GERC: Tree Based Clustering for Gene Expression Data

Ahmed, Hasin Afzal; Mahanta, Priyakshi; Bhattacharyya, Dhruba K.; Kalita, Jugal

doi:10.1109/bibe.2011.54

Cited by 15 publications

(6 citation statements)

References 11 publications

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“…Most of the times, the identified clusters group genes based on similar patterns of expression in time. In a different manner, the identification of Granger causality [6] within a network allows the clustering of genes based on their topological proximity in the network [7,8]. Briefly, Granger causality [6] analysis identifies interaction in terms of temporal precedence (the cause comes before its effect) [6] and may generate a set of sub-networks within which Granger causality is intense among genes.…”

Section: Introductionmentioning

confidence: 99%

Functional clustering of time series gene expression data by Granger causality

Fujita

Severino

Kojima

et al. 2012

BMC Systems Biology

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BackgroundA common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes.ResultsIn this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its consequence.ConclusionsThis kind of analysis can be used as a complementary approach for functional clustering, wherein genes would be clustered not solely based on their expression similarity but on their topological proximity built according to the intensity of Granger causality among them.

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Section: Introductionmentioning

confidence: 99%

Functional clustering of time series gene expression data by Granger causality

Fujita

Severino

Kojima

et al. 2012

BMC Systems Biology

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show abstract

“…Clustering is a popular data analysis tool in genomic studies, particularly in the context of gene-expression microarrays [10][11][12]. Each microarray provides expression measurements for thousands of genes and clustering is a useful exploratory technique to analyze gene expression data since it groups similar genes together and allows biologists to identify groups of potentially meaningful genes, which have related functions or are co-regulated, which in turn helps find the relationships among them in the form of gene regulatory networks [5].…”

Section: Biclustering Of Co-regulated Genesmentioning

confidence: 99%

Analysis of Gene Expression Patterns Using Biclustering

Roy

Bhattacharyya

Kalita

2015

Methods in Molecular Biology

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Mining microarray data to unearth interesting expression profile patterns for discovery of in silico biological knowledge is an emerging area of research in computational biology. A group of functionally related genes may have similar expression patterns under a set of conditions or at some time points. Biclustering is an important data mining tool that has been successfully used to analyze gene expression data for biologically significant cluster discovery. The purpose of this chapter is to introduce interesting patterns that may be observed in expression data and discuss the role of biclustering techniques in detecting interesting functional gene groups with similar expression patterns.

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“…GERC is a polythetic divisive hierarchical clustering algorith m that operates in two distinct steps. This is an extended version of the article [7] where the method was introduced. In the first step of the algorithm, an in itial cluster is formed and this init ial cluster is further processed in the second step to form finer clusters.…”

Section: The Gerc Algorithmmentioning

confidence: 99%

Autotuned Multilevel Clustering of Gene Expression Data

Ahmed¹,

Mahanta²,

Bhattacharyya³

et al. 2012

BIOINFORMATICS

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DNA microarray technology has revolutionized biological and med ical research by enabling bio logists to measure expression levels of thousands of genes in a single experiment. Different computational techniques have been proposed to extract important biological information fro m the massive amount of gene expression data generated by DNA microarray technology. This paper presents a top down hierarchical clustering algorithm that produces a tree of genes called GERC tree (GERC stands for Gene Exp ression Recursive Clustering) along with the generated clusters. GERC tree is an amp le resource of biolog ical information about the genes in an expression dataset. Unlike dendrogram, a GERC tree is not a binary tree. Genes in a leaf node of GERC t ree represent a cluster. The clustering method was used with real-life datasets and the proposed method has been found satisfactory in terms of ho mogeneity, p value and z-score.

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GERC: Tree Based Clustering for Gene Expression Data

Cited by 15 publications

References 11 publications

Functional clustering of time series gene expression data by Granger causality

Functional clustering of time series gene expression data by Granger causality

Analysis of Gene Expression Patterns Using Biclustering

Autotuned Multilevel Clustering of Gene Expression Data

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