mist:methylation inference for single-cell along trajectory
30 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)$mist_pars)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.268368 -0.493016964 0.5097054 0.31225427 -0.11255822
## ENSMUSG00000000003 1.544941 1.542133694 2.8549180 -1.75754011 -2.86007066
## ENSMUSG00000000028 1.294364 0.009540156 0.1068241 0.01793735 -0.02237140
## ENSMUSG00000000037 1.053545 -4.628421848 12.9348016 -5.61607029 -2.73367975
## ENSMUSG00000000049 1.010878 -0.124899476 0.1274451 0.11493762 0.06949836
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.505557 14.999090 3.594427 1.770791
## ENSMUSG00000000003 24.719346 3.384022 7.509212 9.111115
## ENSMUSG00000000028 7.464531 7.644023 3.635891 2.303272
## ENSMUSG00000000037 8.511641 12.921891 6.980450 2.221980
## ENSMUSG00000000049 5.652139 9.128325 2.901747 1.3172753 Step 3: Perform Differential Methylation Analysis Using dmSingle
# Perform differential methylation analysis for the single-group
Dat_sce <- dmSingle(Dat_sce)
# View the top genomic features with drastic methylation changes
head(rowData(Dat_sce)$mist_int)## ENSMUSG00000000037 ENSMUSG00000000003 ENSMUSG00000000001 ENSMUSG00000000049
## 0.062606403 0.032962403 0.011378271 0.008744585
## ENSMUSG00000000028
## 0.0048272504 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)$mist_pars, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.264373 -0.50393635 0.4464748 0.31479556 -0.03650538
## ENSMUSG00000000003 1.507949 1.62616534 3.1017785 -1.83044990 -3.17190169
## ENSMUSG00000000028 1.288418 0.01005007 0.1025316 0.01597944 -0.02290974
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.837911 13.247077 3.809745 1.742368
## ENSMUSG00000000003 24.455615 4.481056 7.310687 9.337942
## ENSMUSG00000000028 7.438908 7.608484 3.761568 2.261506head(rowData(Dat_sce_g2)$mist_pars, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.9203339 -0.6839444 4.537461 -2.727702 -1.2855591
## ENSMUSG00000000003 -0.8179274 -1.3738261 3.783131 -1.392957 -0.9510838
## ENSMUSG00000000028 2.2684264 -17.2559992 82.462057 -117.792832 52.8611262
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.679195 6.387089 3.278315 1.441077
## ENSMUSG00000000003 7.597635 9.543038 4.561997 2.940527
## ENSMUSG00000000028 8.620777 6.288073 3.675560 3.3049147 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)## ENSMUSG00000000028 ENSMUSG00000000037 ENSMUSG00000000003 ENSMUSG00000000001
## 0.04972029 0.03736648 0.03265349 0.02132975
## ENSMUSG00000000049
## 0.012460037.1 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 (2025-08-23 r88802)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.22-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_1.32.0
## [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