CHAI: Consensus Clustering Through Similarity Matrix Integration for Cell-Type Identification.

ABSTRACT: Several methods have been developed to computationally predict cell-types for single cell RNA sequencing (scRNAseq) data. As methods are developed, a common problem for investigators has been identifying the best method they should apply to their specific use-case. To address this challenge, we present CHAI (consensus Clustering tHrough similArIty matrix integratIon for single cell type identification), a wisdom of crowds approach for scRNAseq clustering. CHAI presents two competing methods which aggregate the clustering results from seven state of the art clustering methods: CHAI-AvgSim and CHAI-SNF. Both methods demonstrate improved performance on a diverse selection of benchmarking datasets, besides also outperforming a previous consensus clustering method. We demonstrate CHAI's practic

SUBMITTER: Lodi MK 

PROVIDER: S-EPMC10983883 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature