Compute coassignment probabilities for each label in a reference grouping when compared to an alternative grouping of samples.
This is now deprecated for
A character vector or factor containing one set of groupings, considered to be the reference.
A character vector or factor containing another set of groupings, to be compared to
Logical scalar indicating whether the output matrix should be converted into a per-label summary.
The coassignment probability for each pair of labels in
ref is the probability that a randomly chosen cell from each of the two reference labels will have the same label in
High coassignment probabilities indicate that a particular pair of labels in
ref are frequently assigned to the same label in
alt, which has some implications for cluster stability.
summarize=TRUE, we summarize the matrix of coassignment probabilities into a set of per-label values.
The “self” coassignment probability is simply the diagonal entry of the matrix, i.e., the probability that two cells from the same label in
ref also have the same label in
The “other” coassignment probability is the maximum probability across all pairs involving that label.
ref is well-recapitulated by
alt if the diagonal entries of the matrix is much higher than the sum of the off-diagonal entries.
This manifests as higher values for the self probabilities compared to the other probabilities.
Note that the coassignment probability is closely related to the Rand index-based ratios
broken down by cluster pair in
The off-diagonal coassignment probabilities are simply 1 minus the off-diagonal ratio,
while the on-diagonal values differ only by the lack of consideration of pairs of the same cell in
summarize=FALSE, a numeric matrix is returned with upper triangular entries filled with the coassignment probabilities for each pair of labels in
Otherwise, a DataFrame is returned with one row per label in
ref containing the
other coassignment probabilities.
bootstrapCluster, to compute coassignment probabilities across bootstrap replicates.
pairwiseRand, for another way to compare different clusterings.
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