OLYMPUS: An automated hybrid clustering method in time series gene expression. Case study: Host response after Influenza A (H1N1) infection

Konstantina Dimitrakopoulou, Aristidis G. Vrahatis, Esther Wilk, Athanasios K. Tsakalidis, Anastasios Bezerianos

Research output: Contribution to journalArticle

Abstract

The increasing flow of short time series microarray experiments for the study of dynamic cellular processes poses the need for efficient clustering tools. These tools must deal with three primary issues: first, to consider the multi-functionality of genes; second, to evaluate the similarity of the relative change of amplitude in the time domain rather than the absolute values; third, to cope with the constraints of conventional clustering algorithms such as the assignment of the appropriate cluster number. To address these, we propose OLYMPUS, a novel unsupervised clustering algorithm that integrates Differential Evolution (DE) method into Fuzzy Short Time Series (FSTS) algorithm with the scope to utilize efficiently the information of population of the first and enhance the performance of the latter. Our hybrid approach provides sets of genes that enable the deciphering of distinct phases in dynamic cellular processes.We proved the efficiency of OLYMPUS on synthetic as well as on experimental data. The discriminative power of OLYMPUS provided clusters, which refined the so far perspective of the dynamics of host response mechanisms to Influenza A (H1N1). Our kinetic model sets a timeline for several pathways and cell populations, implicated to participate in host response; yet no timeline was assigned to them (e.g. cell cycle, homeostasis). Regarding the activity of B cells, our approach revealed that some antibody-related mechanisms remain activated until day 60 post infection.The Matlab codes for implementing OLYMPUS, as well as example datasets, are freely accessible via the Web (http://biosignal.med.upatras.gr/wordpress/biosignal/).

Original languageEnglish (US)
Pages (from-to)650-661
Number of pages12
JournalComputer Methods and Programs in Biomedicine
Volume111
Issue number3
DOIs
StatePublished - Sep 2013
Externally publishedYes

Fingerprint

Gene expression
Human Influenza
Cluster Analysis
Time series
Cells
Gene Expression
Clustering algorithms
Genes
Infection
Microarrays
Antibodies
Population
Cell Cycle
Homeostasis
B-Lymphocytes
Kinetics
Experiments
TimeLine

Keywords

  • Dynamic biological process
  • Gene expression data
  • Influenza A kinetic model
  • Short time series

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Health Informatics

Cite this

OLYMPUS : An automated hybrid clustering method in time series gene expression. Case study: Host response after Influenza A (H1N1) infection. / Dimitrakopoulou, Konstantina; Vrahatis, Aristidis G.; Wilk, Esther; Tsakalidis, Athanasios K.; Bezerianos, Anastasios.

In: Computer Methods and Programs in Biomedicine, Vol. 111, No. 3, 09.2013, p. 650-661.

Research output: Contribution to journalArticle

Dimitrakopoulou, Konstantina ; Vrahatis, Aristidis G. ; Wilk, Esther ; Tsakalidis, Athanasios K. ; Bezerianos, Anastasios. / OLYMPUS : An automated hybrid clustering method in time series gene expression. Case study: Host response after Influenza A (H1N1) infection. In: Computer Methods and Programs in Biomedicine. 2013 ; Vol. 111, No. 3. pp. 650-661.
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