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BootstRatio: A web-based statistical analysis of fold-change in qPCR and RT-qPCR data using resampling methods
Ramon Clèries1, Jordi Galvez, Meritxell Espino
1Catalan Plan for Oncology-Institut Català d'Oncologia-IDIBELL, Hospital Duran i Reynals, L'Hospitalet de Llobregat, Barcelona 08907, Spain. r.cleries@iconcologia.net
Computers in Biology and Medicine
|January 25, 2012
Summary
BootstRatio is a new web application that uses resampling methods to determine the statistical significance of gene expression ratios from real-time quantitative polymerase chain reaction (qPCR) experiments without assuming data distribution.
Area of Science:
- Biomedical Sciences
- Bioinformatics
- Statistical Genetics
Background:
- Real-time quantitative polymerase chain reaction (qPCR) is a standard technique for measuring gene expression levels.
- Assessing the statistical significance of relative gene expression (RE) often requires assumptions about data distribution, which can be challenging.
- Existing methods may struggle with accurate significance assessment for gene expression ratios.
Purpose of the Study:
- To introduce BootstRatio, a novel web-based application for robust statistical analysis of gene expression ratios.
- To provide a method for assessing the statistical significance of RE and the probability that RE > 1 using resampling.
- To compare the performance of BootstRatio against a Bayesian approach for estimating the probability that RE > 1.
Main Methods:
- Development of the BootstRatio web application utilizing resampling methods (bootstrapping).
- Application of BootstRatio in two distinct settings: pre-normalized data and raw data with control samples.
- Conducting a simulation study to compare BootstRatio's performance with a Bayesian approach for estimating the probability of RE > 1.
- Analysis of real experimental data to demonstrate BootstRatio's practical performance.
Main Results:
- BootstRatio performs statistical analyses of gene expression ratios without making assumptions about the underlying data distribution.
- The web application handles both pre-normalized data and raw data with provided control samples.
- Simulation results indicate BootstRatio outperforms the Bayesian approach in estimating the probability of RE > 1, except for very small sample sizes (N ≤ 12).
Conclusions:
- BootstRatio offers a reliable and assumption-free method for assessing the statistical significance of gene expression ratios obtained from qPCR.
- The application provides valuable insights into gene expression analysis, particularly the probability of a target gene being upregulated.
- BootstRatio is a valuable tool for researchers in biomedical sciences needing robust statistical analysis of gene expression data.
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