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Shao L, Xue R, Lu X, Liao J, Shao X, Fan X. Identify differential genes and cell subclusters from time-series scRNA-seq data using scTITANS. Comput Struct Biotechnol J 2021; 19:4132-4141. [PMID: 34527187 PMCID: PMC8342909 DOI: 10.1016/j.csbj.2021.07.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 07/20/2021] [Accepted: 07/23/2021] [Indexed: 12/20/2022] Open
Abstract
Time-series single-cell RNA sequencing (scRNA-seq) provides a breakthrough in modern biology by enabling researchers to profile and study the dynamics of genes and cells based on samples obtained from multiple time points at an individual cell resolution. However, cell asynchrony and an additional dimension of multiple time points raises challenges in the effective use of time-series scRNA-seq data for identifying genes and cell subclusters that vary over time. However, no effective tools are available. Here, we propose scTITANS (https://github.com/ZJUFanLab/scTITANS), a method that takes full advantage of individual cells from all time points at the same time by correcting cell asynchrony using pseudotime from trajectory inference analysis. By introducing a time-dependent covariate based on time-series analysis method, scTITANS performed well in identifying differentially expressed genes and cell subclusters from time-series scRNA-seq data based on several example datasets. Compared to current attempts, scTITANS is more accurate, quantitative, and capable of dealing with heterogeneity among cells and making full use of the timing information hidden in biological processes. When extended to broader research areas, scTITANS will bring new breakthroughs in studies with time-series single cell RNA sequencing data.
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Affiliation(s)
- Li Shao
- Hangzhou Normal University, Institute of Translational Medicine, Institute of Hepatology and Metabolic Diseases, The Affiliated Hospital of Hangzhou Normal University, Hangzhou 311121, Zhejiang, China.,Medicine Lab, Alibaba-Zhejiang University Joint Research Center for Future Digital Health, Hangzhou 310018, China
| | - Rui Xue
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xiaoyan Lu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jie Liao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Medicine Lab, Alibaba-Zhejiang University Joint Research Center for Future Digital Health, Hangzhou 310018, China.,Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310058, China
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