Correcting Technical Bias in Clinical Microarray Data
Author Information
Author(s): Eklund Aron C, Szallasi Zoltan
Primary Institution: Children's Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology (CHIP@HST), Harvard Medical School
Hypothesis
Can correcting technical bias in clinical microarray data improve its concordance with known biological relationships?
Conclusion
The method developed to correct for technical bias in clinical microarray data significantly improved the concordance with known biological relationships.
Supporting Evidence
- The bias correction method increased the number of replicate pairs clustering together in a breast cancer data set.
- Bias correction improved the correlation of correlations between gene expression and estrogen receptor status across multiple data sets.
- The method was shown to be more effective than batch adjustment in increasing concordance of replicate samples.
Takeaway
This study shows that fixing errors in clinical data can help scientists better understand how genes work together in diseases like cancer.
Methodology
The study involved developing a method to correct for technical bias in clinical microarray data and testing it on various data sets.
Potential Biases
Potential bias from variable quality and quantity of starting RNA and hybridization conditions.
Limitations
The method may not be optimal for all data sets and relies on the assumption that the bias metrics accurately capture the sources of bias.
Participant Demographics
The study analyzed data from breast cancer patients, specifically 98 surgical specimens.
Digital Object Identifier (DOI)
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