Robust identification of perturbed cell types in single-cell RNA-seq data.

ABSTRACT: Single-cell transcriptomics has emerged as a powerful tool for understanding how different cells contribute to disease progression by identifying cell types that change across diseases or conditions. However, detecting changing cell types is challenging due to individual-to-individual and cohort-to-cohort variability and naive approaches based on current computational tools lead to false positive findings. To address this, we propose a computational tool, scDist, based on a mixed-effects model that provides a statistically rigorous and computationally efficient approach for detecting transcriptomic differences. By accurately recapitulating known immune cell relationships and mitigating false positives induced by individual and cohort variation, we demonstrate that scDist outperforms curren

SUBMITTER: Nicol PB 

PROVIDER: S-EPMC11366752 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature