Description Usage Arguments Value Author(s) References See Also Examples
Performs supervised clustering of predictor variables for large (microarray gene expression) datasets. Works in a greedy forward strategy and optimizes a combination of the Wilcoxon and Margin statistics for finding the clusters.
1 |
x |
Numeric matrix of explanatory variables (p variables in columns, n cases in rows). For example, these can be microarray gene expression data which should be clustered. |
y |
Numeric vector of length n containing the class labels of the individuals. These labels have to be coded by 0 and 1. |
noc |
Integer, the number of clusters that should be searched for on the data. |
genes |
Defaults to |
flip |
Logical, defaults to |
once.per.clust |
Logical, defaults to |
trace |
Integer >= 0; when positive, the output of the internal
loops is provided; |
wilma
returns an object of class "wilma". The functions
print
and summary
are used to obtain an overview of the
clusters that have been found. The function plot
yields a
two-dimensional projection into the space of the first two clusters
that wilma
found. The generic function fitted
returns
the fitted values, these are the cluster representatives. Finally,
predict
is used for classifying test data on the basis of
Wilma's cluster with either the nearest-neighbor-rule, diagonal linear
discriminant analysis, logistic regression or aggregated trees.
An object of class "wilma" is a list containing:
clist |
A list of length |
steps |
Numerical vector of length |
y |
Numeric vector of length n containing the class labels of the individuals. These labels have to be coded by 0 and 1. |
x.means |
A list of length |
noc |
Integer, the number of clusters that has been searched for on the data. |
signs |
Numerical vector of length p, saying whether the ith variable (gene) should be sign-flipped (-1) or not (+1). |
Marcel Dettling, dettling@stat.math.ethz.ch
Marcel Dettling (2002) Supervised Clustering of Genes, see https://stat.ethz.ch/~dettling/supercluster.html
Marcel Dettling and Peter Bühlmann (2002). Supervised Clustering of Genes. Genome Biology, 3(12): research0069.1-0069.15, doi: 10.1186/gb-2002-3-12-research0069 .
Marcel Dettling and Peter Bühlmann (2004). Finding Predictive Gene Groups from Microarray Data. Journal of Multivariate Analysis 90, 106–131, doi: 10.1016/j.jmva.2004.02.012 .
score
, margin
, and for a newer
methodology, pelora
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ## Working with a "real" microarray dataset
data(leukemia, package="supclust")
## Generating random test data: 3 observations and 250 variables (genes)
set.seed(724)
xN <- matrix(rnorm(750), nrow = 3, ncol = 250)
## Fitting Wilma
fit <- wilma(leukemia.x, leukemia.y, noc = 3, trace = 1)
## Working with the output
fit
summary(fit)
plot(fit)
fitted(fit)
## Fitted values and class predictions for the training data
predict(fit, type = "cla")
predict(fit, type = "fitt")
## Predicting fitted values and class labels for test data
predict(fit, newdata = xN)
predict(fit, newdata = xN, type = "cla", classifier = "nnr", noc = c(1,2,3))
predict(fit, newdata = xN, type = "cla", classifier = "dlda", noc = c(1,3))
predict(fit, newdata = xN, type = "cla", classifier = "logreg")
predict(fit, newdata = xN, type = "cla", classifier = "aggtrees")
