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

Carluer, J.-B.; Steinmann, L.; Carre, C.; Mas, A.; Krouk, G.

doi:10.1101/2024.04.17.589552

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bioRxiv · 10.1101/2024.04.17.589552

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

Carluer, J.-B.·Steinmann, L.·Carre, C.·Mas, A.·Krouk, G.

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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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Carluer, J.-B., Steinmann, L., Carre, C., Mas, A., Krouk, G.. 2024-04-20. Classification and Regression Trees to predict Transcription Factor Combinatorial Interaction in scRNA-seq data.. https://doi.org/10.1101/2024.04.17.589552

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