Background: Two or more factor mixed factorial experiments are becoming increasingly common in microarray data analysis. In this case study, the two factors are presence (Patients...
Hao Li, Constance L. Wood, Thomas V. Getchell, Mar...
Background: Many cutting-edge microarray analysis tools and algorithms, including commonly used limma and affy packages in Bioconductor, need sophisticated knowledge of mathematic...
Background: Raw data normalization is a critical step in microarray data analysis because it directly affects data interpretation. Most of the normalization methods currently used...
Sophie Lemoine, Florence Combes, Nicolas Servant, ...
An approximate rank revealing factorization problem with structure constraints on the normalized factors is considered. Examples of structure, motivated by an application in micro...
Background: Normalization is essential in dual-labelled microarray data analysis to remove nonbiological variations and systematic biases. Many normalization methods have been use...
Huiling Xiong, Dapeng Zhang, Christopher J. Martyn...
Background: Gene expression microarray is a powerful technology for genetic profiling diseases and their associated treatments. Such a process involves a key step of biomarker ide...
Zhipeng Cai, Randy Goebel, Mohammad R. Salavatipou...
Background: The Significance Analysis of Microarrays (SAM) is a popular method for detecting significantly expressed genes and controlling the false discovery rate (FDR). Recently...
Background: Though microarray experiments are very popular in life science research, managing and analyzing microarray data are still challenging tasks for many biologists. Most m...
Background: Microarray experimentation requires the application of complex analysis methods as well as the use of non-trivial computer technologies to manage the resultant large d...
Geraint Barton, J. C. Abbott, Norie Chiba, D. W. H...