A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data.

ABSTRACT:

Background

Single-cell RNA sequencing (scRNAseq) data always involves various unwanted variables, which would be able to mask the true signal to identify cell-types. More efficient way of dealing with this issue is to extract low dimension information from high dimensional gene expression data to represent cell-type structure. In the past two years, several powerful matrix factorization tools were developed for scRNAseq data, such as NMF, ZIFA, pCMF and ZINB-WaVE. But the existing approaches either are unable to directly model the raw count of scRNAseq data or are really time-consuming when handling a large number of cells (e.g. n>500).

Results

In this paper, we developed a fast and efficient count-based matrix factorization method (single-cell negative binomial matrix facto

SUBMITTER: Sun S 

PROVIDER: S-EPMC6449882 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature