Description Usage Arguments Details Value Author(s) See Also Examples
computes similarity matrix for the biclustering output based on one of the pairwise similarity indices of biclusters in a given bicluster set
1 | similarity(x, index = "jaccard", type="rows")
|
x |
BiclustSet object containing row and column indicators of bicluster membership, number of biclusters |
index |
similarity index for the biclusters in output |
type |
whether to perform similarity in two dimensions, "both" (recommended for biclustering), row dimension, "rows" (default, requires less computations) or column dimension "cols" |
This function operates on BiclustSet object and computes pairwise similarity based on the common elements between biclusters.
type
variable controls whether similarity index is constructed for all elements, or in one dimension (rows or columns) only.
In general, similarity indices for one dimension (row or column) are higher than for two-dimensions.
Several options for similarity indices are offered: jaccard (default), kulczynski, sensitivity, specificity, sorensen and ochiai indices.
a "similarity" object containing similarity matrix
Tatsiana Khamiakova
HCLtree
, plotSuper
, jaccardMat
,kulczynskiMat
, ochiaiMat
, sensitivityMat
,
specificityMat
,sorensenMat
1 2 3 4 5 6 7 8 9 | #compute sensitivity for BiMAX biclusters
test <- matrix(rnorm(5000), 100, 50)
test[11:20,11:20] <- rnorm(100, 3, 0.1)
test[17:26,21:30] <- rnorm(100, 3, 0.1)
testBin <- binarize(test,2)
res <- biclust(x=testBin, method=BCBimax(), minr=4, minc=4, number=10)
BiMaxBiclustSet <- BiclustSet(res)
SensitivityMatr<- similarity(BiMaxBiclustSet,index="sensitivity", type="rows")
SensitivityMatr
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