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 1.268944 -3.818520e-01 0.2914901  0.28139231  0.04596664
## ENSMUSG00000000003 1.536817  9.611772e-01 3.1903305 -1.68775946 -2.67142591
## ENSMUSG00000000028 1.291467 -3.080192e-05 0.1071733  0.04867304 -0.02096125
## ENSMUSG00000000037 1.048652 -2.930170e+00 7.5497965 -1.13978426 -3.47385148
## ENSMUSG00000000049 1.015904 -2.043675e-01 0.2003745  0.09771344  0.09414582
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  6.010588 13.693273 3.561577 1.895999
## ENSMUSG00000000003 23.944624  8.232259 5.111561 8.478882
## ENSMUSG00000000028  7.855017  6.991929 3.295653 2.606817
## ENSMUSG00000000037  8.980761 11.935988 8.357927 2.295164
## ENSMUSG00000000049  6.007453  8.490531 3.048808 1.282860

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 
##        0.046967872        0.031149846        0.010903139        0.009118700 
## ENSMUSG00000000028 
##        0.005671512

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)$mist_pars, n = 3)
##                      Beta_0       Beta_1    Beta_2      Beta_3      Beta_4
## ENSMUSG00000000001 1.258047 -0.488922810 0.3055507  0.36697657  0.07563823
## ENSMUSG00000000003 1.509964  1.613839833 1.9517978 -1.64480205 -2.14040747
## ENSMUSG00000000028 1.298791 -0.002039645 0.1396054  0.04658121 -0.05638440
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.693721 15.830394 2.987427 1.794977
## ENSMUSG00000000003 24.121958  4.366282 6.092656 8.346126
## ENSMUSG00000000028  7.686776  7.074796 3.538004 2.451185
head(rowData(Dat_sce_g2)$mist_pars, n = 3)
##                        Beta_0      Beta_1    Beta_2       Beta_3    Beta_4
## ENSMUSG00000000001  1.9081492  0.05127459 2.4760443 -1.030953035 -1.655542
## ENSMUSG00000000003 -0.8297118 -0.92028330 2.7930387 -1.025659000 -0.757990
## ENSMUSG00000000028  2.3467502  0.02682188 0.5189964  0.002194115 -0.388745
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.551244  8.211444 3.466165 1.272790
## ENSMUSG00000000003  6.306315 10.356262 4.548397 3.171298
## ENSMUSG00000000028 11.242020  5.927492 3.463530 3.335002

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 
##        0.045390762        0.028042676        0.022744730        0.010874014 
## ENSMUSG00000000028 
##        0.001933617

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 version 4.5.0 Patched (2025-04-21 r88169)
## Platform: aarch64-apple-darwin20
## Running under: macOS Ventura 13.7.1
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
## 
## 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_3.5.2               SingleCellExperiment_1.31.0
##  [3] SummarizedExperiment_1.39.0 Biobase_2.69.0             
##  [5] GenomicRanges_1.61.0        GenomeInfoDb_1.45.0        
##  [7] IRanges_2.43.0              S4Vectors_0.47.0           
##  [9] BiocGenerics_0.55.0         generics_0.1.3             
## [11] MatrixGenerics_1.21.0       matrixStats_1.5.0          
## [13] mist_1.1.0                  BiocStyle_2.37.0           
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.1         dplyr_1.1.4              farver_2.1.2            
##  [4] Biostrings_2.77.0        bitops_1.0-9             fastmap_1.2.0           
##  [7] RCurl_1.98-1.17          GenomicAlignments_1.45.0 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_1.1.6             
## [16] sass_0.4.10              tools_4.5.0              yaml_2.3.10             
## [19] rtracklayer_1.69.0       knitr_1.50               labeling_0.4.3          
## [22] S4Arrays_1.9.0           curl_6.2.2               DelayedArray_0.35.1     
## [25] RColorBrewer_1.1-3       abind_1.4-8              BiocParallel_1.43.0     
## [28] withr_3.0.2              grid_4.5.0               scales_1.4.0            
## [31] MASS_7.3-65              mcmc_0.9-8               tinytex_0.57            
## [34] dichromat_2.0-0.1        cli_3.6.5                mvtnorm_1.3-3           
## [37] rmarkdown_2.29           crayon_1.5.3             httr_1.4.7              
## [40] rjson_0.2.23             cachem_1.1.0             splines_4.5.0           
## [43] parallel_4.5.0           BiocManager_1.30.25      XVector_0.49.0          
## [46] restfulr_0.0.15          vctrs_0.6.5              Matrix_1.7-3            
## [49] jsonlite_2.0.0           SparseM_1.84-2           carData_3.0-5           
## [52] bookdown_0.43            car_3.1-3                MCMCpack_1.7-1          
## [55] Formula_1.2-5            magick_2.8.6             jquerylib_0.1.4         
## [58] glue_1.8.0               codetools_0.2-20         gtable_0.3.6            
## [61] BiocIO_1.19.0            UCSC.utils_1.5.0         tibble_3.2.1            
## [64] pillar_1.10.2            htmltools_0.5.8.1        quantreg_6.1            
## [67] GenomeInfoDbData_1.2.14  R6_2.6.1                 evaluate_1.0.3          
## [70] lattice_0.22-7           Rsamtools_2.25.0         bslib_0.9.0             
## [73] MatrixModels_0.5-4       Rcpp_1.0.14              coda_0.19-4.1           
## [76] SparseArray_1.9.0        xfun_0.52                pkgconfig_2.0.3