methylation-hyper-hypo-classification
Summary
Classify differentially methylated bases into hyper-methylated and hypo-methylated subsets using getMethylDiff() with statistical and effect-size cutoffs. This skill extracts and counts methylation changes between sample groups to identify loci where methylation is significantly elevated or reduced.
When to use
After calculating differential methylation across samples using calculateDiffMeth(), when you need to separately enumerate and extract hyper-methylated (increased methylation) versus hypo-methylated (decreased methylation) bases that meet both statistical significance (q-value < 0.01) and biological relevance (≥25% methylation difference) thresholds.
When NOT to use
- Methylation data has not yet been merged across all samples using unite() or does not contain coverage in all replicates.
- You are working with non-CpG cytosine methylation (e.g., CHG or CHH contexts in plants) without verifying that your q-value and effect-size thresholds remain appropriate.
- Input methylation percentages are binary (fully methylated or unmethylated) rather than continuous; the q-value calculation and effect-size filtering assume heterogeneous methylation percentages.
Inputs
- methylDiff object (output from calculateDiffMeth())
Outputs
- methylDiff object filtered for hyper-methylated bases (type='hyper')
- methylDiff object filtered for hypo-methylated bases (type='hypo')
- Integer counts of hyper-methylated and hypo-methylated bases
How to apply
Apply getMethylDiff() to a methylDiff object produced by calculateDiffMeth(), specifying q-value = 0.01 and percent methylation difference = 25% as joint filtering thresholds. The function automatically selects the appropriate statistical test (Fisher's exact test for small sample sizes, logistic regression for larger cohorts) based on sample count. Extract hyper-methylated bases by setting type='hyper' and hypo-methylated bases with type='hypo', producing separate methylDiff objects. Validate results by confirming that returned base counts align with expected methylation directionality from your experimental design (e.g., treatment vs. control groups).
Related tools
- methylKit (Provides calculateDiffMeth() to perform statistical test selection and p-value calculation, and getMethylDiff() to filter and subset methylation changes by significance and effect size.) — https://github.com/al2na/methylKit
- Bismark (Generates initial methylation call files (input to methRead()) that are later processed through calculateDiffMeth() and getMethylDiff() for hyper/hypo classification.) — https://github.com/FelixKrueger/Bismark
- genomation (Annotates the resulting hyper-methylated and hypo-methylated base sets with gene features (promoters, introns, exons, intergenic regions) for downstream functional interpretation.)
Examples
hyper <- getMethylDiff(methylDiffObject, difference=25, qvalue=0.01, type='hyper'); hypo <- getMethylDiff(methylDiffObject, difference=25, qvalue=0.01, type='hypo')
Evaluation signals
- Hyper-methylated and hypo-methylated base counts are both non-zero, indicating bidirectional methylation changes.
- Returned bases have q-value ≤ 0.01 and absolute percent methylation difference ≥ 25%.
- Base counts for each direction (hyper/hypo) match the vignette-reported or literature-expected values for the same reference dataset.
- The sum of hyper-methylated and hypo-methylated base counts does not exceed the total number of bases in the methylDiff object (no double-counting).
- Hyper-methylated bases show higher methylation percentage in the treatment/case group; hypo-methylated bases show lower methylation percentage in the treatment/case group relative to control.
Limitations
- The q-value < 0.01 and 25% methylation difference thresholds are dataset-dependent; adjusted thresholds may be required for tissues or organisms with naturally lower methylation heterogeneity.
- Classification accuracy depends on adequate coverage (≥10 reads per base by default in methRead()) and balanced sample sizes; Fisher's exact test is used for small groups and logistic regression for larger cohorts, leading to different statistical power.
- Non-CpG methylation contexts (CHG, CHH) are not explicitly handled by the core workflow; users analyzing embryonic stem cells or plant tissues with non-CpG methylation must ensure appropriate context filtering before or after getMethylDiff().
- Overlapping or proximal differentially methylated bases are not automatically merged into regions; bases are classified individually, which may inflate counts if clustering is desired.
Evidence
- [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] Following bit selects the bases that have q-value<0.01 and percent methylation difference larger than 25%: "Following bit selects the bases that have q-value<0.01 and percent methylation difference larger than 25%"
- [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] Traditionally, hypo-methylation is associated with gene transcription (if it is on a regulatory region such as promoters) and hyper-methylation is associated with gene repression: "Traditionally, hypo-methylation is associated with gene transcription (if it is on a regulatory region such as promoters) and hyper-methylation is associated with gene repression"
- [other] Using getMethylDiff() with q-value < 0.01 and 25% methylation difference cutoffs on example methylKit data yields separate hyper-methylated and hypo-methylated base objects, which can be extracted by specifying type="hyper" or type="hypo" parameters: "Using getMethylDiff() with q-value < 0.01 and 25% methylation difference cutoffs on example methylKit data yields separate hyper-methylated and hypo-methylated base objects, which can be extracted by"
1---2name: methylation-hyper-hypo-classification3description: Use when after calculating differential methylation across samples using calculateDiffMeth(), when you need to separately enumerate and extract hyper-methylated (increased methylation) versus hypo-methylated (decreased methylation) bases that meet both statistical significance (q-value < 0.4license: CC-BY-4.05---67# methylation-hyper-hypo-classification89## Summary1011Classify differentially methylated bases into hyper-methylated and hypo-methylated subsets using getMethylDiff() with statistical and effect-size cutoffs. This skill extracts and counts methylation changes between sample groups to identify loci where methylation is significantly elevated or reduced.1213## When to use1415After calculating differential methylation across samples using calculateDiffMeth(), when you need to separately enumerate and extract hyper-methylated (increased methylation) versus hypo-methylated (decreased methylation) bases that meet both statistical significance (q-value < 0.01) and biological relevance (≥25% methylation difference) thresholds.1617## When NOT to use1819- Methylation data has not yet been merged across all samples using unite() or does not contain coverage in all replicates.20- You are working with non-CpG cytosine methylation (e.g., CHG or CHH contexts in plants) without verifying that your q-value and effect-size thresholds remain appropriate.21- Input methylation percentages are binary (fully methylated or unmethylated) rather than continuous; the q-value calculation and effect-size filtering assume heterogeneous methylation percentages.2223## Inputs2425- methylDiff object (output from calculateDiffMeth())2627## Outputs2829- methylDiff object filtered for hyper-methylated bases (type='hyper')30- methylDiff object filtered for hypo-methylated bases (type='hypo')31- Integer counts of hyper-methylated and hypo-methylated bases3233## How to apply3435Apply getMethylDiff() to a methylDiff object produced by calculateDiffMeth(), specifying q-value = 0.01 and percent methylation difference = 25% as joint filtering thresholds. The function automatically selects the appropriate statistical test (Fisher's exact test for small sample sizes, logistic regression for larger cohorts) based on sample count. Extract hyper-methylated bases by setting type='hyper' and hypo-methylated bases with type='hypo', producing separate methylDiff objects. Validate results by confirming that returned base counts align with expected methylation directionality from your experimental design (e.g., treatment vs. control groups).3637## Related tools3839- **methylKit** (Provides calculateDiffMeth() to perform statistical test selection and p-value calculation, and getMethylDiff() to filter and subset methylation changes by significance and effect size.) — https://github.com/al2na/methylKit40- **Bismark** (Generates initial methylation call files (input to methRead()) that are later processed through calculateDiffMeth() and getMethylDiff() for hyper/hypo classification.) — https://github.com/FelixKrueger/Bismark41- **genomation** (Annotates the resulting hyper-methylated and hypo-methylated base sets with gene features (promoters, introns, exons, intergenic regions) for downstream functional interpretation.)4243## Examples4445```46hyper <- getMethylDiff(methylDiffObject, difference=25, qvalue=0.01, type='hyper'); hypo <- getMethylDiff(methylDiffObject, difference=25, qvalue=0.01, type='hypo')47```4849## Evaluation signals5051- Hyper-methylated and hypo-methylated base counts are both non-zero, indicating bidirectional methylation changes.52- Returned bases have q-value ≤ 0.01 and absolute percent methylation difference ≥ 25%.53- Base counts for each direction (hyper/hypo) match the vignette-reported or literature-expected values for the same reference dataset.54- The sum of hyper-methylated and hypo-methylated base counts does not exceed the total number of bases in the methylDiff object (no double-counting).55- Hyper-methylated bases show higher methylation percentage in the treatment/case group; hypo-methylated bases show lower methylation percentage in the treatment/case group relative to control.5657## Limitations5859- The q-value < 0.01 and 25% methylation difference thresholds are dataset-dependent; adjusted thresholds may be required for tissues or organisms with naturally lower methylation heterogeneity.60- Classification accuracy depends on adequate coverage (≥10 reads per base by default in methRead()) and balanced sample sizes; Fisher's exact test is used for small groups and logistic regression for larger cohorts, leading to different statistical power.61- Non-CpG methylation contexts (CHG, CHH) are not explicitly handled by the core workflow; users analyzing embryonic stem cells or plant tissues with non-CpG methylation must ensure appropriate context filtering before or after getMethylDiff().62- Overlapping or proximal differentially methylated bases are not automatically merged into regions; bases are classified individually, which may inflate counts if clustering is desired.6364## Evidence6566- [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"67- [intro] Following bit selects the bases that have q-value<0.01 and percent methylation difference larger than 25%: "Following bit selects the bases that have q-value<0.01 and percent methylation difference larger than 25%"68- [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"69- [intro] Traditionally, hypo-methylation is associated with gene transcription (if it is on a regulatory region such as promoters) and hyper-methylation is associated with gene repression: "Traditionally, hypo-methylation is associated with gene transcription (if it is on a regulatory region such as promoters) and hyper-methylation is associated with gene repression"70- [other] Using getMethylDiff() with q-value < 0.01 and 25% methylation difference cutoffs on example methylKit data yields separate hyper-methylated and hypo-methylated base objects, which can be extracted by specifying type="hyper" or type="hypo" parameters: "Using getMethylDiff() with q-value < 0.01 and 25% methylation difference cutoffs on example methylKit data yields separate hyper-methylated and hypo-methylated base objects, which can be extracted by"
HolobiomicsLab