---
title: "mist:methylation inference for single-cell along trajectory"
output:
  BiocStyle::html_document:
    toc: true
    toc_depth: 2
vignette: >
  %\VignetteIndexEntry{mist_vignette}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

## Introduction

`mist` (Methylation Inference for Single-cell along Trajectory) is an R package for differential methylation (DM) analysis of single-cell DNA methylation (scDNAm) data. The package employs a Bayesian approach to model methylation changes along pseudotime and estimates developmental-stage-specific biological variations. It supports both single-group and two-group analyses, enabling users to identify genomic features exhibiting temporal changes in methylation levels or different methylation patterns between groups.

This vignette demonstrates how to use `mist` for:
1. Single-group analysis.
2. Two-group analysis.

## Installation

To install the latest version of `mist`, run the following commands:

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

# Install mist from GitHub
BiocManager::install("https://github.com/dxd429/mist")
```

From Bioconductor:
```{r, eval = FALSE, warning=FALSE, message=FALSE}
if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}
BiocManager::install("mist")
```

To view the package vignette in HTML format, run the following lines in R:

```{r eval=FALSE,warning=FALSE,message=FALSE}
library(mist)
vignette("mist")
```

## Example Workflow for Single-Group Analysis
In this section, we will estimate parameters and perform differential methylation analysis using single-group data.

# Step 1: Load Example Data
Here we load the example data from GSE121708.

```{r, eval=TRUE, message=FALSE, warning=FALSE}
library(mist)
library(SingleCellExperiment)
# Load sample scDNAm data
Dat_sce <- readRDS(system.file("extdata", "group1_sampleData_sce.rds", package = "mist"))
```

# Step 2: Estimate Parameters Using `estiParam`
```{r, eval=TRUE, message=FALSE, warning=FALSE}
# Estimate parameters for single-group
Dat_sce <- estiParam(
    Dat_sce = Dat_sce,
    Dat_name = "Methy_level_group1",
    ptime_name = "pseudotime"
)

# Check the output
head(rowData(Dat_sce)$mist_pars)
```

# Step 3: Perform Differential Methylation Analysis Using `dmSingle`
```{r, eval=TRUE, message=FALSE, warning=FALSE}
# Perform differential methylation analysis for the single-group
Dat_sce <- dmSingle(Dat_sce)

# View the top genomic features with drastic methylation changes
head(rowData(Dat_sce)$mist_int)
```

# Step 4: Perform Differential Methylation Analysis Using `plotGene`
```{r eval=TRUE, fig.height=5, fig.width=8, message=FALSE, warning=FALSE, out.width='100%'}
# Produce scatterplot with fitted curve of a specific gene
library(ggplot2)
plotGene(Dat_sce = Dat_sce,
         Dat_name = "Methy_level_group1",
         ptime_name = "pseudotime", 
         gene_name = "ENSMUSG00000000037")
```

## Example Workflow for Two-Group Analysis
In this section, we will estimate parameters and perform DM analysis using data from two phenotypic groups.

# Step 1: Load Two-Group Data


```{r, eval=TRUE, message=FALSE, warning=FALSE}
# Load two-group scDNAm data
Dat_sce_g1 <- readRDS(system.file("extdata", "group1_sampleData_sce.rds", package = "mist"))
Dat_sce_g2 <- readRDS(system.file("extdata", "group2_sampleData_sce.rds", package = "mist"))
```

# Step 2: Estimate Parameters Using `estiParam`
```{r, eval=TRUE, message=FALSE, warning=FALSE}
# Estimate parameters for both groups
Dat_sce_g1 <- estiParam(
     Dat_sce = Dat_sce_g1,
     Dat_name = "Methy_level_group1",
     ptime_name = "pseudotime"
 )

Dat_sce_g2 <- estiParam(
     Dat_sce = Dat_sce_g2,
     Dat_name = "Methy_level_group2",
     ptime_name = "pseudotime"
 ) 

# Check the output
head(rowData(Dat_sce_g1)$mist_pars, n = 3)
head(rowData(Dat_sce_g2)$mist_pars, n = 3)
```

# Step 3: Perform Differential Methylation Analysis for Two-Group Comparison Using `dmTwoGroups`
```{r, eval=TRUE, message=FALSE, warning=FALSE}
# Perform DM analysis to compare the two groups
dm_results <- dmTwoGroups(
     Dat_sce_g1 = Dat_sce_g1,
     Dat_sce_g2 = Dat_sce_g2
 )

# View the top genomic features with different temporal patterns between groups
head(dm_results)
```

## Conclusion
`mist` provides a comprehensive suite of tools for analyzing scDNAm data along pseudotime, whether you are working with a single group or comparing two phenotypic groups. With the combination of Bayesian modeling and differential methylation analysis, `mist` is a powerful tool for identifying significant genomic features in scDNAm data.

# Session info {.unnumbered}

```{r sessionInfo, echo=FALSE}
sessionInfo()
```