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.

1 2 |

`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 |

`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 |

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:

Convert R matrix to a

`.arff`

output file.Use the

`.arff`

file as input for the Java code which is called by`system()`

.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.

A Biclust S4 Class object.

Ewoud De Troyer

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*

1 2 3 4 5 6 7 8 9 10 11 | ```
## Not run:
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)
## End(Not run)
``` |

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