MAANOVA: A Software Package for the Analysis of Spotted cDNA Microarray Experiments
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.
KeywordsMicroarray Data Microarray Experiment Data Object Consensus Tree ANOVA Model
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- Cui XQ, Kerr MK, Churchill GA (submitted for publication). Data transformations for normalization of cDNA microarray data.Google Scholar
- Kerr MK, Churchill GA (2001b). Statistical design and the analysis of gene expression microarray data. Genetical Research, 77:123.Google Scholar
- 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.Google Scholar
- Schena M (Ed) (2000). DNA Microarrays: A Practical Approach. Practical Approach Series 205. Oxford University Press: Oxford.Google Scholar
- Tanner JM (1949). Fallacy of per-weight and per-surface area standards, and their relation to spurious correlations. Journal of Applied Physiology, 2:1.Google Scholar
- 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.Google Scholar