Contents

0.1 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.

0.2 Installation

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

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

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

From Bioconductor:

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:

library(mist)
vignette("mist")

0.3 Example Workflow for Single-Group Analysis

In this section, we will estimate parameters and perform differential methylation analysis using single-group data.

1 Step 1: Load Example Data

Here we load the example data from GSE121708.

library(mist)
library(SingleCellExperiment)
# Load sample scDNAm data
Dat_sce <- readRDS(system.file("extdata", "group1_sampleData_sce.rds", package = "mist"))

2 Step 2: Estimate Parameters Using estiParam

# 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)
##                      Beta_0        Beta_1     Beta_2      Beta_3        Beta_4
## ENSMUSG00000000001 1.257692 -0.6952493382 0.61567251  0.53515602 -0.2344914767
## ENSMUSG00000000003 1.581106  0.8747367422 3.81659066 -1.70037645 -3.2294237166
## ENSMUSG00000000028 1.284364 -0.0001793846 0.08159529  0.03056432  0.0001473274
## ENSMUSG00000000037 1.056139 -1.7023323624 4.66504214 -0.32599238 -2.6042201663
## ENSMUSG00000000049 1.031498 -0.0829472601 0.11482518  0.07411521  0.0443292554
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.511900 15.427292 3.299756 1.589265
## ENSMUSG00000000003 24.565991  6.307187 5.536823 9.050213
## ENSMUSG00000000028  8.324742  7.357275 2.935553 2.299307
## ENSMUSG00000000037  9.376057 11.740765 6.979171 2.227382
## ENSMUSG00000000049  6.073160  8.638733 3.099675 1.195544

3 Step 3: Perform Differential Methylation Analysis Using dmSingle

# 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)
## ENSMUSG00000000037 ENSMUSG00000000003 ENSMUSG00000000001 ENSMUSG00000000049 
##        0.033068314        0.032421935        0.013717945        0.007009606 
## ENSMUSG00000000028 
##        0.004656495

4 Step 4: Perform Differential Methylation Analysis Using plotGene

# 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")

4.1 Example Workflow for Two-Group Analysis

In this section, we will estimate parameters and perform DM analysis using data from two phenotypic groups.

5 Step 1: Load Two-Group Data

# 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"))

6 Step 2: Estimate Parameters Using estiParam

# 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)$mist_pars, n = 3)
##                      Beta_0      Beta_1    Beta_2      Beta_3       Beta_4
## ENSMUSG00000000001 1.258457 -0.94912891 0.9374746  0.69322657 -0.421447748
## ENSMUSG00000000003 1.626355  1.52270690 3.3993763 -2.32428415 -3.001825689
## ENSMUSG00000000028 1.290358 -0.02222768 0.1070045  0.04051844 -0.004007954
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.691391 13.641890 3.284727 1.802250
## ENSMUSG00000000003 26.513402  2.946628 6.139663 8.403902
## ENSMUSG00000000028  7.834738  8.306904 3.154024 2.149072
head(rowData(Dat_sce_g2)$mist_pars, n = 3)
##                       Beta_0     Beta_1   Beta_2    Beta_3     Beta_4  Sigma2_1
## ENSMUSG00000000001  1.932540 -0.2894189 3.366104 -2.050875 -1.1790194  6.258029
## ENSMUSG00000000003 -0.828484 -1.1237174 3.507944 -1.332851 -1.0222934  7.184402
## ENSMUSG00000000028  2.391034 -0.6979583 3.626524 -3.433485  0.6108754 12.248834
##                    Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.184055 3.333934 1.403501
## ENSMUSG00000000003 9.709888 4.291719 3.281514
## ENSMUSG00000000028 4.413611 4.053735 3.273897

7 Step 3: Perform Differential Methylation Analysis for Two-Group Comparison Using dmTwoGroups

# 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)
## ENSMUSG00000000037 ENSMUSG00000000003 ENSMUSG00000000001 ENSMUSG00000000049 
##        0.030358735        0.028399948        0.024677912        0.011094711 
## ENSMUSG00000000028 
##        0.004754208

7.1 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

## R Under development (unstable) (2025-01-22 r87618)
## Platform: x86_64-apple-darwin20
## Running under: macOS Monterey 12.7.6
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
## 
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: America/New_York
## tzcode source: internal
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] ggplot2_3.5.1               SingleCellExperiment_1.29.1
##  [3] SummarizedExperiment_1.37.0 Biobase_2.67.0             
##  [5] GenomicRanges_1.59.1        GenomeInfoDb_1.43.4        
##  [7] IRanges_2.41.2              S4Vectors_0.45.2           
##  [9] BiocGenerics_0.53.6         generics_0.1.3             
## [11] MatrixGenerics_1.19.1       matrixStats_1.5.0          
## [13] mist_0.99.18                BiocStyle_2.35.0           
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.1         farver_2.1.2             dplyr_1.1.4             
##  [4] Biostrings_2.75.3        bitops_1.0-9             fastmap_1.2.0           
##  [7] RCurl_1.98-1.16          GenomicAlignments_1.43.0 XML_3.99-0.18           
## [10] digest_0.6.37            lifecycle_1.0.4          survival_3.8-3          
## [13] magrittr_2.0.3           compiler_4.5.0           rlang_1.1.5             
## [16] sass_0.4.9               tools_4.5.0              yaml_2.3.10             
## [19] rtracklayer_1.67.0       knitr_1.49               labeling_0.4.3          
## [22] S4Arrays_1.7.2           curl_6.2.0               DelayedArray_0.33.5     
## [25] abind_1.4-8              BiocParallel_1.41.0      withr_3.0.2             
## [28] grid_4.5.0               colorspace_2.1-1         scales_1.3.0            
## [31] MASS_7.3-64              mcmc_0.9-8               tinytex_0.54            
## [34] cli_3.6.3                mvtnorm_1.3-3            rmarkdown_2.29          
## [37] crayon_1.5.3             httr_1.4.7               rjson_0.2.23            
## [40] cachem_1.1.0             splines_4.5.0            parallel_4.5.0          
## [43] BiocManager_1.30.25      XVector_0.47.2           restfulr_0.0.15         
## [46] vctrs_0.6.5              Matrix_1.7-2             jsonlite_1.8.9          
## [49] SparseM_1.84-2           carData_3.0-5            bookdown_0.42           
## [52] car_3.1-3                MCMCpack_1.7-1           Formula_1.2-5           
## [55] magick_2.8.5             jquerylib_0.1.4          glue_1.8.0              
## [58] codetools_0.2-20         gtable_0.3.6             BiocIO_1.17.1           
## [61] UCSC.utils_1.3.1         munsell_0.5.1            tibble_3.2.1            
## [64] pillar_1.10.1            htmltools_0.5.8.1        quantreg_6.00           
## [67] GenomeInfoDbData_1.2.13  R6_2.5.1                 evaluate_1.0.3          
## [70] lattice_0.22-6           Rsamtools_2.23.1         bslib_0.9.0             
## [73] MatrixModels_0.5-3       Rcpp_1.0.14              coda_0.19-4.1           
## [76] SparseArray_1.7.5        xfun_0.50                pkgconfig_2.0.3