IDclust: Iterative clustering for unsupervised identification of cell types with single cell transcriptomics and epigenomics.

ABSTRACT: The increasing diversity of single-cell datasets require systematic cell type characterization. Clustering is a critical step in single-cell analysis, heavily influencing downstream analyses. However, current unsupervised clustering algorithms rely on biologically irrelevant parameters that require manual optimization and fail to capture hierarchical relationships between clusters. We developed IDclust, a framework that identifies clusters with significant biological features at multiple resolutions using biologically meaningful thresholds like fold change, adjusted P-value and fraction of expressing cells. By iteratively processing and clustering subsets of the dataset, IDclust guarantees that all clusters found have significantly different features and stops only when no more inte

SUBMITTER: Prompsy P 

PROVIDER: S-EPMC11655290 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature