coassignProb {scran} | R Documentation

## Compute coassignment probabilities

### Description

Compute coassignment probabilities for each label in a reference grouping when compared to an alternative grouping of samples.

### Usage

    coassignProb(ref, alt, summarize = FALSE)

### Arguments

`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.

### Details

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.

### Value

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.

### Author(s)

Aaron Lun

### See Also

`bootstrapCluster`, to compute coassignment probabilities across bootstrap replicates.

### Examples

    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)

[Package scran version 1.16.0 Index]