# NanoMethViz

<!-- badges: start -->
[![codecov](https://codecov.io/gh/Shians/NanoMethViz/branch/devel/graph/badge.svg?token=I40WANFT0B)](https://codecov.io/gh/Shians/NanoMethViz)
[![R-CMD-check](https://github.com/Shians/NanoMethViz/workflows/R-CMD-check/badge.svg)](https://github.com/Shians/NanoMethViz/actions)
<!-- badges: end -->

NanoMethViz is a Bioconductor package designed to visualise methylation data from long-read sequencing. It provides a simple interface to plot methylation data and perform dimensionality reduction on a per-read basis.

The package is designed to work with methylation data from the Oxford Nanopore Technologies (ONT) platform. It supports:

- **modBAM files** from dorado and other ONT software
- **Tabular methylation calls** from:
  - modkit (tsv generated by `modkit extract full`)
  - Megalodon (tsv)
  - Nanopolish (tsv)
  - f5c (tsv)

Features are developed for 5mC CpG methylation but will work with other modifications if data is in the correct format. If you would like additional formats supported, please create an issue at https://github.com/Shians/NanoMethViz/issues.

## Installation

Install from Bioconductor:

```r
if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}
BiocManager::install("NanoMethViz")
```

Install the Bioconductor devel version:

```r
if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}
BiocManager::install(version = "devel")
BiocManager::install("NanoMethViz")
```

## Quick Start

To generate a methylation plot, you need three components:

1. **Methylation data**: bgzip-compressed and tabix-indexed file or modBAM file containing methylation calls
2. **Sample annotation**: `data.frame` with at least a `sample` column
3. **Gene/exon annotation**: `data.frame` with columns `gene_id`, `chr`, `strand`, `start`, `end`, `transcript_id`, `symbol`

```r
library(NanoMethViz)

# Load bundled example data
nmr <- load_example_nanomethresult()

# Plot methylation for a single gene
plot_gene(nmr, "Peg3")

# Aggregate methylation across multiple genes
plot_agg_genes(nmr)

# Dimensionality reduction on methylation profiles
bss <- methy_to_bsseq(nmr)
gene_regions <- exons_to_genes(exons(nmr))
lmr <- bsseq_to_log_methy_ratio(bss, gene_regions)
plot_mds(lmr)
plot_pca(lmr)
```

## Typical Workflow

1. **Import methylation calls** from your ONT sequencing software (dorado, modkit, Megalodon, Nanopolish, or f5c).
2. **Convert to tabix format** if needed using `create_tabix_file()` or `modbam_to_tabix()`.
   - For modBAM files, use `ModBamResult` directly without conversion.
   - Data must be sorted by genomic position to meet tabix requirements.
3. **Prepare annotations**: Sample metadata with a `sample` column and gene/exon annotation using `get_exons_hg38()`, `get_exons_mm10()`, or custom annotation.
4. **Build a result object**: `NanoMethResult()` for tabix files or `ModBamResult()` for modBAM files.
5. **Visualise and analyse**: Use plotting functions, query methylation data, perform dimensionality reduction, and export for downstream analysis.
6. **Export results**: Convert to bsseq format for differential analysis tools.

## Importing Data

For `NanoMethResult`, data must be in tabix format (bgzip-compressed and tabix-indexed) and sorted by genomic position. Use `create_tabix_file()` to convert tabular data from modkit, Megalodon, Nanopolish, or f5c.

### modBAM Data

Recent versions of dorado and ONT software store modifications directly in BAM files. These can be used with `ModBamResult` without conversion:

```r
mbr <- ModBamResult(
    methy = ModBamFiles(
        samples = c("sample1", "sample2"),
        c("path/to/sample1.bam", "path/to/sample2.bam")
    ),
    samples = data.frame(sample = c("sample1", "sample2"), group = c("A", "B")),
    exons = exon_tibble
)
```

For sharing, convert to tabix format using `modbam_to_tabix()`.

### Annotations

Use helper functions for common organisms: `get_exons_hg38()`, `get_exons_mm10()`, `get_exons_grcm39()`, or provide custom annotation. See the [Importing Annotations](https://shians.github.io/NanoMethViz/articles/UsersGuide.html#importing-annotations) section of the user's guide for details.

## Common Tasks

**Plotting regions**: `plot_gene()`, `plot_region()` for single genes or genomic intervals; `plot_gene_heatmap()`, `plot_region_heatmap()` for per-read heatmaps.

**Aggregate analysis**: `plot_agg_genes()`, `plot_agg_regions()` for group-level summaries.

**Dimensionality reduction**: Convert to bsseq format with `methy_to_bsseq()`, compute log-methylation ratios with `bsseq_to_log_methy_ratio()`, then visualize with `plot_mds()` or `plot_pca()`.

**Querying data**: Extract methylation values for regions using `query_methy()` or filter with `filter_methy()`.

**Export for downstream analysis**: Use `methy_to_bsseq()` for bsseq/DSS, or `bsseq_to_edger()` for edgeR differential methylation analysis.

See the [function reference](https://shians.github.io/NanoMethViz/reference/index.html) for complete function documentation and examples.

## Data Requirements & Configuration

**Data format requirements**:
- Chromosome naming must be consistent across methylation calls and annotation
- Sample names must match exactly between methylation data and `samples$sample`
- Exon annotation requires columns: `gene_id`, `chr`, `strand`, `start`, `end`, `transcript_id`, `symbol`

**Package options**:
- `NanoMethViz.site_filter` (default: 3) - Remove sites with coverage below threshold
- `NanoMethViz.highlight_col` (default: "grey50") - Color for annotated regions in plots

## Documentation

- User Guide (pkgdown site): https://shians.github.io/NanoMethViz/articles/UsersGuide.html
- Function reference index: https://shians.github.io/NanoMethViz/reference/index.html
- Issue tracker: https://github.com/Shians/NanoMethViz/issues

## Example Plots

### MDS plot

![](man/figures/mds.png)

### Feature aggregation

![](man/figures/agg_genes.png)

### Region methylation plot

![](man/figures/peg3_gene.png)

## License

This project is licensed under Apache License, Version 2.0.
