DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data.

ABSTRACT:

Motivation

Single cell transcriptome sequencing (scRNA-Seq) has become a revolutionary tool to study cellular and molecular processes at single cell resolution. Among existing technologies, the recently developed droplet-based platform enables efficient parallel processing of thousands of single cells with direct counting of transcript copies using Unique Molecular Identifier (UMI). Despite the technology advances, statistical methods and computational tools are still lacking for analyzing droplet-based scRNA-Seq data. Particularly, model-based approaches for clustering large-scale single cell transcriptomic data are still under-explored.

Results

We developed DIMM-SC, a Dirichlet Mixture Model for clustering droplet-based Single Cell transcriptomic data. This approach explic

SUBMITTER: Sun Z 

PROVIDER: S-EPMC6454475 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature