Testing for differential abundance in mass cytometry data
Open accessAbstract
When comparing biological conditions using mass cytometry data, a key challenge is to identify cellular populations that change in abundance. Here, we present a computational strategy for detecting 'differentially abundant' populations by assigning cells to hyperspheres, testing for significant differences between conditions and controlling the spatial false discovery rate. Our method (http://bioconductor.org/packages/cydar) outperforms other approaches in simulations and finds novel patterns of differential abundance in real data.
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Existing clustering-free differential abundance methods either fail to model variability in cell numbers between replicate samples or are mainly built for pairwise comparisons, which restricts their use in more complex experimental designs.
“However, these have different limitations, where they either do not model variability in cell numbers between replicate samples or are primarily designed for pairwise comparisons. This limits their application to datasets with more complex experimental designs”
From Differential abundance testing on single-cell data using k-nearest neighbor graphs (2022) · cites this paper as [6]
Milo handles the non-independence of overlapping neighbourhoods by using a weighted Benjamini-Hochberg procedure in which p-values are weighted by the inverse of neighbourhood connectivity.
“We account for the non-independence of overlapping neighbourhoods by applying a weighted version of the Benjamini-Hochberg (BH) method, where p-values are weighted by the reciprocal of the neighbourhood connectivity”
From Differential abundance testing on single-cell data using k-nearest neighbor graphs (2022) · cites this paper as [6]
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- Authors
- Lun, A. T. L., Richard, A. C., & Marioni, J. C.
- Affiliations
- University of Cambridge (GB)
- European Bioinformatics Institute (GB)
- Corresponding author
- Marioni, John C
- Venue
- Nature Methods
- Published
- 2017-05-15
- Volume
- 14
- Issue
- 7
- Pages
- 707-709
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC6155493
- OpenAlex
- W2612538354
- Cited by
- 181
- References
- 20
- Citations by year
- 2026: 10 · 2025: 17 · 2024: 37 · 2023: 22 · 2022: 21 · 2021: 23 · 2020: 16 · 2019: 15 · 2018: 17 · 2017: 3
- FWCI
- 5.70
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- Cancer Research UK (22231, C14303 / A17197)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- differential abundance, mass cytometry, cell populations, hypersphere, high-dimensional data
- MeSH terms
- Computer Simulation, Flow Cytometry, Image Processing, Computer-Assisted, Software
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