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Maximizing statistical power to detect clinically associated cell states with scPOST

View ORCID ProfileNghia Millard, View ORCID ProfileIlya Korsunsky, Kathryn Weinand, Chamith Y. Fonseka, Aparna Nathan, Joyce B. Kang, View ORCID ProfileSoumya Raychaudhuri
doi: https://doi.org/10.1101/2020.11.23.390682
Nghia Millard
1Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
2Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
3Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
4Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
5Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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  • ORCID record for Nghia Millard
Ilya Korsunsky
1Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
2Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
3Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
4Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
5Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Kathryn Weinand
1Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
2Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
3Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
4Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
5Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Chamith Y. Fonseka
1Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
2Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
3Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
4Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
5Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Aparna Nathan
1Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
2Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
3Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
4Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
5Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Joyce B. Kang
1Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
2Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
3Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
4Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
5Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Soumya Raychaudhuri
1Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA
2Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
3Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
4Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
5Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
6Versus Arthritis Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
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  • For correspondence: soumya@broadinstitute.org
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Abstract

As advances in single-cell technologies enable the unbiased assay of thousands of cells simultaneously, human disease studies are able to identify clinically associated cell states using case-control study designs. These studies require precious clinical samples and costly technologies; therefore, it is critical to employ study design principles that maximize power to detect cell state frequency shifts between conditions, such as disease versus healthy. Here, we present single-cell Power Simulation Tool (scPOST), a method that enables users to estimate power under different study designs. To approximate the specific experimental and clinical scenarios being investigated, scPOST takes prototype (public or pilot) single-cell data as input and generates large numbers of single-cell datasets in silico. We use scPOST to perform power analyses on three independent single-cell datasets that span diverse experimental conditions: a batch-corrected 21-sample rheumatoid arthritis dataset (5,265 cells) from synovial tissue, a 259-sample tuberculosis progression dataset (496,517 memory T cells) from peripheral blood mononuclear cells (PBMCs), and a 30-sample ulcerative colitis dataset (235,229 cells) from intestinal biopsies. Over thousands of simulations, we consistently observe that power to detect frequency shifts in cell states is maximized by larger numbers of independent clinical samples, reduced batch effects, and smaller variation in a cell state’s frequency across samples.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted November 23, 2020.
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Maximizing statistical power to detect clinically associated cell states with scPOST
Nghia Millard, Ilya Korsunsky, Kathryn Weinand, Chamith Y. Fonseka, Aparna Nathan, Joyce B. Kang, Soumya Raychaudhuri
bioRxiv 2020.11.23.390682; doi: https://doi.org/10.1101/2020.11.23.390682
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Maximizing statistical power to detect clinically associated cell states with scPOST
Nghia Millard, Ilya Korsunsky, Kathryn Weinand, Chamith Y. Fonseka, Aparna Nathan, Joyce B. Kang, Soumya Raychaudhuri
bioRxiv 2020.11.23.390682; doi: https://doi.org/10.1101/2020.11.23.390682

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