|
Cluster 1
----------
Accepted
Gen[1] : 174 Score: 0 Margin: 0.402
Gen[2] : 69 Score: 0 Margin: 0.716
Gen[3] : 225 Score: 0 Margin: 0.942
Gen[4] : 216 Score: 0 Margin: 1.025
Gen[5] : 161 Score: 0 Margin: 1.035
gic size changed from 0 to 5
Eliminating -- no reduction --> end{repeat} after 1 step
Final Cluster 1
----------------
Gen: 174 Score: 0 Margin: 0.402
Gen: 69 Score: 0 Margin: 0.716
Gen: 225 Score: 0 Margin: 0.942
Gen: 216 Score: 0 Margin: 1.025
Gen: 161 Score: 0 Margin: 1.035
Cluster 2
----------
Accepted
Gen[1] : 80 Score: 0 Margin: 0.062
Gen[2] : 66 Score: 0 Margin: 0.228
Gen[3] : 119 Score: 0 Margin: 0.400
Gen[4] : 183 Score: 0 Margin: 0.455
Gen[5] : 202 Score: 0 Margin: 0.546
Gen[6] : 146 Score: 0 Margin: 0.552
Gen[7] : 167 Score: 0 Margin: 0.614
Gen[8] : 219 Score: 0 Margin: 0.650
Gen[9] : 217 Score: 0 Margin: 0.664
Gen[10]: 183 Score: 0 Margin: 0.667
Gen[11]: 82 Score: 0 Margin: 0.701
Gen[12]: 183 Score: 0 Margin: 0.712
Gen[13]: 82 Score: 0 Margin: 0.726
gic size changed from 0 to 13
Eliminating -- no reduction --> end{repeat} after 1 step
Final Cluster 2
----------------
Gen: 80 Score: 0 Margin: 0.062
Gen: 66 Score: 0 Margin: 0.228
Gen: 119 Score: 0 Margin: 0.400
Gen: 183 Score: 0 Margin: 0.455
Gen: 202 Score: 0 Margin: 0.546
Gen: 146 Score: 0 Margin: 0.552
Gen: 167 Score: 0 Margin: 0.614
Gen: 219 Score: 0 Margin: 0.650
Gen: 217 Score: 0 Margin: 0.664
Gen: 183 Score: 0 Margin: 0.667
Gen: 82 Score: 0 Margin: 0.701
Gen: 183 Score: 0 Margin: 0.712
Gen: 82 Score: 0 Margin: 0.726
Cluster 3
----------
Accepted
Gen[1] : 59 Score: 5 Margin: -0.301
Gen[2] : 56 Score: 0 Margin: 0.185
Gen[3] : 126 Score: 0 Margin: 0.366
Gen[4] : 104 Score: 0 Margin: 0.390
Gen[5] : 211 Score: 0 Margin: 0.446
Gen[6] : 79 Score: 0 Margin: 0.528
Gen[7] : 104 Score: 0 Margin: 0.587
gic size changed from 0 to 7
Eliminating -- no reduction --> end{repeat} after 1 step
Final Cluster 3
----------------
Gen: 59 Score: 5 Margin: -0.301
Gen: 56 Score: 0 Margin: 0.185
Gen: 126 Score: 0 Margin: 0.366
Gen: 104 Score: 0 Margin: 0.390
Gen: 211 Score: 0 Margin: 0.446
Gen: 79 Score: 0 Margin: 0.528
Gen: 104 Score: 0 Margin: 0.587
Wilma called to fit 3 clusters
Cluster 1 : Contains 5 genes, final score 0, final margin 1.04
Cluster 2 : Contains 13 genes, final score 0, final margin 0.73
Cluster 3 : Contains 7 genes, final score 0, final margin 0.59
'Wilma' object: number of clusters 'noc' = 3
Final Cluster 1
----------------
Gen: 174 Score: 0 Margin: 0.402
Gen: 69 Score: 0 Margin: 0.716
Gen: 225 Score: 0 Margin: 0.942
Gen: 216 Score: 0 Margin: 1.025
Gen: 161 Score: 0 Margin: 1.035
Final Cluster 2
----------------
Gen: 80 Score: 0 Margin: 0.062
Gen: 66 Score: 0 Margin: 0.228
Gen: 119 Score: 0 Margin: 0.400
Gen: 183 Score: 0 Margin: 0.455
Gen: 202 Score: 0 Margin: 0.546
Gen: 146 Score: 0 Margin: 0.552
Gen: 167 Score: 0 Margin: 0.614
Gen: 219 Score: 0 Margin: 0.650
Gen: 217 Score: 0 Margin: 0.664
Gen: 183 Score: 0 Margin: 0.667
Gen: 82 Score: 0 Margin: 0.701
Gen: 183 Score: 0 Margin: 0.712
Gen: 82 Score: 0 Margin: 0.726
Final Cluster 3
----------------
Gen: 59 Score: 5 Margin: -0.301
Gen: 56 Score: 0 Margin: 0.185
Gen: 126 Score: 0 Margin: 0.366
Gen: 104 Score: 0 Margin: 0.390
Gen: 211 Score: 0 Margin: 0.446
Gen: 79 Score: 0 Margin: 0.528
Gen: 104 Score: 0 Margin: 0.587
Predictor 1 Predictor 2 Predictor 3
1 -0.46171740 -0.009992927 -0.3834917
2 -0.07873956 0.063261853 -0.4507562
3 -0.98502735 0.115046790 -0.6588318
4 -0.44679460 0.190811888 -0.3708621
5 -0.60165527 0.160506547 -0.4297218
6 -0.13915781 -0.081445994 -0.5412858
7 -0.60926232 0.233106964 -0.5008374
8 -0.40958990 0.230920699 -0.3737456
9 -0.90332225 -0.205838386 -0.6718678
10 -0.15449904 0.090387741 -0.3621221
11 -0.93321219 -0.166396854 -0.5220501
12 -0.24828228 0.254053226 -0.4432451
13 -0.68080799 -0.275165741 -0.5242771
14 -0.26706279 -0.139745615 -0.4901269
15 -0.59354591 0.013901552 -0.5356795
16 -0.31641321 -0.025168422 -0.6646484
17 -0.12774159 0.243830097 -0.3556577
18 -0.49255028 -0.046791465 -0.3959728
19 -0.10804314 -0.057647582 -0.3670498
20 -0.21844937 -0.020541359 -0.7158141
21 -0.38251170 -0.056594477 -0.4981708
22 -0.52243987 0.041268521 -0.4493086
23 -0.09432030 0.156036729 -0.6578364
24 -0.74887144 0.112149259 -0.4000218
25 -0.31088303 0.155117649 -0.6817786
26 -0.37235541 -0.087960565 -0.4800702
27 -0.65676744 -0.033629112 -0.5413959
28 1.06529951 1.000615155 0.3486579
29 1.40225347 1.385903995 0.5340921
30 1.01794839 1.098640932 0.3200740
31 1.12892126 1.025627213 0.2333483
32 1.25679904 0.979789107 0.2635796
33 0.97891459 1.265071950 0.5098338
34 0.95638559 1.137334725 0.4849519
35 0.97667401 0.992184346 0.2582996
36 1.09683760 0.987807416 0.3759882
37 1.30587995 1.347697651 0.9897420
38 1.09675411 1.178182368 0.2318421
[1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
Predictor 1 Predictor 2 Predictor 3
1 -0.46171740 -0.009992927 -0.3834917
2 -0.07873956 0.063261853 -0.4507562
3 -0.98502735 0.115046790 -0.6588318
4 -0.44679460 0.190811888 -0.3708621
5 -0.60165527 0.160506547 -0.4297218
6 -0.13915781 -0.081445994 -0.5412858
7 -0.60926232 0.233106964 -0.5008374
8 -0.40958990 0.230920699 -0.3737456
9 -0.90332225 -0.205838386 -0.6718678
10 -0.15449904 0.090387741 -0.3621221
11 -0.93321219 -0.166396854 -0.5220501
12 -0.24828228 0.254053226 -0.4432451
13 -0.68080799 -0.275165741 -0.5242771
14 -0.26706279 -0.139745615 -0.4901269
15 -0.59354591 0.013901552 -0.5356795
16 -0.31641321 -0.025168422 -0.6646484
17 -0.12774159 0.243830097 -0.3556577
18 -0.49255028 -0.046791465 -0.3959728
19 -0.10804314 -0.057647582 -0.3670498
20 -0.21844937 -0.020541359 -0.7158141
21 -0.38251170 -0.056594477 -0.4981708
22 -0.52243987 0.041268521 -0.4493086
23 -0.09432030 0.156036729 -0.6578364
24 -0.74887144 0.112149259 -0.4000218
25 -0.31088303 0.155117649 -0.6817786
26 -0.37235541 -0.087960565 -0.4800702
27 -0.65676744 -0.033629112 -0.5413959
28 1.06529951 1.000615155 0.3486579
29 1.40225347 1.385903995 0.5340921
30 1.01794839 1.098640932 0.3200740
31 1.12892126 1.025627213 0.2333483
32 1.25679904 0.979789107 0.2635796
33 0.97891459 1.265071950 0.5098338
34 0.95638559 1.137334725 0.4849519
35 0.97667401 0.992184346 0.2582996
36 1.09683760 0.987807416 0.3759882
37 1.30587995 1.347697651 0.9897420
38 1.09675411 1.178182368 0.2318421
Predictor 1 Predictor 2 Predictor 3
1 -0.08242748 -0.4723073 -0.6131918
2 -0.15667388 -0.5359670 -0.2249549
3 -0.11445314 -0.4265685 0.2245120
1 Predictors 2 Predictors 3 Predictors
1 0 0 0
2 0 0 0
3 0 0 0
1 Predictors 3 Predictors
1 0 0
2 0 0
3 0 0
[1] 0 0 0
[1] 0 0 0
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