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scRNA-seq mixology: towards better benchmarking of single cell RNA-seq analysis methods

View ORCID ProfileLuyi Tian, View ORCID ProfileXueyi Dong, View ORCID ProfileSaskia Freytag, View ORCID ProfileKim-Anh Lê Cao, View ORCID ProfileShian Su, View ORCID ProfileAbolfazl JalalAbadi, View ORCID ProfileDaniela Amann-Zalcenstein, View ORCID ProfileTom S. Weber, View ORCID ProfileAzadeh Seidi, Jafar S. Jabbari, View ORCID ProfileShalin H. Naik, View ORCID ProfileMatthew E. Ritchie
doi: https://doi.org/10.1101/433102
Luyi Tian
1The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, Australia
2Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia
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  • For correspondence: tian.l@wehi.edu.au mritchie@wehi.edu.au
Xueyi Dong
1The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, Australia
3College of Life Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou, Zhejiang Province, 310058, P.R. China
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Saskia Freytag
1The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, Australia
4Harry Perkins Institute of Medical Research, Nedlands, WA 6009, Australia
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Kim-Anh Lê Cao
5Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia
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Shian Su
1The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, Australia
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Abolfazl JalalAbadi
5Melbourne Integrative Genomics, School of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3010, Australia
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Daniela Amann-Zalcenstein
1The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, Australia
2Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia
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Tom S. Weber
1The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, Australia
2Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia
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Azadeh Seidi
6Australian Genome Research Facility, Level 13, Victorian Comprehensive Cancer Centre, 305 Grattan Street, Melbourne, VIC 3000, Australia
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Jafar S. Jabbari
6Australian Genome Research Facility, Level 13, Victorian Comprehensive Cancer Centre, 305 Grattan Street, Melbourne, VIC 3000, Australia
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Shalin H. Naik
1The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, Australia
2Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia
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Matthew E. Ritchie
1The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, Australia
2Department of Medical Biology, The University of Melbourne, Parkville, VIC 3010, Australia
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  • For correspondence: tian.l@wehi.edu.au mritchie@wehi.edu.au
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Abstract

Single cell RNA sequencing (scRNA-seq) technology has undergone rapid development in recent years, bringing with new challenges in data processing and analysis. This has led to an explosion of tailored analysis methods for scRNA-seq data to address various biological questions. However, the current lack of gold-standard benchmark datasets makes it difficult for researchers to systematically evaluate the performance of the many methods available. Here, we designed and carried out a realistic benchmark experiment that included mixtures of single cells or ‘pseudo cells’ created by sampling admixtures of cells or RNA from up to 5 distinct cancer cell lines. Altogether we generated 14 datasets using droplet and plate-based scRNA-seq protocols, compared multiple data analysis methods in combination for tasks ranging from normalization and imputation, to clustering, trajectory analysis and data integration. Evaluation across 3,913 analyses (methods × benchmark dataset combinations) revealed pipelines suited to different types of data for different tasks. Our dataset and analysis present a comprehensive comparison framework for benchmarking most common scRNA-seq analysis tasks.

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Posted March 15, 2019.
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scRNA-seq mixology: towards better benchmarking of single cell RNA-seq analysis methods
Luyi Tian, Xueyi Dong, Saskia Freytag, Kim-Anh Lê Cao, Shian Su, Abolfazl JalalAbadi, Daniela Amann-Zalcenstein, Tom S. Weber, Azadeh Seidi, Jafar S. Jabbari, Shalin H. Naik, Matthew E. Ritchie
bioRxiv 433102; doi: https://doi.org/10.1101/433102
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scRNA-seq mixology: towards better benchmarking of single cell RNA-seq analysis methods
Luyi Tian, Xueyi Dong, Saskia Freytag, Kim-Anh Lê Cao, Shian Su, Abolfazl JalalAbadi, Daniela Amann-Zalcenstein, Tom S. Weber, Azadeh Seidi, Jafar S. Jabbari, Shalin H. Naik, Matthew E. Ritchie
bioRxiv 433102; doi: https://doi.org/10.1101/433102

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