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

Chieh Hubert Lin | Zendy; Ziv BarJoseph | Zendy

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

Author(s) -

Chieh Hubert Lin,

Ziv BarJoseph

Publication year - 2019

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btz296

Subject(s) - computer science , inference , time series , series (stratigraphy) , curse of dimensionality , data mining , probabilistic logic , artificial intelligence , software , dimensionality reduction , process (computing) , algorithm , machine learning , pattern recognition (psychology) , paleontology , biology , programming language , operating system

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.

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