Observer-Biased Analysis of Gene Expression Profiles

Microarray generated gene expression data are characterized by their volume and by the intrinsic background noise. The main task of revealing patterns in gene expression data is typically carried out using clustering analysis, with soft clustering leading the more promising candidate methods. In this chapter, Fuzzy C-Means with a variable Focal Point (FCMFP) is exploited as the first stage in gene expression data analysis. FCMFP is inspired by the observation that the visual perception of a group of similar objects is (highly) dependent on the observer position. This metaphor is used to provide a new analysis insight, with different levels of granularity, over a gene expression dataset.

[1]  J. Bezdek.  Cluster Validity with Fuzzy Sets , 1973 .

[2]  Gerardo Beni,et al.  A Validity Measure for Fuzzy Clustering , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  S. P. Fodor,et al.  Multiplexed biochemical assays with biological chips , 1993, Nature.

[4]  J. Barker,et al.  Developmental kinetics of GAD family mRNAs parallel neurogenesis in the rat spinal cord , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[5]  M. Schena.  Genome analysis with gene expression microarrays. , 1996, BioEssays : news and reviews in molecular, cellular and developmental biology.

[6]  P. Brown,et al.  Parallel human genome analysis: microarray-based expression monitoring of 1000 genes. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[7]  D. Botstein,et al.  The transcriptional program of sporulation in budding yeast. , 1998, Science.

[8]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[9]  G. Church,et al.  Systematic determination of genetic network architecture , 1999, Nature Genetics.

[10]  S. P. Fodor,et al.  High density synthetic oligonucleotide arrays , 1999, Nature Genetics.

[11]  J. Mesirov,et al.  Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Adrian E. Raftery,et al.  Model-based clustering and data transformations for gene expression data , 2001, Bioinform..

[13]  John F. Kolen,et al.  Reducing the time complexity of the fuzzy c-means algorithm , 2002, IEEE Trans. Fuzzy Syst..

[14]  Carl G. Looney,et al.  Interactive clustering and merging with a new fuzzy expected value , 2002, Pattern Recognit..

[15]  Catherine A. Sugar,et al.  Finding the Number of Clusters in a Dataset , 2003 .

[16]  Hong Yan,et al.  A novel OPTOC-based clustering algorithm for gene expression data analysis , 2003, Fourth International Conference on Information, Communications and Signal Processing, 2003 and the Fourth Pacific Rim Conference on Multimedia. Proceedings of the 2003 Joint.

[17]  Doulaye Dembélé,et al.  Fuzzy C-means Method for Clustering Microarray Data , 2003, Bioinform..

[18]  Aidong Zhang,et al.  Cluster analysis for gene expression data: a survey , 2004, IEEE Transactions on Knowledge and Data Engineering.

[19]  Matthias E. Futschik,et al.  Noise-robust Soft Clustering of Gene Expression Time-course Data , 2005, J. Bioinform. Comput. Biol..

[20]  Aize Cao,et al.  A New Cluster Validity for Data Clustering , 2006, Neural Processing Letters.

[21]  Oded Maimon,et al.  Evaluation of gene-expression clustering via mutual information distance measure , 2007, BMC Bioinformatics.

[22]  Sung-Bae Cho,et al.  Fuzzy Bayesian validation for cluster analysis of yeast cell-cycle data , 2006, Pattern Recognit..

[23]  Olga G. Troyanskaya,et al.  Nearest Neighbor Networks: clustering expression data based on gene neighborhoods , 2007, BMC Bioinformatics.

[24]  Witold Pedrycz,et al.  Advances in Fuzzy Clustering and its Applications , 2007 .

[25]  Allan R. Jones,et al.  Genome-wide atlas of gene expression in the adult mouse brain , 2007, Nature.

[26]  Doheon Lee,et al.  Data and text mining Towards clustering of incomplete microarray data without the use of imputation , 2006 .

[27]  Kei-Hoi Cheung,et al.  Advancing translational research with the Semantic Web , 2007, BMC Bioinformatics.

[28]  Paulo Fazendeiro,et al.  A fuzzy clustering algorithm with a variable focal point , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[29]  R. Simon,et al.  Microarray-based expression profiling and informatics. , 2008, Current opinion in biotechnology.

[30]  Heather J. Ruskin,et al.  Techniques for clustering gene expression data , 2008, Comput. Biol. Medicine.

[31]  Michael J. Owen,et al.  A comparison of four clustering methods for brain expression microarray data , 2008, BMC Bioinformatics.

[32]  F. Pépin,et al.  Stromal gene expression predicts clinical outcome in breast cancer , 2008, Nature Medicine.

[33]  Rajagopalan Srinivasan,et al.  NIFTI: An evolutionary approach for finding number of clusters in microarray data , 2008, BMC Bioinformatics.

[34]  Ernesto Picardi,et al.  Is plant mitochondrial RNA editing a source of phylogenetic incongruence? An answer from in silico and in vivo data sets , 2008, BMC Bioinformatics.

[35]  R. Simon,et al.  Analysis of DNA microarray expression data. , 2009, Best practice & research. Clinical haematology.

[36]  Witold Pedrycz,et al.  Modified fuzzy c-means and Bayesian equalizer for nonlinear blind channel , 2009, Appl. Soft Comput..

[37]  M. Gerstein,et al.  RNA-Seq: a revolutionary tool for transcriptomics , 2009, Nature Reviews Genetics.

[38]  S. Abdelhak,et al.  Application of Multi-SOM clustering approach to macrophage gene expression analysis. , 2009, Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases.

[39]  Omid B. Rahimi,et al.  Transcriptome-To-Metabolome™ Biosimulation Reveals Human Hippocampal Hypometabolism with Age and Alzheimer’s Disease , 2011 .

[40]  Fatih Ozsolak,et al.  RNA sequencing: advances, challenges and opportunities , 2011, Nature Reviews Genetics.

[41]  Efstratios F. Georgopoulos,et al.  Efficient Computational Construction of Weighted Protein-Protein Interaction Networks Using Adaptive Filtering Techniques Combined with Natural Selection-Based Heuristic Algorithms , 2012 .

[42]  Burak Eksioglu,et al.  Clustering of high throughput gene expression data , 2012, Comput. Oper. Res..

[43]  Mitchell A. Thornton,et al.  Quantum Computing Approach for Alignment-Free Sequence Search and Classification , 2012 .

[44]  W. D. de Vos,et al.  Microarray analysis reveals marked intestinal microbiota aberrancy in infants having eczema compared to healthy children in at-risk for atopic disease , 2013, BMC Microbiology.

[45]  W. Sanger,et al.  Microarray Studies in Pediatric T-Cell Acute Lymphoblastic Leukemia/Lymphoma: A Report of Four Cases , 2013 .

[46]  Hesham H. Ali,et al.  Bioinformatics: Concepts, Methodologies, Tools, and Applications , 2013 .

[47]  David T. Miller,et al.  Chromosomal microarray impacts clinical management , 2014, Clinical genetics.

[48]  Paulo Fazendeiro,et al.  Observer-Biased Fuzzy Clustering , 2015, IEEE Transactions on Fuzzy Systems.

[49]  International Journal of Knowledge Discovery in Bioinformatics , 2022 .