mist:methylation inference for single-cell along trajectory
8 February 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)$mist_pars)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.245887 -0.57164086 0.47572102 0.32720780 0.01038017
## ENSMUSG00000000003 1.558296 1.73728176 2.75799266 -1.90237797 -2.90536689
## ENSMUSG00000000028 1.303688 -0.01198938 0.11647492 0.02475701 -0.01657044
## ENSMUSG00000000037 1.044776 -3.25999677 9.19036977 -3.82346840 -2.07243969
## ENSMUSG00000000049 1.032187 -0.06907304 0.07676791 0.07735370 0.07607455
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 6.426238 14.900321 3.354172 1.884431
## ENSMUSG00000000003 25.607398 4.201863 6.373195 9.917196
## ENSMUSG00000000028 7.512896 9.464211 3.431837 2.232120
## ENSMUSG00000000037 8.482812 12.846243 6.502279 2.344582
## ENSMUSG00000000049 6.281593 8.979870 3.253743 1.2903693 Step 3: Perform Differential Methylation Analysis Using dmSingle
# Perform differential methylation analysis for the single-group
Dat_sce <- dmSingle(Dat_sce)
# View the top genomic features with drastic methylation changes
head(rowData(Dat_sce)$mist_int)## ENSMUSG00000000037 ENSMUSG00000000003 ENSMUSG00000000001 ENSMUSG00000000049
## 0.047916477 0.033867057 0.012966180 0.007229709
## ENSMUSG00000000028
## 0.0048090014 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)$mist_pars, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.254246 -0.811975164 0.7530509 0.409259079 -0.07615449
## ENSMUSG00000000003 1.571482 1.954090899 2.4176311 -1.708318164 -3.01732264
## ENSMUSG00000000028 1.311981 0.008135466 0.1397775 0.007386868 -0.05224407
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.896316 15.998400 3.331429 1.809799
## ENSMUSG00000000003 27.000789 3.119283 7.770265 8.831189
## ENSMUSG00000000028 7.570086 7.210543 4.132031 2.353869head(rowData(Dat_sce_g2)$mist_pars, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.8961661 -5.48962405 26.4385513 -33.10429348 12.0374625
## ENSMUSG00000000003 -0.8370036 -2.05851301 5.6087376 -2.52313921 -0.9982137
## ENSMUSG00000000028 2.3606222 0.05461162 0.4466327 -0.01775632 -0.3224051
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.217036 6.724608 3.618113 1.547579
## ENSMUSG00000000003 7.502114 10.418126 4.620660 2.934422
## ENSMUSG00000000028 11.345208 5.978956 3.226592 3.4168457 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
## 0.056915987 0.037066930 0.037035005 0.008780471
## ENSMUSG00000000028
## 0.0012865657.1 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) (2026-01-15 r89304)
## 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_4.0.2 SingleCellExperiment_1.33.0
## [3] SummarizedExperiment_1.41.1 Biobase_2.71.0
## [5] GenomicRanges_1.63.1 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_1.3.3 BiocStyle_2.39.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 dplyr_1.2.0 farver_2.1.2
## [4] Biostrings_2.79.4 S7_0.2.1 bitops_1.0-9
## [7] fastmap_1.2.0 RCurl_1.98-1.17 GenomicAlignments_1.47.0
## [10] XML_3.99-0.20 digest_0.6.39 lifecycle_1.0.5
## [13] survival_3.8-6 magrittr_2.0.4 compiler_4.6.0
## [16] rlang_1.1.7 sass_0.4.10 tools_4.6.0
## [19] yaml_2.3.12 rtracklayer_1.71.3 knitr_1.51
## [22] labeling_0.4.3 S4Arrays_1.11.1 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_0.58 dichromat_2.0-0.1 cli_3.6.5
## [37] mvtnorm_1.3-3 rmarkdown_2.30 crayon_1.5.3
## [40] otel_0.2.0 httr_1.4.7 rjson_0.2.23
## [43] cachem_1.1.0 splines_4.6.0 parallel_4.6.0
## [46] BiocManager_1.30.27 XVector_0.51.0 restfulr_0.0.16
## [49] vctrs_0.7.1 Matrix_1.7-4 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_2.9.0 jquerylib_0.1.4 glue_1.8.0
## [61] codetools_0.2-20 gtable_0.3.6 BiocIO_1.21.0
## [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_0.22-7 Rsamtools_2.27.0 cigarillo_1.1.0
## [73] bslib_0.10.0 MatrixModels_0.5-4 Rcpp_1.1.1
## [76] coda_0.19-4.1 SparseArray_1.11.10 xfun_0.56
## [79] pkgconfig_2.0.3