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RapCluster: Bridging the Reproducibility Gap in Clustering Analysis

Lutfi, A.; Warneke, R.; Fischer, L.; Rappsilber, J.

2026-04-15 bioinformatics
10.64898/2026.04.14.718399 bioRxiv
Show abstract

Clustering is ubiquitous across science, yet a text-mining audit of 736,399 open-access articles identified as using clustering (2000-2025) reveals common practice leaves key parameters undocumented or untuned, contributing to the reproducibility crisis in science. We developed an interactive web platform featuring 11 widely adopted clustering algorithms to enable transparent clustering analysis and reporting, aligning practical use with best practices in computational research. Code availabilityThe browser-based clustering analysis platform RapCluster is available for download at https://github.com/lutfia95/RapCluster under MIT License and accessible at https://rappsilberlab.org/rapcluster/ The web version is capacity-limited to 8 GB of memory (ca. 12,000 candidates with 130 features). All source codes used for the analysis are included with the publication.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Bioinformatics
◐ 1204 papers in training set
Top 0.9%
23.2%
Similar papers in this journal
2
BMC Bioinformatics
○ 457 papers in training set
Top 0.3%
13.4%
Similar papers in this journal
3
Briefings in Bioinformatics
◐ 354 papers in training set
Top 1%
7.0%
Similar papers in this journal
4
GigaScience
◐ 212 papers in training set
Top 0.3%
7.0%
Similar papers in this journal
50% of probability mass above
5
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 1%
3.6%
Similar papers in this journal
6
Bioinformatics Advances
◐ 203 papers in training set
Top 2%
3.4%
7
Scientific Reports
○ 3612 papers in training set
Top 37%
2.7%
8
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 2%
2.5%
9
eLife
● 5828 papers in training set
Top 44%
2.2%
10
PLOS ONE
● 5266 papers in training set
Top 42%
2.2%
11
Research Synthesis Methods
○ 20 papers in training set
Top 0.1%
2.2%
12
Nucleic Acids Research
◐ 1281 papers in training set
Top 8%
2.0%
13
PeerJ
◐ 308 papers in training set
Top 5%
1.8%
14
F1000Research
○ 88 papers in training set
Top 1%
1.8%
15
Nature Communications
○ 5641 papers in training set
Top 44%
1.8%
16
Scientific Data
○ 209 papers in training set
Top 1%
1.8%
17
PLOS Biology
● 486 papers in training set
Top 6%
1.4%
18
PLOS Computational Biology
● 1863 papers in training set
Top 16%
1.4%
19
Genome Biology
○ 637 papers in training set
Top 7%
1.2%
20
Analytical Chemistry
● 218 papers in training set
Top 2%
1.2%
21
BioData Mining
○ 22 papers in training set
Top 0.5%
1.0%
22
BMC Research Notes
○ 33 papers in training set
Top 1%
0.6%
23
Frontiers in Bioinformatics
○ 49 papers in training set
Top 2%
0.6%
24
Journal of Medical Internet Research
◐ 87 papers in training set
Top 3%
0.6%
25
iMeta
○ 10 papers in training set
Top 0.2%
0.6%
26
Database
◐ 61 papers in training set
Top 1%
0.6%
27
Cell Reports Methods
○ 165 papers in training set
Top 5%
0.5%
28
Ecological Informatics
○ 33 papers in training set
Top 0.9%
0.5%
29
BMC Biology
○ 265 papers in training set
Top 8%
0.5%
30
Journal of Molecular Biology
○ 232 papers in training set
Top 5%
0.5%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.