BiP

BiP is an algorithm that learns flexible plaid models for an effective discovery of overlapping biclusters.
BiP addresses the limitations of existing plaid models, namely overcomes existing restrictions on the allowed types and structures of biclusters.
BiP makes available different functions to compose contributions from overlapping biclusters, such as weighted and multiplicative functions.
BiP allows the use of different relaxation for noise-tolerant and biologically-meaningful validation of plaid effects.

Authors: Rui Henriques and Sara Madeira

@article{,
	title={Biclustering with Flexible Plaid Models to Unravel Interactions between Biological Processes}, 
	journal={Computational Biology and Bioinformatics, IEEE/ACM Transactions on}, 
	author={Henriques, R. and Madeira, S.}, 
	year={2015}, 
	volume={}, //to appear 
	number={}, //to appear
	pages={}, //to appear
	doi={10.1109/TCBB.2014.2388206}, 
	issn={1545-5963},
}

Synthetic datasets (non-exhaustive set of 6 data instances with background values following Uniform and Normal distributions):

500x50: dataset, hiddenBics
1000x75: dataset, hiddenBics
2000x100: dataset, hiddenBics
Datasets with varying overlapping degree (10%, 20%, 30%, 50%): dataset, hiddenBics

Real datasets:

dlblc.arff (diffuse large-B-cell lymphoma).
From (Rosenwald et al. 2002) consisting of 180 samples and 661 probe sets with skewness of -0.05 and excess kurtosis of 0.35 after standardization.
The goal was to predict the survival after chemotherapy. In (Hoshida et al. 2007) 3 classes were found that can be identified directly by pattern-based biclustering.
hughes.arff (oligonucleotide array for Saccharomyces cerevisiae).
High-resolution genome-wide of S. Cerevisiae (prepared from haploid yeast, collected in the logarithmic phase of growth in YPD medium and hybridized to an Affymetrix tiling).
The original goal, in (David et. al 2006; Lee et. al 2007), was to identify the boundary, structure, and level of coding and noncoding transcripts - study nucleosome occupancy.
gasch.txt (Yeast responses to different stress conditions).
From (Gasch et al. 2000) capturing Saccharomyces cerevisiae response to diverse environmental transitions. DNA microarrays were used to measure changes in transcript levels over time for almost every yeast gene, as cells responded to temperature shocks, hydrogen peroxide, the superoxide-generating drug menadione, the sulfhydryl-oxidizing agent diamide, the disulfide-reducing agent dithiothreitol, hyper- and hypo-osmotic shock, amino acid starvation, nitrogen source depletion, and progression into stationary phase.

Results: Statistical Sheets of BiP (using alternative biclustering solutions) and BCPlaid

Software