Returns the (weighted) consensus matrix given a data matrix
consensus_matrix(data, weights = NULL)
data matrix has rows as samples, columns as replicates
a consensus matrix
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
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.