Intrinsically Bayesian robust classifier for single-cell gene expression trajectories in gene regulatory networks.

ABSTRACT:

Background

Expression-based phenotype classification using either microarray or RNA-Seq measurements suffers from a lack of specificity because pathway timing is not revealed and expressions are averaged across groups of cells. This paper studies expression-based classification under the assumption that single-cell measurements are sampled at a sufficient rate to detect regulatory timing. Thus, observations are expression trajectories. In effect, classification is performed on data generated by an underlying gene regulatory network.

Results

Network regulation is modeled via a Boolean network with perturbation, regulation not fully determined owing to inherent biological randomness. The binary assumption is not critical because the resulting Markov chain characterizes express

SUBMITTER: Karbalayghareh A 

PROVIDER: S-EPMC5872504 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature