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distinct: a novel approach to differential distribution analyses

View ORCID ProfileSimone Tiberi, View ORCID ProfileHelena L Crowell, Pantelis Samartsidis, View ORCID ProfileLukas M Weber, View ORCID ProfileMark D Robinson
doi: https://doi.org/10.1101/2020.11.24.394213
Simone Tiberi
1Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
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  • For correspondence: Simone.Tiberi@uzh.ch
Helena L Crowell
1Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
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Pantelis Samartsidis
2MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
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Lukas M Weber
3Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Mark D Robinson
1Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
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Abstract

We present distinct, a general method for differential analysis of full distributions that is well suited to applications on single-cell data, such as single-cell RNA sequencing and high-dimensional flow or mass cytometry data. High-throughput single-cell data reveal an unprecedented view of cell identity and allow complex variations between conditions to be discovered; nonetheless, most methods for differential expression target differences in the mean and struggle to identify changes where the mean is only marginally affected. distinct is based on a hierarchical non-parametric permutation approach and, by comparing empirical cumulative distribution functions, identifies both differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean. We performed extensive bench-marks across both simulated and experimental datasets from single-cell RNA sequencing and mass cytometry data, where distinct shows favourable performance, identifies more differential patterns than competitors, and displays good control of false positive and false discovery rates. distinct is available as a Bioconductor R package.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • In the presence of nuisance covariates (e.g., batch effects), we previously used a linear model with such covariates as the only predictors. In formula (3), we have replaced this model, with a random effects model with covariates as fixed effects and samples as random effects. This properly accounts for the variance structure across samples: 2 measurements from the same sample are now correlated, while 2 measurements from different samples are not.

Copyright 
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 April 23, 2022.
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distinct: a novel approach to differential distribution analyses
Simone Tiberi, Helena L Crowell, Pantelis Samartsidis, Lukas M Weber, Mark D Robinson
bioRxiv 2020.11.24.394213; doi: https://doi.org/10.1101/2020.11.24.394213
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distinct: a novel approach to differential distribution analyses
Simone Tiberi, Helena L Crowell, Pantelis Samartsidis, Lukas M Weber, Mark D Robinson
bioRxiv 2020.11.24.394213; doi: https://doi.org/10.1101/2020.11.24.394213

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