Statistical analysis of microarrays

Reimers M (2005) Statistical analysis of microarray data. Addict Biol 10 23-35. 

Wittes, J. and H.P. Friedman. Searching for evidence of altered gene expression a comment on statistical analysis of microarray data. J. Natl Cancer Inst. 91 400-401, 1999. 

Several methods for statistical analysis of microarray data are available depending on the experimental setup and the kind of biological question that needs to be addressed. Initial approaches to analyzing microarray data focused on... 

Tai, Y.C. Speed, T.P. (2005). Statistical Analysis of Microarray Time Course Data. Available from... 

Baldi P, Long AD. 2001. A Bayesian framework for the analysis of microarray expression data regularized t-test and statistical inference of gene changes. Bioinformatics 17 509. 

Cheng C et al (2004) Statistical significance threshold criteria for analysis of microarray gene expression data. Stat Appl Genet Mol Biol 3 Article36. doi 10.2202/1544-6115.1064... 

In Sections 2 to 4, we review the technology of synthetic oligonucleotide microarrays and describe some of the popular statistical methods that are used to discover genes with differential expression in simple comparative experiments. A novel Bayesian procedure is introduced in Section 5 to analyze differential expression that addresses some of the limitations of current procedures. We proceed, in Section 6, by discussing the issue of sample size and describe two approaches to sample size determination in screening experiments with microarrays. The first approach is based on the concept of reproducibility, and the second approach uses a Bayesian decision-theoretic criterion to trade off information gain and experimental costs. We conclude, in Section 7, with a discussion of some of the open problems in the design and analysis of microarray experiments that need further research. 

Jane Chang is an Assistant Professor in the Department of Applied Statistics and Operations Research at Bowling Green State University. Her research interests are in optimal experimental design, the design and analysis of microarray experiments, and multiple testing in two-level factorial designs. 

Speed T. (2003). Statistical analysis of gene expression microarray data. Chapman and Hall/CRC, FL, USA. 

Exploratory methods are used not to test hypotheses but rather to get an overview of data. Various clustering methods and ordination are excellent tools for exploratory analysis of microarray data. These unsupervised methods do not require external class or group information. Clusters are generated purely based on the intrinsic similarity of the gene or sample expression profiles. No null hypothesis can be rejected, and p values are not generated to test statistical significance. Methods that... 

Huber, W., von Heydebreck, A., and Vingron, M. 2003. Analysis of microarray gene expression data. In Handbook of Statistical Genetics, 2nd ed. (eds. D.J. Balding, M. Bishop, and C. Cannings). John Wiley Sons Ltd., Chichester, UK. 

Time Course Analysis of Microarray Data for the Pathway of Repa-oductive Development in Female Rainbow Trout Statistical Analysis and Datamining, Vol. 2, pp. 192-208... 

Biochips produce huge data sets. Data collected from microarray experiments are random snapshots with errors, inherently noisy and incomplete. Extracting meaningful information from thousands of data points by means of bioinformatics and statistical analysis is sophisticated and calls for collaboration among researchers from different disciplines. An increasing number of image and data analysis tools, in part freely accessible ( ) to academic researchers and non-profit institutions, is available in the web. Some examples are found in Tables 3 and 4. 

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