Assessing Dye and Slide Bias in Microarray Data
Author Information
Author(s): Lu Ruixiao, Lee Geun-Cheol, Shultz Michael, Dardick Chris, Jung Kihong, Phetsom Jirapa, Jia Yi, Rice Robert H, Goldberg Zelanna, Schnable Patrick S, Ronald Pamela, Rocke David M
Primary Institution: University of California, Davis
Hypothesis
Can we quantify the extent of probe-specific dye and slide bias in two-color microarray data?
Conclusion
Dye and slide biases were significant even after normalization, but our diagnostic method improved analysis performance.
Supporting Evidence
- The method developed provides a graphical diagnostic for assessing dye and slide bias.
- Significant differences in bias were observed even after normalization.
- The study included experiments on human and rice genomic arrays.
Takeaway
This study shows that even after trying to fix problems with dye in experiments, there can still be issues that affect results, but we found a way to measure and improve them.
Methodology
We developed a procedure to quantify dye and slide biases using graphical diagnostics and statistical models.
Potential Biases
Potential biases from specific array facilities could affect the results.
Limitations
The study primarily focused on specific facilities and may not generalize to all microarray platforms.
Participant Demographics
Patients with localized prostate cancer undergoing radiation therapy.
Digital Object Identifier (DOI)
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