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

This function computes the weighted Rand index (wRI), which computes the agreement of two partitions (often a gold standard reference and a clustering result) based on a modified Rand index definition to account for cell type hierarchy.

1 |

`trueclass` |
A vector for the true classes of cells. Often from an outside experiement. |

`cluster` |
A vector for the clustering results. |

`w0` |
An optional square matrix of dimension JxJ, where J is the number of classes provided by 'trueclass'. The (i,j)-th entry is the score of separating two cells of types i and j in different clusters. |

`w1` |
An optional square matrix of dimension JxJ, where J is the number of classes provided by 'trueclass'. The(i,j)-th entry is the score for putting two cells of different types in the same cluster. |

The w0 and w1 matrics can be specified by user based on based on prior biological knowledge. If not given, they will be estimated using 'createWeights' function.

A vector of numerics for values related to the Weighted Rand Index (wRI), including:

`wRI` |
The wRI value. |

`NI1` |
The index with the positive prediction value interpretation: given two subjects are put in the same cluster, the probability that they truely belong to the same type. |

`NI2` |
The index with the negative prediction value interpretation: given two subjects are separated in different clusters, the probability they belong to different types. |

`p1` |
The proportaion of "in-cluster" relationships in the reference, among all pair-wise relationships. |

`p0` |
The proportaion of "seprated in different cluster" relationships in the reference, among all pair-wise relationships. |

Zhijin Wu <zhijin_wu@brown.edu>, Hao Wu <hao.wu@emory.edu>

createWeights, wNMI

1 2 3 4 5 6 7 8 9 10 11 | ```
data(Zhengmix8eq)
weights = createWeights(Y, trueclass)
methods = names(clusterRes)
allRI = matrix(0, nrow=length(methods), ncol=6)
rownames(allRI) = methods
colnames(allRI) = c("RI", "NI1","NI2","wRI","wNI1","wNI2")
for(i in 1:length(clusterRes)) {
allRI[i,1:3] = wRI(trueclass, clusterRes[[i]]) [1:3]
allRI[i,4:6] = wRI(trueclass, clusterRes[[i]], weights$W0, weights$W1)[1:3]
}
barplot(t(allRI[,c(1,4)]), beside=TRUE, ylim=c(0.7,1.05), legend.text=TRUE, xpd=FALSE)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.