Correcting Sequence Biases in Microarray Experiments
Author Information
Author(s): Eugene F Schuster, Eric Blanc, Linda Partridge, Janet M Thornton
Primary Institution: European Bioinformatics Institute
Hypothesis
The present/absent calls in Affymetrix microarray experiments are influenced by probe sequence, particularly the central nucleotide.
Conclusion
Correcting for probe-sequence biases can improve the performance of the MAS 5.0 algorithm in detecting present or absent transcripts.
Supporting Evidence
- Probesets with central T nucleotides are more likely to be falsely called present.
- Using a large dataset allows for better assessment of false discovery rates.
- Methods that correct for probe-sequence biases outperform the MAS 5.0 algorithm.
Takeaway
This study shows that the way we design probes for gene testing can sometimes lead to mistakes in telling if a gene is present or not, but we can fix this to get better results.
Methodology
The study used a large-scale dataset (GoldenSpike) to analyze the influence of probe sequence on present/absent calls and assessed performance using ROC curves.
Potential Biases
There is a risk of false present calls due to the central nucleotide of PM probes, particularly when they are T nucleotides.
Limitations
The study lacks complete knowledge of the sequence of every clone in the dataset, which may affect the accuracy of classifications.
Participant Demographics
The dataset consists of cRNA samples made from 3,859 unique clones derived from Drosophila.
Statistical Information
P-Value
0.06
Statistical Significance
p<0.06
Digital Object Identifier (DOI)
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