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: 7 5 4 4
## Number of Columns: 6 6 5 5
bicluster(tds, res)
## $Bicluster1
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 21 22 22 20 20 22
## [2,] 16 21 22 19 19 20
## [3,] 21 20 22 18 19 24
## [4,] 20 22 24 20 20 20
## [5,] 19 18 23 20 16 20
## [6,] 20 20 20 18 20 21
## [7,] 18 22 22 19 20 20
##
## $Bicluster2
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 21 22 21 25 21 24
## [2,] 23 23 22 22 22 23
## [3,] 18 17 18 18 22 20
## [4,] 19 20 20 20 18 19
## [5,] 20 17 17 21 18 17
##
## $Bicluster3
## [,1] [,2] [,3] [,4] [,5]
## [1,] 16 22 20 17 22
## [2,] 18 19 21 18 21
## [3,] 19 20 17 18 21
## [4,] 17 22 22 18 22
##
## $Bicluster4
## [,1] [,2] [,3] [,4] [,5]
## [1,] 24 19 19 18 19
## [2,] 23 23 17 18 19
## [3,] 24 18 15 17 20
## [4,] 16 16 12 13 15
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: 21
##
## First 5 Cluster sizes:
## BC 1 BC 2 BC 3 BC 4 BC 5
## Number of Rows: 161 88 48 27 17
## Number of Columns: 6 6 6 9 8
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
Your donation will support ongoing availability and give you access to the PDF version of this book. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Popular open source software includes rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984. Copyright © 1995-2021 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0
