology provides a valuable tool for pointing to new TFCI mechanisms and could advance our understanding of Gene Regulatory Networks’ functioning.
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Dates and versions

hal-04626016 , version 1 (26-06-2024)

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Jean Baptiste Carluer, Laura Steinmann, Clément Carré, André Mas, Gabriel Krouk. Classification and Regression Trees to predict Transcription Factor Combinatorial Interaction in scRNA-seq data. 2024. ⟨hal-04626016⟩
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