DNA microarrays are a new and promising biotechnology which allow the monitoring of expression of thousand genes simultaneously.
Image courtesy: http://www.albany.edu/genomics/affymetrix-genechip.html
Microarray technology as one of recent biomedical technologies produces high dimensional data. This makes statistical analysis become challenging.
The statistical components of a microarray experiment involve the following steps (Allison et al. 2006):
(1) Design. The development of an experimental plan to maximize the quality and quantity of information obtained.
(2) Pre-processing. Processing of the microarray image and normalization of the data to remove systematic variation. Other potential preprocessing steps include transformation of data, data filtering and background subtraction.
(3) Inference and/or classification. Inference entails testing statistical hypotheses which are usually about which genes are differentially expressed. Classification refers to analytical approaches that attempt to divide data into classes with no prior information (unsupervised classification) or into predefined classes (supervised classification).
(4) Validation of findings. The process of confirming the veracity of the inferences and conclusions drawn in the study.
I presented an overview of microarray analysis specifically in the use of gene expression profiling in a discussion.