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)$mist_pars)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.274121 -0.471209030 0.3623838 0.32488405 0.01915854
## ENSMUSG00000000003 1.586202 1.098734578 3.6199140 -2.16249453 -2.85873841
## ENSMUSG00000000028 1.283939 -0.009938242 0.0985196 0.04219455 -0.01007775
## ENSMUSG00000000037 1.028149 -4.158435838 11.4899159 -4.41165171 -2.95152100
## ENSMUSG00000000049 1.034583 -0.067620173 0.1188776 0.06162854 0.04176091
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 6.400119 15.139993 3.660511 1.897404
## ENSMUSG00000000003 24.135670 5.753880 5.109565 8.969364
## ENSMUSG00000000028 7.763001 7.708252 3.298996 2.195743
## ENSMUSG00000000037 8.499263 12.026079 7.168603 2.234785
## ENSMUSG00000000049 6.585597 8.304051 3.195952 1.3059273 Step 3: Perform Differential Methylation Analysis Using dmSingle
# Perform differential methylation analysis for the single-group
Dat_sce <- dmSingle(Dat_sce)
# View the top genomic features with drastic methylation changes
head(rowData(Dat_sce)$mist_int)## ENSMUSG00000000037 ENSMUSG00000000003 ENSMUSG00000000001 ENSMUSG00000000049
## 0.059105215 0.032480115 0.011632077 0.007170808
## ENSMUSG00000000028
## 0.0050792494 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)$mist_pars, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.255329 -0.61298767 0.4802841 0.40831766 0.01009127
## ENSMUSG00000000003 1.640251 1.26996570 3.2325638 -2.28401050 -2.52146072
## ENSMUSG00000000028 1.289953 -0.01726036 0.1085411 0.04558792 -0.01854391
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.814364 15.969483 3.412765 2.090464
## ENSMUSG00000000003 24.772595 4.201358 5.057342 8.930926
## ENSMUSG00000000028 7.877231 7.610079 3.156014 2.147512head(rowData(Dat_sce_g2)$mist_pars, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.9278939 -1.5452466 9.308607 -10.045490 2.1314787
## ENSMUSG00000000003 -0.8139109 -3.4412179 9.716602 -5.515422 -0.7456901
## ENSMUSG00000000028 2.3330925 -0.8325805 3.312083 -2.793488 0.4666256
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.663777 6.153624 3.628892 1.512319
## ENSMUSG00000000003 6.430052 10.509734 4.259788 2.938772
## ENSMUSG00000000028 11.316478 5.586455 3.158478 3.0606047 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
## 0.048052450 0.044883921 0.026302083 0.010398669
## ENSMUSG00000000028
## 0.0035384867.1 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 beta (2025-04-02 r88102)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 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_3.5.2 SingleCellExperiment_1.31.0
## [3] SummarizedExperiment_1.39.0 Biobase_2.69.0
## [5] GenomicRanges_1.61.0 GenomeInfoDb_1.45.0
## [7] IRanges_2.43.0 S4Vectors_0.47.0
## [9] BiocGenerics_0.55.0 generics_0.1.3
## [11] MatrixGenerics_1.21.0 matrixStats_1.5.0
## [13] mist_1.1.0 BiocStyle_2.37.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 farver_2.1.2 dplyr_1.1.4
## [4] Biostrings_2.77.0 bitops_1.0-9 fastmap_1.2.0
## [7] RCurl_1.98-1.17 GenomicAlignments_1.45.0 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_1.69.0 knitr_1.50 labeling_0.4.3
## [22] S4Arrays_1.9.0 curl_6.2.2 DelayedArray_0.35.0
## [25] abind_1.4-8 BiocParallel_1.43.0 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_0.49.0 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_1.19.0
## [61] UCSC.utils_1.5.0 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_2.25.0 bslib_0.9.0
## [73] MatrixModels_0.5-4 Rcpp_1.0.14 coda_0.19-4.1
## [76] SparseArray_1.9.0 xfun_0.52 pkgconfig_2.0.3