---
title: "User's Guide"
author: "Shian Su"
output: 
    BiocStyle::html_document:
        toc: true
vignette: >
  %\VignetteIndexEntry{User's Guide}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(fig.width = 7)
knitr::opts_chunk$set(fig.height = 5)
knitr::opts_chunk$set(dpi = 72)

# preload to avoid loading messages
library(NanoMethViz)
library(dplyr)
exon_tibble <- get_exons_mm10()
```

# Installation

To install this package, run

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

BiocManager::install("NanoMethViz")
```

```{r}
library(NanoMethViz)
library(dplyr)
```

# Quick start

## Constructing the NanoMethResult object

To generate a methylation plot we need 3 components:

* methylation data in tabix format
* annotation of exons
* annotation of samples

The methylation information has been modified from the output of nanopolish/f5c.
It has then been compressed and indexed using `bgzip()` and `indexTabix()` from
the `Rsamtools` package.

```{r}
# methylation data stored in tabix file
methy <- system.file(package = "NanoMethViz", "methy_subset.tsv.bgz")

# tabix is just a special gzipped tab-separated-values file
read.table(gzfile(methy), col.names = methy_col_names(), nrows = 6)
```

The exon annotation was obtained from the Mus.musculus package, and joined into
a single table. It is important that the chromosomes share the same convention
as that found in the methylation data.

```{r}
# helper function extracts exons from TxDb package
exon_tibble <- get_exons_mm10()

head(exon_tibble)
```

We will defined the sample annotation ourselves. It is important that the sample
names match those found in the methylation data.

```{r}
sample <- c(
  "B6Cast_Prom_1_bl6", "B6Cast_Prom_1_cast",
  "B6Cast_Prom_2_bl6", "B6Cast_Prom_2_cast",
  "B6Cast_Prom_3_bl6", "B6Cast_Prom_3_cast"
)

group <- c("bl6", "cast", "bl6", "cast", "bl6", "cast")

sample_anno <- data.frame(sample, group, stringsAsFactors = FALSE)

sample_anno
```

For convenience we assemble these three pieces of data into a single object.

```{r}
nmr <- NanoMethResult(methy, sample_anno, exon_tibble)
```

The genes we have available are

* Peg3
* Meg3
* Impact
* Xist
* Brca1
* Brca2

## Basic Plotting

For demonstrative purposes we will plot Peg3.

```{r}
plot_gene(nmr, "Peg3")
```

We can also load in some DMR results to highlight DMR regions.

```{r}
# loading saved results from previous bsseq analysis
bsseq_dmr <- read.table(
    system.file(package = "NanoMethViz", "dmr_subset.tsv.gz"),
    sep = "\t",
    header = TRUE,
    stringsAsFactors = FALSE
)
```

```{r}
plot_gene(nmr, "Peg3", anno_regions = bsseq_dmr)
```

Individual long reads can be visualised using the `spaghetti` argument.

```{r, warning = FALSE}
# warnings have been turned off in this vignette, but this will generally
# generate many warnings as the smoothing for many reads will fail
plot_gene(nmr, "Peg3", anno_regions = bsseq_dmr, spaghetti = TRUE)
```

Alternatively the individual read information can be represented by a heatmap.

```{r, warning = FALSE}
plot_gene(nmr, "Peg3", anno_regions = bsseq_dmr, heatmap = TRUE)
```

# Importing and exporting data

In order to use this package, your data must be converted from the output of
methylation calling software to a tabix indexed bgzipped format. The data needs
to be sorted by genomic position to respect the requirements of the samtools
[tabix](http://www.htslib.org/doc/tabix.html) indexing tool. On Linux and macOS
systems this is done using the bash `sort` utility, which is memory efficient,
but on Windows this is done by loading the entire table and sorting within R.

We currently support output from

* Nanopolish
* f5c
* Megalodon

If you would like any further other formats supported please create an issue
at https://github.com/Shians/NanoMethViz/issues.

## Data example

The conversion can be done using the `create_tabix_file()` function. We provide
example data of nanopolish output within the package, we can look inside to see
how the data looks coming out of nanopolish

```{r}
methy_calls <- system.file(package = "NanoMethViz",
    c("sample1_nanopolish.tsv.gz", "sample2_nanopolish.tsv.gz"))

# have a look at the first 10 rows of methy_data
methy_calls_example <- read.table(
    methy_calls[1], sep = "\t", header = TRUE, nrows = 6)

methy_calls_example
```

We then create a temporary path to store a converted file, this will be deleted
once you exit your R session. Once `create_tabix_file()` is run, it will create
a .bgz file along with its tabix index. Because we have a small amount of data,
we can read in a small portion of it for inspection, do not do this with large
datasets as it decompresses all the data and will take very long to run.

### Megalodon Data

To import data from Megalodon's modification calls, the [per-read modified
bases](https://nanoporetech.github.io/megalodon/file_formats.html#per-read-modified-bases)
file must be generated. This can be done by either adding `--write-mods-text`
argument to Megalodon run or using the `megalodon_extras per_read_text
modified_bases` utility.

## Importing data

```{r, message=F}
# create a temporary file to store the converted data
methy_tabix <- file.path(tempdir(), "methy_data.bgz")
samples <- c("sample1", "sample2")

# you should see messages when running this yourself
create_tabix_file(methy_calls, methy_tabix, samples)

# you don't need to do this with real data
# we have to use gzfile to tell R that we have a gzip compressed file
methy_data <- read.table(
    gzfile(methy_tabix), col.names = methy_col_names(), nrows = 6)

methy_data
```

Now `methy_tabix` will be the path to a tabix object that is ready for use with
NanoMethViz.

## Using BAM files with modification tags

In the latest versions of megalodon and other ONT software, the modifications
are stored in the BAM file. This can be used directly with NanoMethViz using the
`ModBamResult` class as a substitute for `NanoMethResult`.

This requires a `ModBamFiles` object, which is a list of paths to the BAM files
and the sample names. The sample names must match the sample names in the
`sample_anno` data frame.

Because the BAM files contain significantly more data than the tabix files, it
may be useful to convert the BAM files to tabix files using the 
`modbam_to_tabix()` function for sharing the data.

```{r}
# construct with a ModBamFiles object as the methylation data
mbr <- ModBamResult(
    methy = ModBamFiles(
        samples = "sample1",
        system.file(package = "NanoMethViz", "peg3.bam")
    ),
    samples = data.frame(
        sample = "sample1",
        group = 1
    ),
    exons = exon_tibble
)

# use in the same way as you would a NanoMethResult object
plot_gene(mbr, "Peg3")
```

## Exporting data

The methylation data can be exported into formats appropriate for bsseq, DSS, or
edgeR. 

### bsseq and DSS

Both bsseq and DSS make use of the BSSeq object, and these can be obtained from
the NanoMethResult objects using the `methy_to_bsseq()` function.

```{r, message = FALSE}
nmr <- load_example_nanomethresult()
bss <- methy_to_bsseq(nmr)

bss
```

### edgeR

edgeR can also be used to perform differential methylation analysis: https://f1000research.com/articles/6-2055.
BSSeq objects can be converted into the appropriate format using the
`bsseq_to_edger()` function. This can be used to count reads on a per-site
basis or over regions.

```{r}
gene_regions <- exons_to_genes(NanoMethViz::exons(nmr))
edger_mat <- bsseq_to_edger(bss, gene_regions)

edger_mat
```

# Importing Annotations

This package comes with helper functions that import exon annotations from the Bioconductor packages `Homo.sapiens` and `Mus.musculus`. The functions `get_exons_hg19()` and `get_exons_mm10()` simply take data from the respective packages, and reorganise the columns into the following columns:

-   gene_id
-   chr
-   strand
-   start
-   end
-   transcript_id
-   symbol

This is used to provide gene annotations for the gene or region plots.

