Latent cellular analysis robustly reveals subtle diversity in large-scale single-cell RNA-seq data.

ABSTRACT: Single-cell RNA sequencing (scRNA-seq) is a powerful tool for characterizing the cell-to-cell variation and cellular dynamics in populations which appear homogeneous otherwise in basic and translational biological research. However, significant challenges arise in the analysis of scRNA-seq data, including the low signal-to-noise ratio with high data sparsity, potential batch effects, scalability problems when hundreds of thousands of cells are to be analyzed among others. The inherent complexities of scRNA-seq data and dynamic nature of cellular processes lead to suboptimal performance of many currently available algorithms, even for basic tasks such as identifying biologically meaningful heterogeneous subpopulations. In this study, we developed the Latent Cellular Analysis (LCA), a machin

SUBMITTER: Cheng C 

PROVIDER: S-EPMC6902034 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature