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ConsensusClustering (version 1.5.0)

Consensus Clustering

Description

Clustering, or cluster analysis, is a widely used technique in bioinformatics to identify groups of similar biological data points. Consensus clustering is an extension to clustering algorithms that aims to construct a robust result from those clustering features that are invariant under different sources of variation. For the reference, please cite the following paper: Yousefi, Melograna, et. al., (2023) .

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Version

Install

install.packages('ConsensusClustering')

Monthly Downloads

174

Version

1.5.0

License

GPL (>= 3)

Maintainer

Behnam Yousefi

Last Published

July 30th, 2024

Functions in ConsensusClustering (1.5.0)

majority_voting

Consensus mechanism based on majority voting
multiview_kmeans_gen

Multiview K-means generation
gaussian_clusters

Generate clusters of data points from Gaussian distribution with randomly generated parameters
spect_clust_from_adj_mat

Spectral clustering from adjacency matrix
multiview_cluster_gen

Multiview cluster generation
multiview_pam_gen

Multiview PAM (K-medoids) generation
pam_clust_from_adj_mat

PAM (k-medoids) clustering from adjacency matrix
multi_kmeans_gen

Multiple K-means generation
gaussian_clusters_with_param

Generate clusters of data points from Gaussian distribution with given parameters
multiview_clusters

Generate multiview clusters from Gaussian distributions with randomly generated parameters
multi_pam_gen

Multiple PAM (K-medoids) generation
multi_cluster_gen

Multiple cluster generation
cc_cluster_count

Count the number of clusters based on stability score.
adj_conv

Convert adjacency function to the affinity matrix
consensus_matrix

Calculate consensus matrix for data perturbation consensus clustering
connectivity_matrix

Build connectivity matrix
Logit

Logit function
consensus_matrix_data_prtrb

Calculate consensus matrix for data perturbation consensus clustering
adj_mat

Covert data matrix to adjacency matrix
consensus_matrix_multiview

Calculate consensus matrix for multi-data consensus clustering
coCluster_matrix

Calculate the Co-cluster matrix for a given set of clustering results.
cluster_relabel

Relabeling clusters based on cluster similarities
gaussian_mixture_clusters

Generate clusters of data points from Gaussian-mixture-model distributions with randomly generated parameters
generate_multiview

Multiview generation
label_similarity

Similarity between different clusters
generate_gaussian_data

Generate a set of data points from Gaussian distribution
indicator_matrix

Build indicator matrix
generate_data_prtrb

Generation mechanism for data perturbation consensus clustering
generate_method_prtrb

Multiple method generation
hir_clust_from_adj_mat

Hierarchical clustering from adjacency matrix