TY - JOUR AU - Sun, Shiquan AU - Chen, Yabo AU - Liu, Yang AU - Shang, Xuequn PY - 2019 DA - 2019/04/05 TI - A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data JO - BMC Systems Biology SP - 28 VL - 13 IS - 2 AB - 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). SN - 1752-0509 UR - https://doi.org/10.1186/s12918-019-0699-6 DO - 10.1186/s12918-019-0699-6 ID - Sun2019 ER -