Bioconductor Qvalue

qvalue

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

• Version: 2.44.0 · Bioconductor: 3.23 · R: ≥ 4.6
• Imports: ggplot2, reshape2
• Install: BiocManager::install("qvalue")

When to Use

• False Discovery Rate Estimation: When analyzing thousands of features (e.g., differential gene expression) to estimate q-values and control the false discovery rate using qvalue.
• Estimating True Nulls: When you need to estimate the overall proportion of true null hypotheses ($\pi_0$) from a distribution of p-values using pi0est.
• Local FDR Calculation: When you want to calculate the empirical Bayesian posterior probability that a null hypothesis is true, conditional on the observed p-value, using lfdr.
• Empirical P-value Calculation: When you have observed test-statistics and simulated/resampled null statistics and need to compute p-values using empPvals.

When NOT to Use

• Dependent Test Statistics: When tests are strongly dependent or have uncorrected batch effects (indicated by a U-shaped p-value histogram), use the sva package first to correct the data.
• Small-scale hypothesis testing: For experiments with very few tests, use standard Benjamini-Hochberg adjustment because the estimation of $\pi_0$ is highly unstable with small sample sizes.

Data Requirements

• A numeric vector of p-values strictly bounded between 0 and 1, OR a vector of observed statistics and a matrix of empirical null statistics.
• P-values must follow a Uniform(0,1) distribution under the null hypothesis (the right tail of the p-value histogram should be fairly flat).

Key Parameters

• p (required): A vector of p-values.
• fdr.level (NULL): The level at which to control the false discovery rate; returns a logical vector indicating significant tests.
• pfdr (FALSE): Indicator of whether to use the positive false discovery rate (pFDR) to make the estimate more robust for small p-values.
• lambda (seq(0, 0.95, 0.05)): Values of the tuning parameter considered in estimating $\pi_0$.
• pi0.method ("smoother"): Method for automatically handling the tuning parameter in $\pi_0$ estimation ("smoother" or "bootstrap").
• trunc (TRUE): If TRUE, local FDR estimates > 1 are set to 1 in lfdr.
• stat (NULL): Vector of observed test-statistics calculated on the original data for empPvals.
• stat0 (NULL): Matrix of empirical null statistics for empPvals.

Best Practices

• Check P-value Histogram: Always view a histogram of the p-values (hist(p)) before running qvalue to confirm the right tail is flat.
• Evaluate Pi0 Reliability: Use plot(qobj) to view the estimated $\pi_0$ versus the tuning parameter $\lambda$ to gauge the reliability of the $\pi_0$ estimate.
• Summarize Results: Use summary(qobj) to view the $\pi_0$ estimate and the cumulative number of significant calls at various p-value, q-value, and local FDR cutoffs.

Common Pitfalls

• U-shaped p-value histograms: Indicates dependence among variables or a one-sided test on data with two-sided signal. Fix: Compute p-values using a different model or use the sva package to correct for dependence.
• Assuming q-values are adjusted p-values: Users often worry when q-values are smaller than original p-values. Fix: Understand that q-values are population quantities bounded by $\pi_0$, not Bonferroni adjusted p-values.
• Incorrect empirical p-values: Passing statistics where larger values do not indicate more evidence against the null. Fix: Ensure statistics passed to empPvals are constructed such that larger values are "more extreme" (e.g., absolute values of t-statistics).

Alternatives

• sva: For correcting dependent variables and batch effects before computing p-values.
• fdrtool: Estimates local FDR and tail area FDR directly from z-scores or p-values.
• IHW: Incorporates covariates (like base mean expression) to increase statistical power.
• ashr: Uses adaptive shrinkage to model the distribution of effects, providing robust local FDR estimates.

Citations

• Storey JD. (2002). A direct approach to false discovery rates. Journal of the Royal Statistical Society, Series B, 64:479–498.
• Storey JD. (2003). The positive false discovery rate: A Bayesian interpretation and the q-value. Annals of Statistics, 31:2013–2035.
• Storey JD, Tibshirani R. (2003). Statistical significance for genome-wide experiments. Proceedings of the National Academy of Sciences, 100:9440–9445.

References

• Homepage: https://bioconductor.org/packages/qvalue
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/qvalue/inst/doc/qvalue.pdf

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