Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data

Li, Y.; Ma, A.; Wang, Y.; Wang, C.; Chen, S.; Fu, H.; Liu, B.; Ma, Q.

doi:10.1101/2022.12.15.520582

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bioRxiv · 10.1101/2022.12.15.520582

Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data

Li, Y.·Ma, A.·Wang, Y.·Wang, C.·Chen, S.·Fu, H.·Liu, B.·Ma, Q.

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Abstract

Deciphering the intricate relationships between transcription factors (TFs), enhancers, and genes through the inference of enhancer-driven gene regulatory networks is crucial in understanding gene regulatory programs in a complex biological system. This study introduces STREAM, a novel method that leverages a Steiner Forest Problem model, a hybrid biclustering pipeline, and submodular optimization to infer enhancer-driven gene regulatory networks from jointly profiled single-cell transcriptome and chromatin accessibility data. Compared to existing methods, STREAM demonstrates enhanced performance in terms of TF recovery, TF-enhancer relation prediction, and enhancer-gene discovery. Application of STREAM to an Alzheimers disease dataset and a diffuse small lymphocytic lymphoma dataset reveals its ability to identify TF-enhancer-gene relationships associated with pseudotime, as well as key TF-enhancer-gene relationships and TF cooperation underlying tumor cells.

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Li, Y., Ma, A., Wang, Y., Wang, C., Chen, S., Fu, H., Liu, B., Ma, Q.. 2022-12-15. Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data. https://doi.org/10.1101/2022.12.15.520582

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