The ability for the variability of a sample to be attributed by a factor other than through random noise. This is dependent first upon the overall distribution of the samples, though most researchers assume that the random variations are gaussian. Due to systematic factors such as dye binding affinities as well as the nonlinear binding behavior in microarrays, normalization is required before the use of this gaussian assumption
Published in Chapter:
Assessing the Information Content of Microarray Time Series
E. Yang (Rutgers University, USA) and I. P. Androulakis (Rutgers University, USA)
Copyright: © 2008
|Pages: 8
DOI: 10.4018/978-1-59904-889-5.ch017
Abstract
While the rise of microarrays has heralded a new era in molecular biology with its ability to measure the expression level of thousands of genes at once, the usefulness of microarrays is exigent upon the ability to obtain accurate gene expression data for the individual genes (Bowtell, 1999; Brown & Botstein, 1999; Cheung, Morley, Aguilar, Massimi, Kucherlapati, & Childs, 1999). However, there has been significant criticism as to how meaningful the information derived via microarrays is. In cases where one has attempted to find genes that correlated to types of cancer or survival rate, it was found that different analysis techniques would often times yield radically different set of genes, calling into question the validity of the overall experiment itself (Dupuy & Simon, 2007). It is our contention that part of the problem associated with microarrays is that there does not exist a coherent method for dealing with data quality, and if a coherent method for dealing with data quality existed, many of the criticisms of microarrays could be addressed.