Section: Genomics
Topic: Genetics/genomics

Bulk-based hypothesis weighing increases power in single-cell differential expression analysis

Germain, Pierre-Luc1, 2  ; Wang, Jiayi2, 3 ; Robinson, Mark D.2, 3 
1 D-HEST Institute for Neuroscience, ETH Zurich, Switzerland
2 Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
3 SIB Swiss Institute of Bioinformatics, Switzerland

Corresponding author(s): Germain, Pierre-Luc (pierre-luc.germain@hest.ethz.ch)

10.24072/pcjournal.663 - Peer Community Journal, Volume 5 (2025), article no. e136

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Due to the costs of single-cell sequencing, sample sizes are often relatively limited, sometimes leading to poorly reproducible results. In many contexts, however, larger bulk RNAseq data is available for the same conditions or experimental paradigm, which can be used as additional evidence of a generalizable differential expression pattern. Here, we show how such data can be used, via bulk-based hypothesis weighing (bbhw), to increase the power and robustness of single-cell differential state analysis. We find that all methods improve performance, with the best results obtained by applying a grouped Benjamini-Hochberg procedure on bins based on proportion-adjusted significance (PAS). These methods are implemented in the muscat package, and should be applicable to a broader range of scenarios.

Published online:
DOI: 10.24072/pcjournal.663
Type: Research article
Keywords: single-cell sequencing; differential analysis; multiple testing correction; false discovery rate; hypothesis weighing; power

Germain, Pierre-Luc 1, 2; Wang, Jiayi 2, 3; Robinson, Mark D. 2, 3

1 D-HEST Institute for Neuroscience, ETH Zurich, Switzerland
2 Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
3 SIB Swiss Institute of Bioinformatics, Switzerland
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights 
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Germain, P.-L.; Wang, J.; Robinson, M. D. Bulk-based hypothesis weighing increases power in single-cell differential expression analysis. Peer Community Journal, Volume 5 (2025), article  no. e136. https://doi.org/10.24072/pcjournal.663

PCI peer reviews and recommendation, and links to data, scripts, code and supplementary information: 10.24072/pci.genomics.100437

Conflict of interest of the recommender and peer reviewers:
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article.

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