Clustering of Gene Expression Profiles Applied to Marine Research

This work presents the results of applying two clustering techniques to gene expression data from the mussel Mytilus galloprovincialis. The objective of the study presented in this paper was to cluster the different genes involved in the experiment, in order to find those most closely related based on their expression patterns. A self-organising map (SOM) and the k-means algorithm were used, partitioning the input data into nine clusters. The resulting clusters were then analysed using Gene Ontology (GO) data, obtaining results that suggest that SOM clusters could be more homogeneous than those obtained by the k-means technique.

[1]  Young‐Seuk Park,et al.  Evaluation of Changes in Effluent Quality from Industrial Complexes on the Korean Nationwide Scale Using a Self-Organizing Map , 2012, International journal of environmental research and public health.

[2]  T. Kohonen.  Self-organized formation of topographically correct feature maps , 1982 .

[3]  W. C. Kock.  Monitoring bio-available marine contaminants with mussels (Mytilus edulis L) in the Netherlands , 1986 .

[4]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993, Knowl. Acquis..

[5]  M. Robles,et al.  University of Birmingham High throughput functional annotation and data mining with the Blast2GO suite , 2022 .

[6]  G. Gibson,et al.  Microarray Analysis , 2020, Definitions.

[7]  Ronald W. Davis,et al.  Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.

[8]  Marco Piastra,et al.  Self-organizing adaptive map: autonomous learning of curves and surfaces from point samples. , 2013, Neural networks : the official journal of the International Neural Network Society.

[9]  F. Galgani,et al.  Western Mediterranean coastal waters--monitoring PCBs and pesticides accumulation in Mytilus galloprovincialis by active mussel watching: the Mytilos project. , 2010, Journal of environmental monitoring : JEM.

[10]  Juan Miguel García-Gómez,et al.  BIOINFORMATICS APPLICATIONS NOTE Sequence analysis Manipulation of FASTQ data with Galaxy , 2005 .

[11]  Simon X. Yang,et al.  Dynamic Task Assignment and Path Planning of Multi-AUV System Based on an Improved Self-Organizing Map and Velocity Synthesis Method in Three-Dimensional Underwater Workspace , 2013, IEEE Transactions on Cybernetics.

[12]  Joaquín Dopazo,et al.  Genome analysis Advance Access publication February 18, 2011 B2G-FAR, a species-centered GO annotation repository , 2022 .

[13]  Blanca Laffon,et al.  Monitoring of the impact of Prestige oil spill on Mytilus galloprovincialis from Galician coast. , 2006, Environment international.

[14]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[15]  J. Boer,et al.  Dietary patterns derived from principal component- and k-means cluster analysis: long-term association with coronary heart disease and stroke. , 2013, Nutrition, metabolism, and cardiovascular diseases : NMCD.

[16]  Homin K. Lee,et al.  Coexpression analysis of human genes across many microarray data sets. , 2004, Genome research.

[17]  Bin Dai,et al.  Using self-organizing map (SOM) and support vector machine (SVM) for classification of selectivity of ACAT inhibitors , 2012, Molecular Diversity.

[18]  N. Chang,et al.  Identification of spatiotemporal nutrient patterns in a coastal bay via an integrated k-means clustering and gravity model. , 2012, Journal of environmental monitoring : JEM.

[19]  Qiang Wang,et al.  The oyster genome reveals stress adaptation and complexity of shell formation , 2012, Nature.

[20]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[21]  Teuvo Kohonen,et al.  Essentials of the self-organizing map , 2013, Neural Networks.

[22]  Stefan Götz,et al.  Blast2GO: A Comprehensive Suite for Functional Analysis in Plant Genomics , 2007, International journal of plant genomics.

[23]  Ning Ma,et al.  Evaluation of clustering algorithms for gene expression data using gene ontology annotations. , 2012, Chinese medical journal.

[24]  P. Brown,et al.  Yeast microarrays for genome wide parallel genetic and gene expression analysis. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Zhilu Wu,et al.  Hybrid Radar Emitter Recognition Based on Rough k-Means Classifier and Relevance Vector Machine , 2013, Sensors.

[26]  M. Spanoghe,et al.  Use of Self-Organizing Map to Analyze Images of Fungi Colonies Grown from Triticum aestivum Seeds Disinfected by Ozone Treatment , 2012, International journal of microbiology.

[27]  Flavio Mignone,et al.  Gene Expression Rhythms in the Mussel Mytilus galloprovincialis (Lam.) across an Annual Cycle , 2011, PloS one.

[28]  Paul Stolee,et al.  K-means cluster analysis of rehabilitation service users in the Home Health Care System of Ontario: examining the heterogeneity of a complex geriatric population. , 2012, Archives of physical medicine and rehabilitation.

[29]  Aixia Yan,et al.  Discriminating of ATP competitive Src kinase inhibitors and decoys using self-organizing map and support vector machine , 2013, Molecular Diversity.

[30]  Zhaoyang Feng,et al.  Profiling a Caenorhabditis elegans behavioral parametric dataset with a supervised K-means clustering algorithm identifies genetic networks regulating locomotion , 2011, Journal of Neuroscience Methods.

[31]  Robert C. Welsh,et al.  Using a self-organizing map algorithm to detect age-related changes in functional connectivity during rest in autism spectrum disorders , 2011, Brain Research.

[32]  Tong Zhou,et al.  Extracting gene expression patterns and identifying co-expressed genes from microarray data reveals biologically responsive processes , 2007, BMC Bioinformatics.

[33]  D. Schaid,et al.  Using the gene ontology to scan multilevel gene sets for associations in genome wide association studies , 2012, Genetic epidemiology.

[34]  J. Lefman,et al.  Automated correlation and classification of secondary ion mass spectrometry images using a k-means cluster method. , 2012, The Analyst.

[35]  Guanghao Sun,et al.  A novel infection screening method using a neural network and k-means clustering algorithm which can be applied for screening of unknown or unexpected infectious diseases. , 2012, The Journal of infection.

[36]  Y. Tabuchi,et al.  Microarray and gene ontology analyses reveal downregulation of DNA repair and apoptotic pathways in diethylstilbestrol-exposed testicular Leydig cells. , 2012, The Journal of toxicological sciences.