Methods and approaches in the analysis of gene expression data.

The application of high-density DNA array technology to monitor gene transcription has been responsible for a real paradigm shift in biology. The majority of research groups now have the ability to measure the expression of a significant proportion of the human genome in a single experiment, resulting in an unprecedented volume of data being made available to the scientific community. As a consequence of this, the storage, analysis and interpretation of this information present a major challenge. In the field of immunology the analysis of gene expression profiles has opened new areas of investigation. The study of cellular responses has revealed that cells respond to an activation signal with waves of co-ordinated gene expression profiles and that the components of these responses are the key to understanding the specific mechanisms which lead to phenotypic differentiation. The discovery of 'cell type specific' gene expression signatures have also helped the interpretation of the mechanisms leading to disease progression. Here we review the principles behind the most commonly used data analysis methods and discuss the approaches that have been employed in immunological research.

[1]  T. Hughes,et al.  Signaling and circuitry of multiple MAPK pathways revealed by a matrix of global gene expression profiles. , 2000, Science.

[2]  B. Williams,et al.  Identification of genes differentially regulated by interferon α, β, or γ using oligonucleotide arrays , 1998 .

[3]  Bernd Fritzke,et al.  Growing cell structures--A self-organizing network for unsupervised and supervised learning , 1994, Neural Networks.

[4]  Joshua M. Stuart,et al.  MICROARRAY EXPERIMENTS : APPLICATION TO SPORULATION TIME SERIES , 1999 .

[5]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[6]  C. Goodnow,et al.  erratum: How self-tolerance and the immunosuppressive drug FK506 prevent B-cell mitogenesis , 2000, Nature.

[7]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[8]  P S Meltzer,et al.  Construction of a representative cDNA library from prostatic intraepithelial neoplasia. , 1996, Cancer research.

[9]  P. Marrack,et al.  Activation changes the spectrum but not the diversity of genes expressed by T cells. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[10]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

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

[12]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[13]  Eric R. Ziegel,et al.  Applied Multivariate Data Analysis , 2002, Technometrics.

[14]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[15]  Williams,et al.  Identification of genes differentially regulated by interferon a , b , or g using oligonucleotide arrays , 1998 .

[16]  Bernhard O. Palsson,et al.  Cancer cell lines , 1999 .

[17]  Saulo Alves de Araujo,et al.  Identification of novel keloid biomarkers through Profiling of Tissue Biopsies versus Cell Cultures in Keloid Margin specimens Compared to adjacent Normal Skin , 2010, Eplasty.

[18]  Jae K. Lee,et al.  Mining and Visualizing Large Anticancer Drug Discovery Databases , 2000, J. Chem. Inf. Comput. Sci..

[19]  Christian A. Rees,et al.  Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[20]  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.

[21]  J. Barker,et al.  Large-scale temporal gene expression mapping of central nervous system development. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[22]  R. W. Davis,et al.  Discovery and analysis of inflammatory disease-related genes using cDNA microarrays. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

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

[24]  Christian A. Rees,et al.  Systematic variation in gene expression patterns in human cancer cell lines , 2000, Nature Genetics.

[25]  Ina Ruck,et al.  USA , 1969, The Lancet.

[26]  P. Törönen,et al.  Analysis of gene expression data using self‐organizing maps , 1999, FEBS letters.

[27]  Alfonso Valencia,et al.  A hierarchical unsupervised growing neural network for clustering gene expression patterns , 2001, Bioinform..

[28]  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.

[29]  P F Lemkin,et al.  Coordinate regulation of the expression of axonal proteins by the axonal microenvironment. , 1986, Developmental biology.

[30]  D. Bowtell,et al.  Options available — from start to finish — for obtaining expression data by microarray , 1999, Nature Genetics.

[31]  D. Steiner,et al.  Expression profiling of pancreatic beta-cells: glucose regulation of secretory and metabolic pathway genes. , 2000, Diabetes.

[32]  J Taylor,et al.  Global approaches to quantitative analysis of gene-expression patterns observed by use of two-dimensional gel electrophoresis. , 1984, Clinical chemistry.

[33]  B. Tabachnick,et al.  Using Multivariate Statistics , 1983 .

[34]  M. Jackson,et al.  Gene expression profiles of laser-captured adjacent neuronal subtypes , 1999, Nature Medicine.

[35]  D. Lockhart,et al.  Expression monitoring by hybridization to high-density oligonucleotide arrays , 1996, Nature Biotechnology.

[36]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[37]  D. Bowtell,et al.  Options available—from start to finish—for obtaining expression data by microarray , 1999, Nature Genetics.

[38]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[39]  C. Rosenow,et al.  Monitoring gene expression using DNA microarrays. , 2000, Current opinion in microbiology.

[40]  C. Müller,et al.  Large-scale clustering of cDNA-fingerprinting data. , 1999, Genome research.

[41]  Laurie J. Heyer,et al.  Exploring expression data: identification and analysis of coexpressed genes. , 1999, Genome research.

[42]  J. Claverie.  Computational methods for the identification of differential and coordinated gene expression. , 1999, Human molecular genetics.

[43]  E. Zanders,et al.  Gene expression analysis as an aid to the identification of drug targets. , 2000, Pharmacogenomics.

[44]  D. Hafler,et al.  Multiple differences in gene expression in regulatory Valpha 24Jalpha Q T cells from identical twins discordant for type I diabetes. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[45]  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.

[46]  Jan Mous,et al.  Touching base , 2000, Nature Genetics.

[47]  L. Liotta,et al.  Microdissection, microchip arrays, and molecular analysis of tumor cells (primary and metastases). , 1998, Seminars in radiation oncology.

[48]  Ron Shamir,et al.  Clustering Gene Expression Patterns , 1999, J. Comput. Biol..

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

[50]  B. Everitt,et al.  Applied Multivariate Data Analysis. , 1993 .

[51]  D Haussler,et al.  Knowledge-based analysis of microarray gene expression data by using support vector machines. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[52]  J. Dopazo,et al.  Phylogenetic Reconstruction Using an Unsupervised Growing Neural Network That Adopts the Topology of a Phylogenetic Tree , 1997, Journal of Molecular Evolution.

[53]  David G. Morris,et al.  Global analysis of gene expression in pulmonary fibrosis reveals distinct programs regulating lung inflammation and fibrosis. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[54]  M Roederer,et al.  Gene microarray identification of redox and mitochondrial elements that control resistance or sensitivity to apoptosis. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[55]  D. Botstein,et al.  The transcriptional program in the response of human fibroblasts to serum. , 1999, Science.

[56]  D. Botstein,et al.  A gene expression database for the molecular pharmacology of cancer , 2000, Nature Genetics.

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