Statistical Test Comparison

statistical-test-comparison

Summary

Compare statistical test stringency and multiple-testing correction outcomes (q-value distributions, test type, variance adjustment) between corrected and uncorrected differential methylation analyses to verify that overdispersion correction produces more conservative significance thresholds.

When to use

When you have run differential methylation analysis in methylKit and need to validate whether overdispersion correction (overdispersion='MN') produces appropriately stringent statistical tests. Apply this skill when comparing a corrected run (with overdispersion='MN' and test='Chisq') against a parallel uncorrected baseline (overdispersion=FALSE) to confirm the correction adjusts variance for excess dispersion and makes p-value/q-value thresholds more conservative.

When NOT to use

• Input methylBase object has <3 replicates per group — Fisher's exact test (not logistic regression or F-test) will be used, and overdispersion correction assumptions may not hold.
• Samples have already been filtered to remove low-coverage bases or PCR bias artifacts — comparison may conflate the effects of coverage filtering with overdispersion correction.
• Goal is exploratory rather than hypothesis-testing — a single uncorrected run may suffice; side-by-side comparison adds computational burden without validation value.

Inputs

• methylBase object (unified methylation data across samples and sites)
• methylKit R package with dataSim() simulation or imported bisulfite sequencing data

Outputs

• q-value distribution from overdispersion='MN' corrected run
• q-value distribution from uncorrected (overdispersion=FALSE) baseline run
• comparative summary statistics (mean, median, range of q-values per method)
• count of significantly differential sites at fixed q-value threshold (e.g., q < 0.01) per method

How to apply

Execute calculateDiffMeth() on a unified methylBase object twice: once with overdispersion='MN' and test='Chisq', and once with overdispersion=FALSE (or default uncorrected mode). Extract the q-value distributions from both runs and compare their central tendencies, ranges, and proportions of sites passing a common significance cutoff (e.g., q < 0.01). The overdispersion='MN' mode applies a scaling parameter φ = X²/(N−P) to adjust variance as φ·n_i·π̂_i·(1−π̂_i), and automatically switches to an F-test from Chi-square, which should result in higher (more stringent) q-values on average. Verify that the corrected method produces visibly elevated median and mean q-values and a smaller proportion of sites meeting a fixed q-value threshold compared to the uncorrected baseline.

Related tools

• methylKit (R package providing calculateDiffMeth(), dataSim(), and methylBase objects for differential methylation analysis with tunable overdispersion correction) — https://github.com/al2na/methylKit
• Bismark (Bisulfite sequencing mapper and methylation caller; produces alignment files that feed into methylKit via methRead()) — https://github.com/FelixKrueger/Bismark
• MethylDackel (Alternative methylation metrics extractor from BAM/CRAM files; output can be converted to methylKit-compatible format) — https://github.com/dpryan79/MethylDackel

Examples

# Generate simulated methylBase; compare overdispersion-corrected vs. uncorrected
library(methylKit)
mBase <- dataSim(replicates=6, sites=1000, treatment=c(1,1,1,0,0,0))
mDiff_corrected <- calculateDiffMeth(mBase, overdispersion='MN', test='Chisq')
mDiff_uncorrected <- calculateDiffMeth(mBase, overdispersion=FALSE, test='Chisq')
# Extract and compare q-value distributions
qvals_corr <- mDiff_corrected@data$qvalue
qvals_uncorr <- mDiff_uncorrected@data$qvalue
print(c(mean(qvals_corr), mean(qvals_uncorr)))  # Should show higher mean for corrected

Evaluation signals

• Mean and median q-values from overdispersion='MN' run are visibly higher (more stringent) than uncorrected baseline, indicating variance inflation correction is active.
• Proportion of sites passing a fixed q-value threshold (e.g., q < 0.01) is smaller in the overdispersion='MN' run, confirming more conservative multiple-testing correction.
• Overdispersion='MN' run automatically uses F-test instead of Chi-square test, which can be verified by inspecting the internal test statistic column or by checking that the number of distinct q-value magnitudes differs from uncorrected output.
• Scaling parameter φ is >1 for the majority of sites, confirming that variance adjustment is being applied (φ represents excess variance relative to binomial expectation).
• Q-value rank order is largely preserved between corrected and uncorrected runs (i.e., top differentially methylated sites remain concordant), indicating the correction refines stringency without reversing relative significance.

Limitations

• Overdispersion correction assumes that observed variance exceeds binomial expectations; if data are underdispersed or binomially distributed, the correction may over-penalize significance and yield false negatives.
• Comparison is most informative when sample sizes are ≥3 per group; with very small sample numbers, Fisher's exact test is used instead of logistic regression, and overdispersion correction is not applicable.
• The choice of q-value threshold (e.g., 0.01 vs. 0.05) affects the magnitude of difference observed between corrected and uncorrected runs; a threshold too permissive may obscure the correction's effect, while too stringent may yield too few differentially methylated sites for robust comparison.
• Simulated data from dataSim() may not fully capture the complexity of real bisulfite sequencing artifacts (e.g., C-to-T conversion bias, strand asymmetry), so validation on empirical data is recommended.

Evidence

• [other] The calculateDiffMeth() function with overdispersion='MN' parameter calculates a scaling parameter φ = X²/(N-P) to adjust variance as φ·n_i·π̂_i·(1-π̂_i), which makes statistical tests more stringent by correcting for variance in excess of binomial expectations and automatically switches from Chisq to F-test.: "calculateDiffMeth() function with overdispersion='MN' parameter calculates a scaling parameter φ = X²/(N-P) to adjust variance as φ·n_i·π̂_i·(1−π̂_i), which makes statistical tests more stringent by"
• [intro] After q-value calculation, we can select the differentially methylated regions/bases based on q-value and percent methylation difference cutoffs.: "After q-value calculation, we can select the differentially methylated regions/bases based on q-value and percent methylation difference cutoffs"
• [intro] The calculateDiffMeth() function is the main function to calculate differential methylation. Depending on the sample size per each set it will either use Fisher's exact or logistic regression.: "The calculateDiffMeth() function is the main function to calculate differential methylation. Depending on the sample size per each set it will either use Fisher's exact or logistic regression"
• [intro] We start by reading in the methylation call data from bisulfite sequencing with methRead function. Reading in the data this way will return a methylRawList object.: "We start by reading in the methylation call data from bisulfite sequencing with methRead function. Reading in the data this way will return a methylRawList object"
• [readme] methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing.: "methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing"