ICAT: a novel algorithm to robustly identify cell states following perturbations in single-cell transcriptomes.

ABSTRACT:

Motivation

The detection of distinct cellular identities is central to the analysis of single-cell RNA sequencing (scRNA-seq) experiments. However, in perturbation experiments, current methods typically fail to correctly match cell states between conditions or erroneously remove population substructure. Here, we present the novel, unsupervised algorithm Identify Cell states Across Treatments (ICAT) that employs self-supervised feature weighting and control-guided clustering to accurately resolve cell states across heterogeneous conditions.

Results

Using simulated and real datasets, we show ICAT is superior in identifying and resolving cell states compared with current integration workflows. While requiring no a priori knowledge of extant cell states or discriminatory marker

SUBMITTER: Hawkins DY 

PROVIDER: S-EPMC10172037 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature