Returns the (weighted) consensus matrix given a data matrix

consensus_matrix(data, weights = NULL)

## Arguments

data data matrix has rows as samples, columns as replicates a vector of weights for each algorithm used in meta-consensus clustering. Must have length(weights) equal to ncol(data).

## Value

a consensus matrix

## 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.

## Note

When consensus is calculated over bootstrap samples, not every sample is used in each replication. Thus, there will be scenarios where two samples are never chosen together in any bootstrap samples. This typically happens when the number of replications is small. The coordinate in the consensus matrix for such pairs of samples is NaN from a 0 / 0 computation. These entries are coerced to 0.

Derek Chiu

## 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)