RT Journal Article
SR Electronic
T1 Identifying Gene Expression Programs of Cell-type Identity and Cellular Activity with Single-Cell RNA-Seq
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 310599
DO 10.1101/310599
A1 Kotliar, Dylan
A1 Veres, Adrian
A1 Nagy, M. Aurel
A1 Tabrizi, Shervin
A1 Hodis, Eran
A1 Melton, Douglas A.
A1 Sabeti, Pardis C.
YR 2018
UL http://biorxiv.org/content/early/2018/11/20/310599.abstract
AB Identifying gene expression programs underlying both cell-type identity and cellular activities (e.g. life-cycle processes, responses to environmental cues) is crucial for understanding the organization of cells and tissues. Although single-cell RNA-Seq (scRNA-Seq) can quantify transcripts in individual cells, each cell’s expression profile may be a mixture of both types of programs, making them difficult to disentangle. Here we illustrate and enhance the use of matrix factorization as a solution to this problem. We show with simulations that a method that we call consensus non-negative matrix factorization (cNMF) accurately infers identity and activity programs, including the relative contribution of programs in each cell. Applied to published brain organoid and visual cortex scRNA-Seq datasets, cNMF refines the hierarchy of cell types and identifies both expected (e.g. cell cycle and hypoxia) and intriguing novel activity programs. We propose that one of the novel programs may reflect a neurosecretory phenotype and a second may underlie the formation of neuronal synapses. We make cNMF available to the community and illustrate how this approach can provide key insights into gene expression variation within and between cell types.