Microarray data

Natsoulis G, El Ghaoui L, Lanckriet GR, Tolley AM, Leroy F, Dunlea S, et al. Classification of a large microarray data set algorithm comparison and analysis of drug signatures. Genome Res 2005 15 724-36. 

Busold CH, Winter S, Hauser N, Bauer A, Dippon J, Hoheisel JD, et al. Integration of GO annotations in Correspondence Analysis facilitating the interpretation of microarray data. Bioinformatics 2005 21 2424-9. 

Dahlquist KD, Salomonis N, Vranizan K, Lawlor SC, Conklin BR. GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat Genet 2002 31 19-20. 

Microarray data cannot be analyzed by purely brute force techniques to generate a causal model of a set of biological processes because the data represents gene expression patterns that are only correlated with temporal processes of interest in the organism. Lander (1999) comments on this problem as follows ... 

Further below, I will refer to a Bayesian causal network approach that does attempt to infer causation from microarray data. Furthermore, as Lander suggests, the microarray data, suitably constrained, may be used to generate causal hypotheses that can then be tested in other experiments and contexts. Thus, there are strategies that may be able to address this difficulty of determining causation. 

Fig. 5.2 In vitro microarray data from xenobiotic treatment (see color plates, p. XXXI).

Quackenbush, J. (2002). Microarray data normalization and transformation. Nat. Genet. 32 (Suppl.), 496-501. 

Yang Y.H., Dudoit S., Luu P., Lin D.M., Peng V., Ngai J., Speed T., Normalization for cDNA microarray data a robust composite method addressing single and multiple slide systematic variation, Nucl Acid Res. 2002 30 el5. 

Dudoit, S., Gentleman, R. C., and Quackenbush, J., Open source software for the analysis of microarray data, Biotechniques, Suppl., 45, 2003. 

Fare TL et al (2003) Effects of atmospheric ozone on microarray data quality. Anal Chem... 

Use of Genomic Microarray Data with Standard, Short-Term Toxicology Studies to Guide Study Design or Species Selection... 

Eor every microarray experiment the first and most important step is experimental design. A badly designed experiment can render microarray data unsuitable for addressing the experimental questions or worse, lead the investigator to draw false conclusions. Furthermore, failed microarray experiments can be very costly both in terms of resources and time. There are many issues that must be addressed when planning a cDNA microarray experiment, some intuitive, others requiring considerable thought. 

Although microarray experiments generate vast amounts of data, typically, the experimental question can be answered with only a small fraction of this information. By sharing complete datasets with the research community (published results or results that will not be published) the full utility of microarray results can be realized. By conforming to the MIAME standards, microarray data become more interpretable and extensible. 

Normalization of cDNA microarray data is a very important step in the process of data analysis. With current technology, systematic hias is unavoidable and must he dealt with in a sensible manner. Furthermore, normalization methods need to be consistently apphed to all raw data. Using different normalization methods on different datasets may introduce bias and thereby decrease the validity of the data. Normahzed data should be free of systematic bias and should thereby provide a truer representation of the biological variance. Furthermore, normahzed data increases the validity of shde to shde comparisons. 

To examine the microarray data from the perspective of functional pathways... 

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