Statistical approaches to analyzing multichip data

James Roy Johnson; Patrick Hurban; Jeff Woessner; Craig M. Liddell

doi:10.1117/12.427988

4 June 2001 Statistical approaches to analyzing multichip data

James Roy Johnson, Patrick Hurban, Jeff Woessner, Craig M. Liddell

Proceedings Volume 4266, Microarrays: Optical Technologies and Informatics; (2001) https://doi.org/10.1117/12.427988
Event: BiOS 2001 The International Symposium on Biomedical Optics, 2001, San Jose, CA, United States

Abstract

Processing large quantities of micro-arrays designed for high-throughput gene expression profiling presents a completely new set of challenges that must be addressed if biologically meaningful data are to be generated that can undergo statistical analysis. Sources of variation fall naturally into two classes: instrument and biological variation. Each source of variation must be adequately addressed by controlling systematic instrument and operation error, building empirically derived error models, and adequately characterizing the variability observed in biological controls. Finally, the tools used to derive biological meaning from gene expression profiling data must closely tie to the error models and the processes used to generate these data. Robust statistical techniques are appropriate methods for analysis of gene expression profiling data derived from micro-arrays, where adequate characterization of the sources of variation are quantified. No matter how complex or powerful the analysis tools may be, if they are not designed and utilized in this context then the results may remain questionable. At Paradigm Genetics the implementation of these techniques within the gene expression profile platform with the mustard plant, Arabidopsis thaliana, are providing a basis for integrated analysis of micro-array observed data.

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James Roy Johnson, Patrick Hurban, Jeff Woessner, and Craig M. Liddell "Statistical approaches to analyzing multichip data", Proc. SPIE 4266, Microarrays: Optical Technologies and Informatics, (4 June 2001); https://doi.org/10.1117/12.427988

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KEYWORDS

Statistical analysis

Control systems

Data modeling

Profiling

Biological research

Error analysis

Process modeling

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