Statistical Treatment for Illumina BeadChip and Affymetrix GeneChip Data
Author Information
Author(s): Wong Wing-Cheong, Loh Marie, Eisenhaber Frank
Primary Institution: Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore
Hypothesis
Different statistical treatments are necessary for Illumina BeadChip and Affymetrix GeneChip data to improve biological interpretation.
Conclusion
Processing Illumina BeadChip data with a modified statistical procedure improves the identification of biologically relevant genes.
Supporting Evidence
- The modified statistical treatment led to improved identification of differentially expressed genes.
- More biologically relevant pathways were elucidated using the new summary statistic.
- Concordance between Affymetrix and Illumina results improved with the new method.
Takeaway
This study shows that using the right math helps scientists find important genes in data from a specific type of experiment.
Methodology
The study involved re-evaluating datasets using a new summary statistic for Illumina BeadChip data and comparing results with Affymetrix GeneChip data.
Limitations
The study relies on previously published datasets and may not account for all variables in new experiments.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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