diceR (version 3.0.0)

consensus_matrix: Consensus matrix

Description

Returns the (weighted) consensus matrix given a data matrix

Usage

consensus_matrix(data, weights = NULL)

Value

a consensus matrix

Arguments

data

data matrix has rows as samples, columns as replicates

weights

a vector of weights for each algorithm used in meta-consensus clustering. Must have length(weights) equal to ncol(data).

Author

Derek Chiu

Details

Given a vector of cluster assignments, we first calculate the connectivity matrix and indicator matrix. A connectivity matrix has a 1 if both samples are in the same cluster, and 0 otherwise. An indicator matrix has a 1 if both samples were selected to be used in a subsample of a consensus clustering algorithm, and 0 otherwise. Summation of connectivity matrices and indicator matrices is performed over different subsamples of the data. The consensus matrix is calculated by dividing the aggregated connectivity matrices by the aggregated indicator matrices.

If a meta-consensus matrix is desired, where consensus classes of different clustering algorithms are aggregated, we can construct a weighted meta-consensus matrix using weights.

Examples

set.seed(2)
x <- replicate(100, rbinom(100, 4, 0.2))
w <- rexp(100)
w <- w / sum(w)
cm1 <- consensus_matrix(x)
cm2 <- consensus_matrix(x, weights = w)