| name | bioconductor-consensusclusterplus |
| description | algorithm for determining cluster count and membership by stability evidence in unsupervised analysis |
| when_to_use | Use when: Unsupervised class discovery from gene expression data (e.g., microarray data).; Determining optimal cluster count and membership using consensus clustering resampling.; Generating consensus matrices, CDF plots, and tracking plots to assess cluster stability.; Calculating cluster-consensus and item-consensus metrics using the calcICL function.. Not for: For extremely large datasets without pre-computing distance matrices (use a pre-computed dist object instead to save time).; If you need non-resampling based clustering (use standard hclust or kmeans instead). |
| user-invocable | false |
ConsensusClusterPlus
Dependencies & Environment
Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
- Version: 1.76.0 · Bioconductor: 3.23 · R: ≥ 4.6
- Depends: ALL, cluster
- Imports: Biobase, ALL, cluster
- System requirements: URL
- Install:
BiocManager::install("ConsensusClusterPlus")
When to Use
- Unsupervised class discovery from gene expression data (e.g., microarray data).
- Determining optimal cluster count and membership using consensus clustering resampling.
- Generating consensus matrices, CDF plots, and tracking plots to assess cluster stability.
- Calculating cluster-consensus and item-consensus metrics using the
calcICL function.
When NOT to Use
- For extremely large datasets without pre-computing distance matrices (use a pre-computed
dist object instead to save time).
- If you need non-resampling based clustering (use standard
hclust or kmeans instead).
Data Requirements
- A matrix of numerical values where columns are samples (items) and rows are features (e.g., genes).
- Alternatively, a pre-computed distance matrix (
dist object) can be provided.
- Data is typically filtered for highly variable features (e.g., top 5000 genes by
mad) and median-centered using sweep and median.
Key Parameters
- d: The input data matrix or pre-computed distance matrix.
- maxK: The maximum evaluated cluster count (e.g., 6).
- reps: The number of resamplings to perform (e.g., 50 for testing, 1000 in practice).
- pItem: The proportion of items to resample in each iteration (e.g., 0.8).
- pFeature: The proportion of features to resample in each iteration (e.g., 1).
- clusterAlg: The clustering algorithm to use (e.g., "hc", "pam", "km", or a custom function).
- distance: The distance metric to use (e.g., "pearson", "spearman", "euclidean", "manhattan").
- plot: The output format for graphical results (e.g., "png", "pdf", "pngBMP").
Best Practices
- Filter the dataset to the most informative/variable genes using
mad before clustering.
- Median-center the data using
sweep and median if using Pearson correlation distance.
- Use a high number of
reps (e.g., 1,000) and a higher maxK (e.g., 20) in real-world practice.
- Run
calcICL after ConsensusClusterPlus to generate and plot cluster-consensus and item-consensus metrics.
Common Pitfalls
- Computation time is too long for large datasets with feature resampling. Fix: Provide a pre-computed distance matrix to
d or use plot="pngBMP" to avoid plotting massive dendrograms.
- Results change between runs making analysis irreproducible. Fix: Always specify a random
seed parameter in the ConsensusClusterPlus call.
- Consensus matrix dendrogram fails to plot for large datasets. Fix: Use
plot="pngBMP" which uses the bitmap function rather than png.
Alternatives
- cluster: For standard clustering algorithms like
diana or pam without consensus resampling.
- NMF: For non-negative matrix factorization based clustering.
- mclust: For model-based clustering.
Citations
- Monti, S., Tamayo, P., Mesirov, J., Golub, T. (2003) Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data. Machine Learning, 52, 91-118.
- Wilkerson, M.D., Hayes, D.N. (2010). ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics, 26(12):1572-3.
References
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