Cell-attribute aware community detection improves differential abundance testing from single-cell RNA-Seq data.

ABSTRACT: Variations of cell-type proportions within tissues could be informative of biological aging and disease risk. Single-cell RNA-sequencing offers the opportunity to detect such differential abundance patterns, yet this task can be statistically challenging due to the noise in single-cell data, inter-sample variability and because such patterns are often of small effect size. Here we present a differential abundance testing paradigm called ELVAR that uses cell attribute aware clustering when inferring differentially enriched communities within the single-cell manifold. Using simulated and real single-cell and single-nucleus RNA-Seq datasets, we benchmark ELVAR against an analogous algorithm that uses Louvain for clustering, as well as local neighborhood-based methods, demonstrating that ELVAR

SUBMITTER: Maity AK 

PROVIDER: S-EPMC10241145 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature