bioRxivBioinformaticsPreprint
ECCO: A Python UI for Performing Ensemble Clustering Combined with Cluster Optimization on Omics Data
Brady David HislopiD, Kenna Brown, Hunter HasskampiD, Connor Boone, Mark GreenwoodiD, Chelsea M. HeveraniD, Ronald K. JuneiD
Abstract
Modern biological research often leverages clustering to elucidate disease endotypes and underlying mechanisms. Mono-cluster solutions remain the predominant method; however, this approach has concerning pitfalls, motivating the need for ensemble clustering methods. We present Ensemble Clustering Combined with Cluster Optimization (ECCO), an open-source Python UI that provides a fast, scalable framework for ensemble clustering of large-scale data. It includes zero-code integration of novel ensemble clustering methods and many pre- and post-processing functionalities, enabling researchers to efficiently integrate advanced clustering methodologies into their analysis pipelines.
- Citations
- 4
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Hypercluster: a flexible tool for parallelized unsupervised clustering optimization
Lili M. Blumenberg, Kelly V. Ruggles·BMC BioinformaticsPeer-reviewed·Sep 29, 2020
- ClusterEnG: An interactive educational web resource for clustering big data
Mohith Manjunath, Yi Zhang, … Jun S. Song·PeerJ Computer SciencePeer-reviewed·May 21, 2018
- Spectrum: Fast density-aware spectral clustering for single and multi-omic data
Christopher R. John, David Watson, … Myles Lewis·BioinformaticsPeer-reviewed·Sep 6, 2019
- clusterProfiler: An universal enrichment tool for functional and comparative study
Tianzhi Wu, Erqiang Hu, … Guangchuang Yu·The InnovationPeer-reviewed·Jul 1, 2021
- Consensus clustering applied to multi-omic disease subtyping
Galadriel Brière, Élodie Darbo, … Raluca Uricaru·BMC BioinformaticsPeer-reviewed·Jul 6, 2021
- A hyperparameter-randomized ensemble approach for robust clustering across diverse datasets
Sarah M. Goggin, Eli R. Zunder·bioRxiv·Dec 18, 2023