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DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
Published on: March 15, 2011
Assessing quality and normalization of microarrays: case studies using neurological genomic data.
A D Hershey1, D Burdine, C Liu
1Department of Pediatrics, Divisions of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. andrew.hershey@chmcc.org
Acta Neurologica Scandinavica
|January 22, 2008
Summary
Standardizing genomic analysis of microarray data improves accuracy for neurological disease research. Removing technical outliers before analysis enhances gene identification sensitivity and specificity.
Area of Science:
- Genomics
- Bioinformatics
- Neuroscience
Background:
- Genomic analysis using microarray tools aids in understanding neurological diseases.
- Analyzing large microarray datasets presents significant complexity.
- Established standard methods for microarray data analysis are lacking.
Purpose of the Study:
- To analyze the sensitivity and specificity of various gene identification methods.
- To present a standardized approach for microarray data analysis in neurological research.
Main Methods:
- Utilized Affymetrix HG-U133 plus 2.0 microarray datasets from chronic migraine and new-onset epilepsy.
- Compared data normalization and gene change identification methods.
- Employed housekeeping and gender-related genes for specificity and sensitivity testing.
Main Results:
- Identified 5-10% of microarrays as potential outliers due to technical errors.
- Consistent outlier identification across dChip and Bioconductor analysis platforms.
- Robust multichip average analysis outperformed per-gene normalization for specificity and sensitivity.
Conclusions:
- Technical variations in microarray preprocessing contribute to outliers.
- Outlier removal is crucial before performing genomic analysis.
- Standardized genomic analysis techniques improve result accuracy and reliability.
