ensemble: Ensemble Methods for Bicluster Algorithms In biclust: BiCluster Algorithms

Description

Calculates an ensemble of biclusters from different parameter setting of possible different bicluster algorithms.

Usage

 ```1 2``` ```ensemble(x, confs, rep = 1, maxNum = 5, similar = jaccard2, thr = 0.8, simthr =0.7, subs = c(1, 1), bootstrap = FALSE, support = 0, combine=firstcome, ...) ```

Arguments

 `x` Data Matrix `confs` Matrix containing parameter sets `rep` Number of repetitions for each parameter set `maxNum` Maximum number of biclusters taken from each run `similar` Function to produce a similarity matrix of bicluster `thr` Threshold for similarity `simthr` Proportion of row column combinations in bicluster `subs` Vector of proportion of rows and columns for subsampling. Default c(1,1) means no subsampling. `bootstrap` Should bootstrap sampling be used (logical: replace=bootstrap). `support` Wich proportion of the runs must contain the bicluster to have enough support to report it (between 0 and 1). `combine` Function to combine the single bicluster only firstcome and hcl for hierarchical clustering are possible at the moment. `...` Arguments past to the combine function.

Details

Two different kinds (or both combined) of ensebmbling is possible. Ensemble of repeated runs or ensemble of runs on subsamples.

Value

Return an object of class Biclust

Author(s)

Sebastian Kaiser [email protected]

See Also

`Biclust-class`, `plaid.grid`, `bimax.grid`

Examples

 ```1 2 3 4 5 6``` ```data(BicatYeast) ensemble.plaid <- ensemble(BicatYeast,plaid.grid()[1:5],rep=1,maxNum=2, thr=0.5, subs = c(1,1)) ensemble.plaid x <- binarize(BicatYeast) ensemble.bimax <- ensemble(x,bimax.grid(),rep=10,maxNum=2,thr=0.5, subs = c(0.8,0.8)) ensemble.bimax ```

Example output

```Loading required package: MASS
Loading required package: grid
Loading required package: colorspace
Loading required package: lattice
[1] "Support:"
[1] 0
[1] "Number of Bicluster:"
[1] 3 3 2 1 1

An object of class Biclust

call:
ensemble(x = BicatYeast, confs = plaid.grid()[1:5], rep = 1,
maxNum = 2, thr = 0.5, subs = c(1, 1))

Number of Clusters found:  5

First  5  Cluster sizes:
BC 1 BC 2 BC 3 BC 4 BC 5
Number of Rows:      46   71  137   11   30
Number of Columns:    4    4    6   10    7

[1] "Threshold:  0.3969381"
[1] "Support:"
[1] 0
[1] "Number of Bicluster:"
[1] 4 3 3 2 2 2 1 1 1 1 1 1 1 1

An object of class Biclust

call:
ensemble(x = x, confs = bimax.grid(), rep = 10, maxNum = 2, thr = 0.5,
subs = c(0.8, 0.8))

Number of Clusters found:  14

First  5  Cluster sizes:
BC 1 BC 2 BC 3 BC 4 BC 5
Number of Rows:      11    8    8   11   11
Number of Columns:   10    9    9   10   10
```

biclust documentation built on May 31, 2017, 4:22 a.m.