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 1.252500 -0.65627290 0.70757457  0.23985558 -0.03628037
## ENSMUSG00000000003 1.618793  1.50901707 3.68033672 -2.42221397 -3.19007526
## ENSMUSG00000000028 1.302410 -0.02340125 0.16306488  0.05435516 -0.04378651
## ENSMUSG00000000037 1.053623 -3.04024499 8.14409641 -2.87735440 -2.18581877
## ENSMUSG00000000049 1.020903 -0.07552968 0.08312542  0.08562614  0.07853311
##                     Sigma2_1  Sigma2_2 Sigma2_3  Sigma2_4
## ENSMUSG00000000001  5.754893 13.615396 3.236498  1.974745
## ENSMUSG00000000003 26.016786  2.858984 5.831721 10.354450
## ENSMUSG00000000028  7.319067  6.723698 3.713901  2.447121
## ENSMUSG00000000037  9.373451 12.441672 7.133176  2.236819
## ENSMUSG00000000049  5.770292  8.683505 2.805825  1.238576

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 
##        0.044342637        0.034544520        0.013967177        0.007755732 
## ENSMUSG00000000028 
##        0.006476657

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)$mist_pars, n = 3)
##                      Beta_0      Beta_1    Beta_2      Beta_3       Beta_4
## ENSMUSG00000000001 1.257608 -0.88458756 0.8188123  0.34991159  0.015610076
## ENSMUSG00000000003 1.659472  1.53316450 2.9833405 -2.04249189 -2.846070133
## ENSMUSG00000000028 1.303149 -0.02460535 0.1175911  0.05575988 -0.009933118
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.674036 13.296370 3.170302 1.974657
## ENSMUSG00000000003 27.789259  4.908695 5.454961 9.324031
## ENSMUSG00000000028  7.770776  7.037927 3.234474 2.304566
head(rowData(Dat_sce_g2)$mist_pars, n = 3)
##                        Beta_0     Beta_1    Beta_2     Beta_3     Beta_4
## ENSMUSG00000000001  1.9176025 -0.2638276 3.7053244 -2.5251135 -1.0773195
## ENSMUSG00000000003 -0.8274889 -1.1292678 3.0090084 -0.7648040 -1.0734199
## ENSMUSG00000000028  2.3334589 -0.0392941 0.8481836 -0.1647699 -0.5094589
##                     Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.739275 5.735277 3.585038 1.318027
## ENSMUSG00000000003  8.040950 9.715496 4.968265 2.888158
## ENSMUSG00000000028 11.114791 5.529628 3.129615 3.187064

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 
##        0.049762869        0.029590177        0.027207410        0.010659001 
## ENSMUSG00000000028 
##        0.003246012

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 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_1.35.1
##  [3] SummarizedExperiment_1.43.0 Biobase_2.73.1             
##  [5] GenomicRanges_1.65.0        Seqinfo_1.3.0              
##  [7] IRanges_2.47.2              S4Vectors_0.51.5           
##  [9] BiocGenerics_0.59.9         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_2.81.3        S7_0.2.2                 bitops_1.0-9            
##  [7] fastmap_1.2.0            RCurl_1.98-1.19          GenomicAlignments_1.49.0
## [10] XML_3.99-0.23            digest_0.6.39            lifecycle_1.0.5         
## [13] survival_3.8-6           magrittr_2.0.5           compiler_4.6.1          
## [16] rlang_1.2.0              sass_0.4.10              tools_4.6.1             
## [19] yaml_2.3.12              rtracklayer_1.73.0       knitr_1.51              
## [22] labeling_0.4.3           S4Arrays_1.13.0          curl_7.1.0              
## [25] DelayedArray_0.39.3      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_7.3-65             
## [34] mcmc_0.9-8               cli_3.6.6                mvtnorm_1.4-1           
## [37] rmarkdown_2.31           crayon_1.5.3             httr_1.4.8              
## [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_1.7-5             jsonlite_2.0.0           SparseM_1.84-2          
## [52] carData_3.0-6            car_3.1-5                MCMCpack_1.7-1          
## [55] Formula_1.2-5            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_0.22-9           Rsamtools_2.29.0        
## [70] cigarillo_1.3.0          bslib_0.11.0             MatrixModels_0.5-4      
## [73] coda_0.19-4.1            SparseArray_1.13.2       xfun_0.59               
## [76] buildtools_1.0.0         pkgconfig_2.0.3