An Integrated QAP-Based Approach to Visualize Patterns of Gene Expression Similarity

Inostroza-Ponta, Mario; Mendes, Alexandre; Berretta, Regina; Moscato, Pablo

doi:10.1007/978-3-540-76931-6_14

Cited by 17 publications

(9 citation statements)

References 13 publications

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“…In this section, we report on the results of applying our second method of differential gene (co-)expression analysis. A kNN-MST clustering algorithm [172] , [173] was applied to the detection p-value prefiltered whole genome mRNA expression data set (19,000 probes) to produce a clustering of nodes. More formally, the clustering result is a forest, a collection of disjoint graphs such that each component is a tree (a connected graph with no cycles).…”

Section: Resultsmentioning

confidence: 99%

A Transcription Factor Map as Revealed by a Genome-Wide Gene Expression Analysis of Whole-Blood mRNA Transcriptome in Multiple Sclerosis

et al. 2010

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BackgroundSeveral lines of evidence suggest that transcription factors are involved in the pathogenesis of Multiple Sclerosis (MS) but complete mapping of the whole network has been elusive. One of the reasons is that there are several clinical subtypes of MS and transcription factors that may be involved in one subtype may not be in others. We investigate the possibility that this network could be mapped using microarray technologies and contemporary bioinformatics methods on a dataset derived from whole blood in 99 untreated MS patients (36 Relapse Remitting MS, 43 Primary Progressive MS, and 20 Secondary Progressive MS) and 45 age-matched healthy controls.Methodology/Principal FindingsWe have used two different analytical methodologies: a non-standard differential expression analysis and a differential co-expression analysis, which have converged on a significant number of regulatory motifs that are statistically overrepresented in genes that are either differentially expressed (or differentially co-expressed) in cases and controls (e.g., V$KROX_Q6, p-value <3.31E-6; V$CREBP1_Q2, p-value <9.93E-6, V$YY1_02, p-value <1.65E-5).Conclusions/SignificanceOur analysis uncovered a network of transcription factors that potentially dysregulate several genes in MS or one or more of its disease subtypes. The most significant transcription factor motifs were for the Early Growth Response EGR/KROX family, ATF2, YY1 (Yin and Yang 1), E2F-1/DP-1 and E2F-4/DP-2 heterodimers, SOX5, and CREB and ATF families. These transcription factors are involved in early T-lymphocyte specification and commitment as well as in oligodendrocyte dedifferentiation and development, both pathways that have significant biological plausibility in MS causation.

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Section: Resultsmentioning

confidence: 99%

A Transcription Factor Map as Revealed by a Genome-Wide Gene Expression Analysis of Whole-Blood mRNA Transcriptome in Multiple Sclerosis

et al. 2010

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“…To compare our method with other clustering methods, we applied six well-known distance-based clustering techniques on the Complete dataset and the five reduced datasets ( G1-G5 ). The selected clustering methods for comparisons are: a well-known implementation of the k-means method named k-means++ [ 81 ], one unsupervised graph-based clustering algorithm named MST-kNN [ 32 ], and four popular hierarchical clustering methods: 1)Complete-linkage, 2)Average-linkage, 3)Single-linkage and 4)Ward’s algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…MST-kNN is an unsupervised graph clustering technique proposed by Inostroza-Ponta et. al [ 32 ]. This method works by partitioning the minimum spanning tree using the k-nearest neighbour graph with an adaptive determination of the number of clusters.…”

Section: Resultsmentioning

confidence: 99%

“…That study resulted in a set of marker words that can distinguish between plays written by Fletcher, Jonson, Middleton and Shakespeare compared to the remainder of the authors. Recently, Arefin et al [ 31 ] used this dataset to test the performance of their new graph clustering method incorporating paraclique identification and a clustering method known as MST- k NN [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

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A Novel Clustering Methodology Based on Modularity Optimisation for Detecting Authorship Affinities in Shakespearean Era Plays

et al. 2016

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In this study we propose a novel, unsupervised clustering methodology for analyzing large datasets. This new, efficient methodology converts the general clustering problem into the community detection problem in graph by using the Jensen-Shannon distance, a dissimilarity measure originating in Information Theory. Moreover, we use graph theoretic concepts for the generation and analysis of proximity graphs. Our methodology is based on a newly proposed memetic algorithm (iMA-Net) for discovering clusters of data elements by maximizing the modularity function in proximity graphs of literary works. To test the effectiveness of this general methodology, we apply it to a text corpus dataset, which contains frequencies of approximately 55,114 unique words across all 168 written in the Shakespearean era (16th and 17th centuries), to analyze and detect clusters of similar plays. Experimental results and comparison with state-of-the-art clustering methods demonstrate the remarkable performance of our new method for identifying high quality clusters which reflect the commonalities in the literary style of the plays.

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“…Then, we use the result of the MSTkNN clustering algorithm [40] to produce the integrated clustering/layout of the 79 samples.…”

Section: Resultsmentioning

confidence: 99%

QAPgrid: A Two Level QAP-Based Approach for Large-Scale Data Analysis and Visualization

2011

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BackgroundThe visualization of large volumes of data is a computationally challenging task that often promises rewarding new insights. There is great potential in the application of new algorithms and models from combinatorial optimisation. Datasets often contain “hidden regularities” and a combined identification and visualization method should reveal these structures and present them in a way that helps analysis. While several methodologies exist, including those that use non-linear optimization algorithms, severe limitations exist even when working with only a few hundred objects.Methodology/Principal FindingsWe present a new data visualization approach (QAPgrid) that reveals patterns of similarities and differences in large datasets of objects for which a similarity measure can be computed. Objects are assigned to positions on an underlying square grid in a two-dimensional space. We use the Quadratic Assignment Problem (QAP) as a mathematical model to provide an objective function for assignment of objects to positions on the grid. We employ a Memetic Algorithm (a powerful metaheuristic) to tackle the large instances of this NP-hard combinatorial optimization problem, and we show its performance on the visualization of real data sets.Conclusions/SignificanceOverall, the results show that QAPgrid algorithm is able to produce a layout that represents the relationships between objects in the data set. Furthermore, it also represents the relationships between clusters that are feed into the algorithm. We apply the QAPgrid on the 84 Indo-European languages instance, producing a near-optimal layout. Next, we produce a layout of 470 world universities with an observed high degree of correlation with the score used by the Academic Ranking of World Universities compiled in the The Shanghai Jiao Tong University Academic Ranking of World Universities without the need of an ad hoc weighting of attributes. Finally, our Gene Ontology-based study on Saccharomyces cerevisiae fully demonstrates the scalability and precision of our method as a novel alternative tool for functional genomics.

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An Integrated QAP-Based Approach to Visualize Patterns of Gene Expression Similarity

Cited by 17 publications

References 13 publications

A Transcription Factor Map as Revealed by a Genome-Wide Gene Expression Analysis of Whole-Blood mRNA Transcriptome in Multiple Sclerosis

A Transcription Factor Map as Revealed by a Genome-Wide Gene Expression Analysis of Whole-Blood mRNA Transcriptome in Multiple Sclerosis

A Novel Clustering Methodology Based on Modularity Optimisation for Detecting Authorship Affinities in Shakespearean Era Plays

QAPgrid: A Two Level QAP-Based Approach for Large-Scale Data Analysis and Visualization

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