A stochastic model dissects cell states in biological transition processes.

ABSTRACT: Many biological processes, including differentiation, reprogramming, and disease transformations, involve transitions of cells through distinct states. Direct, unbiased investigation of cell states and their transitions is challenging due to several factors, including limitations of single-cell assays. Here we present a stochastic model of cellular transitions that allows underlying single-cell information, including cell-state-specific parameters and rates governing transitions between states, to be estimated from genome-wide, population-averaged time-course data. The key novelty of our approach lies in specifying latent stochastic models at the single-cell level, and then aggregating these models to give a likelihood that links parameters at the single-cell level to observables at the po

SUBMITTER: Armond JW 

PROVIDER: S-EPMC3894565 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature