Airpart: interpretable statistical models for analyzing allelic imbalance in single-cell datasets.

ABSTRACT:

Motivation

Allelic expression analysis aids in detection of cis-regulatory mechanisms of genetic variation, which produce allelic imbalance (AI) in heterozygotes. Measuring AI in bulk data lacking time or spatial resolution has the limitation that cell-type-specific (CTS), spatial- or time-dependent AI signals may be dampened or not detected.

Results

We introduce a statistical method airpart for identifying differential CTS AI from single-cell RNA-sequencing data, or dynamics AI from other spatially or time-resolved datasets. airpart outputs discrete partitions of data, pointing to groups of genes and cells under common mechanisms of cis-genetic regulation. In order to account for low counts in single-cell data, our method uses a Generalized Fused Lasso with Binomial likelih

SUBMITTER: Mu W 

PROVIDER: S-EPMC9113279 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature