---
title: "methcp: User’s Guide"
author: "Boying Gong"
date: "`r Sys.Date()`"
vignette: >
    %\VignetteIndexEntry{methcp: User’s Guide}
    %\VignetteEngine{knitr::rmarkdown}
    \usepackage[utf8]{inputenc}
output: 
    BiocStyle::html_document:
        toc_float: true
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = FALSE)
```

# Introduction

`methcp` is a differentially methylated region 
(DMR) detecting method for whole-genome bisulfite sequencing (WGBS) 
data. It is applicable for a wide range of experimental designs.
In this document, we provide examples for two-group comparisons and 
time-course analysis.  

`methcp` identifies DMRs based on change point detection, which 
naturally segments the genome and provides region-level 
differential analysis. We direct the interested reader to our paper
[here](https://link.springer.com/chapter/10.1007/978-3-030-17083-7_5)

Load packages. 

```{r, message=FALSE}
library(bsseq)
library(MethCP)
```

# Two-group comparison

In this section, we use the CpG methylation data from an Arabidopsis dataset 
available from GEO with accession number
[GSM954584](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM954584). We 
take a subset of chromosome 1 and 2 from each of the samples and perform 
differential analysis between the wild-type plants and the H2A.Z mutant 
plants, which we refer to as `treatment` and `control` in the rest of 
the document. 

## Read data

We use a well-developed Bioconductor package `bsseq` to load the store the raw 
data. Below is an example of how to read raw counts using `bsseq`. 

We provide a helper function `createBsseqObject` to create a bsseq object
when the data for each sample is stored in a separate text file. 

For more operations regarding the `bsseq` object, or to create a `bsseq` 
object customized to your file format, please refer to their 
[User's Guide](http://bioconductor.org/packages/release/bioc/html/bsseq.html). 

```{r readData}
# The dataset is consist of 6 samples. 3 samples are H2A.Z mutant 
# plants, and 3 samples are controls.
sample_names <- c(
    paste0("control", seq_len(3)), 
    paste0("treatment", seq_len(3))
)

# Get the vector of file path and names 
raw_files <- system.file(
    "extdata", paste0(sample_names, ".txt"), package = "MethCP")

# load the data
bs_object <- createBsseqObject(
    files = raw_files, sample_names = sample_names, 
    chr_col = 'Chr', pos_col = 'Pos', m_col = "M", cov_col = 'Cov')
```

The `bsseq` object.

```{r showBSobject}
bs_object
```

Header of the raw file for one of the samples.

```{r}
dt <- read.table(
            raw_files[1], stringsAsFactors = FALSE, header = TRUE)
head(dt)
```

## Calculate the statistics

We calculate the per-cytosine statistics using two different test `DSS` and 
`methylKit` using the function `calcLociStat`. This function returns a 
`MethCP` object that is used for the future segmentation step. We allow 
parallelized computing when there are multiple chromosomes in the dataset.

```{r calcStat}
# the sample names of the two groups to compare. They should be subsets of the 
# sample names provided when creating the `bsseq` objects.
group1 <- paste0("control", seq_len(3))
group2 <- paste0("treatment", seq_len(3))

# Below we calculate the per-cytosine statistics using two different 
# test `DSS` and `methylKit`. The users may pick one of the two for their
#  application.
# obj_DSS <- calcLociStat(bs_object, group1, group2, test = "DSS")
obj_methylKit <- calcLociStat(
    bs_object, group1, group2, test = "methylKit")
```

```{r obj_methylKit}
obj_methylKit
```

In cases the user wants to use their pre-calculated test statistics for 
experiments other than two-group comparison and time course data, we use the 
calculated statistics and create a `MethCP` object.

```{r createmethcp}
data <- data.frame(
    chr = rep("Chr01", 5),
    pos = c(2, 5, 9, 10, 18),
    effect.size = c(1,-1, NA, 9, Inf),
    pvals = c(0, 0.1, 0.9, NA, 0.02))
obj <- MethCPFromStat(
    data, test.name="myTest",
    pvals.field = "pvals",
    effect.size.field="effect.size",
    seqnames.field="chr",
    pos.field="pos"
)
```

```{r}
obj
```

## Segmentation

`segmentMethCP` performs segmentation on a `MethCP` object. We allow 
parallelized computing when there are multiple chromosomes in the dataset. 
Different from `calcLociStat` function in the previous section, we do not put 
any constraint on the number of cores used. Please see the documentation for 
adjusting the parameters used in the segmentation.

```{r segmentation}
# obj_DSS <- segmentMethCP(
#     obj_DSS, bs_object, region.test = "weighted-coverage")

obj_methylKit <- segmentMethCP(
    obj_methylKit, bs_object, region.test = "fisher")
```

```{r}
obj_methylKit
```

## Significant regions

Use function `getSigRegion` on a `MethCP` object to get the list of DMRs.

```{r}
# region_DSS <- getSigRegion(obj_DSS)
# head(region_DSS)
```

```{r}
region_methylKit <- getSigRegion(obj_methylKit)
head(region_methylKit)
```

# A time-course example

MethCP is flexible for a wide variety of experimental designs. We apply MethCP 
on an Arabidopsis thaliana seed germination dataset available from the GEO 
with accession number 
[https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE94712](GSE94712) 
The data is generated from two replicates of dry seed and germinating seeds of 
wild-type plants (Col-0) and ros1 dml2 dml3 (rdd) triple demethylase mutant 
plants at 0-4 days after imbibition for 4 days (DAI). For cytosine-based 
statistics, we fit linear models on the methylation ratios and test the 
differences between the time coefficient of condition Col-0 and condition rdd.

Read the meta data.

```{r}
meta_file <- system.file(
    "extdata", "meta_data.txt", package = "MethCP")
meta <- read.table(meta_file, sep = "\t", header = TRUE)

head(meta)
```

Read the counts data.

```{r}
# Get the vector of file path and names 
raw_files <- system.file(
    "extdata", paste0(meta$SampleName, ".tsv"), package = "MethCP")

# read files
bs_object <- createBsseqObject(
    files = raw_files, sample_names = meta$SampleName, 
    chr_col = 1, pos_col = 2, m_col = 4, cov_col = 5, header = TRUE)
```

Apply coverage filter to make sure each loci has total coverage (summed across 
samples) more than 3 for each condition.

```{r}
groups <- split(seq_len(nrow(meta)), meta$Condition)
coverages <- as.data.frame(getCoverage(bs_object, type = "Cov"))
filter <- rowSums(coverages[, meta$SampleName[groups[[1]]]] != 0) >= 3 &
    rowSums(coverages[, meta$SampleName[groups[[2]]]] != 0) >= 3
bs_object <- bs_object[filter, ]
```

Calculate the statistics. A dataframe of the meta data will be passed to 
function `calcLociStatTimeCourse`. Note that there must be columns named 
`Condition`, `Time` and `SampleName` in the dataframe.

```{r}
obj <- calcLociStatTimeCourse(bs_object, meta)
```

```{r}
obj
```

Segmentation.

```{r}
obj <- segmentMethCP(obj, bs_object, region.test = "stouffer")
```

Get the DMRs.

```{r}
regions <- getSigRegion(obj)
```

```{r}
head(regions)
```


# Session info

```{r}
sessionInfo() 
```
