Author

F.-M. Kerckhof

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Microbiology· 2 Bioengineering· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Assay:flow cytometry· 2 Assay:metagenomics· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:differential expression· 1

Frequent Co-Authors

N. Boon· 2 shared P. Rubbens· 2 shared R. Props· 2 shared W. Waegeman· 2 shared A. Skirtach· 1 shared C. Garcia-Timermans· 1 shared J. Heyse· 1 shared L. Bijnens· 1 shared

Activity by Year

2018-2020 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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2 shared papersMicrobiology

N. Boon

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3 papers

Recent·Most cited

MicrobiologybioRxivPreprintJun 19, 2020

PhenoGMM: Gaussian mixture modelling of microbial cytometry data enables efficient predictions of biodiversity

P. Rubbens, R. Props, F.-M. Kerckhof…·W. Waegeman

PhenoGMM uses Gaussian Mixture Models to predict microbial diversity from flow cytometry data, integrating with 16S rRNA sequencing for improved accuracy.

DOI 10.1101/641464

flow cytometrymetagenomics

𝕏 PostBluesky

BioengineeringbioRxivPreprintMay 24, 2019

Discriminating bacterial phenotypes at the population and single-cell level: a comparison of flow cytometry and Raman spectroscopy fingerprinting

C. Garcia-Timermans, P. Rubbens, J. Heyse…·N. Boon

This paper compares flow cytometry and Raman spectroscopy for analyzing bacterial phenotypic heterogeneity, showing flow cytometry's population-level resolution and Raman's single-cell biochemical detail.

DOI 10.1101/545681

E. coliflow cytometry

𝕏 PostBluesky

MicrobiologybioRxivSep 27, 2018

A unified framework for unconstrained and constrained ordination of microbiome read count data

S. Hawinkel, F.-M. Kerckhof, L. Bijnens, O. Thas

This paper introduces a unified ordination framework for microbiome data that addresses overdispersion and sequencing depth differences, offering improved visualization and analysis.

DOI 10.1371/journal.pone.0205474

metagenomicsstatistical model

𝕏 PostBluesky