19.2 Biclustering
THIS SECTION IS UNDER DEVELOPMENT. PLEASE CHECK BACK LATER
20200902
library(biclust)
<- matrix(rbinom(400, 50, 0.4), 20, 20)
tds <- biclust(tds, method=BCCC(), delta=1.5, alpha=1, number=10)
res res
##
## An object of class Biclust
##
## call:
## biclust(x=tds, method=BCCC(), delta=1.5, alpha=1, number=10)
##
## Number of Clusters found: 4
##
## First 4 Cluster sizes:
## BC 1 BC 2 BC 3 BC 4
## Number of Rows: 6 6 5 3
## Number of Columns: 7 5 7 4
bicluster(tds, res)
## $Bicluster1
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 17 16 19 21 22 17 19
## [2,] 18 20 17 22 24 17 18
## [3,] 19 20 24 25 26 20 21
## [4,] 17 16 15 19 23 17 20
## [5,] 20 19 18 22 23 20 19
## [6,] 16 20 20 23 23 19 20
##
## $Bicluster2
## [,1] [,2] [,3] [,4] [,5]
## [1,] 19 17 23 17 20
## [2,] 20 19 22 19 17
## [3,] 20 19 24 24 21
## [4,] 22 16 22 20 20
## [5,] 21 17 23 20 17
## [6,] 22 19 25 24 22
##
## $Bicluster3
## [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,] 18 18 18 19 19 19 20
## [2,] 15 20 19 23 18 21 17
## [3,] 18 18 19 18 19 21 18
## [4,] 22 22 21 22 21 24 20
## [5,] 18 21 20 24 21 22 19
##
## $Bicluster4
## [,1] [,2] [,3] [,4]
## [1,] 18 18 21 20
## [2,] 19 20 25 19
## [3,] 15 15 21 16
parallelCoordinates(tds, res, number=4)
data(BicatYeast)
<- discretize(BicatYeast)
tds <- biclust(tds, method=BCXmotifs(), alpha=0.05, number=50)
res res
##
## An object of class Biclust
##
## call:
## biclust(x=tds, method=BCXmotifs(), alpha=0.05, number=50)
##
## Number of Clusters found: 20
##
## First 5 Cluster sizes:
## BC 1 BC 2 BC 3 BC 4 BC 5
## Number of Rows: 160 76 59 37 21
## Number of Columns: 6 8 6 6 9
parallelCoordinates(BicatYeast, res, number=4)
plotclust(res, tds)
<- tribble(~x, ~y,
tds 1, 1,
2, 1,
1, 0,
4, 7,
3, 5,
3, 6)
<- biclust(as.matrix(tds), method=BCCC(), delta=50, alpha=0, number=5)
res res
##
## An object of class Biclust
##
## call:
## biclust(x=as.matrix(tds), method=BCCC(), delta=50, alpha=0,
## number=5)
##
## There was one cluster found with
## 6 Rows and 2 columns
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