BiBitR (version 0.3.1)

bibit: The BiBit Algorithm

Description

A R-wrapper which directly calls the original Java code for the BiBit algorithm (http://eps.upo.es/bigs/BiBit.html) and transforms it to the output format of the Biclust R package.

Usage

bibit(matrix = NULL, minr = 2, minc = 2, arff_row_col = NULL,
  output_path = NULL)

Arguments

matrix

The binary input matrix.

minr

The minimum number of rows of the Biclusters.

minc

The minimum number of columns of the Biclusters.

arff_row_col

If you want to circumvent the internal R function to convert the matrix to .arff format, provide the pathname of this file here. Additionally, two .csv files should be provided containing 1 column of row and column names. These two files should not contain a header or quotes around the names, simply 1 column with the names. (Example: arff_row_col=c("...\\data\\matrix.arff","...\\data\\rownames.csv","...\\data\\colnames.csv")) Note: These files can be generated with the make_arff_row_col function. Warning: Should you use the write.arff function from the foreign package, remember to transpose the matrix first.

output_path

If as output, the original txt output of the Java code is desired, provide the outputh path here (without extension). In this case the bibit function will skip the transformation to a Biclust class object and simply return NULL. (Example: output_path="...\\out\\bibitresult")

(Description Output: The following information about every bicluster generated will be printed in the output file: number of rows, number of columns, name of rows and name of columns.

Value

A Biclust S4 Class object.

Details

This function uses the original Java code directly (with the intended input and output). Because the Java code was not refactored, the rJava package could not be used. The bibit function does the following:

1. Convert R matrix to a .arff output file.

2. Use the .arff file as input for the Java code which is called by system().

3. The outputted .txt file from the Java BiBit algorithm is read in and transformed to a Biclust object.

Because of this, there is a chance of overhead when applying the algorithm on large datasets. Make sure your machine has enough RAM available when applying to big data.

References

Domingo S. Rodriguez-Baena, Antonia J. Perez-Pulido and Jesus S. Aguilar-Ruiz (2011), "A biclustering algorithm for extracting bit-patterns from binary datasets", Bioinformatics

Examples

data <- matrix(sample(c(0,1),100*100,replace=TRUE,prob=c(0.9,0.1)),nrow=100,ncol=100)
data[1:10,1:10] <- 1 # BC1
data[11:20,11:20] <- 1 # BC2
data[21:30,21:30] <- 1 # BC3
data <- data[sample(1:nrow(data),nrow(data)),sample(1:ncol(data),ncol(data))]
result <- bibit(data,minr=5,minc=5)
result
MaxBC(result)