Assessing the Information Content of Microarray Time Series

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.