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Cellular stages of biological processes have been characterized using fluorescence-activated cell sorting and genetic perturbations, charting a limited landscape of cellular states. Time series transcriptome data can help define new cellular states at the molecular level since the analysis of transcriptional changes can provide information on cell states and transitions. However, existing methods for inferring cell states from transcriptome data use additional information such as prior knowledge on cell types or cell-type-specific markers to reduce the complexity of data. In this study, we present a novel time series clustering framework to infer TRAnscriptomic Cellular States (TRACS) only from time series transcriptome data by integrating Gaussian process regression, shape-based distance, and ranked pairs algorithm in a single computational framework. TRACS determines patterns that correspond to hidden cellular states by clustering gene expression data. TRACS was used to analyse single-cell and bulk RNA sequencing data and successfully generated cluster networks that reflected the characteristics of key stages of biological processes. Thus, TRACS has a potential to help reveal unknown cellular states and transitions at the molecular level using only time series transcriptome data. TRACS is implemented in Python and available at http://github.com/BML-cbnu/TRACS/ .

Titel:	Inferring transcriptomic cell states and transitions only from time series transcriptome data.
Autor/in / Beteiligte Person:	Jo, K ; Sung, I ; Lee, D ; Jang, H ; Kim, S
Zeitschrift:	Scientific reports, Jg. 11 (2021-06-15), Heft 1, S. 12566
Veröffentlichung:	London : Nature Publishing Group, copyright 2011-, 2021
Medientyp:	academicJournal
ISSN:	2045-2322 (electronic)
DOI:	10.1038/s41598-021-91752-9
Schlagwort:	Algorithms Cluster Analysis Gene Regulatory Networks genetics Humans RNA genetics Gene Expression Profiling statistics & numerical data Sequence Analysis, RNA statistics & numerical data Single-Cell Analysis statistics & numerical data Transcriptome genetics
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article; Research Support, Non-U.S. Gov't Language: English [Sci Rep] 2021 Jun 15; Vol. 11 (1), pp. 12566. <i>Date of Electronic Publication: </i>2021 Jun 15. MeSH Terms: Gene Expression Profiling / statistics & numerical data ; Sequence Analysis, RNA / statistics & numerical data ; Single-Cell Analysis / statistics & numerical data ; Transcriptome / genetics ; Algorithms ; Cluster Analysis ; Gene Regulatory Networks / genetics ; Humans ; RNA / genetics References: Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 1, 257–272 (2019). (PMID: 10.1038/s41576-019-0093-7) ; Chen, L. & Wong, G. Transcriptome informatics. in Encyclopedia of Bioinformatics and Computational Biology, vol. 2 324–340 (2018). ; Liu, Y. et al. Transcriptional landscape of the human cell cycle. Proc. Natl. Acad. Sci. 114, 3473–3478 (2017). (PMID: 2828923210.1073/pnas.16176361145380023) ; van Galen, P. et al. 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Inferring transcriptomic cell states and transitions only from time series transcriptome data.