For other annotations, they will most likely be able to be imported using `rtracklayer::import()` and manipulated into the desired format. As an example, we can use a small sample of the C. Elegans gene annotation provided by ENSEMBL. `rtracklayer` will import the annotation as a `GRanges` object, this can be coerced into a data.frame and manipulated using `dplyr`.

```{r}
anno <- rtracklayer::import(system.file(package = "NanoMethViz", "c_elegans.gtf.gz"))

head(anno)
```

```{r}
anno <- anno %>%
    as.data.frame() %>%
    dplyr::rename(
        chr = "seqnames",
        symbol = "gene_name"
    ) %>%
    dplyr::select("gene_id", "chr", "strand", "start", "end", "transcript_id", "symbol")

head(anno)
```

## Importing Annotations

Annotations can be simplified if full exon and isoform information is not required. For example, gene-body annotation can be represented as single exon genes. For example we can take the example dataset and transform the isoform annotations of Peg3 into a single gene-body block. The helper function `exons_to_genes()` can help with this common conversion.

```{r, message = FALSE}
nmr <- load_example_nanomethresult()

plot_gene(nmr, "Peg3")
```

```{r}
new_exons <- NanoMethViz::exons(nmr) %>%
    exons_to_genes() %>%
    mutate(transcript_id = gene_id)

NanoMethViz::exons(nmr) <- new_exons

plot_gene(nmr, "Peg3")
```

# Dimensionality reduction

Dimensionality reduction is used to represent high dimensional data in a more tractable form. It is commonly used in RNA-seq analysis, where each sample is characterised by tens of thousands of gene expression values, to visualise samples in a 2D plane with distances between points representing similarity and dissimilarity. For RNA-seq the data used is generally gene counts, for methylation there are generally two relevant count matrices, the count of methylated bases, and the count of methylated bases. The information from these two matrices can be combined by taking log-methylation ratios as done in Chen et al. 2018.

### Preparing data for dimensionality reduction

It is assumed that users of this package have imported the data into the gzipped tabix format. From there, further processing is required to create the log-methylation-ratio matrix used in dimensionality reduction. Namely we go through the BSseq format as it is easily coerced into the desired matrix and is itself useful for various other analyses.

```{r, message = FALSE}
# convert to bsseq
bss <- methy_to_bsseq(nmr)
bss
```

We can generate the log-methylation-ratio based on individual methylation sites or computed over genes, or other feature types. Aggregating over features will generally provide more stable and robust results, here we will use genes.

```{r}
# create gene annotation from exon annotation
gene_anno <- exons_to_genes(NanoMethViz::exons(nmr))

# create log-methylation-ratio matrix
lmr <- bsseq_to_log_methy_ratio(bss, regions = gene_anno)
```

NanoMethViz currently provides two options, a MDS plot based on the limma implementation of MDS, and a PCA plot using BiocSingular.

```{r}
plot_mds(lmr) +
    ggtitle("MDS Plot")

plot_pca(lmr) +
    ggtitle("PCA Plot")
```

Additional coloring and labeling options can be provided via arguments to either function. Further customisations can be done using typical ggplot2 commands.

```{r}
new_labels <- gsub("B6Cast_Prom_", "", colnames(lmr))
new_labels <- gsub("(\\d)_(.*)", "\\2 \\1", new_labels)
groups <- gsub(" \\d", "", new_labels)

plot_mds(lmr, labels = new_labels, groups = groups) +
    ggtitle("MDS Plot") +
    scale_colour_brewer(palette = "Set1")
```

Points can also be plotted without labels by setting `labels = NULL`.

```{r}
plot_mds(lmr, labels = NULL, groups = groups) +
    ggtitle("MDS Plot") +
    scale_colour_brewer(palette = "Set1")
```