Peter Rubbens
- ORCID iD
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0000-0001-5595-4758
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- Journal Article
- A1
- open access
Predicting the presence and abundance of bacterial taxa in environmental communities through flow cytometric fingerprinting
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- Journal Article
- A1
- open access
Computational analysis of microbial flow cytometry data
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PhenoGMM : Gaussian mixture modeling of cytometry data quantifies changes in microbial community structure
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Cytometric fingerprints of gut microbiota predict Crohn's disease state
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Discriminating bacterial phenotypes at the population and single‐cell level : a comparison of flow cytometry and Raman spectroscopy fingerprinting
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Fingerprinting microbial communities through flow cytometry and Raman spectroscopy
(2019) -
- PhD Thesis
- open access
Machine learning approaches for microbial flow cytometry at the single-cell and community level
(2019) -
- Journal Article
- A1
- open access
Randomized lasso links microbial taxa with aquatic functional groups inferred from flow cytometry
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Learning single‐cell distances from cytometry data
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Cyt-Geist : current and future challenges in cytometry : reports of the CYTO 2018 conference workshops