Statistical analysis of array data: Inferring changes
Pierre Baldi, G. Wesley Hatfield · Cambridge University Press eBooks · 2002
Problems and common approaches Although many data analysis techniques have been applied to DNA array data, the field is still evolving and the methods have not yet reached a level of maturity [1]. Even very basic issues of signal-to-noise ratios are still being sorted out. Gene expression array data can be analyzed on at least three levels of increasing complexity. The first level is that of single genes, where one seeks to establish whether each gene in isolation behaves differently in a control versus an experimental or treatment situation. Here experimental/treatment is to be taken, of course, in a very broad sense: essentially any situation different from the control. Differential single-gene expression analysis can be used, for instance, to establish gene targets for drug development. The second level is multiple genes, where clusters of genes are analyzed in terms of common functionalities, interactions, co-regulation, etc. Gene co-expression can provide, for instance, a simple means of gaining leads to the functions of many genes for which information is not available currently. This level includes also leveraging DNA array data information to analyze DNA regulatory regions and finding regulatory motifs. Finally, the third level attempts to infer and understand the underlying gene and protein networks that ultimately are responsible for the patterns observed. Other issues of calibration, quality control, and comparison across different experiments and technologies are addressed in Chapter 7 (see, for instance, also [2, 3]).