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Updated: Aug 22, 2026
Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
Published on: July 29, 2022
Multiple-testing strategy for analyzing cDNA array data on gene expression
Robert R Delongchamp1, John F Bowyer, James J Chen
1Division of Biometry and Risk Assessment, National Center for Toxicological Research, Jefferson, Arkansas 72079, USA. rdelongchamp@nctr.fda.gov
Abstract:
An objective of many functional genomics studies is to estimate treatment-induced changes in gene expression. cDNA arrays interrogate each tissue sample for the levels of mRNA for hundreds to tens of thousands of genes, and the use of this technology leads to a multitude of treatment contrasts. By-gene hypotheses tests evaluate the evidence supporting no effect, but selecting a significance level requires dealing with the multitude of comparisons. The p-values from these tests order the genes such that a p-value cutoff divides the genes into two sets. Ideally one set would contain the affected genes and the other would contain the unaffected genes. However, the set of genes selected as affected will have false positives, i.e., genes that are not affected by treatment. Likewise, the other set of genes, selected as unaffected, will contain false negatives, i.e., genes that are affected. A plot of the observed p-values (1 - p) versus their expectation under a uniform [0, 1] distribution allows one to estimate the number of true null hypotheses. With this estimate, the false positive rates and false negative rates associated with any p-value cutoff can be estimated. When computed for a range of cutoffs, these rates summarize the ability of the study to resolve effects. In our work, we are more interested in selecting most of the affected genes rather than protecting against a few false positives. An optimum cutoff, i.e., the best set given the data, depends upon the relative cost of falsely classifying a gene as affected versus the cost of falsely classifying a gene as unaffected. We select the cutoff by a decision-theoretic method analogous to methods developed for receiver operating characteristic curves. In addition, we estimate the false discovery rate and the false nondiscovery rate associated with any cutoff value. Two functional genomics studies that were designed to assess a treatment effect are used to illustrate how the methods allowed the investigators to determine a cutoff to suit their research goals.
Insights
This study introduces a method to optimize gene expression analysis in functional genomics. It helps researchers select the best cutoff for identifying treatment-induced gene changes while estimating false positive and negative rates.
Area of Science:
- Genomics
- Bioinformatics
- Statistical Genetics
Background:
- Functional genomics studies aim to detect treatment-induced gene expression changes using technologies like cDNA arrays.
- Analyzing numerous gene expression comparisons presents challenges in selecting appropriate significance levels and managing multiple testing.
- Existing methods struggle to balance false positives and false negatives, especially when prioritizing the identification of affected genes.
Purpose of the Study:
- To develop a statistical framework for optimizing the selection of significant genes in functional genomics studies.
- To provide methods for estimating false positive and false negative rates across various significance cutoffs.
- To enable researchers to determine an optimal cutoff based on the relative costs of misclassification.
Main Methods:
- Utilized p-value distribution plots to estimate the number of true null hypotheses.
- Applied a decision-theoretic approach, analogous to receiver operating characteristic (ROC) curves, to select an optimal p-value cutoff.
- Estimated false discovery rate (FDR) and false nondiscovery rate (FNR) for different cutoff values.
Main Results:
- The proposed method allows for the estimation of true null hypotheses, aiding in the interpretation of p-values.
- The decision-theoretic approach provides a data-driven way to select a cutoff that balances the trade-off between identifying true positives and minimizing false positives/negatives.
- Demonstrated the application of these methods in two functional genomics studies to determine research-goal-specific cutoffs.
Conclusions:
- The developed statistical methods enhance the analysis of gene expression data from functional genomics studies.
- Researchers can effectively determine optimal significance cutoffs to maximize the discovery of treatment-affected genes while controlling error rates.
- These techniques improve the resolution and reliability of findings in gene expression analysis.
