Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments

Luyi Tian; Xueyi Dong; Saskia Freytag; Kim-Anh Le Cao; Shian Su; Abolfazl JalalAbadi; Daniela Amann-Zalcenstein; Tom S Weber; Azadeh Seidi; Jafar S Jabbari; Shalin H Naik; Matthew E Ritchie

Journal article

Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments

Luyi Tian, Xueyi Dong, Saskia Freytag, Kim-Anh Le Cao, Shian Su, Abolfazl JalalAbadi, Daniela Amann-Zalcenstein, Tom S Weber, Azadeh Seidi, Jafar S Jabbari, Shalin H Naik, Matthew E Ritchie

NATURE METHODS | NATURE PUBLISHING GROUP | Published : 2019

DOI: 10.1038/s41592-019-0425-8

Abstract

Single cell RNA-sequencing (scRNA-seq) technology has undergone rapid development in recent years, leading to an explosion in the number of tailored data analysis methods. However, the current lack of gold-standard benchmark datasets makes it difficult for researchers to systematically compare the performance of the many methods available. Here, we generated a realistic benchmark experiment that included single cells and admixtures of cells or RNA to create 'pseudo cells' from up to five distinct cancer cell lines. In total, 14 datasets were generated using both droplet and plate-based scRNA-seq protocols. We compared 3,913 combinations of data analysis methods for tasks ranging from normali..

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University of Melbourne Researchers

Kim-Anh Le Cao Author Mathematics and Statistics

Saskia Freytag Author Medical Biology (w.e.h.i.)

Matthew Ritchie Author Medical Biology (w.e.h.i.)

Tom Weber Author Medical Biology (w.e.h.i.)

Shalin Naik Author Medical Biology (w.e.h.i.)

Related Projects (1)

DEVELOPMENT OF STATISTICAL METHODOLOGIES AND APPLICATION TO CLINICAL CANCER STUDIES

Integrating different layers of information coming from the recent ‘-omics’ technologies can help improving the treatment and the prevention..

Grants

Awarded by National Health and Medical Research Council (NHMRC)

Awarded by Silicon Valley Community Foundation

Funding Acknowledgements

We thank C. Weeden and M.-L. Asselin-Labat for providing the cell lines used in this study, J. Schreuder and D. Lin for assistance in conducting experiments and I. Virshup for assistance in the data integration analysis. This work was supported by funding from the National Health and Medical Research Council (NHMRC) Project Grants (No. GNT1143163 to M.E.R., No. GNT1124812 to S.H.N. and M.E.R., and No. GNT1062820 to S.H.N.), Fellowship Nos. GNT1104924 to M.E.R. and GNT1087415 to K.A.L.C., the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation (grant no. 2018-182819 to MER and no. 2018-182885 to K.A.L.C.), a Melbourne Research Scholarship to L.T., the Genomics Innovation Hub, the Victorian State Government Operational Infrastructure Support and Australian Government NHMRC IRIISS.