mist:methylation inference for single-cell along trajectory
9 January 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
estiParamSingle - 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
estiParamTwoGroups - 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", "small_sampleData_sce.rds", package = "mist"))2 Step 2: Estimate Parameters Using estiParamSingle
# Estimate parameters for single-group
Dat_sce <- estiParamSingle(
Dat_sce = Dat_sce,
Dat_name = "Methy_level_group1",
ptime_name = "pseudotime"
)
# Check the output
head(rowData(Dat_sce)$mist_pars)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.251821 -0.8298828 0.71185133 0.47356436 -0.100648275
## ENSMUSG00000000003 1.541433 1.9426357 2.66996046 -2.00873161 -2.948861743
## ENSMUSG00000000028 1.268217 -0.0146444 0.09725288 0.04459067 -0.004776211
## ENSMUSG00000000037 1.060106 -2.0275698 5.24558881 -0.89507053 -2.276672217
## ENSMUSG00000000049 1.016816 -0.1346197 0.15546302 0.09704615 0.062586513
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 6.102466 14.318164 3.464031 1.735114
## ENSMUSG00000000003 26.378111 4.884270 7.225007 9.163833
## ENSMUSG00000000028 7.538908 8.134058 2.970255 2.317636
## ENSMUSG00000000037 9.822060 12.605745 7.191436 2.221882
## ENSMUSG00000000049 5.742192 9.096224 2.807598 1.2563303 Step 3: Perform Differential Methylation Analysis Using dmSingle
# Perform differential methylation analysis for the single-group
Dat_sce <- dmSingle(Dat_sce)
# View the top genomic features with drastic methylation changes
head(rowData(Dat_sce)$mist_int)## ENSMUSG00000000003 ENSMUSG00000000037 ENSMUSG00000000001 ENSMUSG00000000049
## 0.035204394 0.033684470 0.016134207 0.008551738
## ENSMUSG00000000028
## 0.0051631354 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 <- readRDS(system.file("extdata", "small_sampleData_sce.rds", package = "mist"))6 Step 2: Estimate Parameters Using estiParamTwoGroups
# Estimate parameters for both groups
Dat_sce <- estiParamTwo(
Dat_sce = Dat_sce,
Dat_name_g1 = "Methy_level_group1",
Dat_name_g2 = "Methy_level_group2",
ptime_name_g1 = "pseudotime",
ptime_name_g2 = "pseudotime_g2"
)
# Check the output
head(rowData(Dat_sce)$mist_pars_group1, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.251117 -1.24339490 1.2852404 0.65851897 -0.401992034
## ENSMUSG00000000003 1.610158 1.88177269 2.3122802 -1.78509481 -2.775867523
## ENSMUSG00000000028 1.276944 -0.04699542 0.1334145 0.06866795 0.007253987
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.911935 14.374977 3.299854 1.926787
## ENSMUSG00000000003 26.318934 3.687068 5.759587 9.469006
## ENSMUSG00000000028 7.465450 7.732377 2.844887 2.487678head(rowData(Dat_sce)$mist_pars_group2, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.9125108 -3.618325 19.878396 -26.800633 10.4805747
## ENSMUSG00000000003 -0.8242179 -1.276835 3.622506 -1.453831 -0.8421272
## ENSMUSG00000000028 2.3290821 -1.831904 8.601193 -10.278182 3.6263711
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.409877 6.433782 3.506305 1.642253
## ENSMUSG00000000003 6.847387 10.197469 4.528897 2.961215
## ENSMUSG00000000028 11.623755 5.327985 3.491826 3.4927157 Step 3: Perform Differential Methylation Analysis for Two-Group Comparison Using dmTwoGroups
# Perform DM analysis to compare the two groups
Dat_sce <- dmTwoGroups(Dat_sce)
# View the top genomic features with different temporal patterns between groups
head(rowData(Dat_sce)$mist_int_2group)## ENSMUSG00000000001 ENSMUSG00000000037 ENSMUSG00000000003 ENSMUSG00000000049
## 0.034716066 0.031906532 0.031560902 0.010995756
## ENSMUSG00000000028
## 0.0072251967.1 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) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 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
##
## 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_3.5.1 SingleCellExperiment_1.29.1
## [3] SummarizedExperiment_1.37.0 Biobase_2.67.0
## [5] GenomicRanges_1.59.1 GenomeInfoDb_1.43.2
## [7] IRanges_2.41.2 S4Vectors_0.45.2
## [9] BiocGenerics_0.53.3 generics_0.1.3
## [11] MatrixGenerics_1.19.0 matrixStats_1.5.0
## [13] mist_0.99.16 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 farver_2.1.2 dplyr_1.1.4
## [4] Biostrings_2.75.3 bitops_1.0-9 fastmap_1.2.0
## [7] RCurl_1.98-1.16 GenomicAlignments_1.43.0 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_1.1.4
## [16] sass_0.4.9 tools_4.5.0 yaml_2.3.10
## [19] rtracklayer_1.67.0 knitr_1.49 labeling_0.4.3
## [22] S4Arrays_1.7.1 curl_6.1.0 DelayedArray_0.33.3
## [25] abind_1.4-8 BiocParallel_1.41.0 withr_3.0.2
## [28] grid_4.5.0 colorspace_2.1-1 scales_1.3.0
## [31] MASS_7.3-64 mcmc_0.9-8 tinytex_0.54
## [34] cli_3.6.3 mvtnorm_1.3-2 rmarkdown_2.29
## [37] crayon_1.5.3 httr_1.4.7 rjson_0.2.23
## [40] cachem_1.1.0 zlibbioc_1.53.0 splines_4.5.0
## [43] parallel_4.5.0 BiocManager_1.30.25 XVector_0.47.2
## [46] restfulr_0.0.15 vctrs_0.6.5 Matrix_1.7-1
## [49] jsonlite_1.8.9 SparseM_1.84-2 carData_3.0-5
## [52] bookdown_0.42 car_3.1-3 MCMCpack_1.7-1
## [55] Formula_1.2-5 magick_2.8.5 jquerylib_0.1.4
## [58] snow_0.4-4 glue_1.8.0 codetools_0.2-20
## [61] gtable_0.3.6 BiocIO_1.17.1 UCSC.utils_1.3.0
## [64] munsell_0.5.1 tibble_3.2.1 pillar_1.10.1
## [67] htmltools_0.5.8.1 quantreg_5.99.1 GenomeInfoDbData_1.2.13
## [70] R6_2.5.1 evaluate_1.0.1 lattice_0.22-6
## [73] Rsamtools_2.23.1 bslib_0.8.0 MatrixModels_0.5-3
## [76] Rcpp_1.0.13-1 coda_0.19-4.1 SparseArray_1.7.2
## [79] xfun_0.50 pkgconfig_2.0.3