sharp (version 1.4.6)

ConsensusScore: Consensus score

Description

Computes the consensus score from the consensus matrix, matrix of co-sampling counts and consensus clusters. The score is a z statistic for the comparison of the co-membership proportions observed within and between the consensus clusters.

Usage

ConsensusScore(prop, K, theta)

Value

A consensus score.

Arguments

prop

consensus matrix.

K

matrix of co-sampling counts.

theta

consensus co-membership matrix.

Details

To calculate the consensus score, the features are classified as being stably selected or not (in selection) or as being in the same consensus cluster or not (in clustering). In selection, the quantities \(X_w\) and \(X_b\) are defined as the sum of the selection counts for features that are stably selected or not, respectively. In clustering, the quantities \(X_w\) and \(X_b\) are defined as the sum of the co-membership counts for pairs of items in the same consensus cluster or in different consensus clusters, respectively.

Conditionally on this classification, and under the assumption that the selection (or co-membership) probabilities are the same for all features (or item pairs) in each of these two categories, the quantities \(X_w\) and \(X_b\) follow binomial distributions with probabilities \(p_w\) and \(p_b\), respectively.

In the most unstable situation, we suppose that all features (or item pairs) would have the same probability of being selected (or co-members). The consensus score is the z statistic from a z test where the null hypothesis is \(p_w \leq p_b\).

The consensus score increases with stability.

See Also

Other stability metric functions: FDP(), PFER(), StabilityMetrics(), StabilityScore()

Examples

# \donttest{
# Data simulation
set.seed(2)
simul <- SimulateClustering(
  n = c(30, 30, 30),
  nu_xc = 1
)
plot(simul)

# Consensus clustering
stab <- Clustering(
  xdata = simul$data
)
stab$Sc[3]

# Calculating the consensus score
theta <- CoMembership(Clusters(stab, argmax_id = 3))
ConsensusScore(
  prop = (stab$coprop[, , 3])[upper.tri(stab$coprop[, , 3])],
  K = stab$sampled_pairs[upper.tri(stab$sampled_pairs)],
  theta = theta[upper.tri(theta)]
)
# }