Biclustering Methods: Biological Relevance and Application in Gene Expression Analysis

Abstract

DNA microarray technologies are used extensively to profile the expression levels of thousands of genes under various conditions, yielding extremely large data-matrices. Thus, analyzing this information and extracting biologically relevant knowledge becomes a considerable challenge. A classical approach for tackling this challenge is to use clustering (also known as one-way clustering) methods where genes (or respectively samples) are grouped together based on the similarity of their expression profiles across the set of all samples (or respectively genes). An alternative approach is to develop biclustering methods to identify local patterns in the data. These methods extract subgroups of genes that are co-expressed across only a subset of samples and may feature important biological or medical implications. In this study we evaluate 13 biclustering and 2 clustering (k-means and hierarchical) methods. We use several approaches to compare their performance on two real gene expression data sets. For this purpose we apply four evaluation measures in our analysis: (1) we examine how well the considered (bi)clustering methods differentiate various sample types; (2) we evaluate how well the groups of genes discovered by the (bi)clustering methods are annotated with similar Gene Ontology categories; (3) we evaluate the capability of the methods to differentiate genes that are known to be specific to the particular sample types we study and (4) we compare the running time of the algorithms. In the end, we conclude that as long as the samples are well defined and annotated, the contamination of the samples is limited, and the samples are well replicated, biclustering methods such as Plaid and SAMBA are useful for discovering relevant subsets of genes and samples.

Region-Based Correspondence Between 3D Shapes via Spatially Smooth Biclustering

“…The biclustering literature includes a wide range of approaches, comprehensively reviewed in [21,28]. A recent trend is to use matrix factorization tools [4,11,13,19,31,44], most of which relying on the concept of latent block models [11,26,31,34,44].…”

“…The choice of coherence criterion controls the type of biclusters to be retrieved [21,28]. A standard choice is that entries in the same bicluster have similar values, significantly different from the other entries of the matrix.…”

“…Biclustering differs from standard clustering, which treats whole rows/columns, in that the former is able to focus on local information in portions of rows and portions of columns. Many biclustering methods have been proposed, differing in the type of biclusters that can be retrieved, as well as in the adopted criteria and algorithms, as comprehensively reviewed in [21,28].…”

Iterative segmented least square method for functional microRNA-mRNA module discovery in breast cancer

“…A seminal work, a node-deletion biclustering algorithm by Cheng and Church (2000), was successful in finding co-regulation patterns in yeast and human. Since then, many biclustering algorithms have been developed in bioinfomartics (Oghabian et al, 2014).…”

QtBiVis: a software toolbox for visual analysis of biclustering experiment

“…Biclustering algorithms have been applied to identify groups of genes that show resemblance under particular subsection of conditions. Multiple biclustering methods have been developed so far [2], [3], [4]. Different metrics have been adapted to measure gene expression level [5], [6].…”