UniBic is an elementary method by which biologically meaningful trend-preserving biclusters can be readily identified from noisy and complex large data. The basic idea is to apply the longest common subsequence (LCS) framework to selected pairs of rows in an index matrix derived from an input data matrix to locate a seed for each bicluster to be identified.
Citing us: Wang, Z., Li, G., Robinson, R. W., Huang, X. (2016). UniBic: Sequential row-based biclustering algorithm for analysis of gene expression data. Scientific Reports, 6.
This software provides a biclustering module for microarray data. For a set of genes and a set of conditions, the program outputs a block-like structure which shows uniform trending-preserving pattern within the block, the block would contain only subsets of all given genes under subsets of all given conditions.
Certain parts of the code uses open-source data structure library codes, including:
- fib http://resnet.uoregon.edu/~gurney_j/jmpc/fib.html, copyright information in fib.c
- Mark A. Weiss's data structure codes http://www.cs.fiu.edu/~weiss/
Simply put "unibic1.0.tar.gz" in any directory,
$ tar zxvf unibic1.0.tar.gz
enter the folder "unibic1.0" and type "make" then the compiled codes are within the same directory as the source.
The major program in the provided package is unibic, it can parse two
formats of files, discrete data and continuous data, and examples for each
are provided. See help and look at all available options.
$ ./unibic -h
Take a look at toy_example (discrete data) first. And try to run clustering
$ ./unibic -i toy_example -d
-d is important here since it tells the program that this is discrete data.
Then look at a continuous data "example". Try to run
$ ./unibic -i example -f .25
This restricts no two blocks overlap more than 0.25 of the size of each one. And the other parameters are default value.
For each input file, our program generates three output files, namely, '.blocks' file, '.chars'file and '.rules' file.
In '.blocks' file, you can see all the biclusters the program found, especially, we use a blank line to separate the positively and the negatively (if any) correlated genes in each bicluster.
As to '.chars' file, it provides the qualitative matrix of the microarray data to users with some details of how to discrete the data in '.rules' file. You can find further details about how to represent a microarray dataset with a qualitative matrix in our paper.
Version 1.0
- latest version
Any questions, problems, bugs are welcome and should be dumped to Zhenjia Wang '[email protected]'
Creation: Dec. 22, 2014