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 1.257015 -0.65800319  0.6704662   0.32695987 -0.08499720
## ENSMUSG00000000003 1.471712 -1.98260993 21.6039110 -31.83201490 12.07746686
## ENSMUSG00000000028 1.271675 -0.02297926  0.1485149   0.02632135 -0.01554607
## ENSMUSG00000000037 1.030785 -3.32235755  9.4311356  -3.99782693 -2.10490453
## ENSMUSG00000000049 1.007810 -0.15717413  0.1539206   0.11523266  0.06806295
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.828935 12.959841 4.167344 1.873990
## ENSMUSG00000000003 22.864332  6.077657 6.864039 9.200033
## ENSMUSG00000000028  6.976134  7.393192 3.089782 2.181497
## ENSMUSG00000000037  8.678554 12.579597 7.849311 2.080902
## ENSMUSG00000000049  6.054738  8.846405 2.712944 1.164296

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 
##        0.048409699        0.042298489        0.014047292        0.008632123 
## ENSMUSG00000000028 
##        0.005896315

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)$mist_pars, n = 3)
##                      Beta_0       Beta_1     Beta_2       Beta_3       Beta_4
## ENSMUSG00000000001 1.253647 -0.592552440 0.65865851   0.27654906 -0.095457532
## ENSMUSG00000000003 1.650630  0.494163681 9.21592020 -12.59531173  2.536845310
## ENSMUSG00000000028 1.280625  0.003525885 0.08087772   0.03246609 -0.001020308
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.278502 15.802456 3.468277 1.836472
## ENSMUSG00000000003 24.718098  2.537163 5.278427 9.015078
## ENSMUSG00000000028  7.432052  6.877211 3.113405 2.122135
head(rowData(Dat_sce_g2)$mist_pars, n = 3)
##                        Beta_0    Beta_1    Beta_2     Beta_3     Beta_4
## ENSMUSG00000000001  1.8990254 -1.350243  6.779308  -4.465056 -1.1466590
## ENSMUSG00000000003 -0.8209654 -1.929623  5.501995  -2.777593 -0.7370982
## ENSMUSG00000000028  2.2941125 -2.552121 10.631275 -11.706371  3.7471348
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.334782  6.657836 3.549807 1.316962
## ENSMUSG00000000003  6.392179 10.688400 4.686338 2.841845
## ENSMUSG00000000028 10.169537  4.504655 3.258572 3.180957

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 
##        0.047826754        0.036407517        0.023629350        0.011315551 
## ENSMUSG00000000028 
##        0.009396238

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) (2025-11-04 r88984)
## Platform: aarch64-apple-darwin20
## Running under: macOS Ventura 13.7.8
## 
## Matrix products: default
## BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.6-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_4.0.1               SingleCellExperiment_1.33.0
##  [3] SummarizedExperiment_1.41.0 Biobase_2.71.0             
##  [5] GenomicRanges_1.63.1        Seqinfo_1.1.0              
##  [7] IRanges_2.45.0              S4Vectors_0.49.0           
##  [9] BiocGenerics_0.57.0         generics_0.1.4             
## [11] MatrixGenerics_1.23.0       matrixStats_1.5.0          
## [13] mist_1.3.1                  BiocStyle_2.39.0           
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.1         dplyr_1.1.4              farver_2.1.2            
##  [4] Biostrings_2.79.4        S7_0.2.1                 bitops_1.0-9            
##  [7] fastmap_1.2.0            RCurl_1.98-1.17          GenomicAlignments_1.47.0
## [10] XML_3.99-0.20            digest_0.6.39            lifecycle_1.0.5         
## [13] survival_3.8-3           magrittr_2.0.4           compiler_4.6.0          
## [16] rlang_1.1.6              sass_0.4.10              tools_4.6.0             
## [19] yaml_2.3.12              rtracklayer_1.71.3       knitr_1.51              
## [22] labeling_0.4.3           S4Arrays_1.11.1          curl_7.0.0              
## [25] DelayedArray_0.37.0      RColorBrewer_1.1-3       abind_1.4-8             
## [28] BiocParallel_1.45.0      withr_3.0.2              grid_4.6.0              
## [31] scales_1.4.0             MASS_7.3-65              mcmc_0.9-8              
## [34] tinytex_0.58             dichromat_2.0-0.1        cli_3.6.5               
## [37] mvtnorm_1.3-3            rmarkdown_2.30           crayon_1.5.3            
## [40] otel_0.2.0               httr_1.4.7               rjson_0.2.23            
## [43] cachem_1.1.0             splines_4.6.0            parallel_4.6.0          
## [46] BiocManager_1.30.27      XVector_0.51.0           restfulr_0.0.16         
## [49] vctrs_0.6.5              Matrix_1.7-4             jsonlite_2.0.0          
## [52] SparseM_1.84-2           carData_3.0-5            bookdown_0.46           
## [55] car_3.1-3                MCMCpack_1.7-1           Formula_1.2-5           
## [58] magick_2.9.0             jquerylib_0.1.4          glue_1.8.0              
## [61] codetools_0.2-20         gtable_0.3.6             BiocIO_1.21.0           
## [64] tibble_3.3.0             pillar_1.11.1            htmltools_0.5.9         
## [67] quantreg_6.1             R6_2.6.1                 evaluate_1.0.5          
## [70] lattice_0.22-7           Rsamtools_2.27.0         cigarillo_1.1.0         
## [73] bslib_0.9.0              MatrixModels_0.5-4       Rcpp_1.1.0.8.2          
## [76] coda_0.19-4.1            SparseArray_1.11.10      xfun_0.55               
## [79] pkgconfig_2.0.3