CellHarmony: cell-level matching and holistic comparison of single-cell transcriptomes.

ABSTRACT: To understand the molecular pathogenesis of human disease, precision analyses to define alterations within and between disease-associated cell populations are desperately needed. Single-cell genomics represents an ideal platform to enable the identification and comparison of normal and diseased transcriptional cell populations. We created cellHarmony, an integrated solution for the unsupervised analysis, classification, and comparison of cell types from diverse single-cell RNA-Seq datasets. cellHarmony efficiently and accurately matches single-cell transcriptomes using a community-clustering and alignment strategy to compute differences in cell-type specific gene expression over potentially dozens of cell populations. Such transcriptional differences are used to automatically identify dist

SUBMITTER: DePasquale EAK 

PROVIDER: S-EPMC6868361 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature