COCA (Cluster-of-Clusters Algorithm)

Overview

COCA is a multi-platform integrative unsupervised clustering method that combines separate clustering solutions from distinct data types (e.g., copy number, DNA methylation, mRNA expression) into a consensus cluster assignment. Used to define robust molecular groups across omics layers PMID:34433969.

Used by

  • PMID:34433969 — COCA integration of six CNA clusters, six DNA methylation clusters, and six mRNA clusters from 121 fresh-frozen meningiomas converged on four stable molecular groups (MG1–MG4) independently associated with recurrence-free survival (log-rank P = 5 x 10^-15) and superior to WHO grade, individual-datatype clustering, or methylation-only classification for predicting time to recurrence PMID:34433969.
  • COCA (Cluster of Clusters Analysis) used for inter-cohort cluster integration in the TCGA esophageal/stomach study, enabling comparison of oesophageal subtypes with TCGA lung squamous, head-and-neck squamous, and gastric adenocarcinoma datasets PMID:28052061.
  • Cluster of Cluster Assignments (COCA) used for integrative cross-platform clustering across WES, methylation, RNA-seq, miRNA, and RPPA data in 412 BLCA tumors PMID:28988769

Notes

  • COCA outperformed single-datatype clustering approaches in the meningioma cohort; MG classification was an independent predictor of recurrence after adjusting for WHO grade, resection extent, and radiotherapy PMID:34433969.
  • Corpus-grown slug; not present in canonical ontology.

Sources

This page was processed by entity-page-writer on 2026-04-11. - PMID:28052061

This page was processed by wiki-cli on 2026-05-14. - PMID:28988769

This page was processed by entity-page-writer on 2026-05-15.