ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking

By Matthew D. Wilkerson, D. Hayes. Bioinform., 2010

4,827 citations recorded by the source corpus.

Summary: Unsupervised class discovery is a highly useful technique in cancer research, where intrinsic groups sharing biological characteristics may exist but are unknown. The consensus clustering (CC) method provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset. ConsensusClusterPlus implements the CC method in R and extends it with new functionality and visualizations including item tracking, item-consensus and cluster-consensus plots. These new features provide users with detailed information that enable more specific decisions in unsupervised class discovery. Availability: ConsensusClusterPlus is open source software, written in R, under GPL-2, and available through the Bioconductor project (http://www.bioconductor.org/). Contact: mwilkers@med.unc.edu Supplementary Information: Supplementary data are available at Bioinformatics online.

Subfields: Molecular Biology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/f09c52301704d1c89380f90b50b682dcc3f9d80f

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In Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Intrinsic subtypes of high-grade bladder cancer reflect the hallmarks of breast cancer biology (2014, methodology): “tering was performed as described previously (37), with 90% resampling and 1,000 iterations.”
Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer (2018, methodology): “Unsupervised consensus clustering using Consensus Cluster Plus (Wilkerson and Hayes, 2010) was performed on 10,165 tumors with 15,363 genes.”
Gene Body Methylation can alter Gene Expression and is a Therapeutic Target in Cancer (2014, methodology): “The ‘‘ConsensusClusterPlus’’ package from Bioconductor was used to cluster DNA methylation probes into different groups with variant remethylation patterns (Wilkerson and Hayes, 2010).”
TANRIC: An interactive open platform to explore the function of lncRNAs in cancer (2015, positive): “Analysis of tumor subtypes revealed by lncRNA expression To classify tumor subtypes based on lncRNA expression, for each cancer type, we selected the 500 lncRNAs with the most variable expression pattern and used ConsensusClusterPlus (19) to classify the tumor samples into sample clusters (subtypes)”
Inhibiting DNA methylation causes an interferon response in cancer via dsRNA including endogenous retroviruses (2015, positive): “Consensus hierarchical clustering was performed with the ConsensusClusterPlus R-package (Wilkerson and Hayes, 2010)”
TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data (2015, neutral): “D2 and ConsensusClusterPlus (31).”
Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma. (2018, methodology): “Analytical Methods We carried out unsupervised consensus clustering on the most variable 1% of CpG probes (3,859 of 385,857 probes), using the ConsensusClusterPlus (Wilkerson and Hayes, 2010) R package, with Euclidean distance and PAM.”
Comprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas. (2017, methodology): “Consensus clustering was implemented with the ConsensusClusterPlus package in R (Wilkerson and Hayes, 2010).”
Integrated Genomic Characterization of Pancreatic Ductal Adenocarcinoma (2017, positive): “For the 76 high-purity samples, unsupervised consensus clustering (Wilkerson and Hayes, 2010), applied to expression profiles for a subset of 360 highly variant lncRNAs, identified two clusters that were independent of purity (p = 0.”
Comprehensive molecular characterization of gastric adenocarcinoma (2014, neutral): “Focal amplifications targeted oncogenes such as ERBB2, CCNE1, KRAS, MYC, EGFR, CDK6, GATA4, GATA6 and ZNF217.”
Multi-platform analysis of 12 cancer types reveals molecular classification within and across tissues-of-origin (2014, methodology): “The binary matrix was then used in the ConsensusClusterPlus Rpackage (Wilkerson and Hayes, 2010) to identify patterns of relationship among the samples.”
Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer. (2017, neutral): “…≥ 1) and highly variable across the n=80 tumor cohort (≥ 95th FPKM variance percentile) from the matrix of 8167 lncRNAs (above), and identified groups of samples with similar abundance profiles by unsupervised consensus clustering with ConsensusClusterPlus (CCP) 1.20.0 (Wilkerson and Hayes, 2010).”
The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma (2018, methodology): “The K-value of 6 was selected according to the consensus cumulative distribution function, where K > 6 did not produce any appreciable increase in consensus (Monti et al., 2003; Wilkerson and Hayes, 2010).”
Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma (2019, methodology): “Consensus clustering was performed using the R packages ConsensusClusterPlus (Monti et al., 2003; Wilkerson and Hayes, 2010) within the Cell.”