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Artuur Couckuyt

dblp:404/8858 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2024
0000-0001-7858-6521ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › single-cell analysis › cytometry data analysis
cell population clustering		 0.812024
Efficient cytometry analysis with FlowSOM in Python boosts interoperability with other single-cell tools · Bioinform. 2024
Bioinformatics and computational biology › single-cell analysis
cytometry data analysis		 0.812024
Efficient cytometry analysis with FlowSOM in Python boosts interoperability with other single-cell tools · Bioinform. 2024
Bioinformatics and computational biology › single-cell analysis
single-cell data integration		 0.212024
Efficient cytometry analysis with FlowSOM in Python boosts interoperability with other single-cell tools · Bioinform. 2024
Bioinformatics and computational biology › single-cell analysis
single-cell omics		 0.212024
Efficient cytometry analysis with FlowSOM in Python boosts interoperability with other single-cell tools · Bioinform. 2024

Methods — techniques the papers use, named apart from their topics

self-organizing map · 0.8clustering · 0.8
YearPublicationVenuePosition
2024 Efficient cytometry analysis with FlowSOM in Python boosts interoperability with other single-cell tools
abstract
MOTIVATION: We describe a new Python implementation of FlowSOM, a clustering method for cytometry data. RESULTS: This implementation is faster than the original version in R, better adapted to work with single-cell omics data including integration with current single-cell data structures and includes all the original visualizations, such as the star and pie plot. AVAILABILITY AND IMPLEMENTATION: The FlowSOM Python implementation is freely available on GitHub: https://github.com/saeyslab/FlowSOM_Python.
Artuur Couckuyt, Benjamin Rombaut 0001, Yvan Saeys, Sofie Van Gassen
 Bioinform.1