ArticleNucleic acids research2021
cola: an R/Bioconductor package for consensus partitioning through a general framework.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
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- Ancestry and somatic profile predict acral melanoma origin and prognosis.medRxiv : the preprint server for health sciences · 2025Article
- Uncovering the molecular signature of feline diffuse iris melanoma through transcriptomic analysis of disease severity.Scientific reports · 2025Article
- Outcome-associated factors in a molecularly defined cohort of central neurocytoma.Acta neuropathologica · 2025Article
- DNMT3A-dependent DNA methylation shapes the endothelial enhancer landscape.Nucleic acids research · 2025Article
- Comprehensive molecular profiling of FH-deficient renal cell carcinoma identifies molecular subtypes and potential therapeutic targets.Nature communications · 2025Article
- Subtyping Burkitt Lymphoma by DNA Methylation.Genes, chromosomes & cancer · 2025Article
- Oronasal mucosal melanoma is defined by two transcriptional subtypes in humans and dogs with implications for diagnosis and therapy.The Journal of pathology · 2025Article
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- NAB2::STAT6 fusions and genome-wide DNA methylation profiling: Predictors of patient outcomes in meningeal solitary fibrous tumors.Brain pathology (Zurich, Switzerland) · 2024Article
- Optimizing hybrid ensemble feature selection strategies for transcriptomic biomarker discovery in complex diseases.NAR genomics and bioinformatics · 2024Article
- Identification of disease phenotypes in acetylcholine receptor-antibody myasthenia gravis using proteomics-based consensus clustering.EBioMedicine · 2024Article
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3 authors.
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Abstract
Classification of high-throughput genomic data is a powerful method to assign samples to subgroups with specific molecular profiles. Consensus partitioning is the most widely applied approach to reveal subgroups by summarizing a consensus classification from a list of individual classifications generated by repeatedly executing clustering on random subsets of the data. It is able to evaluate the stability of the classification. We implemented a new R/Bioconductor package, cola, that provides a general framework for consensus partitioning. With cola, various parameters and methods can be user-defined and easily integrated into different steps of an analysis, e.g., feature selection, sample classification or defining signatures. cola provides a new method named ATC (ability to correlate to other rows) to extract features and recommends spherical k-means clustering (skmeans) for subgroup classification. We show that ATC and skmeans have better performance than other commonly used methods by a comprehensive benchmark on public datasets. We also benchmark key parameters in the consensus partitioning procedure, which helps users to select optimal parameter values. Moreover, cola provides rich functionalities to apply multiple partitioning methods in parallel and directly compare their results, as well as rich visualizations. cola can automate the complete analysis and generates a comprehensive HTML report.
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