HolobiomicsLab 
methylation-object-merging-and-union
Summary
Merge methylation call objects across multiple bisulfite sequencing samples to create a unified methylBase object containing only bases with coverage in all samples. This is a prerequisite for comparative differential methylation analysis, ensuring consistent base-pair resolution across the sample cohort.
When to use
You have loaded individual methylation call files as methylRawList objects from bisulfite sequencing experiments (via methRead()) and need to perform base-level comparative analysis across two or more samples. The goal is to identify a common set of covered loci before calculating differential methylation statistics.
When NOT to use
- Input is already a methylBase object; merging is not needed.
- You wish to perform single-sample methylation statistics or visualization without cross-sample comparison.
- Coverage is highly uneven across samples and you prefer to use coverage-weighted or missing-data-imputation strategies instead of complete-case deletion.
Inputs
- methylRawList object (collection of methylRaw objects, one per sample, from methRead())
- Methylation call files in text format with per-base cytosine calls
Outputs
- methylBase object (merged methylation matrix with bases covered in all samples)
How to apply
Apply the unite() function to a methylRawList object to merge all samples and retain only those base-pair locations covered in every sample in the cohort. The resulting methylBase object contains a consistent set of bases suitable for downstream statistical testing via calculateDiffMeth(). This union operation is essential because differential methylation analysis requires paired observations across all samples; bases missing in even one sample are excluded. The merger also standardizes the coverage landscape, reducing heterogeneity that would inflate false positives in Fisher's exact test or logistic regression.
Related tools
- methylKit (R package providing methRead() and unite() functions for loading and merging methylation call objects) — https://github.com/al2na/methylKit
- Bismark (Bisulfite alignment and methylation calling tool producing input call files for methRead()) — https://github.com/FelixKrueger/Bismark
- MethylDackel (Alternative tool for extracting per-base methylation metrics from BAM/CRAM files, producing input-compatible call files) — https://github.com/dpryan79/MethylDackel
Examples
myMethBaseObj <- unite(myMethRawList, destrand=FALSE)
Evaluation signals
- Output methylBase object contains only bases with non-zero coverage across all samples in the input methylRawList.
- Total row count of methylBase is ≤ the row count of any individual methylRaw input (due to union operation).
- Column count equals the number of samples in the input methylRawList (one coverage and one methylation percentage column per sample).
- No missing values (NA) appear in the coverage or methylation percentage columns of the output methylBase.
- Subsequent calculateDiffMeth() produces a methylDiff object with valid q-values and percent methylation differences for all rows.
Limitations
- The union operation (bases covered in ALL samples) can substantially reduce statistical power if samples have highly divergent coverage profiles or sequencing depths, as rare or low-coverage regions are discarded.
- Bases with coverage below the default threshold (10 reads per base, set during methRead()) are already filtered prior to merging; subsequent filtering cannot recover these loci.
- No imputation or interpolation is performed; missing coverage in any sample results in complete exclusion of that base from the merged object.
- Large cohorts with heterogeneous library sizes may lose >50% of individual base calls due to the complete-case requirement.
Evidence
- [intro] In order to do further analysis, we will need to get the bases covered in all samples. The following function will merge all samples to one object for base-pair locations that are covered in all: "In order to do further analysis, we will need to get the bases covered in all samples. The following function will merge all samples to one object for base-pair locations that are covered in all"
- [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"
- [other] Merge samples across all bases with unite() function to create a methylBase object containing only bases covered in all samples.: "Merge samples across all bases with unite() function to create a methylBase object containing only bases covered in all samples"