bibit {BiBitR} | R Documentation |
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.
bibit(matrix = NULL, minr = 2, minc = 2, arff_row_col = NULL, output_path = NULL)
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
## 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)