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 1.273834 -0.65972580  0.43732096  0.44908860  0.06066583
## ENSMUSG00000000003 1.573845  1.53724378  3.34530736 -2.43547564 -2.74948505
## ENSMUSG00000000028 1.282101 -0.02231334  0.08071836  0.05308882  0.03893988
## ENSMUSG00000000037 1.014334 -4.31155102 11.63361853 -4.15429631 -3.17858018
## ENSMUSG00000000049 1.014305 -0.07780254  0.09615202  0.10392588  0.05045784
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  6.206545 12.577874 3.453982 1.946593
## ENSMUSG00000000003 24.950254  5.617174 6.399881 8.979302
## ENSMUSG00000000028  7.232447  8.622535 2.807800 2.374178
## ENSMUSG00000000037  8.472973 11.967949 6.927886 2.318852
## ENSMUSG00000000049  5.771766  7.229728 2.803567 1.260335

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 
##        0.061798357        0.033668794        0.015191906        0.007966331 
## ENSMUSG00000000028 
##        0.005963045

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)$mist_pars, n = 3)
##                      Beta_0       Beta_1     Beta_2       Beta_3      Beta_4
## ENSMUSG00000000001 1.260517 -0.525112249  0.3445328   0.37246090  0.07527110
## ENSMUSG00000000003 1.597635 -1.700175225 21.6886875 -30.52166819 10.17835075
## ENSMUSG00000000028 1.295251 -0.002119131  0.0608122   0.03458285  0.01687154
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.793386 14.343460 3.036675 1.911447
## ENSMUSG00000000003 25.327072  3.884224 6.425982 8.631422
## ENSMUSG00000000028  7.860522  7.889817 2.922350 2.184507
head(rowData(Dat_sce_g2)$mist_pars, n = 3)
##                        Beta_0      Beta_1    Beta_2       Beta_3    Beta_4
## ENSMUSG00000000001  1.9306169  -0.4660029  3.689067   -1.9988461 -1.359871
## ENSMUSG00000000003 -0.7885939  -1.1791219  3.157027   -0.8643921 -1.066566
## ENSMUSG00000000028  2.2829420 -16.8843747 78.641683 -108.7158063 47.147584
##                    Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.570074 8.228614 2.961350 1.518357
## ENSMUSG00000000003 7.982322 9.805420 4.527244 2.764936
## ENSMUSG00000000028 8.771821 4.627577 4.435838 3.315517

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)
## ENSMUSG00000000028 ENSMUSG00000000037 ENSMUSG00000000003 ENSMUSG00000000001 
##        0.051888577        0.051454243        0.038313131        0.022067042 
## ENSMUSG00000000049 
##        0.009251298

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 RC (2025-04-04 r88126 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
## 
## Matrix products: default
##   LAPACK version 3.12.1
## 
## locale:
## [1] LC_COLLATE=C                          
## [2] LC_CTYPE=English_United States.utf8   
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.utf8    
## 
## 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_1.30.0
##  [3] SummarizedExperiment_1.38.0 Biobase_2.68.0             
##  [5] GenomicRanges_1.60.0        GenomeInfoDb_1.44.0        
##  [7] IRanges_2.42.0              S4Vectors_0.46.0           
##  [9] BiocGenerics_0.54.0         generics_0.1.3             
## [11] MatrixGenerics_1.20.0       matrixStats_1.5.0          
## [13] mist_1.0.0                  BiocStyle_2.36.0           
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.1         farver_2.1.2             dplyr_1.1.4             
##  [4] Biostrings_2.76.0        bitops_1.0-9             fastmap_1.2.0           
##  [7] RCurl_1.98-1.17          GenomicAlignments_1.44.0 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_1.68.0       knitr_1.50               labeling_0.4.3          
## [22] S4Arrays_1.8.0           curl_6.2.2               DelayedArray_0.34.0     
## [25] abind_1.4-8              BiocParallel_1.42.0      withr_3.0.2             
## [28] grid_4.5.0               colorspace_2.1-1         scales_1.3.0            
## [31] MASS_7.3-65              mcmc_0.9-8               tinytex_0.57            
## [34] cli_3.6.4                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_0.48.0           restfulr_0.0.15         
## [46] vctrs_0.6.5              Matrix_1.7-3             jsonlite_2.0.0          
## [49] SparseM_1.84-2           carData_3.0-5            bookdown_0.43           
## [52] car_3.1-3                MCMCpack_1.7-1           Formula_1.2-5           
## [55] magick_2.8.6             jquerylib_0.1.4          glue_1.8.0              
## [58] codetools_0.2-20         gtable_0.3.6             BiocIO_1.18.0           
## [61] UCSC.utils_1.4.0         munsell_0.5.1            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_2.24.0         bslib_0.9.0             
## [73] MatrixModels_0.5-4       Rcpp_1.0.14              coda_0.19-4.1           
## [76] SparseArray_1.8.0        xfun_0.52                pkgconfig_2.0.3