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 1.229565 -0.906256230 0.9449425  0.35219988 -0.10106566
## ENSMUSG00000000003 1.577763  1.731825389 1.7484500 -0.93807626 -2.85938265
## ENSMUSG00000000028 1.300256  0.001863788 0.1011794  0.02698799 -0.00596132
## ENSMUSG00000000037 1.034239 -3.035795349 8.4141149 -3.02967490 -2.31930084
## ENSMUSG00000000049 1.019115 -0.094677922 0.1213672  0.09206358  0.06155379
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.446638 13.691372 2.975291 1.843944
## ENSMUSG00000000003 25.142947  3.850606 4.932253 9.010189
## ENSMUSG00000000028  8.051416  8.230083 3.207845 2.425138
## ENSMUSG00000000037  8.414027 12.808300 6.652033 2.324699
## ENSMUSG00000000049  5.719849  8.771707 3.386213 1.337142

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 
##        0.046127342        0.031131709        0.017782415        0.008339259 
## ENSMUSG00000000028 
##        0.005156739

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)$mist_pars, n = 3)
##                      Beta_0       Beta_1     Beta_2      Beta_3      Beta_4
## ENSMUSG00000000001 1.234728 -0.755156479 0.83968604  0.30669009 -0.14096961
## ENSMUSG00000000003 1.614334  2.016753535 1.14998350 -1.09656827 -2.38203882
## ENSMUSG00000000028 1.295590  0.008139507 0.09355377  0.02067561 -0.01258234
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.416661 13.691300 3.321143 1.725017
## ENSMUSG00000000003 25.582497  2.993087 5.228878 8.759563
## ENSMUSG00000000028  7.976525  8.361564 3.614812 2.267689
head(rowData(Dat_sce_g2)$mist_pars, n = 3)
##                       Beta_0     Beta_1    Beta_2     Beta_3     Beta_4
## ENSMUSG00000000001  1.909795 -0.9023612  5.114487  -3.265287 -1.0988102
## ENSMUSG00000000003 -0.852604 -1.1100448  3.286372  -1.426551 -0.7038767
## ENSMUSG00000000028  2.319481 -2.6649750 11.983062 -13.691708  4.4514022
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.730759  6.271297 3.069641 1.437109
## ENSMUSG00000000003  6.545853 10.123953 4.588248 3.216118
## ENSMUSG00000000028 10.944550  5.346765 3.831982 3.309672

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 ENSMUSG00000000028 
##        0.047265158        0.028838316        0.022959994        0.010442367 
## ENSMUSG00000000049 
##        0.009436176

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) (2025-01-21 r87610 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_1.29.1
##  [3] SummarizedExperiment_1.37.0 Biobase_2.67.0             
##  [5] GenomicRanges_1.59.1        GenomeInfoDb_1.43.3        
##  [7] IRanges_2.41.2              S4Vectors_0.45.2           
##  [9] BiocGenerics_0.53.4         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_1.7.1           curl_6.1.0               DelayedArray_0.33.4     
## [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_1.7-1             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_5.99.1         
## [67] GenomeInfoDbData_1.2.13  R6_2.5.1                 evaluate_1.0.3          
## [70] lattice_0.22-6           Rsamtools_2.23.1         bslib_0.8.0             
## [73] MatrixModels_0.5-3       Rcpp_1.0.14              coda_0.19-4.1           
## [76] SparseArray_1.7.4        xfun_0.50                pkgconfig_2.0.3