Continuous-state HMMs for modeling time-series single-cell RNA-Seq data.

ABSTRACT:

Motivation

Methods for reconstructing developmental trajectories from time-series single-cell RNA-Seq (scRNA-Seq) data can be largely divided into two categories. The first, often referred to as pseudotime ordering methods are deterministic and rely on dimensionality reduction followed by an ordering step. The second learns a probabilistic branching model to represent the developmental process. While both types have been successful, each suffers from shortcomings that can impact their accuracy.

Results

We developed a new method based on continuous-state HMMs (CSHMMs) for representing and modeling time-series scRNA-Seq data. We define the CSHMM model and provide efficient learning and inference algorithms which allow the method to determine both the structure of the branching

SUBMITTER: Lin C 

PROVIDER: S-EPMC6853676 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature