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 1.251235 -0.52615638  0.39023560  0.42033359 -0.02599344
## ENSMUSG00000000003 1.574206  1.37512009  2.98043018 -1.90416699 -2.75332804
## ENSMUSG00000000028 1.296041 -0.11933866  0.32250371  0.01783067 -0.06873215
## ENSMUSG00000000037 1.019019 -5.34840936 15.08173981 -7.55292794 -2.19672655
## ENSMUSG00000000049 1.030708 -0.04910964  0.08233504  0.08448160  0.04374166
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.302492 14.105541 3.168315 1.768467
## ENSMUSG00000000003 23.126506  3.678297 5.065776 8.225515
## ENSMUSG00000000028  8.011839  7.335632 3.133549 2.474678
## ENSMUSG00000000037  7.911133 12.757499 6.926176 2.288500
## ENSMUSG00000000049  5.957227  9.363629 3.675403 1.251859

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 
##        0.070602135        0.032255830        0.013094748        0.007337344 
## ENSMUSG00000000028 
##        0.007173050

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)$mist_pars, n = 3)
##                      Beta_0     Beta_1    Beta_2      Beta_3      Beta_4
## ENSMUSG00000000001 1.231054 -0.3937353 0.2563001  0.31254017  0.08997116
## ENSMUSG00000000003 1.589262  1.7666945 2.0598294 -1.87113319 -2.28233559
## ENSMUSG00000000028 1.279408 -0.1063847 0.3053657  0.01241552 -0.05933072
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.157675 13.200676 3.481040 1.983486
## ENSMUSG00000000003 23.404223  3.164957 4.816241 8.137398
## ENSMUSG00000000028  7.697486  8.123531 3.225649 2.504152
head(rowData(Dat_sce_g2)$mist_pars, n = 3)
##                        Beta_0    Beta_1   Beta_2     Beta_3     Beta_4
## ENSMUSG00000000001  1.9062137 -1.281048 7.456582  -7.233937  0.9351495
## ENSMUSG00000000003 -0.7985744 -1.395990 4.233355  -2.179590 -0.5941231
## ENSMUSG00000000028  2.3322469 -2.168141 9.505404 -11.870873  4.7186224
##                     Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.318967 6.845212 3.175845 1.434011
## ENSMUSG00000000003  6.212816 9.234135 4.477713 2.792110
## ENSMUSG00000000028 11.495604 6.533785 3.152168 3.372169

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 
##        0.063200015        0.031270699        0.022519373        0.010453134 
## ENSMUSG00000000028 
##        0.005222698

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 4.6.0 (2026-04-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 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-r0.3.26.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_1.34.0
##  [3] SummarizedExperiment_1.42.0 Biobase_2.72.0             
##  [5] GenomicRanges_1.64.0        Seqinfo_1.2.0              
##  [7] IRanges_2.46.0              S4Vectors_0.50.0           
##  [9] BiocGenerics_0.58.0         generics_0.1.4             
## [11] MatrixGenerics_1.24.0       matrixStats_1.5.0          
## [13] mist_1.4.0                  BiocStyle_2.40.0           
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_1.2.1         dplyr_1.2.1              farver_2.1.2            
##  [4] Biostrings_2.80.0        S7_0.2.2                 bitops_1.0-9            
##  [7] fastmap_1.2.0            RCurl_1.98-1.18          GenomicAlignments_1.48.0
## [10] XML_3.99-0.23            digest_0.6.39            lifecycle_1.0.5         
## [13] survival_3.8-6           magrittr_2.0.5           compiler_4.6.0          
## [16] rlang_1.2.0              sass_0.4.10              tools_4.6.0             
## [19] yaml_2.3.12              rtracklayer_1.72.0       knitr_1.51              
## [22] S4Arrays_1.12.0          labeling_0.4.3           curl_7.1.0              
## [25] DelayedArray_0.38.0      RColorBrewer_1.1-3       abind_1.4-8             
## [28] BiocParallel_1.46.0      withr_3.0.2              sys_3.4.3               
## [31] grid_4.6.0               scales_1.4.0             MASS_7.3-65             
## [34] mcmc_0.9-8               cli_3.6.6                mvtnorm_1.3-7           
## [37] rmarkdown_2.31           crayon_1.5.3             httr_1.4.8              
## [40] rjson_0.2.23             cachem_1.1.0             splines_4.6.0           
## [43] parallel_4.6.0           BiocManager_1.30.27      XVector_0.52.0          
## [46] restfulr_0.0.16          vctrs_0.7.3              Matrix_1.7-5            
## [49] jsonlite_2.0.0           SparseM_1.84-2           carData_3.0-6           
## [52] car_3.1-5                MCMCpack_1.7-1           Formula_1.2-5           
## [55] maketools_1.3.2          jquerylib_0.1.4          glue_1.8.1              
## [58] codetools_0.2-20         gtable_0.3.6             BiocIO_1.22.0           
## [61] tibble_3.3.1             pillar_1.11.1            htmltools_0.5.9         
## [64] quantreg_6.1             R6_2.6.1                 evaluate_1.0.5          
## [67] lattice_0.22-9           Rsamtools_2.28.0         cigarillo_1.2.0         
## [70] bslib_0.10.0             MatrixModels_0.5-4       coda_0.19-4.1           
## [73] SparseArray_1.12.0       xfun_0.57                buildtools_1.0.0        
## [76] pkgconfig_2.0.3