T Boes1, M Neuhäuser. 1. Institut für Medizinische Informatik, Biometrie und Epidemiologie, Universitätsklinikum Essen, Hufelandstr. 55, 45122 Essen, Germany. tanja.boes@medizin.uni-essen.de
Abstract
OBJECTIVES: The high density oligonucleotide microarrays from Affymetrix (Affymetrix GeneChips) are very popular in biomedical research. They enable to study the expression of thousands of genes simultaneously. In experiments with multiple arrays, normalization techniques are used to reduce the so-called obscuring variation, i.e. the technical variation that is of non-biological origin. Several different normalization methods have been proposed during the last years. METHODS: We review published results about the comparison of normalization methods proposed for Affymetrix GeneChips. RESULTS: The quantile normalization seems to perform favorably regarding precision (low variance), accuracy (low bias), and practicability (low computing time). However, according to very recent results, this normalization method can have an impact on the biological variability and, therefore, appears to be less than optimal from this point of view. CONCLUSION: Although the quantile normalization may be recommendable, more investigations based on more data sets are needed so that the different normalization methods can be evaluated on widely differing data.
OBJECTIVES: The high density oligonucleotide microarrays from Affymetrix (Affymetrix GeneChips) are very popular in biomedical research. They enable to study the expression of thousands of genes simultaneously. In experiments with multiple arrays, normalization techniques are used to reduce the so-called obscuring variation, i.e. the technical variation that is of non-biological origin. Several different normalization methods have been proposed during the last years. METHODS: We review published results about the comparison of normalization methods proposed for Affymetrix GeneChips. RESULTS: The quantile normalization seems to perform favorably regarding precision (low variance), accuracy (low bias), and practicability (low computing time). However, according to very recent results, this normalization method can have an impact on the biological variability and, therefore, appears to be less than optimal from this point of view. CONCLUSION: Although the quantile normalization may be recommendable, more investigations based on more data sets are needed so that the different normalization methods can be evaluated on widely differing data.
Authors: Denise V Kratschmar; Diego Calabrese; Jo Walsh; Adam Lister; Julia Birk; Christian Appenzeller-Herzog; Pierre Moulin; Chris E Goldring; Alex Odermatt Journal: PLoS One Date: 2012-05-11 Impact factor: 3.240
Authors: Nil Turan; Susana Kalko; Anna Stincone; Kim Clarke; Ayesha Sabah; Katherine Howlett; S John Curnow; Diego A Rodriguez; Marta Cascante; Laura O'Neill; Stuart Egginton; Josep Roca; Francesco Falciani Journal: PLoS Comput Biol Date: 2011-09-01 Impact factor: 4.475