Gene microarray data analysis using parallel point-symmetry-based clustering

Anasua Sarkar, Ujjwal Maulik

Research output: Contribution to journalArticlepeer-review

Abstract

Identification of co-expressed genes is the central goal in microarray gene expression analysis. Point-symmetry-based clustering is an important unsupervised learning technique for recognising symmetrical convex- or nonconvex-shaped clusters. To enable fast clustering of large microarray data, we propose a distributed time-efficient scalable approach for point-symmetrybased K-Means algorithm. A natural basis for analysing gene expression data using symmetry-based algorithm is to group together genes with similar symmetrical expression patterns. This new parallel implementation also satisfies linear speedup in timing without sacrificing the quality of clustering solution on large microarray data sets. The parallel point-symmetry-based K-Means algorithm is compared with another new parallel symmetry-based K-Means and existing parallel K-Means over eight artificial and benchmark microarray data sets, to demonstrate its superiority, in both timing and validity. The statistical analysis is also performed to establish the significance of this message-passing-interface based point-symmetry K-Means implementation. We also analysed the biological relevance of clustering solutions.

Original languageEnglish
Pages (from-to)277-300
Number of pages24
JournalInternational Journal of Data Mining and Bioinformatics
Volume11
Issue number3
DOIs
Publication statusPublished - 2015 Jan 1
Externally publishedYes

Free keywords

  • Bioinformatics
  • Cluster validity measures
  • Clustering algorithm
  • K-Means algorithm
  • Microarray gene expression
  • Parallel algorithm
  • Point-symmetry based distance

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