Biclustering is a popular approach to analyze patterns in a dataset, especially those of biological origin such as gene expression data. Biclustering performs better than classical clustering techniques under certain data sets, since it can simultaneously cluster both rows and columns of matrix unlike the latter. As a result, submatrices exhibiting unique patterns can be revealed helping us to better understand the relationship between row and column variables.
BIDEAL is a unified interactive application that would allow researchers to use forefront Biclustering techniques with minimal efforts. BIDEAL also facilitates preprocessing of data which includes removing NAN spots, binarization, discretization, normalization. It allows to visualize and interpret the result using HeatMap, Gene Profile Map, Cluster Plot. Furthermore, it also provides six Bicluster quality indices for Bicluster validation.
BIDEAL is freely available for use and further development under license. A tutorial is provided for usage as well as a brief developer's guide to help you understand the internal structure of the project.
- User can load any data in .txt/.csv/.dat/ data-numeric format. Data must consists of numerical values corresponding to the genes for different conditions
- Sample dataset i.e. Gene Expression Data is also provided in package.
- Biclustering result will appear in a new pop up window.
- Indices of rows and columns of the gene expression dataset with their corresponding Bicluster number will automatically save in a two different .csv files. Results can be validated and visualized.
- Double click on BIDEAL.exe. or Open Linux command prompt and type ./run_BIDEAL.sh
- Load the dataset.
- Next preprocess your data according to your need using preprocessing options available.
- Select the biclustering algorithm to be executed. If needed, change the parameters of algorithm. Default values are given.
- After execution Result window will be popped up.
- A dialogue box will be appeared to save result in .mat file. These results can be validated and visualized if required.
Please cite the following paper:
N. K. Verma, T. Sharma, S. Dixit, P. Agrawal, S. Sengupta, and V. Singh,
BIDEAL: A Toolbox for Bicluster Analysis - Generation, Visualization and Validation, SN COMPUTER SCIENCE, Springer Journal, 2, 24 (2021). https://doi.org/10.1007/s42979-020-00411-9