mist:methylation inference for single-cell along trajectory
22 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
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)$mist_pars)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.262168 -0.45848577 0.3860424 0.25592535 0.04228796
## ENSMUSG00000000003 1.486194 0.82677917 6.2557564 -7.00088978 -0.26377178
## ENSMUSG00000000028 1.282562 -0.08961659 0.2194913 0.05096871 -0.02954715
## ENSMUSG00000000037 1.031421 -3.50242554 10.2365773 -4.77857574 -1.96278669
## ENSMUSG00000000049 1.015701 -0.13145110 0.1770878 0.09438084 0.03773343
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 6.289928 13.959799 3.770757 1.859867
## ENSMUSG00000000003 23.774740 4.458471 8.147946 9.299482
## ENSMUSG00000000028 8.078568 8.321215 2.913104 2.380414
## ENSMUSG00000000037 8.870319 12.908047 7.330478 2.128084
## ENSMUSG00000000049 5.776777 9.121084 3.572700 1.2439293 Step 3: Perform Differential Methylation Analysis Using dmSingle
# Perform differential methylation analysis for the single-group
Dat_sce <- dmSingle(Dat_sce)
# View the top genomic features with drastic methylation changes
head(rowData(Dat_sce)$mist_int)## ENSMUSG00000000037 ENSMUSG00000000003 ENSMUSG00000000001 ENSMUSG00000000049
## 0.050513676 0.035503376 0.011228769 0.008567133
## ENSMUSG00000000028
## 0.0067079904 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)$mist_pars, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.261142 -0.60380709 0.5409062 0.2867432 0.03171628
## ENSMUSG00000000003 1.572144 0.53062879 6.9571271 -8.7676111 1.14495632
## ENSMUSG00000000028 1.273395 -0.04626301 0.1704434 0.0457484 -0.02823613
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.819875 15.928278 3.441753 1.846513
## ENSMUSG00000000003 25.067763 4.468332 6.808220 9.135659
## ENSMUSG00000000028 8.183641 7.775493 3.049279 2.527337head(rowData(Dat_sce_g2)$mist_pars, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.9220036 -0.9153696 5.725854 -4.189998 -0.7956022
## ENSMUSG00000000003 -0.8361485 -2.0700920 5.664806 -2.503635 -1.0378510
## ENSMUSG00000000028 2.3020459 -0.5006646 2.293696 -2.135752 0.5424604
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 6.031045 6.868264 3.586493 1.419703
## ENSMUSG00000000003 6.706098 11.982201 4.575758 3.068558
## ENSMUSG00000000028 11.577432 4.312868 3.451771 3.4152977 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
## 0.038913246 0.033327989 0.024177175 0.010985338
## ENSMUSG00000000028
## 0.0023021927.1 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) (2025-01-20 r87609)
## 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 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_3.5.1 SingleCellExperiment_1.29.1
## [3] SummarizedExperiment_1.37.0 Biobase_2.67.0
## [5] GenomicRanges_1.59.1 GenomeInfoDb_1.43.3
## [7] IRanges_2.41.2 S4Vectors_0.45.2
## [9] BiocGenerics_0.53.4 generics_0.1.3
## [11] MatrixGenerics_1.19.1 matrixStats_1.5.0
## [13] mist_0.99.18 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_1.1.5
## [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_0.33.4
## [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_1.3-3 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_0.47.2 restfulr_0.0.15
## [46] vctrs_0.6.5 Matrix_1.7-1 jsonlite_1.8.9
## [49] SparseM_1.84-2 carData_3.0-5 bookdown_0.42
## [52] car_3.1-3 MCMCpack_1.7-1 Formula_1.2-5
## [55] magick_2.8.5 jquerylib_0.1.4 glue_1.8.0
## [58] codetools_0.2-20 gtable_0.3.6 BiocIO_1.17.1
## [61] UCSC.utils_1.3.1 munsell_0.5.1 tibble_3.2.1
## [64] pillar_1.10.1 htmltools_0.5.8.1 quantreg_5.99.1
## [67] GenomeInfoDbData_1.2.13 R6_2.5.1 evaluate_1.0.3
## [70] lattice_0.22-6 Rsamtools_2.23.1 bslib_0.8.0
## [73] MatrixModels_0.5-3 Rcpp_1.0.14 coda_0.19-4.1
## [76] SparseArray_1.7.4 xfun_0.50 pkgconfig_2.0.3