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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.

2022-12-30 genomics
10.1101/2022.12.29.522232 bioRxiv
Show 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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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.