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 1.254944 -0.68624064  0.68003271  0.27480272  0.005883444
## ENSMUSG00000000003 1.600655  1.70113214  3.03387115 -2.18713018 -2.892133999
## ENSMUSG00000000028 1.326387 -0.02107965  0.09199239  0.05194276  0.015911692
## ENSMUSG00000000037 1.020234 -5.30171036 14.15643078 -6.12772977 -2.737958417
## ENSMUSG00000000049 1.025713 -0.06609732  0.08317797  0.05857072  0.059142265
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.292717 14.636110 3.769187 1.965028
## ENSMUSG00000000003 24.102798  3.608860 5.927295 8.687267
## ENSMUSG00000000028  9.005452  7.272410 3.435735 2.538691
## ENSMUSG00000000037  8.682910 11.476334 7.513607 2.153677
## ENSMUSG00000000049  6.211663  7.958283 3.012329 1.237730

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 
##        0.069941369        0.033425201        0.014848534        0.006174708 
## ENSMUSG00000000028 
##        0.005496859

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)$mist_pars, n = 3)
##                      Beta_0      Beta_1    Beta_2      Beta_3        Beta_4
## ENSMUSG00000000001 1.265158 -0.54170526 0.4409279  0.26480738  6.396932e-02
## ENSMUSG00000000003 1.558915  1.28673718 3.1616723 -1.54105545 -3.192248e+00
## ENSMUSG00000000028 1.321757 -0.07391479 0.1450761  0.08784784  5.691108e-05
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.935776 14.351450 3.502479 1.716379
## ENSMUSG00000000003 24.254295  6.143700 5.548887 8.561998
## ENSMUSG00000000028  8.600133  6.722864 3.735727 2.597490
head(rowData(Dat_sce_g2)$mist_pars, n = 3)
##                        Beta_0      Beta_1     Beta_2       Beta_3     Beta_4
## ENSMUSG00000000001  1.9018146  -3.6837802  19.014784  -23.1873171  7.7165870
## ENSMUSG00000000003 -0.8323118  -0.8014328   2.422573   -0.6748569 -0.8747437
## ENSMUSG00000000028  2.3267649 -21.9483226 106.516574 -153.3382950 69.0295274
##                    Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.358080 6.059127 4.407252 1.513229
## ENSMUSG00000000003 6.375058 9.335479 4.701191 2.998822
## ENSMUSG00000000028 9.335452 6.870896 4.669407 3.286500

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 ENSMUSG00000000001 ENSMUSG00000000003 
##        0.062564514        0.051749523        0.031259081        0.027272725 
## ENSMUSG00000000049 
##        0.009347043

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) (2025-03-01 r87860 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
## 
## Matrix products: default
##   LAPACK version 3.12.0
## 
## 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_3.5.1               SingleCellExperiment_1.29.2
##  [3] SummarizedExperiment_1.37.0 Biobase_2.67.0             
##  [5] GenomicRanges_1.59.1        GenomeInfoDb_1.43.4        
##  [7] IRanges_2.41.3              S4Vectors_0.45.4           
##  [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_2.75.4        bitops_1.0-9             fastmap_1.2.0           
##  [7] RCurl_1.98-1.17          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_1.67.1       knitr_1.50               labeling_0.4.3          
## [22] S4Arrays_1.7.3           curl_6.2.2               DelayedArray_0.33.6     
## [25] abind_1.4-8              BiocParallel_1.41.2      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_0.56            
## [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_0.47.2           restfulr_0.0.15         
## [46] vctrs_0.6.5              Matrix_1.7-3             jsonlite_1.9.1          
## [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_2.8.6             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_6.1            
## [67] GenomeInfoDbData_1.2.14  R6_2.6.1                 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_1.7.7        xfun_0.51                pkgconfig_2.0.3