Orthogonal projections to latent structures as a strategy for microarray data normalization

Background: During generation of microarray data, various forms of systematic biases are frequently introduced which limits accuracy and precision of the results. In order to properly estimate biological effects, these biases must be identified and discarded. Results: We introduce a normalization strategy for multi-channel microarray data based on orthogonal projections to latent structures (OPLS); a multivariate regression method. The effect of applying the normalization methodology on single-channel Affymetrix data as well as dualchannel cDNA data is illustrated. We provide a parallel comparison to a wide range of commonly employed normalization methods with diverse properties and strengths based on sensitivity and specificity from external (spike-in) controls. On the illustrated data sets, the OPLS normalization strategy exhibits leading average true negative and true positive rates in comparison to other evaluated methods. Conclusion: The OPLS methodology identifies joint variatio...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Max Bylesjö, Daniel Eriksson, Andreas Sjödin, Stefan Jansson, Thomas Moritz, Johan Trygg
• Keywords: BMCBI 2007, Microarray Data, Normalization Strategy, OPLS Normalization Strategy