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2008/9 Catalogue
Library Recommendation
 

Summary
Apr 2006, Vol. 7, No. 3, Pages 511-519 , DOI 10.2217/14622416.7.3.511
(doi:10.2217/14622416.7.3.511)

Collaborative Study: chronic fatigue syndrome – Review
Statistical challenges with gene expression studies
Jennifer Shoemaker
Duke University, Department of Biostatistics and Bioinformatics, 2424 Erwin Road, Hock Plaza, Suite 802, Durham, NC 27705 USA.



Studies that include high-throughput data, such as gene expression data, raise unique issues with respect to study design and analysis. At the same time, they should be viewed through the lens (albeit a modified one) of standard scientific approach that involves such issues as specifying objectives (even if the study is mainly hypothesis generating or exploratory), a careful consideration of design, including sample size and replication, deciding whether to include technical replication in addition to biological replication, and ensuring that the methods of analysis are appropriate for the objective.

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Cited by

Xiaohua Douglas Zhang. (2009) A method for effectively comparing gene effects in multiple conditions in RNAi and expression-profiling research. Pharmacogenomics 10:3, 345-358
Online publication date: 1-Mar-2009.
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Keywords:
bioinformatics
class comparison
class discovery
class prediction
clinical application
high-throughput technologies
objectives
study design