Fast, sensitive and accurate integration of single-cell data with Harmony.

ABSTRACT: The emerging diversity of single-cell RNA-seq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies, because biological and technical differences are interspersed. We present Harmony (https://github.com/immunogenomics/harmony), an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms while requiring fewer computational resources. Harmo

SUBMITTER: Korsunsky I 

PROVIDER: S-EPMC6884693 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature