R: The Plaid Model Bicluster algorithm

BCPlaid {biclust} | R Documentation

## The Plaid Model Bicluster algorithm

### Description

Performs Plaid Model Biclustering as described in Turner et al., 2003. This is an improvement of original 'Plaid Models for Gene Expression Data' (Lazzeroni and Owen, 2002). This algorithm models data matrices to a sum of layers, the model is fitted to data through minimization of error.

### Usage

    ## S4 method for signature 'matrix,BCPlaid'
    biclust(x, method=BCPlaid(), cluster="b", fit.model = y ~ m + a + b,
      background = TRUE, background.layer = NA, background.df = 1, row.release = 0.7,
      col.release = 0.7, shuffle = 3, back.fit = 0, max.layers = 20, iter.startup = 5,
      iter.layer = 10, verbose = TRUE)

### Arguments

`x` | The data matrix where biclusters have to be found
`method` | Here BCPlaid, to perform Plaid algorithm
`cluster` | 'r', 'c' or 'b', to cluster rows, columns or both (default 'b')
`fit.model` | Model (formula) to fit each layer. Usually, a linear model is used, that estimates three parameters: m (constant for all elements in the bicluster), a(contant for all rows in the bicluster) and b (constant for all columns). Thus, default is: y ~ m + a + b.
`background` | If 'TRUE' the method will consider that a background layer (constant for all rows and columns) is present in the data matrix.
`background.layer` | If background='TRUE' a own background layer (Matrix with dimension of x) can be specified.
`background.df` | Degrees of Freedom of backround layer if background.layer is specified.
`shuffle` | Before a layer is added, it's statistical significance is compared against a number of layers obtained by random defined by this parameter. Default is 3, higher numbers could affect time performance.
`iter.startup` | Number of iterations to find starting values
`iter.layer` | Number of iterations to find each layer
`back.fit` | After a layer is added, additional iterations can be done to refine the fitting of the layer (default set to 0)
`row.release` | Scalar in [0,1](with interval recommended [0.5-0.7]) used as threshold to prune rows in the layers depending on row homogeneity
`col.release` | As above, with columns
`max.layers` | Maximum number of layer to include in the model
`verbose` | If 'TRUE' prints extra information on progress.

### Value

Returns an Biclust object.

### Author(s)

Adaptation of original code from Heather Turner from Rodrigo Santamaria rodri@usal.es. rodri@usal.es

### References

Heather Turner et al, "Improved biclustering of microarray data demonstrated through systematic performance tests",Computational Statistics and Data Analysis, 2003, vol. 48, pages 235-254.

Lazzeroni and Owen, "Plaid Models for Gene Expression Data", Standford University, 2002.

### Examples

      #Random matrix with embedded bicluster
      test <- matrix(rnorm(5000),100,50)
      test[11:20,11:20] <- rnorm(100,3,0.3)
      res<-biclust(test, method=BCPlaid())
      res

      #microarray matrix
      data(BicatYeast)
      res<-biclust(BicatYeast, method=BCPlaid(), verbose=FALSE)
      res

[Package biclust version 2.0.2 Index]