UINMF performs mosaic integration of single-cell multi-omic datasets using nonnegative matrix factorization.

ABSTRACT: Single-cell genomic technologies provide an unprecedented opportunity to define molecular cell types in a data-driven fashion, but present unique data integration challenges. Many analyses require "mosaic integration", including both features shared across datasets and features exclusive to a single experiment. Previous computational integration approaches require that the input matrices share the same number of either genes or cells, and thus can use only shared features. To address this limitation, we derive a nonnegative matrix factorization algorithm for integrating single-cell datasets containing both shared and unshared features. The key advance is incorporating an additional metagene matrix that allows unshared features to inform the factorization. We demonstrate that incorporating

SUBMITTER: Kriebel AR 

PROVIDER: S-EPMC8828882 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature