Bioconductor Metapod

metapod

Dependencies & Environment

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

• Version: 1.20.0 · Bioconductor: 3.23 · R: ≥ 4.6
• Imports: Rcpp
• System requirements: C++11
• Install: BiocManager::install("metapod")

When to Use

• Genomic Window Aggregation: Combining p-values across adjacent genomic windows in ChIP-seq (e.g., inside csaw) using grouped functions like groupedSimes.
• Multi-Batch Marker Detection: Merging statistics from multiple batches or pairwise comparisons during marker gene detection (e.g., inside scran) using parallel functions like parallelSimes.
• Consensus Direction Summarization: Determining the overall direction of effect (up, down, or mixed) across multiple tests using summarizeParallelDirection or summarizeGroupedDirection.

When NOT to Use

• For simple multiple testing correction on a single flat list of independent p-values, use base R's p.adjust instead because metapod is designed for hierarchical or multi-test aggregation.
• For combining raw effect sizes or variances directly across independent studies, use metafor instead because metapod operates strictly on p-values and their directions.

Data Requirements

• Input Format: A numeric vector of p-values with a grouping factor (for grouped* functions) or a list of parallel numeric vectors of p-values (for parallel* functions).
• Normalization State: P-values must be properly calibrated (uniform under the null hypothesis).
• Minimum Size: At least 2 p-values per group or parallel list to combine.

Key Parameters

• method ("simes"): The method used to combine p-values in wrapper functions like combineGroupedPValues (e.g., "simes", "holm-min").
• log.p (FALSE): Logical indicating whether the input p-values are log-transformed and whether to return log-transformed output.
• influential: A logical vector (returned by combining functions) passed to summarizeParallelDirection to only consider tests that contributed to the final p-value.
• weights: Numeric vector of weights to apply to individual tests (supported by Simes, Stouffer, and Holm-min methods).

Best Practices

• Use Simes' method (parallelSimes or groupedSimes) when you expect dependencies between tests (e.g., overlapping genomic windows) as it is robust to such dependencies.
• Set log.p = TRUE when dealing with extremely small p-values to prevent underflow to zero during calculation.
• Use summarizeParallelDirection or summarizeGroupedDirection on the influential tests to avoid noise from non-significant tests when determining the consensus log-fold change direction.

Common Pitfalls

• Assuming Independence Incorrectly: Using Fisher's method (groupedFisher or parallelFisher) on highly correlated tests. Fix: Switch to Simes' method or the minimum Holm approach (groupedHolmMin), which do not require independence.
• Underflow of P-values: Generating zero p-values due to numerical limits when combining highly significant tests. Fix: Pass log-transformed p-values and set log.p = TRUE.
• Misinterpreting Direction Counts: Using countParallelDirection without understanding that it applies Benjamini-Hochberg or Holm corrections internally. Fix: Read the documentation for countParallelDirection carefully, or use summarizeParallelDirection for a simpler consensus.

Alternatives

• poolr: For combining p-values while accounting for correlation using various methods.
• survcomp: Contains classical p-value combining methods (Fisher, Stouffer) but lacks genomic-specific optimizations and grouped/parallel vector handling.
• metafor: For traditional meta-analysis combining effect sizes and standard errors rather than just p-values.

Citations

• Lun, A. T. L. (2021). metapod: Meta-Analyses on P-Values of Differential Analyses. R package.

References

• Homepage: https://bioconductor.org/packages/metapod
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/metapod/inst/doc/metapod.html

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