A polynomial time biclustering algorithm for finding approximate expression patterns in gene expression time series

<p>Abstract</p> <p>Background</p> <p>The ability to monitor the change in expression patterns over time, and to observe the emergence of coherent temporal responses using gene expression time series, obtained from microarray experiments, is critical to advance our understanding of complex biological processes. In this context, biclustering algorithms have been recognized as an important tool for the discovery of local expression patterns, which are crucial to unravel potential regulatory mechanisms. Although most formulations of the biclustering problem are NP-hard, when working with time series expression data the interesting biclusters can be restricted to those with contiguous columns. This restriction leads to a tractable problem and enables the design of efficient biclustering algorithms able to identify all maximal contiguous column coherent biclusters.</p> <p>Methods</p> <p>In this work, we propose <it>e</it>-CCC-Biclustering, a biclustering algorithm that finds and reports all maximal contiguous column coherent biclusters with approximate expression patterns in time polynomial in the size of the time series gene expression matrix. This polynomial time complexity is achieved by manipulating a discretized version of the original matrix using efficient string processing techniques. We also propose extensions to deal with missing values, discover anticorrelated and scaled expression patterns, and different ways to compute the errors allowed in the expression patterns. We propose a scoring criterion combining the statistical significance of expression patterns with a similarity measure between overlapping biclusters.</p> <p>Results</p> <p>We present results in real data showing the effectiveness of <it>e</it>-CCC-Biclustering and its relevance in the discovery of regulatory modules describing the transcriptomic expression patterns occurring in <it>Saccharomyces cerevisiae </it>in response to heat stress. In particular, the results show the advantage of considering approximate patterns when compared to state of the art methods that require exact matching of gene expression time series.</p> <p>Discussion</p> <p>The identification of co-regulated genes, involved in specific biological processes, remains one of the main avenues open to researchers studying gene regulatory networks. The ability of the proposed methodology to efficiently identify sets of genes with similar expression patterns is shown to be instrumental in the discovery of relevant biological phenomena, leading to more convincing evidence of specific regulatory mechanisms.</p> <p>Availability</p> <p>A prototype implementation of the algorithm coded in Java together with the dataset and examples used in the paper is available in <url>http://kdbio.inesc-id.pt/software/e-ccc-biclustering</url>.</p

A biclustering algorithm based on a Bicluster Enumeration Tree: application to DNA microarray data

<p>Abstract</p> <p>Background</p> <p>In a number of domains, like in DNA microarray data analysis, we need to cluster simultaneously rows (genes) and columns (conditions) of a data matrix to identify groups of rows coherent with groups of columns. This kind of clustering is called <it>biclustering</it>. Biclustering algorithms are extensively used in DNA microarray data analysis. More effective biclustering algorithms are highly desirable and needed.</p> <p>Methods</p> <p>We introduce <it>BiMine</it>, a new enumeration algorithm for biclustering of DNA microarray data. The proposed algorithm is based on three original features. First, <it>BiMine </it>relies on a new evaluation function called <it>Average Spearman's rho </it>(ASR). Second, <it>BiMine </it>uses a new tree structure, called <it>Bicluster Enumeration Tree </it>(BET), to represent the different biclusters discovered during the enumeration process. Third, to avoid the combinatorial explosion of the search tree, <it>BiMine </it>introduces a parametric rule that allows the enumeration process to cut tree branches that cannot lead to good biclusters.</p> <p>Results</p> <p>The performance of the proposed algorithm is assessed using both synthetic and real DNA microarray data. The experimental results show that <it>BiMine </it>competes well with several other biclustering methods. Moreover, we test the biological significance using a gene annotation web-tool to show that our proposed method is able to produce biologically relevant biclusters. The software is available upon request from the authors to academic users.</p

Unsupervised fuzzy pattern discovery in gene expression data

2010-2011 > Academic research: refereed > Publication in refereed journalpublished_fina

DeBi: Discovering Differentially Expressed Biclusters using a Frequent Itemset Approach

<p>Abstract</p> <p>Background</p> <p>The analysis of massive high throughput data via clustering algorithms is very important for elucidating gene functions in biological systems. However, traditional clustering methods have several drawbacks. Biclustering overcomes these limitations by grouping genes and samples simultaneously. It discovers subsets of genes that are co-expressed in certain samples. Recent studies showed that biclustering has a great potential in detecting marker genes that are associated with certain tissues or diseases. Several biclustering algorithms have been proposed. However, it is still a challenge to find biclusters that are significant based on biological validation measures. Besides that, there is a need for a biclustering algorithm that is capable of analyzing very large datasets in reasonable time.</p> <p>Results</p> <p>Here we present a fast biclustering algorithm called DeBi (Differentially Expressed BIclusters). The algorithm is based on a well known data mining approach called frequent itemset. It discovers maximum size homogeneous biclusters in which each gene is strongly associated with a subset of samples. We evaluate the performance of DeBi on a yeast dataset, on synthetic datasets and on human datasets.</p> <p>Conclusions</p> <p>We demonstrate that the DeBi algorithm provides functionally more coherent gene sets compared to standard clustering or biclustering algorithms using biological validation measures such as Gene Ontology term and Transcription Factor Binding Site enrichment. We show that DeBi is a computationally efficient and powerful tool in analyzing large datasets. The method is also applicable on multiple gene expression datasets coming from different labs or platforms.</p

