Microarray analysis has
been used to find new subclasses in disease states and to identify biologic markers
associated with disease. In a recent review, John Quackenbush of Dana-Farber Cancer
Institute in Boston describes how microarray technology works and discusses whether
patterns in gene expression profiles can be used to classify disease.
He wrote about a two-color and a single-color
array for generating data and about confocal laser scanning for measuring the fluorescence
intensity of each probe. Data are normalized to account for differences in labeling,
hybridization and detection methods, filtered through a selected set of criteria,
then represented in matrix form to indicate the levels of gene expression.
Among the ways to measure data in expression
profiles, the author noted Pearson’s correlation coefficient distance for
evaluating similarities in patterns, which are important in tumor classification.
Data can be analyzed either by supervised or unsupervised methods, the latter of
which disregard prior knowledge about the samples and are useful in identifying
new subgroups. Hierarchical and k-means clustering are mentioned as commonly used
Various methods of analysis can result
in a variety of sets of significant genes. Although the best way to compensate for
this is under active debate, Quackenbush presents several potential solutions, including
publicizing data that meet industry standards. He also believes that gene selection
criteria must be established and a method for validation developed.
Despite current limitations of classification,
Quackenbush applauded the success of expression profiling with microarrays and said
he believes that it will lead to advances in the diagnosis and treatment of disease.
(The New England Journal of Medicine, June 8, 2006, p. 2463.)
MORE FROM PHOTONICS MEDIA