SnATAC-Express infers Gene Expression from Prioritized Chromatin Accessibility Peaks using Machine Learning.

ABSTRACT:

Background

Single cell multi-omic investigation opens-up new opportunities to understand mechanisms of gene regulation. Existing methods for inferring transcript abundance from chromatin accessibility fail to prioritize the most relevant peaks and tend to assume positive associations between ATAC peaks and RNA counts. We hypothesize that gene regulation can be modeled as a function of combined positive and negative interactions among peaks and that causal regulatory variants are enriched in the vicinity of the most critical peaks.

Results

A machine learning pipeline leveraging single nuclear multiomic transcriptome and chromatin accessibility data is developed to model gene expression as a function of ATAC peak intensity. Multiome data was available for 18 immune cell types

SUBMITTER: Brown M 

PROVIDER: S-EPMC12330625 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature