ICAT: A Novel Algorithm to Robustly Identify Cell States Following Perturbations in Single Cell Transcriptomes

Motivation The detection of distinct cellular identities is central to the analysis of single-cell RNA sequencing 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 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 to current integration workflows. While requiring no a priori knowledge of extant cell states or discriminatory marker genes, ICAT is robust to low signal strength, high perturbation severity, and disparate cell type proportions. We empirically validate ICAT in a developmental model and find that only ICAT identifies a perturbation-unique cellular response. Taken together, our results demonstrate that ICAT offers a significant improvement in defining cellular responses to perturbation in single-cell RNA sequencing data. Availability and implementation https://github.com/BradhamLab/icat