Comparison of Affymetrix Data Normalization Methods
Author Information
Author(s): Reija Autio, Sami Kilpinen, Matti Saarela, Olli Kallioniemi, Sampsa Hautaniemi, Jaakko Astola
Primary Institution: Tampere University of Technology
Hypothesis
What are the best normalization methods for integrating gene expression data from different Affymetrix array generations?
Conclusion
The AGC method provides significantly more comparable values across different Affymetrix datasets than methods without it.
Supporting Evidence
- The AGC method improved correlations between technical replicates.
- Normalization methods were assessed using multiple criteria for comparability.
- AGC normalization significantly increased classification accuracy of samples.
Takeaway
This study looked at different ways to make gene data from various experiments easier to compare, and found that one method worked best.
Methodology
The study compared five normalization methods using data from 6,926 Affymetrix experiments across five array generations.
Potential Biases
Potential biases due to differences in experimental conditions and preprocessing methods.
Limitations
The study may not generalize to other microarray platforms beyond Affymetrix.
Statistical Information
P-Value
p<0.01
Statistical Significance
p<0.01
Digital Object Identifier (DOI)
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