Molecular profiling has become a fundamental part of biomedical research, but much more importantly, it is now becoming a component of health care [1–3]. New clinical trials are identifying the benefits of treatments supported by micro array data. This device for highly parallel measurements of gene expression was once restricted to pure research laboratories. It has now taken its place next to pathology reports and imaging devices as a tool to elaborate alterations in genetic pathways in order to provide clues to disease status and progression. It is this new paradigm that puts pressure on the mathematical algorithms and data manipulations that normalize molecular profiling data. In order to obtain high precision, we must correct for inherent, repeatable biases in raw expression data. It is incumbent upon the biologist to understand, even anecdotally, the underlying principals of the profiling devices themselves, from image acquisition to processing and data normalization. With this understanding, one knows whether data are being appropriately processed. In this chapter we describe several Affymetrix normalization methods along with a way to compare normalization methods using biologically interpretable analyses. We provide data that illuminates not only how normalization affects array-to-array precision, but also how one can use the gene ontology and gene regulatory and metabolic pathway tools to quickly interpret how normalization can affect the results.
|Original language||English (US)|
|Title of host publication||Methods in Microarray Normalization|
|Number of pages||22|
|State||Published - Jan 1 2008|
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)