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Incorporation of gene-specific variability improves expression analysis using high-density DNA microarrays
Vikram Budhraja1, Edward Spitznagel, W Timothy Schaiff
1Department of Obstetrics and Gynecology, and Cell Biology and Physiology, Washington University School of Medicine, St, Louis, MO 63110, USA. vik.budhraja@mssm.edu <vik.budhraja@mssm.edu>
BMC Biology
|December 3, 2003
Summary
Reproducibility in gene expression analysis is crucial. This study introduces a novel method to quantify probe set-specific variability, improving the accuracy of DNA microarray data analysis without needing technical replicates.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Assessing data reproducibility is vital for microarray technology applications in biological pathway and disease state exploration.
- Technical variability in data analysis is largely dependent on signal intensity.
- Reproducibility of individual probe sets has not been previously addressed.
Purpose of the Study:
- To analyze the probe-specific contribution to gene expression variability.
- To develop a novel method for quantifying probe set-specific variability.
- To devise a formula for determining expression changes using probe set- and intensity-specific variability.
Main Methods:
- Utilized an extensive replicate dataset from human placental trophoblast.
- Analyzed probe-specific contributions to gene expression variability.
- Developed a quantitative method for probe set-specific variability and a formula for expression change determination.
Main Results:
- Signal variability is dependent on signal intensity and is probe set-specific.
- A novel method was developed to quantify probe set-specific variability.
- A formula was devised to determine expression changes incorporating probe set- and intensity-specific variability, even without technical replicates.
Conclusions:
- Incorporating probe set-specific variability offers a superior strategy compared to arbitrary fold-change thresholds.
- The study recommends integrating this approach into all gene expression change computations using high-density DNA microarrays.
- A Java application for the T-score is available online.