Inferring transcriptomic cell states and transitions only from time series transcriptome data

Abstract 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...

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Autores principales:	Kyuri Jo, Inyoung Sung, Dohoon Lee, Hyuksoon Jang, Sun Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ebe464cf98454be49c3a2751bd8479cd
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spelling	oai:doaj.org-article:ebe464cf98454be49c3a2751bd8479cd2021-12-02T17:23:26ZInferring transcriptomic cell states and transitions only from time series transcriptome data10.1038/s41598-021-91752-92045-2322https://doaj.org/article/ebe464cf98454be49c3a2751bd8479cd2021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91752-9https://doaj.org/toc/2045-2322Abstract 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/ .Kyuri JoInyoung SungDohoon LeeHyuksoon JangSun KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kyuri Jo Inyoung Sung Dohoon Lee Hyuksoon Jang Sun Kim Inferring transcriptomic cell states and transitions only from time series transcriptome data
description	Abstract 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/ .
format	article
author	Kyuri Jo Inyoung Sung Dohoon Lee Hyuksoon Jang Sun Kim
author_facet	Kyuri Jo Inyoung Sung Dohoon Lee Hyuksoon Jang Sun Kim
author_sort	Kyuri Jo
title	Inferring transcriptomic cell states and transitions only from time series transcriptome data
title_short	Inferring transcriptomic cell states and transitions only from time series transcriptome data
title_full	Inferring transcriptomic cell states and transitions only from time series transcriptome data
title_fullStr	Inferring transcriptomic cell states and transitions only from time series transcriptome data
title_full_unstemmed	Inferring transcriptomic cell states and transitions only from time series transcriptome data
title_sort	inferring transcriptomic cell states and transitions only from time series transcriptome data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ebe464cf98454be49c3a2751bd8479cd
work_keys_str_mv	AT kyurijo inferringtranscriptomiccellstatesandtransitionsonlyfromtimeseriestranscriptomedata AT inyoungsung inferringtranscriptomiccellstatesandtransitionsonlyfromtimeseriestranscriptomedata AT dohoonlee inferringtranscriptomiccellstatesandtransitionsonlyfromtimeseriestranscriptomedata AT hyuksoonjang inferringtranscriptomiccellstatesandtransitionsonlyfromtimeseriestranscriptomedata AT sunkim inferringtranscriptomiccellstatesandtransitionsonlyfromtimeseriestranscriptomedata
_version_	1718381001630023680

Inferring transcriptomic cell states and transitions only from time series transcriptome data

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