Triclustering Algorithm for 3D Gene Expression Data Analysis using Order Preserving Triclustering (OPTricluster)

Dea Siska, Devvi Sarwinda, Titin Siswantining, Saskya Mary Soemartojo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Triclustering is the expansion of clustering and biclustering methods that works on three-dimensional (3D) data. This method is generally implemented in the analysis of 3D gene expression data to find gene expression profiles. This data consists of three dimensions: genes, experimental conditions, and time points. Triclustering can group these dimensions simultaneously and form a 3D cluster called a tricluster. Order Preserving Triclustering (OPTricluster) is a triclustering algorithm that uses a pattern-based approach and is used to analyze short time-series data (3-8 time points). The OPTricluster forms the tricluster by identifying genes with the same pattern of change in expression across time points under several experimental conditions. In contrast to most triclustering algorithms that only focus on similarities between experimental conditions, OPTricluster considers the similarities and differences between them. In this study, OPTricluster was implemented with several scenarios in gene expression data of yellow fever patients after vaccination. The lowest average Tricluster Diffusion (TD) score indicates the scenario with the best triclustering result. For this case, we found that the scenario with threshold of 1.6 is the scenario that produced triclusters with better quality (lowest average TD score) than the other scenarios. These triclusters represent gene expression profiles that show the biological relationship among those patients, including anomalies found in patients.

Original languageEnglish
Title of host publicationICICoS 2020 - Proceeding
Subtitle of host publication4th International Conference on Informatics and Computational Sciences
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728195261
DOIs
Publication statusPublished - 10 Nov 2020
Event4th International Conference on Informatics and Computational Sciences, ICICoS 2020 - Semarang, Indonesia
Duration: 10 Nov 202011 Nov 2020

Publication series

NameICICoS 2020 - Proceeding: 4th International Conference on Informatics and Computational Sciences

Conference

Conference4th International Conference on Informatics and Computational Sciences, ICICoS 2020
Country/TerritoryIndonesia
CitySemarang
Period10/11/2011/11/20

Keywords

  • 3D gene expression data
  • order preserving
  • short time series
  • tricluster diffusion
  • triclustering

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