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 1.244686 -0.81521594  0.79419789  0.39555169 -0.09851152
## ENSMUSG00000000003 1.561398  1.61542519  1.23713812 -1.05523630 -1.97553772
## ENSMUSG00000000028 1.279488 -0.02616037  0.15784720  0.06282856 -0.06946061
## ENSMUSG00000000037 1.040205 -4.33514882 11.36284552 -3.84775352 -3.16810665
## ENSMUSG00000000049 1.027572 -0.04846733  0.05959365  0.04682151  0.06883517
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.560717 14.436743 3.328805 1.837981
## ENSMUSG00000000003 27.588143  3.088556 6.738168 9.543057
## ENSMUSG00000000028  8.021890  7.646123 3.371750 2.236593
## ENSMUSG00000000037  8.798536 12.992227 7.074879 2.423262
## ENSMUSG00000000049  6.416601  9.168663 3.226694 1.098108

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 ENSMUSG00000000028 
##        0.060807199        0.027168011        0.016406937        0.005716521 
## ENSMUSG00000000049 
##        0.005677504

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)$mist_pars, n = 3)
##                      Beta_0      Beta_1    Beta_2      Beta_3      Beta_4
## ENSMUSG00000000001 1.246424 -0.84896219 0.7104918  0.45790635 -0.04013731
## ENSMUSG00000000003 1.537399  1.84748833 2.4944883 -1.40403156 -3.29451833
## ENSMUSG00000000028 1.281298 -0.02041193 0.1193233  0.05389544 -0.02926960
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.631472 14.358915 3.160913 1.723290
## ENSMUSG00000000003 27.346189  4.243303 6.604580 9.481243
## ENSMUSG00000000028  8.202197  7.066100 3.298992 2.108500
head(rowData(Dat_sce_g2)$mist_pars, n = 3)
##                        Beta_0      Beta_1    Beta_2     Beta_3    Beta_4
## ENSMUSG00000000001  1.8992299  -0.6489073  3.745480  -1.599641 -1.604709
## ENSMUSG00000000003 -0.8618975  -1.7326219  4.599261  -1.500699 -1.320210
## ENSMUSG00000000028  2.2756041 -10.3961054 47.527492 -64.664168 27.673899
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.381909  6.963481 3.054193 1.499863
## ENSMUSG00000000003  7.734154 12.012355 5.560490 3.346497
## ENSMUSG00000000028 10.033125  6.967584 3.456964 2.902706

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 ENSMUSG00000000028 ENSMUSG00000000001 
##        0.054396566        0.035900379        0.028945190        0.024561876 
## ENSMUSG00000000049 
##        0.009215543

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) (2025-01-20 r87609)
## 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 3.12.0
## 
## 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_3.5.1               SingleCellExperiment_1.29.1
##  [3] SummarizedExperiment_1.37.0 Biobase_2.67.0             
##  [5] GenomicRanges_1.59.1        GenomeInfoDb_1.43.4        
##  [7] IRanges_2.41.2              S4Vectors_0.45.2           
##  [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_2.75.3        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_1.7.2           curl_6.2.0               DelayedArray_0.33.5     
## [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_1.7-2             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_6.00           
## [67] GenomeInfoDbData_1.2.13  R6_2.5.1                 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_1.7.5        xfun_0.50                pkgconfig_2.0.3