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

• Published in: Computer methods and programs in biomedicine 2013-09, Vol.111 (3), p.650-661
• Main Authors: Dimitrakopoulou, Konstantina, Vrahatis, Aristidis G, Wilk, Esther, Tsakalidis, Athanasios K, Bezerianos, Anastasios
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Automation; Biological and medical sciences; Cell Cycle; Cellular; Clustering; Computer science; control theory; systems; Data processing. List processing. Character string processing; Dynamic biological process; Dynamics; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; Fuzzy; Fuzzy Logic; Gene expression; Gene expression data; Gene Expression Regulation, Viral; Genes; Homeostasis; Host-Pathogen Interactions; Humans; Immunity, Innate; Inference from stochastic processes; time series analysis; Influenza A kinetic model; Influenza A Virus, H1N1 Subtype - isolation & purification; Influenza, Human - immunology; Influenza, Human - virology; Internal Medicine; Mathematics; Memory organisation. Data processing; Molecular and cellular biology; Molecular genetics; Other; Probability and statistics; Sciences and techniques of general use; Short time series; Software; Statistics; Time series
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2013.05.025

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/ ).