Statistical Bioinformatics Webinar: Kriebel -- Mosaic Integration of Single-cell Multi-omic Datasets Using Non-negative Matrix Factorization

Presented by Ms.  April Kriebel (University of Michigan, USA) 

Single-cell genomic technologies provide an unprecedented opportunity to define
molecular cell types in a data-driven fashion, but present unique data integration
challenges.  Integration analyses often involve datasets with partially overlapping
features, including both shared features that occur in all 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 novel non-negative 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 R package LIGER