Computing reliable gene expression levels from microarray experiments is a sophisticated process with many potential pitfalls. Quality control is one of the most important steps i...
Background: Normalization is an important step for microarray data analysis to minimize biological and technical variations. Choosing a suitable approach can be critical. The defa...
Abstract. Many efforts have been involved in association study of quantitative phenotypes and expressed genes. The key issue is how to efficiently identify phenotype-associated gen...
Functional Data Analysis (FDA) is used for datasets that are more meaningfully represented in the functional form. Functional principal component analysis, for instance, is used to...
Background: When publishing large-scale microarray datasets, it is of great value to create supplemental websites where either the full data, or selected subsets corresponding to ...
Christian A. Rees, Janos Demeter, John C. Matese, ...