QC and Affymetrix data

Quality Control (QC) assessment is a crucial first step in successful data analysis: before any comparisons can be performed it is necessary to check that there were no problems with sample processing, and that arrays are of sufficient quality to be included in a study. The Affymetrix platform has a collection of QC metrics and accompanying guidelines that aid the identification of problematic arrays. Detailed information about these can be found in the Affymetrix ‘Data Analysis Fundamentals Manual’ (located at http://www.affymetrix.com). Here, we provide a summary (and our own interpretation) of these metrics, and show how the QC functions within the simpleaffy package in BioConductor can be used to assess array quality. Simpleaffy provides high level functions for reading Affymetrix .CEL files, phenotypic data, and then computing simple things from them, such as t-tests, fold changes etc. It makes heavy use of the affy package within BioConductor and also has some basic scatter plot functions and mechanisms for generating high resolution journal figures.