Microarray Analysis of mRNAs: Experimental Design and Data Analysis Fundamentals
- 3.1k Downloads
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.
Key wordsMicroarray Gene expression Experiment design Normalisation Clustering
- 1.Derisi, J. (2001) Overview of nucleic acid arrays. Curr Protoc Mol Biol. Chapter 22, Unit 22.1.Google Scholar
- 3.Vinciotti, V., Khanin, R., D’Alimonte, D., et al. An experimental evaluation of a loop versus a reference design for two-channel microarrays. Bioinformatics. 21, 492–501.Google Scholar
- 4.Lee, M.L., Kuo, F.C., Whitmore, G.A., Sklar, J. (2000) Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. Proc Natl Acad Sci USA. 97, 9834–9.Google Scholar
- 9.Li, C., Hung, Wong, W. (2001) Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application. Genome Biol. 2, RESEARCH0032.Google Scholar
- 20.Raychaudhuri, S., Stuart, J.M., Altman, R.B. (2000) Principal components analysis to summarize microarray experiments: application to sporulation time series. Pac Symp Biocomput. 455–66.Google Scholar
- 21.Ashburner, M., Ball, C.A., Blake, J.A., et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 25, 25–9.Google Scholar