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Systematic comparative analysis of single cell RNA-sequencing methods

Jiarui Ding, Xian Adiconis, Sean K. Simmons, Monika S. Kowalczyk, Cynthia C. Hession, Nemanja D. Marjanovic, Travis K. Hughes, Marc H. Wadsworth, Tyler Burks, Lan T. Nguyen, John Y. H. Kwon, Boaz Barak, William Ge, Amanda J. Kedaigle, Shaina Carroll, Shuqiang Li, Nir Hacohen, Orit Rozenblatt-Rosen, Alex K. Shalek, Alexandra-Chloé Villani, Aviv Regev, View ORCID ProfileJoshua Z. Levin
doi: https://doi.org/10.1101/632216
Jiarui Ding
1Broad Institute of MIT & Harvard, Cambridge, MA USA
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Xian Adiconis
1Broad Institute of MIT & Harvard, Cambridge, MA USA
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Sean K. Simmons
1Broad Institute of MIT & Harvard, Cambridge, MA USA
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Monika S. Kowalczyk
1Broad Institute of MIT & Harvard, Cambridge, MA USA
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Cynthia C. Hession
1Broad Institute of MIT & Harvard, Cambridge, MA USA
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Nemanja D. Marjanovic
1Broad Institute of MIT & Harvard, Cambridge, MA USA
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Travis K. Hughes
1Broad Institute of MIT & Harvard, Cambridge, MA USA
2Institute for Medical Engineering & Science, Dept. of Chemistry, MIT, Cambridge, MA USA
3Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA USA
4Koch Institute of Integrative Cancer Research, Cambridge, MA USA
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Marc H. Wadsworth
1Broad Institute of MIT & Harvard, Cambridge, MA USA
2Institute for Medical Engineering & Science, Dept. of Chemistry, MIT, Cambridge, MA USA
3Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA USA
4Koch Institute of Integrative Cancer Research, Cambridge, MA USA
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Tyler Burks
1Broad Institute of MIT & Harvard, Cambridge, MA USA
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Lan T. Nguyen
1Broad Institute of MIT & Harvard, Cambridge, MA USA
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John Y. H. Kwon
1Broad Institute of MIT & Harvard, Cambridge, MA USA
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Boaz Barak
5McGovern Institute for Brain Research and Department of Brain & Cognitive Sciences, MIT, Cambridge, MA USA
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William Ge
1Broad Institute of MIT & Harvard, Cambridge, MA USA
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Amanda J. Kedaigle
1Broad Institute of MIT & Harvard, Cambridge, MA USA
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Shaina Carroll
1Broad Institute of MIT & Harvard, Cambridge, MA USA
2Institute for Medical Engineering & Science, Dept. of Chemistry, MIT, Cambridge, MA USA
3Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA USA
4Koch Institute of Integrative Cancer Research, Cambridge, MA USA
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Shuqiang Li
1Broad Institute of MIT & Harvard, Cambridge, MA USA
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Nir Hacohen
1Broad Institute of MIT & Harvard, Cambridge, MA USA
6Center for Cancer Research, Dept. of Medicine, Massachusetts General Hospital, Boston, MA USA
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Orit Rozenblatt-Rosen
1Broad Institute of MIT & Harvard, Cambridge, MA USA
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Alex K. Shalek
1Broad Institute of MIT & Harvard, Cambridge, MA USA
2Institute for Medical Engineering & Science, Dept. of Chemistry, MIT, Cambridge, MA USA
3Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA USA
4Koch Institute of Integrative Cancer Research, Cambridge, MA USA
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Alexandra-Chloé Villani
1Broad Institute of MIT & Harvard, Cambridge, MA USA
6Center for Cancer Research, Dept. of Medicine, Massachusetts General Hospital, Boston, MA USA
7Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Charlestown, MA USA
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Aviv Regev
1Broad Institute of MIT & Harvard, Cambridge, MA USA
4Koch Institute of Integrative Cancer Research, Cambridge, MA USA
8Howard Hughes Medical Institute, Dept. of Biology, MIT, Cambridge, MA USA
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Joshua Z. Levin
1Broad Institute of MIT & Harvard, Cambridge, MA USA
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  • ORCID record for Joshua Z. Levin
  • For correspondence: jlevin@broadinstitute.org
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ABSTRACT

A multitude of single-cell RNA sequencing methods have been developed in recent years, with dramatic advances in scale and power, and enabling major discoveries and large scale cell mapping efforts. However, these methods have not been systematically and comprehensively benchmarked. Here, we directly compare seven methods for single cell and/or single nucleus profiling from three types of samples – cell lines, peripheral blood mononuclear cells and brain tissue – generating 36 libraries in six separate experiments in a single center. To analyze these datasets, we developed and applied scumi, a flexible computational pipeline that can be used for any scRNA-seq method. We evaluated the methods for both basic performance and for their ability to recover known biological information in the samples. Our study will help guide experiments with the methods in this study as well as serve as a benchmark for future studies and for computational algorithm development.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted May 09, 2019.
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Systematic comparative analysis of single cell RNA-sequencing methods
Jiarui Ding, Xian Adiconis, Sean K. Simmons, Monika S. Kowalczyk, Cynthia C. Hession, Nemanja D. Marjanovic, Travis K. Hughes, Marc H. Wadsworth, Tyler Burks, Lan T. Nguyen, John Y. H. Kwon, Boaz Barak, William Ge, Amanda J. Kedaigle, Shaina Carroll, Shuqiang Li, Nir Hacohen, Orit Rozenblatt-Rosen, Alex K. Shalek, Alexandra-Chloé Villani, Aviv Regev, Joshua Z. Levin
bioRxiv 632216; doi: https://doi.org/10.1101/632216
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Systematic comparative analysis of single cell RNA-sequencing methods
Jiarui Ding, Xian Adiconis, Sean K. Simmons, Monika S. Kowalczyk, Cynthia C. Hession, Nemanja D. Marjanovic, Travis K. Hughes, Marc H. Wadsworth, Tyler Burks, Lan T. Nguyen, John Y. H. Kwon, Boaz Barak, William Ge, Amanda J. Kedaigle, Shaina Carroll, Shuqiang Li, Nir Hacohen, Orit Rozenblatt-Rosen, Alex K. Shalek, Alexandra-Chloé Villani, Aviv Regev, Joshua Z. Levin
bioRxiv 632216; doi: https://doi.org/10.1101/632216

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