Bi-Force: large-scale bicluster editing and its application to gene expression data biclustering

Peng Sun,Jiong Guo,Richard Röttger,Jan Baumbach,Nora K Speicher

doi:10.1093/nar/gku201

Abstract

The explosion of the biological data has dramatically reformed today's biological research. The need to integrate and analyze high-dimensional biological data on a large scale is driving the development of novel bioinformatics approaches. Biclustering, also known as ‘simultaneous clustering’ or ‘co-clustering’, has been successfully utilized to discover local patterns in gene expression data and similar biomedical data types. Here, we contribute a new heuristic: ‘Bi-Force’. It is based on the weighted bicluster editing model, to perform biclustering on arbitrary sets of biological entities, given any kind of pairwise similarities. We first evaluated the power of Bi-Force to solve dedicated bicluster editing problems by comparing Bi-Force with two existing algorithms in the BiCluE software package. We then followed a biclustering evaluation protocol in a recent review paper from Eren et al. (2013) (A comparative analysis of biclustering algorithms for gene expressiondata. Brief. Bioinform., 14:279–292.) and compared Bi-Force against eight existing tools: FABIA, QUBIC, Cheng and Church, Plaid, BiMax, Spectral, xMOTIFs and ISA. To this end, a suite of synthetic datasets as well as nine large gene expression datasets from Gene Expression Omnibus were analyzed. All resulting biclusters were subsequently investigated by Gene Ontology enrichment analysis to evaluate their biological relevance. The distinct theoretical foundation of Bi-Force (bicluster editing) is more powerful than strict biclustering. We thus outperformed existing tools with Bi-Force at least when following the evaluation protocols from Eren et al. Bi-Force is implemented in Java and integrated into the open source software package of BiCluE. The software as well as all used datasets are publicly available at http://biclue.mpi-inf.mpg.de.

Highlights

The enormous amount of available biological data from laboratories around the world has greatly re-shaped today’s biological studies
We first evaluated the power of BiForce to solve dedicated bicluster editing problems by comparing Bi-Force with two existing algorithms in the BiCluE software package
We have presented Bi-Force, the yet fastest software for solving the bicluster editing problem

Summary

Introduction

The enormous amount of available biological data from laboratories around the world has greatly re-shaped today’s biological studies. Clustering is commonly accepted as a powerful approach to explore gene expression datasets [3]. Given a pairwise similarity function transformed into a similarity matrix, clustering algorithms seek to partition the data items into a list of disjoint groups, such that the similarities within each group are maximized and those between different groups are minimized. Biclustering allows to ‘simultaneously’ partition both rows and columns. For instance, gene expression datasets for different cellular conditions, biclustering is more powerful in capturing biologically meaningful subsets of condition-specific genes. Biclustering approaches are generally capable of discovering such local patterns. They have proven useful for various types of gene expression data analysis [5] but should work on other omics datasets, proteomics or metabolomics, for instance [6]

Objectives

Methods

Results

Conclusion