This preprint is under consideration at a Nature Portfolio Journal. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Nature journals have partnered with Research Square to offer In Review, a journal-integrated preprint deposition service.
Article
Matthias Heinig
Helmholtz Zentrum München
Corresponding Author
ORCiD: https://orcid.org/0000-0002-5612-1720
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Single cell RNA-seq revolutionizes transcriptomics by providing cell type resolution for interindividual differential gene expression and expression quantitative trait loci analyses. However, efficient power analysis methods accounting for the characteristics of single cell data and interindividual comparison are missing.
Here we present a statistical framework for design and power analysis of multi-sample single cell genomics experiments. The model relates sample size, number of cells per individual and sequencing depth to the power of detecting differentially expressed genes within cell types. It enables fast systematic comparison of alternative experimental designs and optimization for a limited budget. We evaluated data driven priors for a range of applications and single cell platforms. In many settings, shallow sequencing of high numbers of cells leads to higher overall power than deep sequencing of fewer cells.
The model including priors is implemented as an R package scPower and is accessible as a web tool.
Keywords
experimental design, power analysis, single cell genomics, eQTL, differential gene expression
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Yes there is potential Competing Interest. FJT reports receiving consulting fees from Roche Diagnostics GmbH and Cellarity Inc., and ownership interest in Cellarity, Inc. and Dermagnostix. The other authors declare that they have no competing interests.
This is a list of supplementary files associated with this preprint. Click to download.
Loading...
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 25 Mar, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Learn more about our company.
This preprint is under consideration at a Nature Portfolio Journal. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Nature journals have partnered with Research Square to offer In Review, a journal-integrated preprint deposition service.
Article
Matthias Heinig
Helmholtz Zentrum München
Corresponding Author
ORCiD: https://orcid.org/0000-0002-5612-1720
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 25 Mar, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Single cell RNA-seq revolutionizes transcriptomics by providing cell type resolution for interindividual differential gene expression and expression quantitative trait loci analyses. However, efficient power analysis methods accounting for the characteristics of single cell data and interindividual comparison are missing.
Here we present a statistical framework for design and power analysis of multi-sample single cell genomics experiments. The model relates sample size, number of cells per individual and sequencing depth to the power of detecting differentially expressed genes within cell types. It enables fast systematic comparison of alternative experimental designs and optimization for a limited budget. We evaluated data driven priors for a range of applications and single cell platforms. In many settings, shallow sequencing of high numbers of cells leads to higher overall power than deep sequencing of fewer cells.
The model including priors is implemented as an R package scPower and is accessible as a web tool.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Loading...