Dataset Information

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 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 AND IMPLEMENTATION:Software and Supporting website: www.andrew.cmu.edu/user/chiehl1/CSHMM/. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Lin C

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

REPOSITORIES: biostudies-literature

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

Lin Chieh C Bar-Joseph Ziv Z

Bioinformatics (Oxford, England) 20191101 22

<h4>Motivation</h4>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 ac ...[more]

PMID: 31038684

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Project description:Dynamic expression data, nowadays obtained using high-throughput RNA sequencing, are essential to monitor transient gene expression changes and to study the dynamics of their transcriptional activity in the cell or response to stimuli. Several methods for data selection, clustering and functional analysis are available; however, these steps are usually performed independently, without exploiting and integrating the information derived from each step of the analysis.Here we present FunPat, an R package for time series RNA sequencing data that integrates gene selection, clustering and functional annotation into a single framework. FunPat exploits functional annotations by performing for each functional term, e.g. a Gene Ontology term, an integrated selection-clustering analysis to select differentially expressed genes that share, besides annotation, a common dynamic expression profile.FunPat performance was assessed on both simulated and real data. With respect to a stand-alone selection step, the integration of the clustering step is able to improve the recall without altering the false discovery rate. FunPat also shows high precision and recall in detecting the correct temporal expression patterns; in particular, the recall is significantly higher than hierarchical, k-means and a model-based clustering approach specifically designed for RNA sequencing data. Moreover, when biological replicates are missing, FunPat is able to provide reproducible lists of significant genes. The application to real time series expression data shows the ability of FunPat to select differentially expressed genes with high reproducibility, indirectly confirming high precision and recall in gene selection. Moreover, the expression patterns obtained as output allow an easy interpretation of the results.A novel analysis pipeline was developed to search the main temporal patterns in classes of genes similarly annotated, improving the sensitivity of gene selection by integrating the statistical evidence of differential expression with the information on temporal profiles and the functional annotations. Significant genes are associated to both the most informative functional terms, avoiding redundancy of information, and the most representative temporal patterns, thus improving the readability of the results. FunPat package is provided in R/Bioconductor at link: http://sysbiobig.dei.unipd.it/?q=node/79.

Dataset Information

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

Publications

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

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