mist:methylation inference for single-cell along trajectory
28 April 2026
Contents
- 0.1 Introduction
- 0.2 Installation
- 0.3 Example Workflow for Single-Group Analysis
- 1 Step 1: Load Example Data
- 2 Step 2: Estimate Parameters Using
estiParam - 3 Step 3: Perform Differential Methylation Analysis Using
dmSingle - 4 Step 4: Perform Differential Methylation Analysis Using
plotGene - 5 Step 1: Load Two-Group Data
- 6 Step 2: Estimate Parameters Using
estiParam - 7 Step 3: Perform Differential Methylation Analysis for Two-Group Comparison Using
dmTwoGroups - Session info
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)$mist_pars)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.241430 -0.946276838 0.98904128 0.63023084 -0.404308573
## ENSMUSG00000000003 1.657151 1.857718252 1.75129146 -1.45275018 -2.494631598
## ENSMUSG00000000028 1.295288 0.005517982 0.06840954 0.02774444 0.009360037
## ENSMUSG00000000037 1.014316 -5.787860284 15.88533378 -7.65842290 -2.489882372
## ENSMUSG00000000049 1.031597 -0.095340182 0.09328418 0.09031981 0.068639395
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.220367 14.921429 3.304151 1.839973
## ENSMUSG00000000003 26.612791 2.870299 5.578274 9.147422
## ENSMUSG00000000028 8.146372 7.488457 3.792368 2.344544
## ENSMUSG00000000037 8.757038 12.296174 6.580017 2.145654
## ENSMUSG00000000049 6.529040 8.730833 3.361353 1.2008833 Step 3: Perform Differential Methylation Analysis Using dmSingle
# Perform differential methylation analysis for the single-group
Dat_sce <- dmSingle(Dat_sce)
# View the top genomic features with drastic methylation changes
head(rowData(Dat_sce)$mist_int)## ENSMUSG00000000037 ENSMUSG00000000003 ENSMUSG00000000001 ENSMUSG00000000049
## 0.074433574 0.029544311 0.017794048 0.007196965
## ENSMUSG00000000028
## 0.0045042844 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)$mist_pars, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.253127 -1.067169533 1.04843538 0.7110666 -0.42610311
## ENSMUSG00000000003 1.599050 1.810972137 2.10867367 -1.4769419 -2.76115614
## ENSMUSG00000000028 1.295239 0.004494557 0.06604265 0.0268299 0.00784607
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.849404 13.805629 3.228885 1.775344
## ENSMUSG00000000003 25.659544 3.898626 6.104902 9.195432
## ENSMUSG00000000028 8.244557 7.203092 3.787727 2.277945head(rowData(Dat_sce_g2)$mist_pars, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.9265891 -0.5609386 5.200186 -5.5350773 0.7766728
## ENSMUSG00000000003 -0.8245876 -0.8678986 2.440746 -0.7806363 -0.7219310
## ENSMUSG00000000028 2.3124320 -1.5233449 7.958763 -9.7557929 3.4046674
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.933531 6.008405 3.161434 1.428969
## ENSMUSG00000000003 7.120333 10.336919 4.426956 3.098995
## ENSMUSG00000000028 11.627920 4.922777 3.981777 3.3290757 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
## 0.053349442 0.029496422 0.026799784 0.012356129
## ENSMUSG00000000028
## 0.0074579917.1 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 version 4.6.0 Patched (2026-04-24 r89963)
## Platform: aarch64-apple-darwin23
## Running under: macOS Tahoe 26.3.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## 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_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] labeling_0.4.3 S4Arrays_1.12.0 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 grid_4.6.0
## [31] scales_1.4.0 MASS_7.3-65 mcmc_0.9-8
## [34] tinytex_0.59 dichromat_2.0-0.1 cli_3.6.6
## [37] mvtnorm_1.3-7 rmarkdown_2.31 crayon_1.5.3
## [40] otel_0.2.0 httr_1.4.8 rjson_0.2.23
## [43] cachem_1.1.0 splines_4.6.0 parallel_4.6.0
## [46] BiocManager_1.30.27 XVector_0.52.0 restfulr_0.0.16
## [49] vctrs_0.7.3 Matrix_1.7-5 jsonlite_2.0.0
## [52] SparseM_1.84-2 carData_3.0-6 bookdown_0.46
## [55] car_3.1-5 MCMCpack_1.7-1 Formula_1.2-5
## [58] magick_2.9.1 jquerylib_0.1.4 glue_1.8.1
## [61] codetools_0.2-20 gtable_0.3.6 BiocIO_1.22.0
## [64] tibble_3.3.1 pillar_1.11.1 htmltools_0.5.9
## [67] quantreg_6.1 R6_2.6.1 evaluate_1.0.5
## [70] lattice_0.22-9 Rsamtools_2.28.0 cigarillo_1.2.0
## [73] bslib_0.10.0 MatrixModels_0.5-4 Rcpp_1.1.1-1.1
## [76] coda_0.19-4.1 SparseArray_1.12.0 xfun_0.57
## [79] pkgconfig_2.0.3