mist:methylation inference for single-cell along trajectory
18 May 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)$mist_pars)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.237303 -0.80538598 0.72080928 0.4251259 -0.07355208
## ENSMUSG00000000003 1.484392 1.55615013 2.29650933 -1.0260499 -3.04916491
## ENSMUSG00000000028 1.285119 -0.01975652 0.07208422 0.0544290 0.03540316
## ENSMUSG00000000037 1.040071 -2.12303509 5.59232341 -1.1147840 -2.30649070
## ENSMUSG00000000049 1.034663 -0.05313817 0.05847330 0.0787287 0.05831636
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.201994 13.511180 3.769499 1.734025
## ENSMUSG00000000003 24.754051 7.041535 6.700874 9.279612
## ENSMUSG00000000028 8.114469 7.558136 2.850906 2.239709
## ENSMUSG00000000037 9.080661 12.493077 6.019883 2.241691
## ENSMUSG00000000049 6.341110 10.504352 2.972976 1.2009293 Step 3: Perform Differential Methylation Analysis Using dmSingle
# Perform differential methylation analysis for the single-group
Dat_sce <- dmSingle(Dat_sce)
# View the top genomic features with drastic methylation changes
head(rowData(Dat_sce)$mist_int)## ENSMUSG00000000037 ENSMUSG00000000003 ENSMUSG00000000001 ENSMUSG00000000049
## 0.035617687 0.033548117 0.016230431 0.006389468
## ENSMUSG00000000028
## 0.0056273984 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)$mist_pars, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.249787 -0.941790050 1.03699703 0.55030569 -0.397928949
## ENSMUSG00000000003 1.563741 1.909010731 2.38367139 -1.54510712 -3.123347562
## ENSMUSG00000000028 1.277546 0.003077572 0.06487294 0.04207186 0.002180267
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.380056 14.148860 3.427506 1.702339
## ENSMUSG00000000003 24.876215 4.640167 5.722674 9.491763
## ENSMUSG00000000028 7.812907 7.689459 2.978111 2.096568head(rowData(Dat_sce_g2)$mist_pars, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4 Sigma2_1
## ENSMUSG00000000001 1.8859317 -2.013795 10.39180 -10.968199 2.460508 5.237160
## ENSMUSG00000000003 -0.8639267 -3.909302 10.16810 -4.128963 -2.133202 6.007425
## ENSMUSG00000000028 2.3366162 -2.763743 13.50098 -16.986315 6.320165 11.135932
## Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 6.565495 3.180021 1.346843
## ENSMUSG00000000003 11.719358 4.839551 2.993808
## ENSMUSG00000000028 5.164648 3.783526 3.0877237 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
## 0.06131446 0.05511296 0.02639441 0.01061977
## ENSMUSG00000000028
## 0.010482467.1 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 (2026-05-01 r89994)
## 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_1.35.1
## [3] SummarizedExperiment_1.43.0 Biobase_2.73.1
## [5] GenomicRanges_1.65.0 Seqinfo_1.3.0
## [7] IRanges_2.47.1 S4Vectors_0.51.2
## [9] BiocGenerics_0.59.2 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_2.81.1 S7_0.2.2 bitops_1.0-9
## [7] fastmap_1.2.0 RCurl_1.98-1.18 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_4.6.0
## [16] rlang_1.2.0 sass_0.4.10 tools_4.6.0
## [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_0.39.2 RColorBrewer_1.1-3 abind_1.4-8
## [28] BiocParallel_1.47.0 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] BiocBaseUtils_1.15.1 cachem_1.1.0 splines_4.6.0
## [46] parallel_4.6.0 BiocManager_1.30.27 XVector_0.53.0
## [49] restfulr_0.0.16 vctrs_0.7.3 Matrix_1.7-5
## [52] jsonlite_2.0.0 SparseM_1.84-2 carData_3.0-6
## [55] bookdown_0.46 car_3.1-5 MCMCpack_1.7-1
## [58] Formula_1.2-5 magick_2.9.1 jquerylib_0.1.4
## [61] glue_1.8.1 codetools_0.2-20 gtable_0.3.6
## [64] BiocIO_1.23.3 tibble_3.3.1 pillar_1.11.1
## [67] htmltools_0.5.9 quantreg_6.1 R6_2.6.1
## [70] evaluate_1.0.5 lattice_0.22-9 Rsamtools_2.29.0
## [73] cigarillo_1.3.0 bslib_0.11.0 MatrixModels_0.5-4
## [76] Rcpp_1.1.1-1.1 coda_0.19-4.1 SparseArray_1.13.2
## [79] xfun_0.57 pkgconfig_2.0.3