Global Rank-Invariant Set Normalization for Microarray Data
Author Information
Author(s): Carl R. Pelz, Molly Kulesz-Martin, Grover Bagby, Rosalie C. Sears
Primary Institution: Oregon Health and Sciences University
Hypothesis
Can a new normalization method reduce systematic distortions in microarray data?
Conclusion
The GRSN method effectively reduces non-linear technical variation in microarray data, improving the accuracy of gene selection and pathway analysis.
Supporting Evidence
- GRSN effectively corrects non-linear technical variation in microarray datasets.
- The method improves statistical performance in gene selection.
- GRSN enhances pathway enrichment analysis results.
Takeaway
This study introduces a new way to clean up messy data from gene tests, making it easier to find important genes.
Methodology
The GRSN method uses a global set of rank-invariant transcripts to normalize microarray data after it has been processed by other methods.
Potential Biases
There is a risk of introducing noise if not applied correctly, but the method aims to minimize this risk.
Limitations
The method may not reduce random variation and could increase variance for some genes.
Statistical Information
P-Value
0.0010
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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