Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development.

ABSTRACT: Transcriptional programs control cellular lineage commitment and differentiation during development. Understanding of cell fate has been advanced by studying single-cell RNA-sequencing (RNA-seq) but is limited by the assumptions of current analytic methods regarding the structure of data. We present single-cell topological data analysis (scTDA), an algorithm for topology-based computational analyses to study temporal, unbiased transcriptional regulation. Unlike other methods, scTDA is a nonlinear, model-independent, unsupervised statistical framework that can characterize transient cellular states. We applied scTDA to the analysis of murine embryonic stem cell (mESC) differentiation in vitro in response to inducers of motor neuron differentiation. scTDA resolved asynchrony and continuity i

SUBMITTER: Rizvi AH 

PROVIDER: S-EPMC5569300 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature