Predicting regulators of epithelial cell state through regularized regression analysis of single cell multiomic sequencing

Ledru, N.; Wilson, P. C.; Muto, Y.; Yoshimura, Y.; Wu, H.; Asthana, A.; Tullius, S. G.; Waikar, S. S.; Orlando, G.; Humphreys, B.

doi:10.1101/2022.12.29.522232

bioRxiv · 10.1101/2022.12.29.522232

Predicting regulators of epithelial cell state through regularized regression analysis of single cell multiomic sequencing

Ledru, N.·Wilson, P. C.·Muto, Y.·Yoshimura, Y.·Wu, H.·Asthana, A.·Tullius, S. G.·Waikar, S. S.·Orlando, G.·Humphreys, B.

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Abstract

Chronic disease processes are marked by cell-specific transcriptomic and epigenomic changes. Single nucleus joint RNA- and ATAC-seq offers an opportunity to study the gene regulatory networks underpinning these changes in order to identify key regulatory drivers. We developed a regularized regression approach, RENIN, (Regulatory Network Inference) to construct genome-wide parametric gene regulatory networks using multiomic datasets. We generated a single nucleus multiomic dataset from seven adult human kidney biopsies and applied RENIN to study drivers of a failed injury response associated with kidney disease. We demonstrate that RENIN is highly effective tool at predicting key cis- and trans-regulatory elements.

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Ledru, N., Wilson, P. C., Muto, Y., Yoshimura, Y., Wu, H., Asthana, A., Tullius, S. G., Waikar, S. S., Orlando, G., Humphreys, B.. 2022-12-30. Predicting regulators of epithelial cell state through regularized regression analysis of single cell multiomic sequencing. https://doi.org/10.1101/2022.12.29.522232

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