Description Usage Arguments Details Value Author(s) See Also Examples

Compute coassignment probabilities for each label in a reference grouping when compared to an alternative grouping of samples.
This is now deprecated for `pairwiseRand`

.

1 | ```
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, to be compared to |

`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.

Note that the coassignment probability is closely related to the Rand index-based ratios
broken down by cluster pair in `pairwiseRand`

with `mode="ratio"`

and `adjusted=FALSE`

.
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 `pairwiseRand`

.

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.

`pairwiseRand`

, for another way to compare different clusterings.

1 2 3 4 5 6 7 8 9 | ```
library(scuttle)
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)
``` |

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