Tempora: Cell trajectory inference using time-series single-cell RNA sequencing data

Single-cell RNA sequencing (scRNA-seq) can map cell types, states and transitions during dynamic biological processes such as tissue development and regeneration. Many trajectory inference methods have been developed to order cells by their progression through a dynamic process. However, when time s...

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Bibliographic Details
Published in:	PLoS computational biology 2020-09, Vol.16 (9), p.e1008205-e1008205
Main Authors:	Tran, Thinh N, Bader, Gary D
Format:	Article
Language:	eng
Subjects:	Algorithms Analysis Animals Biological activity Biology and Life Sciences Cells, Cultured Computational Biology - methods Datasets Gene expression Gene Expression Profiling - methods Gene sequencing Humans Inference Information processing Medicine and Health Sciences Methods Mice Musculoskeletal system Myoblasts - metabolism Regeneration Reproducibility of Results Research and Analysis Methods Ribonucleic acid RNA RNA - genetics RNA - metabolism RNA sequencing Sequence Analysis, RNA - methods Signal processing Single-Cell Analysis - methods Software Time series
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Summary:	Single-cell RNA sequencing (scRNA-seq) can map cell types, states and transitions during dynamic biological processes such as tissue development and regeneration. Many trajectory inference methods have been developed to order cells by their progression through a dynamic process. However, when time series data is available, most of these methods do not consider the available time information when ordering cells and are instead designed to work only on a single scRNA-seq data snapshot. We present Tempora, a novel cell trajectory inference method that orders cells using time information from time-series scRNA-seq data. In performance comparison tests, Tempora inferred known developmental lineages from three diverse tissue development time series data sets, beating state of the art methods in accuracy and speed. Tempora works at the level of cell clusters (types) and uses biological pathway information to help identify cell type relationships. This approach increases gene expression signal from single cells, processing speed, and interpretability of the inferred trajectory. Our results demonstrate the utility of a combination of time and pathway information to supervise trajectory inference for scRNA-seq based analysis.
ISSN:	1553-7358 1553-734X 1553-7358