mist:methylation inference for single-cell along trajectory
4 November 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)$mist_pars)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.254570 -0.66428126 0.5890856 0.32220381 0.02693085
## ENSMUSG00000000003 1.587354 1.81821615 1.7484529 -1.42884779 -2.40340675
## ENSMUSG00000000028 1.298808 -0.01870284 0.1195747 0.03277182 -0.01212524
## ENSMUSG00000000037 1.016930 -4.89567929 13.2765761 -5.84533783 -2.53502124
## ENSMUSG00000000049 1.018608 -0.08200303 0.1084319 0.07221070 0.05115999
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.534338 13.642131 3.734521 1.963797
## ENSMUSG00000000003 25.883281 3.656273 6.115603 8.737515
## ENSMUSG00000000028 7.466891 7.260971 3.797514 2.277551
## ENSMUSG00000000037 8.788247 13.565056 6.795162 2.303394
## ENSMUSG00000000049 5.943850 8.263436 3.067052 1.1848173 Step 3: Perform Differential Methylation Analysis Using dmSingle
# Perform differential methylation analysis for the single-group
Dat_sce <- dmSingle(Dat_sce)
# View the top genomic features with drastic methylation changes
head(rowData(Dat_sce)$mist_int)## ENSMUSG00000000037 ENSMUSG00000000003 ENSMUSG00000000001 ENSMUSG00000000049
## 0.065803405 0.030241906 0.014715770 0.006987605
## ENSMUSG00000000028
## 0.0051413054 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)$mist_pars, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.242179 -0.678591685 0.76905422 0.30193115 -0.104913967
## ENSMUSG00000000003 1.565390 1.753905814 2.61003512 -1.89259634 -2.751706140
## ENSMUSG00000000028 1.297740 -0.008799848 0.08440254 0.02836873 -0.001974097
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.484694 13.926588 3.432080 2.237804
## ENSMUSG00000000003 24.393702 4.177242 6.405818 9.494624
## ENSMUSG00000000028 7.957134 7.774183 2.993894 2.207018head(rowData(Dat_sce_g2)$mist_pars, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.9017308 -3.696403 20.312050 -26.381107 9.6227984
## ENSMUSG00000000003 -0.8381918 -2.396281 7.951101 -6.373335 0.8607604
## ENSMUSG00000000028 2.3491996 -2.456967 10.653108 -11.927032 3.8272402
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.539401 6.406628 4.075735 1.336894
## ENSMUSG00000000003 6.772438 10.604057 4.962369 3.162399
## ENSMUSG00000000028 11.075112 4.658120 3.647120 3.5471627 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
## 0.062070012 0.033195085 0.031254865 0.010393782
## ENSMUSG00000000028
## 0.0093439817.1 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 Under development (unstable) (2025-10-20 r88955)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.23-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [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: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] ggplot2_4.0.0 SingleCellExperiment_1.33.0
## [3] SummarizedExperiment_1.41.0 Biobase_2.71.0
## [5] GenomicRanges_1.63.0 Seqinfo_1.1.0
## [7] IRanges_2.45.0 S4Vectors_0.49.0
## [9] BiocGenerics_0.57.0 generics_0.1.4
## [11] MatrixGenerics_1.23.0 matrixStats_1.5.0
## [13] mist_1.3.0 BiocStyle_2.39.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 dplyr_1.1.4 farver_2.1.2
## [4] Biostrings_2.79.1 S7_0.2.0 bitops_1.0-9
## [7] fastmap_1.2.0 RCurl_1.98-1.17 GenomicAlignments_1.47.0
## [10] XML_3.99-0.19 digest_0.6.37 lifecycle_1.0.4
## [13] survival_3.8-3 magrittr_2.0.4 compiler_4.6.0
## [16] rlang_1.1.6 sass_0.4.10 tools_4.6.0
## [19] yaml_2.3.10 rtracklayer_1.71.0 knitr_1.50
## [22] labeling_0.4.3 S4Arrays_1.11.0 curl_7.0.0
## [25] DelayedArray_0.37.0 RColorBrewer_1.1-3 abind_1.4-8
## [28] BiocParallel_1.45.0 withr_3.0.2 grid_4.6.0
## [31] scales_1.4.0 MASS_7.3-65 mcmc_0.9-8
## [34] tinytex_0.57 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_4.6.0 parallel_4.6.0 BiocManager_1.30.26
## [46] XVector_0.51.0 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_1.21.0 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_2.27.0 cigarillo_1.1.0 bslib_0.9.0
## [73] MatrixModels_0.5-4 Rcpp_1.1.0 coda_0.19-4.1
## [76] SparseArray_1.11.1 xfun_0.54 pkgconfig_2.0.3