Normalization for Affymetrix GeneChips.

T Boes, M Neuhäuser
Author Information
  1. T Boes: 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.

MeSH Term

Algorithms
Computer Simulation
DNA Probes
Gene Expression Profiling
Mathematical Computing
Models, Genetic
Models, Statistical
Oligonucleotide Array Sequence Analysis
Reference Values
Sequence Analysis, DNA

Chemicals

DNA Probes

Word Cloud

Created with Highcharts 10.0.0normalizationAffymetrixGeneChipsmethodslowvariationdifferentproposedresultsquantilecandataOBJECTIVES:highdensityoligonucleotidemicroarrayspopularbiomedicalresearchenablestudyexpressionthousandsgenessimultaneouslyexperimentsmultiplearraystechniquesusedreduceso-calledobscuringietechnicalnon-biologicaloriginSeverallastyearsMETHODS:reviewpublishedcomparisonRESULTS:seemsperformfavorablyregardingprecisionvarianceaccuracybiaspracticabilitycomputingtimeHoweveraccordingrecentmethodimpactbiologicalvariabilitythereforeappearslessoptimalpointviewCONCLUSION:AlthoughmayrecommendableinvestigationsbasedsetsneededevaluatedwidelydifferingNormalization

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