ScRCMF: Identification of Cell Subpopulations and Transition States From Single-Cell Transcriptomes.

ABSTRACT: Single cell technologies provide an unprecedented opportunity to explore the heterogeneity in a biological process at the level of single cells. One major challenge in analyzing single cell data is to identify cell subpopulations, stable cell states, and cells in transition between states. To elucidate the transition mechanisms in cell fate dynamics, it is highly desirable to quantitatively characterize cellular states and intermediate states. Here, we present scRCMF, an unsupervised method that identifies stable cell states and transition cells by adopting a nonlinear optimization model that infers the latent substructures from a gene-cell matrix. We incorporate a random coefficient matrix-based regularization into the standard nonnegative matrix decomposition model to improve the reliabi

SUBMITTER: Zheng X 

PROVIDER: S-EPMC7250043 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature