Identification of cell types from single cell data using stable clustering

Abstract Single-cell RNA-seq (scRNASeq) has become a powerful technique for measuring the transcriptome of individual cells. Unlike the bulk measurements that average the gene expressions over the individual cells, gene measurements at individual cells can be used to study several different tissues...

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Autores principales:	Azam Peyvandipour, Adib Shafi, Nafiseh Saberian, Sorin Draghici
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ac0671e32d6d48e88007358b3fd7ccde
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id	oai:doaj.org-article:ac0671e32d6d48e88007358b3fd7ccde
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spelling	oai:doaj.org-article:ac0671e32d6d48e88007358b3fd7ccde2021-12-02T16:26:22ZIdentification of cell types from single cell data using stable clustering10.1038/s41598-020-66848-32045-2322https://doaj.org/article/ac0671e32d6d48e88007358b3fd7ccde2020-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-66848-3https://doaj.org/toc/2045-2322Abstract Single-cell RNA-seq (scRNASeq) has become a powerful technique for measuring the transcriptome of individual cells. Unlike the bulk measurements that average the gene expressions over the individual cells, gene measurements at individual cells can be used to study several different tissues and organs at different stages. Identifying the cell types present in the sample from the single cell transcriptome data is a common goal in many single-cell experiments. Several methods have been developed to do this. However, correctly identifying the true cell types remains a challenge. We present a framework that addresses this problem. Our hypothesis is that the meaningful characteristics of the data will remain despite small perturbations of data. We validate the performance of the proposed method on eight publicly available scRNA-seq datasets with known cell types as well as five simulation datasets with different degrees of the cluster separability. We compare the proposed method with five other existing methods: RaceID, SNN-Cliq, SINCERA, SEURAT, and SC3. The results show that the proposed method performs better than the existing methods.Azam PeyvandipourAdib ShafiNafiseh SaberianSorin DraghiciNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Azam Peyvandipour Adib Shafi Nafiseh Saberian Sorin Draghici Identification of cell types from single cell data using stable clustering
description	Abstract Single-cell RNA-seq (scRNASeq) has become a powerful technique for measuring the transcriptome of individual cells. Unlike the bulk measurements that average the gene expressions over the individual cells, gene measurements at individual cells can be used to study several different tissues and organs at different stages. Identifying the cell types present in the sample from the single cell transcriptome data is a common goal in many single-cell experiments. Several methods have been developed to do this. However, correctly identifying the true cell types remains a challenge. We present a framework that addresses this problem. Our hypothesis is that the meaningful characteristics of the data will remain despite small perturbations of data. We validate the performance of the proposed method on eight publicly available scRNA-seq datasets with known cell types as well as five simulation datasets with different degrees of the cluster separability. We compare the proposed method with five other existing methods: RaceID, SNN-Cliq, SINCERA, SEURAT, and SC3. The results show that the proposed method performs better than the existing methods.
format	article
author	Azam Peyvandipour Adib Shafi Nafiseh Saberian Sorin Draghici
author_facet	Azam Peyvandipour Adib Shafi Nafiseh Saberian Sorin Draghici
author_sort	Azam Peyvandipour
title	Identification of cell types from single cell data using stable clustering
title_short	Identification of cell types from single cell data using stable clustering
title_full	Identification of cell types from single cell data using stable clustering
title_fullStr	Identification of cell types from single cell data using stable clustering
title_full_unstemmed	Identification of cell types from single cell data using stable clustering
title_sort	identification of cell types from single cell data using stable clustering
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/ac0671e32d6d48e88007358b3fd7ccde
work_keys_str_mv	AT azampeyvandipour identificationofcelltypesfromsinglecelldatausingstableclustering AT adibshafi identificationofcelltypesfromsinglecelldatausingstableclustering AT nafisehsaberian identificationofcelltypesfromsinglecelldatausingstableclustering AT sorindraghici identificationofcelltypesfromsinglecelldatausingstableclustering
_version_	1718384074073047040

Identification of cell types from single cell data using stable clustering

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