Goulphar: rapid access and expertise for standard two-color microarray normalization methods

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 are included in the R/BioConductor packages but it is often difficult to identify the most appropriate method. Furthermore, the use of R commands for functions and graphics can introduce mistakes that are difficult to trace. We present here a script written in R that provides a flexible means of access to and monitoring of data normalization for two-color microarrays. This script combines the power of BioConductor and R analysis functions and reduces the amount of R programming required. Results: Goulphar was developed in and runs using the R language and environment. It combines and extends functions found in BioConductor packages (limma and marray) to correct for dye biases and spatial artifacts. Goulphar provides a wide range of optional and customizable filters for excluding incorrect signals during the ...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Sophie Lemoine, Florence Combes, Nicolas Servant, Stéphane Le Crom
• Keywords: BMCBI 2006, Data Normalization, Microarray Data Analysis, Raw Data Normalization