Hybrid Clustering of single-cell gene-expression and cell spatial information via integrated NMF and k-means

MotivationRecent advances in single cell transcriptomics have allowed us to examine the identity of single cells, which has led to the discovery of new cell types and high resolution maps of cell type composition in tissues. Technologies that measure multiple modalities of single cell data provide a more comprehensive picture of a cell, but they also create challenges for data integration tasks. ResultsIn our work, we jointly consider the spatial location and gene expression profiles of cells to determine their identity. Specifically, we have developed scHybridNMF (single-cell Hybrid Nonnegative Matrix Factorization), which performs cell type identification by incorporating single cell gene expression data with cell location data. We combined nonnegative matrix factorization (NMF) with k-means clustering to cohesively represent high-dimensional gene expression data and low-dimensional location data, respectively. We show that scHybridNMF can utilize location data to improve cell type clustering. In particular, we show that under multiple scenarios, including the cases where there is a small number of genes profiled and the location data is noisy, scHybridNMF outperforms sparse NMF, k-means, and an existing method (HMRF) that also uses cell location and gene expression data for cell type identification. Availabilityhttps://github.com/soobleck/scHybridNMF Contacthpark@cc.gatech.edu, xiuwei.zhang@gatech.edu