Microarray analysis of mRNAs: experimental design and data analysis fundamentals.

Microarray technology has made it possible to quantify gene expression of thousands of genes in a single experiment. With the technological advancement, it is now possible to quantify expression of all known genes using a single microarray chip. With this volume of data and the possibility of improper quantification of expression beyond our control, the challenge lies in appropriate experimental design and the data analysis.This chapter describes the different types of experimental design for experiments involving microarray analysis (with their specific advantages and disadvantages). It considers the optimum number of replicates for a particular type of experiment. Additionally, this chapter describes the fundamentals of data analysis and the data analysis pipeline to be followed in most common types of microarray experiment.

[1]  Cheng Li,et al.  Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application , 2001, Genome Biology.

[2]  Yoav Benjamini,et al.  Identifying differentially expressed genes using false discovery rate controlling procedures , 2003, Bioinform..

[3]  Purvesh Khatri,et al.  Ontological analysis of gene expression data: current tools, limitations, and open problems , 2005, Bioinform..

[4]  Jin Hwan Do,et al.  Clustering approaches to identifying gene expression patterns from DNA microarray data. , 2008, Molecules and cells.

[5]  Crispin J. Miller,et al.  Cell Culture , 2010, Cell.

[6]  G. A. Whitmore,et al.  Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Xiaohui Liu,et al.  An experimental evaluation of a loop versus a reference design for two-channel microarrays , 2005, Bioinform..

[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]  T. Speed,et al.  Summaries of Affymetrix GeneChip probe level data. , 2003, Nucleic acids research.

[10]  C. Perou,et al.  Molecular portraits and 70-gene prognosis signature are preserved throughout the metastatic process of breast cancer. , 2005, Cancer research.

[11]  Terry Speed,et al.  Normalization of cDNA microarray data. , 2003, Methods.

[12]  Michael Peacock,et al.  Hierarchical Clustering Analysis of Tissue Microarray Immunostaining Data Identifies Prognostically Significant Groups of Breast Carcinoma , 2004, Clinical Cancer Research.

[13]  R. Tibshirani,et al.  Repeated observation of breast tumor subtypes in independent gene expression data sets , 2003, Proceedings of the National Academy of Sciences of the United States of America.

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

[15]  William Stafford Noble,et al.  The effect of replication on gene expression microarray experiments , 2003, Bioinform..

[16]  R. Tibshirani,et al.  Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[17]  F. Bertucci,et al.  Gene expression profiling of breast cell lines identifies potential new basal markers , 2006, Oncogene.

[18]  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.

[19]  A. Nobel,et al.  The molecular portraits of breast tumors are conserved across microarray platforms , 2006, BMC Genomics.

[20]  G. Churchill,et al.  Statistical design and the analysis of gene expression microarray data. , 2007, Genetical research.

[21]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.