estiParam
dmSingle
plotGene
estiParam
dmTwoGroups
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.
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")
In this section, we will estimate parameters and perform differential methylation analysis using single-group 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"))
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)$mist_pars)
## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.229358 -0.79756013 0.71079382 0.48500686 -0.05384609
## ENSMUSG00000000003 1.619208 1.39649835 3.21481717 -2.92635838 -1.91747149
## ENSMUSG00000000028 1.289347 -0.05432853 0.11266552 0.04860176 0.03355944
## ENSMUSG00000000037 1.053122 -2.80965904 7.47428896 -1.85139688 -2.79623749
## ENSMUSG00000000049 1.014522 -0.10578958 0.09803603 0.10502108 0.07339598
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.363294 12.393336 3.259193 2.280623
## ENSMUSG00000000003 24.911560 5.027838 5.874613 8.546568
## ENSMUSG00000000028 8.150715 6.979663 2.941894 2.301342
## ENSMUSG00000000037 9.244015 14.408558 6.787336 2.322025
## ENSMUSG00000000049 5.862191 9.491265 2.878722 1.189985
dmSingle
# Perform differential methylation analysis for the single-group
Dat_sce <- dmSingle(Dat_sce)
# View the top genomic features with drastic methylation changes
head(rowData(Dat_sce)$mist_int)
## ENSMUSG00000000037 ENSMUSG00000000003 ENSMUSG00000000001 ENSMUSG00000000049
## 0.043843088 0.030109795 0.018454756 0.007884151
## ENSMUSG00000000028
## 0.005706508
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")
In this section, we will estimate parameters and perform DM analysis using data from two phenotypic groups.
# 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"))
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)$mist_pars, n = 3)
## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.242371 -0.48803127 0.3108058 0.36450019 0.077777241
## ENSMUSG00000000003 1.561865 1.53239077 2.3139768 -1.31965981 -2.750816467
## ENSMUSG00000000028 1.277313 -0.02187773 0.1104212 0.02668885 -0.003651618
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.417616 13.607535 3.679199 1.900187
## ENSMUSG00000000003 24.096764 5.591455 5.942960 9.131139
## ENSMUSG00000000028 7.823941 6.474997 2.969883 2.133866
head(rowData(Dat_sce_g2)$mist_pars, n = 3)
## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.9088445 -1.205858 6.792645 -5.441605 -0.3016342
## ENSMUSG00000000003 -0.8338466 -3.597110 10.450255 -6.204598 -0.6458703
## ENSMUSG00000000028 2.3006964 -11.520292 54.271785 -76.637126 34.1651335
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.407086 6.18872 3.421180 1.519962
## ENSMUSG00000000003 6.015730 10.58480 4.623419 2.889453
## ENSMUSG00000000028 9.407655 6.44427 3.619148 3.388481
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
## 0.046689237 0.045513589 0.030662015 0.024507811
## ENSMUSG00000000049
## 0.009830224
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.
## R version 4.5.0 RC (2025-04-04 r88126)
## Platform: x86_64-apple-darwin20
## Running under: macOS Monterey 12.7.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
##
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## 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