Single Cell Clustering Based on Cell-Pair Differentiability Correlation and Variance Analysis

Single Cell Clustering Based on Cell-Pair Differentiability Correlation and Variance Analysis Abstract Motivation The rapid advancement of single cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. Identification of intercellular transcriptomic heterogeneity is one of the most critical tasks in single-cell RNA-sequencing studies. Results We propose a new cell similarity measure based on cell-pair differentiability correlation, which is derived from gene differential pattern among all cell pairs. Through plugging into the framework of hierarchical clustering with this new measure, we further develop a variance analysis based clustering algorithm ‘Corr’ that can determine cluster number automatically and identify cell types accurately. The robustness and superiority of the proposed algorithm are compared with representative algorithms: SNN-Cliq and several other state-of-the-art clustering methods, on many benchmark or real scRNA-Seq datasets in terms of both internal criteria (clustering number and accuracy) and external criteria (purity, adjusted rand index, F1-measure). Moreover, differentiability vector with our new measure provides a new means in identifying potential biomarkers from cancer related single cell data sets even with strong noise. Prognosis analyses from independent datasets of cancers confirmed the effectiveness of our ‘Corr’ method. Implementation and Availability The source code (Matlab) is available at http://sysbio.sibcb.ac.cn/cb/chenlab/soft/Corr–SourceCodes.zip. Contact lnchen@sibs.ac.cn or hyh0110@berkeley.edu Supplementary information Supplementary data are available at Bioinformatics online. © The Author(s) (2018). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bioinformatics Oxford University Press

Single Cell Clustering Based on Cell-Pair Differentiability Correlation and Variance Analysis

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Publisher
Oxford University Press
Copyright
© The Author(s) (2018). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
ISSN
1367-4803
eISSN
1460-2059
D.O.I.
10.1093/bioinformatics/bty390
Publisher site
See Article on Publisher Site

Abstract

Abstract Motivation The rapid advancement of single cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. Identification of intercellular transcriptomic heterogeneity is one of the most critical tasks in single-cell RNA-sequencing studies. Results We propose a new cell similarity measure based on cell-pair differentiability correlation, which is derived from gene differential pattern among all cell pairs. Through plugging into the framework of hierarchical clustering with this new measure, we further develop a variance analysis based clustering algorithm ‘Corr’ that can determine cluster number automatically and identify cell types accurately. The robustness and superiority of the proposed algorithm are compared with representative algorithms: SNN-Cliq and several other state-of-the-art clustering methods, on many benchmark or real scRNA-Seq datasets in terms of both internal criteria (clustering number and accuracy) and external criteria (purity, adjusted rand index, F1-measure). Moreover, differentiability vector with our new measure provides a new means in identifying potential biomarkers from cancer related single cell data sets even with strong noise. Prognosis analyses from independent datasets of cancers confirmed the effectiveness of our ‘Corr’ method. Implementation and Availability The source code (Matlab) is available at http://sysbio.sibcb.ac.cn/cb/chenlab/soft/Corr–SourceCodes.zip. Contact lnchen@sibs.ac.cn or hyh0110@berkeley.edu Supplementary information Supplementary data are available at Bioinformatics online. © The Author(s) (2018). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

Journal

BioinformaticsOxford University Press

Published: May 16, 2018

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