mist:methylation inference for single-cell along trajectory
15 April 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)$mist_pars)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.241292 -0.62490620 0.51857314 0.33853903 0.050143708
## ENSMUSG00000000003 1.583575 1.70737077 3.25832457 -2.57507155 -2.684669917
## ENSMUSG00000000028 1.286394 -0.01733794 0.10311323 0.03097087 -0.003909041
## ENSMUSG00000000037 1.033816 -3.73638198 10.30363194 -3.27337060 -3.336715718
## ENSMUSG00000000049 1.032356 -0.06913694 0.07363689 0.07877664 0.091803005
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.602400 15.452745 3.124282 1.948904
## ENSMUSG00000000003 25.776135 3.708126 7.970795 8.993499
## ENSMUSG00000000028 8.401626 6.767990 2.853196 2.156276
## ENSMUSG00000000037 8.955372 14.419091 7.011103 2.407441
## ENSMUSG00000000049 6.294127 9.362584 3.411021 1.3739723 Step 3: Perform Differential Methylation Analysis Using dmSingle
# Perform differential methylation analysis for the single-group
Dat_sce <- dmSingle(Dat_sce)
# View the top genomic features with drastic methylation changes
head(rowData(Dat_sce)$mist_int)## ENSMUSG00000000037 ENSMUSG00000000003 ENSMUSG00000000001 ENSMUSG00000000049
## 0.055857718 0.033659140 0.014710837 0.007765350
## ENSMUSG00000000028
## 0.0047597084 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)$mist_pars, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.262988 -0.56669940 0.5616504 0.26777072 -0.02316883
## ENSMUSG00000000003 1.550099 1.16247844 3.9640714 -2.31759563 -3.07198215
## ENSMUSG00000000028 1.276870 -0.04839283 0.1346460 0.04474141 0.01704771
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.811874 13.983816 3.735228 1.784201
## ENSMUSG00000000003 24.519159 7.826680 6.209487 9.607665
## ENSMUSG00000000028 7.814366 7.737493 2.905221 2.324463head(rowData(Dat_sce_g2)$mist_pars, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.9258027 -1.144325 6.715761 -6.5918728 0.942872
## ENSMUSG00000000003 -0.8507717 -1.008402 2.702022 -0.5298433 -1.114373
## ENSMUSG00000000028 2.3100747 -7.982103 35.501428 -46.4747577 19.084240
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.614912 6.423551 2.962210 1.908368
## ENSMUSG00000000003 6.639421 10.882583 4.628323 2.977315
## ENSMUSG00000000028 10.258500 6.289765 3.882762 3.0805827 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 ENSMUSG00000000028 ENSMUSG00000000001
## 0.04818409 0.02892537 0.02253438 0.02091419
## ENSMUSG00000000049
## 0.010513247.1 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 4.5.0 RC (2025-04-04 r88126)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-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_3.5.2 SingleCellExperiment_1.30.0
## [3] SummarizedExperiment_1.38.0 Biobase_2.68.0
## [5] GenomicRanges_1.60.0 GenomeInfoDb_1.44.0
## [7] IRanges_2.42.0 S4Vectors_0.46.0
## [9] BiocGenerics_0.54.0 generics_0.1.3
## [11] MatrixGenerics_1.20.0 matrixStats_1.5.0
## [13] mist_1.0.0 BiocStyle_2.36.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 farver_2.1.2 dplyr_1.1.4
## [4] Biostrings_2.76.0 bitops_1.0-9 fastmap_1.2.0
## [7] RCurl_1.98-1.17 GenomicAlignments_1.44.0 XML_3.99-0.18
## [10] digest_0.6.37 lifecycle_1.0.4 survival_3.8-3
## [13] magrittr_2.0.3 compiler_4.5.0 rlang_1.1.6
## [16] sass_0.4.10 tools_4.5.0 yaml_2.3.10
## [19] rtracklayer_1.68.0 knitr_1.50 labeling_0.4.3
## [22] S4Arrays_1.8.0 curl_6.2.2 DelayedArray_0.34.0
## [25] abind_1.4-8 BiocParallel_1.42.0 withr_3.0.2
## [28] grid_4.5.0 colorspace_2.1-1 scales_1.3.0
## [31] MASS_7.3-65 mcmc_0.9-8 tinytex_0.57
## [34] cli_3.6.4 mvtnorm_1.3-3 rmarkdown_2.29
## [37] crayon_1.5.3 httr_1.4.7 rjson_0.2.23
## [40] cachem_1.1.0 splines_4.5.0 parallel_4.5.0
## [43] BiocManager_1.30.25 XVector_0.48.0 restfulr_0.0.15
## [46] vctrs_0.6.5 Matrix_1.7-3 jsonlite_2.0.0
## [49] SparseM_1.84-2 carData_3.0-5 bookdown_0.43
## [52] car_3.1-3 MCMCpack_1.7-1 Formula_1.2-5
## [55] magick_2.8.6 jquerylib_0.1.4 glue_1.8.0
## [58] codetools_0.2-20 gtable_0.3.6 BiocIO_1.18.0
## [61] UCSC.utils_1.4.0 munsell_0.5.1 tibble_3.2.1
## [64] pillar_1.10.2 htmltools_0.5.8.1 quantreg_6.1
## [67] GenomeInfoDbData_1.2.14 R6_2.6.1 evaluate_1.0.3
## [70] lattice_0.22-7 Rsamtools_2.24.0 bslib_0.9.0
## [73] MatrixModels_0.5-4 Rcpp_1.0.14 coda_0.19-4.1
## [76] SparseArray_1.8.0 xfun_0.52 pkgconfig_2.0.3