SCOUP: a probabilistic model based on the Ornstein-Uhlenbeck process to analyze single-cell expression data during differentiation.
BACKGROUND
Single-cell technologies make it possible to quantify the comprehensive states of individual cells, and have the power to shed light on cellular differentiation in particular. Although several methods have been developed to fully analyze the single-cell expression data, there is still room for improvement in the analysis of differentiation.


RESULTS
In this paper, we propose a novel method SCOUP to elucidate differentiation process. Unlike previous dimension reduction-based approaches, SCOUP describes the dynamics of gene expression throughout differentiation directly, including the degree of differentiation of a cell (in pseudo-time) and cell fate. SCOUP is superior to previous methods with respect to pseudo-time estimation, especially for single-cell RNA-seq. SCOUP also successfully estimates cell lineage more accurately than previous method, especially for cells at an early stage of bifurcation. In addition, SCOUP can be applied to various downstream analyses. As an example, we propose a novel correlation calculation method for elucidating regulatory relationships among genes. We apply this method to a single-cell RNA-seq data and detect a candidate of key regulator for differentiation and clusters in a correlation network which are not detected with conventional correlation analysis.


CONCLUSIONS
We develop a stochastic process-based method SCOUP to analyze single-cell expression data throughout differentiation. SCOUP can estimate pseudo-time and cell lineage more accurately than previous methods. We also propose a novel correlation calculation method based on SCOUP. SCOUP is a promising approach for further single-cell analysis and available at https://github.com/hmatsu1226/SCOUP.
Bioinformatics Research Unit, Advanced Center for Computing and Communication, RIKEN, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan. hirotaka.matsumoto@riken.jp.|Department of Computational Biology and Medical Sciences, Faculty of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan. hirotaka.matsumoto@riken.jp.
Hirotaka
H
Matsumoto
Department of Computational Biology and Medical Sciences, Faculty of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan.
Hisanori
H
Kiryu
D000465:Algorithms; D019540:Area Under Curve; D002454:Cell Differentiation / Q000235:genetics*; D002460:Cell Line; D019070:Cell Lineage; D016000:Cluster Analysis; D005786:Gene Expression Regulation; D063990:Gene Ontology; D053263:Gene Regulatory Networks; D006801:Humans; D015233:Models, Statistical; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D059010:Single-Cell Analysis / Q000379:methods*; D013269:Stochastic Processes; D013997:Time Factors; D014157:Transcription Factors / Q000378:metabolism
D014157:Transcription Factors
Differentiation analysis; Ornstein–Uhlenbeck process; Single-cell transcriptomics