mist:methylation inference for single-cell along trajectory
29 October 2025
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)$mist_pars)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.248307 -0.32076637 0.23208752 0.25004153 0.06893146
## ENSMUSG00000000003 1.592233 1.66580639 2.26253608 -1.85226041 -2.33987443
## ENSMUSG00000000028 1.284370 -0.00608650 0.10429107 0.04707951 -0.01972436
## ENSMUSG00000000037 1.024039 -3.87345822 10.31656683 -3.41003908 -3.05071729
## ENSMUSG00000000049 1.027007 -0.04912175 0.05783529 0.07452194 0.05228232
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.459454 12.469965 3.764669 1.785071
## ENSMUSG00000000003 26.183231 6.219113 5.565072 8.797245
## ENSMUSG00000000028 7.949602 8.220084 3.258056 2.220716
## ENSMUSG00000000037 8.918608 12.812793 7.212077 2.128206
## ENSMUSG00000000049 6.066276 8.849229 2.980045 1.1578913 Step 3: Perform Differential Methylation Analysis Using dmSingle
# Perform differential methylation analysis for the single-group
Dat_sce <- dmSingle(Dat_sce)
# View the top genomic features with drastic methylation changes
head(rowData(Dat_sce)$mist_int)## ENSMUSG00000000037 ENSMUSG00000000003 ENSMUSG00000000001 ENSMUSG00000000049
## 0.055556615 0.030537733 0.010302694 0.006123931
## ENSMUSG00000000028
## 0.0053250924 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)$mist_pars, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.242068 -0.44050624 0.22410813 0.34686864 0.14668732
## ENSMUSG00000000003 1.639647 1.77956229 2.43843580 -1.94648773 -2.60354521
## ENSMUSG00000000028 1.289386 -0.02279722 0.08811998 0.05518299 0.02541573
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.091809 14.518908 3.203293 1.822668
## ENSMUSG00000000003 26.263682 3.356110 5.939064 8.954333
## ENSMUSG00000000028 7.688547 7.493357 3.101238 2.328775head(rowData(Dat_sce_g2)$mist_pars, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.9239899 -0.3251663 3.252234 -1.911634 -1.1376255
## ENSMUSG00000000003 -0.8531602 -1.6923463 5.911375 -4.964182 0.8175813
## ENSMUSG00000000028 2.2902083 -6.4640277 29.189754 -37.807580 15.2146883
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.539727 6.164198 2.831261 1.463195
## ENSMUSG00000000003 6.425620 10.997343 5.212602 3.113019
## ENSMUSG00000000028 9.814264 5.467274 3.754816 3.6075547 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 ENSMUSG00000000028
## 0.063565921 0.029265786 0.021157990 0.019653982
## ENSMUSG00000000049
## 0.0099476147.1 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 4.5.1 Patched (2025-09-10 r88807)
## Platform: aarch64-apple-darwin20
## Running under: macOS Ventura 13.7.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/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_4.0.0 SingleCellExperiment_1.32.0
## [3] SummarizedExperiment_1.40.0 Biobase_2.70.0
## [5] GenomicRanges_1.62.0 Seqinfo_1.0.0
## [7] IRanges_2.44.0 S4Vectors_0.48.0
## [9] BiocGenerics_0.56.0 generics_0.1.4
## [11] MatrixGenerics_1.22.0 matrixStats_1.5.0
## [13] mist_1.2.0 BiocStyle_2.38.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 dplyr_1.1.4 farver_2.1.2
## [4] Biostrings_2.78.0 S7_0.2.0 bitops_1.0-9
## [7] fastmap_1.2.0 RCurl_1.98-1.17 GenomicAlignments_1.46.0
## [10] XML_3.99-0.19 digest_0.6.37 lifecycle_1.0.4
## [13] survival_3.8-3 magrittr_2.0.4 compiler_4.5.1
## [16] rlang_1.1.6 sass_0.4.10 tools_4.5.1
## [19] yaml_2.3.10 rtracklayer_1.70.0 knitr_1.50
## [22] labeling_0.4.3 S4Arrays_1.10.0 curl_7.0.0
## [25] DelayedArray_0.36.0 RColorBrewer_1.1-3 abind_1.4-8
## [28] BiocParallel_1.44.0 withr_3.0.2 grid_4.5.1
## [31] scales_1.4.0 MASS_7.3-65 mcmc_0.9-8
## [34] tinytex_0.57 dichromat_2.0-0.1 cli_3.6.5
## [37] mvtnorm_1.3-3 rmarkdown_2.30 crayon_1.5.3
## [40] httr_1.4.7 rjson_0.2.23 cachem_1.1.0
## [43] splines_4.5.1 parallel_4.5.1 BiocManager_1.30.26
## [46] XVector_0.50.0 restfulr_0.0.16 vctrs_0.6.5
## [49] Matrix_1.7-4 jsonlite_2.0.0 SparseM_1.84-2
## [52] carData_3.0-5 bookdown_0.45 car_3.1-3
## [55] MCMCpack_1.7-1 Formula_1.2-5 magick_2.9.0
## [58] jquerylib_0.1.4 glue_1.8.0 codetools_0.2-20
## [61] gtable_0.3.6 BiocIO_1.20.0 tibble_3.3.0
## [64] pillar_1.11.1 htmltools_0.5.8.1 quantreg_6.1
## [67] R6_2.6.1 evaluate_1.0.5 lattice_0.22-7
## [70] Rsamtools_2.26.0 cigarillo_1.0.0 bslib_0.9.0
## [73] MatrixModels_0.5-4 Rcpp_1.1.0 coda_0.19-4.1
## [76] SparseArray_1.10.0 xfun_0.53 pkgconfig_2.0.3