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Bioinformatics. 2019 Apr 30. pii: btz296. doi: 10.1093/bioinformatics/btz296. [Epub ahead of print]

Continuous State HMMs for Modeling Time Series Single Cell RNA-Seq Data.

Author information

1
Machine Learning Dept. School of Computer Science, Carnegie Mellon University.
2
Computational Biology Dept., School of Computer Science, Carnegie Mellon University.

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 process and the assignment of cells to these branches. Analyzing several developmental single cell datasets we show that the CSHMM method accurately infers branching topology and correctly and continuously assign cells to paths, improving upon prior methods proposed for this task. Analysis of genes based on the continuous cell assignment identifies known and novel markers for different cell types.

AVAILABILITY:

Software and Supporting website: www.andrew.cmu.edu/user/chiehl1/CSHMM/.

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

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