coassignProb {scran} | R Documentation |
Compute coassignment probabilities for each label in a reference grouping when compared to an alternative grouping of samples.
coassignProb(ref, alt, summarize = FALSE)
ref |
A character vector or factor containing one set of groupings, considered to be the reference. |
alt |
A character vector or factor containing another set of groupings. |
summarize |
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 alt
.
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.
When 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 alt
.
The “other” coassignment probability is the maximum probability across all pairs involving that label.
In general, 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.
If summarize=FALSE
, a numeric matrix is returned with upper triangular entries filled with the coassignment probabilities for each pair of labels in ref
.
Otherwise, a DataFrame is returned with one row per label in ref
containing the self
and other
coassignment probabilities.
Aaron Lun
bootstrapCluster
, to compute coassignment probabilities across bootstrap replicates.
library(scater) sce <- mockSCE(ncells=200) sce <- logNormCounts(sce) clust1 <- kmeans(t(logcounts(sce)),3)$cluster clust2 <- kmeans(t(logcounts(sce)),5)$cluster coassignProb(clust1, clust2) coassignProb(clust1, clust2, summarize=TRUE)