The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
install.packages("BiBitR")install.packages("devtools") # If not yet installed on your R Version
devtools::install_github("hadley/devtools") # Only run this if your currently installed
# devtools version is <= 1.12 (recursive dependencies bug)
devtools::install_github("ewouddt/BiBitR")Should the installation of BiBitR or
devtools::install_github("hadley/devtools") throw an error,
please install the dependencies manually, then try:
install.packages(c("flexclust","biclust"))
devtools::install_github("ewouddt/BiBitR")BiBitR is a simple R wrapper which directly calls the
original Java code for applying the BiBit algorithm after which the
output is transformed to a Biclust S4 class object. The
original Java code can be found at http://eps.upo.es/bigs/BiBit.html by Domingo S.
Rodriguez-Baena, Antonia J. Perez-Pulido and Jesus S. Aguilar-Ruiz.
More details about the BiBit algorithm can be found in:
The bibit 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:
.arff output file..arff file as input for the Java code which is
called by system()..txt file from the Java BiBit algorithm
is read in and transformed to a Biclust object.Because of this procedure, 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.
Note: If you want to circumvent the internal R
function to convert the matrix to .arff format, look at the
documentation of the arff_row_col parameter of the
bibit function. You can input the original input files for
the java algorithm with this parameter. The original input files can
also be generated with the make_arff_row_col function in
the package.
The BiBit algorithm was also slightly adapted to allow some noise in
the biclusters. This can be done with the bibit2 function
in the BiBitR package. It is the same function as
bibit, but with an additional new noise parameter which
allows 0’s in the discovered biclusters.
bibit2 follows the same steps as described in the
Details section above. Following the general steps of the BiBit
algorithm, the allowance for noise in the biclusters is inserted in the
original algorithm as such:
noise parameter. Those rows that match completely or those
within the allowed noise range are added to bicluster.Note: Biclusters are only saved if they satisfy the
minr and minc parameter settings and if the
bicluster is not already contained completely within another
bicluster.
What you will end up with are biclusters not only consisting out of 1’s, but biclusters in which 2 rows (the starting pair) are all 1’s and in which the other rows could contain 0’s (= noise).
Note: Because of the extra checks involved in the noise allowance, using noise might increase the computation time a little bit.
The noise parameter determines the amount of zero’s
allowed in the bicluster (i.e. in the extra added rows to the starting
row pair) and can take on the following values:
noise=0: No noise allowed. This gives the same result
as using the bibit function.0<noise<1: The noise parameter will be a noise
percentage. The number of allowed 0’s in a (extra) row in the bicluster
will depend on the column size of the bicluster. More specifically
zeros_allowed = ceiling(noise * columnsize). For example
for noise=0.10 and a bicluster column size of
5, the number of allowed 0’s would be 1.noise>=1: The noise parameter will be the number of
allowed 0’s in a (extra) row in the bicluster independent from the
column size of the bicluster. In this noise option, the noise parameter
should be an integer.Normally you would apply BiBit (with/without noise) directly on the binary data. However due to the nature of the algorithm, namely starting an exhaustive search from each row-pair, it is also possible to look for specific patterns of interest.
To do this, simply add 2 (artificial) identical rows which contain the pattern/motif of interest (e.g. 2 rows with 1’s in specific columns and 0 everywhere else). You can do this multiple times if multiple patterns/motifs are of interest.
This procedure is currently implemented in the package in the
bibit3 function. It allows to drive the BiBit algorithm to
only look for one or multiple full or sub patterns (which increases the
speed). See the Documentation of bibit3 for more info.
The package also allows you extend the bibit biclusters in the column
dimension. More info can be found in the documentation of the
bibit2, bibit3 and
bibit_columnextension functions. The goal of the procedure
is:
Looking for Noisy Biclusters in large data using BiBit
(bibit2) often results in many (overlapping) biclusters. In
order decrease the number of biclusters and find larger meaningful
patterns which make up noisy biclusters, the following workflow
(BiBitWorkflow) can be applied. Note that this workflow is
primarily used for data where there are many more rows (e.g. patients)
than columns (e.g. symptoms). For example the workflow would discover
larger meaningful symptom patterns which, conditioned on the allowed
noise/zeros, subsets of the patients share.
A Biclust S4 Class object.
library(BiBitR)
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))]
result1 <- bibit(data,minr=2,minc=2)
result1
MaxBC(result1)
result2 <- bibit2(data,minr=5,minc=5,noise=0.2)
result2
MaxBC(result2)
result3 <- bibit2(data,minr=5,minc=5,noise=3)
result3
MaxBC(result3)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.