mist:methylation inference for single-cell along trajectory

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.

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")

Example Workflow for Single-Group Analysis

In this section, we will estimate parameters and perform differential methylation analysis using single-group data.

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"))

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 1.249248 -0.71767498 0.6842368  0.35146006 -0.063711149
## ENSMUSG00000000003 1.592751  1.91873102 3.0015338 -2.06739654 -3.267105523
## ENSMUSG00000000028 1.305245 -0.02315654 0.1045082  0.05525021  0.003511649
## ENSMUSG00000000037 1.056482 -2.25305755 6.3854627 -1.84305958 -2.289509189
## ENSMUSG00000000049 1.012156 -0.15545487 0.1357529  0.12581692  0.081600949
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.648572 13.240991 3.170574 1.738837
## ENSMUSG00000000003 25.561452  4.065887 6.138043 8.493933
## ENSMUSG00000000028  8.130091  7.677151 3.351898 2.432341
## ENSMUSG00000000037  9.411593 12.185644 6.546693 2.017261
## ENSMUSG00000000049  5.853656  9.732647 2.937522 1.242974

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 
##        0.037025249        0.035295010        0.014728459        0.008861108 
## ENSMUSG00000000028 
##        0.005703748

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")

Example Workflow for Two-Group Analysis

In this section, we will estimate parameters and perform DM analysis using data from two phenotypic groups.

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"))

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)$mist_pars, n = 3)
##                      Beta_0      Beta_1    Beta_2      Beta_3      Beta_4
## ENSMUSG00000000001 1.226009 -0.56750106 0.5033689  0.32183909 -0.01544113
## ENSMUSG00000000003 1.692348  1.72618777 2.7809332 -1.77369318 -3.16074088
## ENSMUSG00000000028 1.296351 -0.01171263 0.1223256  0.04293191 -0.02809809
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.371369 14.224508 3.286640 1.811517
## ENSMUSG00000000003 25.849596  2.956217 4.882090 9.154818
## ENSMUSG00000000028  8.050587  7.622305 3.400091 2.448553
head(rowData(Dat_sce_g2)$mist_pars, n = 3)
##                        Beta_0      Beta_1    Beta_2       Beta_3     Beta_4
## ENSMUSG00000000001  1.9092669 -0.97561200 5.8754484 -4.764809915 -0.2800561
## ENSMUSG00000000003 -0.8332051 -2.60339159 6.9014182 -2.481684463 -1.8076820
## ENSMUSG00000000028  2.3875015 -0.03515755 0.4793889 -0.005604446 -0.2564841
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.541996  6.962222 3.349928 1.355418
## ENSMUSG00000000003  6.574111 11.909578 4.671003 2.925385
## ENSMUSG00000000028 12.490540  4.415887 3.775675 3.435670

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 
##        0.047078521        0.042271192        0.022827777        0.010962495 
## ENSMUSG00000000028 
##        0.001475434

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 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 26.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so;  LAPACK version 3.12.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] ggplot2_4.0.3               SingleCellExperiment_1.35.2
##  [3] SummarizedExperiment_1.43.0 Biobase_2.73.2             
##  [5] GenomicRanges_1.65.4        Seqinfo_1.3.2              
##  [7] IRanges_2.47.5              S4Vectors_0.51.10          
##  [9] BiocGenerics_0.59.12        generics_0.1.4             
## [11] MatrixGenerics_1.25.0       matrixStats_1.5.0          
## [13] mist_1.5.0                  BiocStyle_2.41.0           
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.1         dplyr_1.2.1              farver_2.1.2            
##  [4] Biostrings_2.81.9        S7_0.2.2                 bitops_1.1-0            
##  [7] fastmap_1.2.0            RCurl_1.98-1.20          GenomicAlignments_1.49.2
## [10] XML_3.99-0.24            digest_0.6.39            lifecycle_1.0.5         
## [13] survival_3.8-12          magrittr_2.0.5           compiler_4.6.1          
## [16] rlang_1.3.0              sass_0.4.10              tools_4.6.1             
## [19] yaml_2.3.12              rtracklayer_1.73.0       knitr_1.52              
## [22] labeling_0.4.3           S4Arrays_1.13.1          curl_8.0.0              
## [25] DelayedArray_0.39.7      RColorBrewer_1.1-3       abind_1.4-8             
## [28] BiocParallel_1.47.0      withr_3.0.3              sys_3.4.3               
## [31] grid_4.6.1               scales_1.4.0             MASS_7.3-66             
## [34] mcmc_0.9-8               cli_3.6.6                mvtnorm_1.4-2           
## [37] rmarkdown_2.32           crayon_1.5.3             httr_1.4.9              
## [40] rjson_0.2.23             BiocBaseUtils_1.15.1     cachem_1.1.0            
## [43] splines_4.6.1            parallel_4.6.1           BiocManager_1.30.27     
## [46] XVector_0.53.0           restfulr_0.0.17          vctrs_0.7.3             
## [49] Matrix_1.7-6             jsonlite_2.0.0           SparseM_1.84-2          
## [52] carData_3.0-6            car_3.1-5                MCMCpack_1.7-1          
## [55] Formula_1.2-6            maketools_1.3.2          jquerylib_0.1.4         
## [58] glue_1.8.1               codetools_0.2-20         gtable_0.3.6            
## [61] BiocIO_1.23.3            tibble_3.3.1             pillar_1.11.1           
## [64] htmltools_0.5.9          quantreg_6.1             R6_2.6.1                
## [67] evaluate_1.0.5           lattice_0.23-1           Rsamtools_2.29.0        
## [70] cigarillo_1.3.1          bslib_0.12.0             MatrixModels_0.5-4      
## [73] coda_0.19-4.1            SparseArray_1.13.3       xfun_0.61               
## [76] buildtools_1.0.0         pkgconfig_2.0.3