QServer: A Biclustering Server for Prediction and Assessment of Co-Expressed Gene Clusters

BACKGROUND: Biclustering is a powerful technique for identification of co-expressed gene groups under any (unspecified) substantial subset of given experimental conditions, which can be used for elucidation of transcriptionally co-regulated genes. RESULTS: We have previously developed a biclustering algorithm, QUBIC, which can solve more general biclustering problems than previous biclustering algorithms. To fully utilize the analysis power the algorithm provides, we have developed a web server, QServer, for prediction, computational validation and analyses of co-expressed gene clusters. Specifically, the QServer has the following capabilities in addition to biclustering by QUBIC: (i) prediction and assessment of conserved cis regulatory motifs in promoter sequences of the predicted co-expressed genes; (ii) functional enrichment analyses of the predicted co-expressed gene clusters using Gene Ontology (GO) terms, and (iii) visualization capabilities in support of interactive biclustering analyses. QServer supports the biclustering and functional analysis for a wide range of organisms, including human, mouse, Arabidopsis, bacteria and archaea, whose underlying genome database will be continuously updated. CONCLUSION: We believe that QServer provides an easy-to-use and highly effective platform useful for hypothesis formulation and testing related to transcription co-regulation

Identifying Modules of Coexpressed Transcript Units and Their Organization of Saccharopolyspora erythraea from Time Series Gene Expression Profiles

BACKGROUND: The Saccharopolyspora erythraea genome sequence was released in 2007. In order to look at the gene regulations at whole transcriptome level, an expression microarray was specifically designed on the S. erythraea strain NRRL 2338 genome sequence. Based on these data, we set out to investigate the potential transcriptional regulatory networks and their organization. METHODOLOGY/PRINCIPAL FINDINGS: In view of the hierarchical structure of bacterial transcriptional regulation, we constructed a hierarchical coexpression network at whole transcriptome level. A total of 27 modules were identified from 1255 differentially expressed transcript units (TUs) across time course, which were further classified in to four groups. Functional enrichment analysis indicated the biological significance of our hierarchical network. It was indicated that primary metabolism is activated in the first rapid growth phase (phase A), and secondary metabolism is induced when the growth is slowed down (phase B). Among the 27 modules, two are highly correlated to erythromycin production. One contains all genes in the erythromycin-biosynthetic (ery) gene cluster and the other seems to be associated with erythromycin production by sharing common intermediate metabolites. Non-concomitant correlation between production and expression regulation was observed. Especially, by calculating the partial correlation coefficients and building the network based on Gaussian graphical model, intrinsic associations between modules were found, and the association between those two erythromycin production-correlated modules was included as expected. CONCLUSIONS: This work created a hierarchical model clustering transcriptome data into coordinated modules, and modules into groups across the time course, giving insight into the concerted transcriptional regulations especially the regulation corresponding to erythromycin production of S. erythraea. This strategy may be extendable to studies on other prokaryotic microorganisms

A Visual Data Mining Tool that Facilitates Reconstruction of Transcription Regulatory Networks

Background: Although the use of microarray technology has seen exponential growth, analysis of microarray data remains a challenge to many investigators. One difficulty lies in the interpretation of a list of differentially expressed genes, or in how to plan new experiments given that knowledge. Clustering methods can be used to identify groups of genes with similar expression patterns, and genes with unknown function can be provisionally annotated based on the concept of ‘‘guilt by association’’, where function is tentatively inferred from the known functions of genes with similar expression patterns. These methods frequently suffer from two limitations: (1) visualization usually only gives access to group membership, rather than specific information about nearest neighbors, and (2) the resolution or quality of the relationships are not easily inferred. Methodology/Principal Findings: We have addressed these issues by improving the precision of similarity detection over that of a single experiment and by creating a tool to visualize tractable association networks: we (1) performed metaanalysis computation of correlation coefficients for all gene pairs in a heterogeneous data set collected from 2,145 publicly available micorarray samples in mouse, (2) filtered the resulting distribution of over 130 million correlation coefficients to build new, more tractable distributions from the strongest correlations, and (3) designed and implemented a new Web based tool (StarNet