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

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 unshared features significantly improves integration of single-cell RNA-seq, spatial transcriptomic, SNARE-seq, and cross-species datasets. We have incorporated the UINMF algorithm into the open-source LIGER R package (https://github.com/welch-lab/liger).

Automated summary

Single-cell genomic technologies offer a unique opportunity for defining molecular cell types. This study introduces a nonnegative matrix factorization algorithm for integrating single-cell datasets with shared and unshared features. The incorporation of unshared features significantly improves the integration of various types of single-cell datasets.