BicFinder: a biclustering algorithm for microarray data analysis

RUBic: rapid unsupervised biclustering

Biclustering of biologically meaningful binary information is essential in many applications related to drug discovery, like protein-protein interactions and gene expressions. However, for robust performance in recently emerging large health datasets, it is important for new biclustering algorithms to be scalable and fast. We present a rapid unsupervised biclustering (RUBic) algorithm that achieves this objective with a novel encoding and search strategy. RUBic significantly reduces the computational overhead on both synthetic and experimental datasets shows significant computational benefits, with respect to several state-of-the-art biclustering algorithms. In 100 synthetic binary datasets, our method took [Formula: see text] s to extract 494,872 biclusters. In the human PPI database of size [Formula: see text], our method generates 1840 biclusters in [Formula: see text] s. On a central nervous system embryonic tumor gene expression dataset of size 712,940, our algorithm takes   101 min to produce 747,069 biclusters, while the recent competing algorithms take significantly more time to produce the same result. RUBic is also evaluated on five different gene expression datasets and shows significant speed-up in execution time with respect to existing approaches to extract significant KEGG-enriched bi-clustering. RUBic can operate on two modes, base and flex, where base mode generates maximal biclusters and flex mode generates less number of clusters and faster based on their biological significance with respect to KEGG pathways. The code is available at ( https://github.com/CMATERJU-BIOINFO/RUBic ) for academic use only.

POPBic: Pathway-Based Order Preserving Biclustering Algorithm Towards the Analysis of Gene Expression Data

To understand the underlying biological mechanisms of gene expression data, it is important to discover the groups of genes that have similar expression patterns under certain subsets of conditions. Biclustering algorithms have been effective in analyzing large-scale gene expression data. Recently, traditional biclustering has been improved by introducing biological knowledge along with the expression data during the biclustering process. In this paper, we propose the Pathway-based Order Preserving Biclustering (POPBic) algorithm by incorporating Kyoto Encyclopedia of Genes and Genomes (KEGG) based on the hypothesis that two genes sharing similar pathways are likely to be similar. The basic principle of the POPBic approach is to apply the concept of Longest Common Subsequence between a pair of genes which have a high number of common pathways. The algorithm identifies the expression patterns from data using two major steps: (i) selection of significant seed genes and (ii) extraction of biclusters. We performe exhaustive experimentation with the POPBic algorithm using synthetic dataset to evaluate the bicluster model, finding its robustness in the presence of noise and identifying overlapping biclusters. We demonstrate that POPBic is able to discover biologically significant biclusters for four cancer microarray gene expression datasets. POPBic has been found to perform consistently well in comparison to its closest competitors.

A systematic comparative evaluation of biclustering techniques

BACKGROUND

Biclustering techniques are capable of simultaneously clustering rows and columns of a data matrix. These techniques became very popular for the analysis of gene expression data, since a gene can take part of multiple biological pathways which in turn can be active only under specific experimental conditions. Several biclustering algorithms have been developed in the past recent years. In order to provide guidance regarding their choice, a few comparative studies were conducted and reported in the literature. In these studies, however, the performances of the methods were evaluated through external measures that have more recently been shown to have undesirable properties. Furthermore, they considered a limited number of algorithms and datasets.

RESULTS

We conducted a broader comparative study involving seventeen algorithms, which were run on three synthetic data collections and two real data collections with a more representative number of datasets. For the experiments with synthetic data, five different experimental scenarios were studied: different levels of noise, different numbers of implanted biclusters, different levels of symmetric bicluster overlap, different levels of asymmetric bicluster overlap and different bicluster sizes, for which the results were assessed with more suitable external measures. For the experiments with real datasets, the results were assessed by gene set enrichment and clustering accuracy.

CONCLUSIONS

We observed that each algorithm achieved satisfactory results in part of the biclustering tasks in which they were investigated. The choice of the best algorithm for some application thus depends on the task at hand and the types of patterns that one wants to detect.

Pattern-driven neighborhood search for biclustering of microarray data

BACKGROUND

Biclustering aims at finding subgroups of genes that show highly correlated behaviors across a subgroup of conditions. Biclustering is a very useful tool for mining microarray data and has various practical applications. From a computational point of view, biclustering is a highly combinatorial search problem and can be solved with optimization methods.

RESULTS

We describe a stochastic pattern-driven neighborhood search algorithm for the biclustering problem. Starting from an initial bicluster, the proposed method improves progressively the quality of the bicluster by adjusting some genes and conditions. The adjustments are based on the quality of each gene and condition with respect to the bicluster and the initial data matrix. The performance of the method was evaluated on two well-known microarray datasets (Yeast cell cycle and Saccharomyces cerevisiae), showing that it is able to obtain statistically and biologically significant biclusters. The proposed method was also compared with six reference methods from the literature.

CONCLUSIONS

The proposed method is computationally fast and can be applied to discover significant biclusters. It can also used to effectively improve the quality of existing biclusters provided by other biclustering methods.