Abstract
We describe a software package called MAANOVA (MicroArray ANalysis Of VAriance). MAANOVA is a collection of functions for statistical analysis of gene expression data from two-color cDNA microarray experiments. It is available in both the Matlab and R programming environments and can be run on any platform that supports these packages. MAANOVA allows the user to assess data quality, apply data transformations, estimate relative gene expression from designed experiments with ANOVA models, evaluate and interpret ANOVA models, formally test for differential expression of genes and estimate false-discovery rates, produce graphical summaries of expression patterns, and perform cluster analysis with bootstrapping. The development of MAANOVA was motivated by the need to analyze microarray data that arise from sophisticated designed experiments. MAANOVA provides specialized functions for microarray analysis in an open-ended format within flexible computing environments. MAANOVA functions can be used alone or in co mbination with other functions for the rigorous statistical analysis of microarray data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Baldi P, Long AD (2001). A Bayesian framework for the analysis of microarray expression data: Regularized t-test and statistical inferences of gene changes. Bioinformatics, 17:509.
Benjamini Y, Hochberg Y (1995). Controlling the false discovery rate: A practical and powerful approach to multiple-testing. Journal of the Royal Statistical Society, Series B, 57:289.
Callow MJ, Dudoit S, Gong EL, Speed TP, Rubin E (2000). Microarray expression profiling identifies genes with altered expression in HDL deficient mice. Genome Research, 10:2022.
Chu S, DeRisi J, Eisen M, Mullholland J, Botstein D, Brown PO (1998). Science, 282:699.
Cui XQ, Kerr MK, Churchill GA (submitted for publication). Data transformations for normalization of cDNA microarray data.
Dudoit S, Yang YH, Speed TP, Callow MJ (2002). Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Statistica Sinica, 12:111–139.
Felsenstein J (1985). Confidence limits on phylogenies—An approach using the bootstrap. Evolution 39:783.
Jin W, Riley RM, Wolfinger RD, White KP, Passador-Gurgel G, Gibson G (2001). The contribution of sex, genotype and age to transcriptional variance in Drosophila melanogaster. Nature Genetics, 29:389.
Kerr MK, Churchill GA (2001a). Experimental design for gene expression microarrays. Biostatistics, 2:183.
Kerr MK, Churchill GA (2001b). Statistical design and the analysis of gene expression microarray data. Genetical Research, 77:123.
Kerr MK, Churchill GA (2001c). Bootstrapping cluster analysis: Assessing the reliability of conclusion from microarray experiments. Proceedings of the National Academy of Sciences USA, 98:8961.
Kerr MK, Martin, M., Churchill GA (2000). Analysis of variance for gene expression microarray data. Journal of Computational Biology, 7:819.
Kerr MK, Leiter EH, Picard L, Churchill GA (2002). Sources of variation in microarray experiments. In: Computational and Statistical Approaches to Genomics, Zhang I, Shmulevich I (Eds), p 41. Kluwer Academic Publishers: Amsterdam.
Lönnstedt I, Speed TP (2002). Replicated microarray data. Statistica Sinica, 12:31.
Margush T, McMorris FR (1981). Consensus n-trees. Bulletin of Mathematical Biology, 43:239.
Pritchard CC, Hsu L, Delrow J, Nelson PS (2001). Project normal: Defining normal variation in mouse gene expression Proceedings of the National Academy of Sciences USA, 98:13266.
Rocke DM, Durbin B (2001). A model for measurement error for gene expression arrays. Journal of Computational Biology, 8:557.
Schena M (Ed) (2000). DNA Microarrays: A Practical Approach. Practical Approach Series 205. Oxford University Press: Oxford.
Seber GAF (1977). Linear Regression Analysis. Wiley: New York.
Tanner JM (1949). Fallacy of per-weight and per-surface area standards, and their relation to spurious correlations. Journal of Applied Physiology, 2:1.
Tusher VG, Tibshirani R, Chu G (2001). Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences USA, 98:5116.
Westfall PH, Young SS (1993). Resampling-based multiple testing: Examples and methods for p-value adjustment. Wiley Series in Probability and Mathematical Statistics, Wiley: New York.
Wolfinger RD, Gibson G, Wolfinger ED, Bennett L, Hamadeh H, Bushel P, Ashfari C, Paules RS (2001). Assessing gene significance from cDNA microarray expression data via mixed-models. Journal of Computational Biology, 8:625.
Yang YH, Buckley MJ, Dudoit S, Speed TP (2002). Comparison of methods for image analysis on cDNA microarray data. Journal of Computational Graph Statistics, 11:108.
Yang YH, Dudoit S, Luu P, Lin DM, Peng V, Ngai J, Speed TP. (2001). Normalization for cDNA microarray data: A robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Research, 30:e15.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag New York, Inc.
About this chapter
Cite this chapter
Wu, H., Kerr, M.K., Cui, X., Churchill, G.A. (2003). MAANOVA: A Software Package for the Analysis of Spotted cDNA Microarray Experiments. In: Parmigiani, G., Garrett, E.S., Irizarry, R.A., Zeger, S.L. (eds) The Analysis of Gene Expression Data. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/0-387-21679-0_14
Download citation
DOI: https://doi.org/10.1007/0-387-21679-0_14
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-95577-3
Online ISBN: 978-0-387-21679-9
eBook Packages: Springer Book Archive