mist:methylation inference for single-cell along trajectory
8 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)$mist_pars)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.243936 -1.06711067 1.08376804 0.59527307 -0.316911023
## ENSMUSG00000000003 1.642493 1.66834253 2.90598286 -1.95605056 -2.967474060
## ENSMUSG00000000028 1.266281 -0.00540059 0.07414763 0.03730413 0.002710052
## ENSMUSG00000000037 1.047687 -1.61015616 4.35304170 -1.08515565 -1.566171962
## ENSMUSG00000000049 1.019200 -0.09791241 0.14456928 0.08316001 0.037967421
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.613181 15.262305 3.208331 1.923053
## ENSMUSG00000000003 26.467477 4.507429 5.647065 9.781749
## ENSMUSG00000000028 7.867970 7.802508 2.934401 2.252336
## ENSMUSG00000000037 9.374480 12.748678 7.949599 2.228485
## ENSMUSG00000000049 5.733095 7.942586 3.438195 1.2585053 Step 3: Perform Differential Methylation Analysis Using dmSingle
# Perform differential methylation analysis for the single-group
Dat_sce <- dmSingle(Dat_sce)
# View the top genomic features with drastic methylation changes
head(rowData(Dat_sce)$mist_int)## ENSMUSG00000000003 ENSMUSG00000000037 ENSMUSG00000000001 ENSMUSG00000000049
## 0.032115754 0.028780572 0.019736744 0.007950688
## ENSMUSG00000000028
## 0.0045370804 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)$mist_pars_group1, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.242625 -0.878997743 0.7746688 0.53607422 -0.14591769
## ENSMUSG00000000003 1.605070 2.120766967 2.5468170 -2.20043075 -2.88327765
## ENSMUSG00000000028 1.264522 -0.001564345 0.0961375 0.03691856 -0.01323275
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.671474 15.832997 3.064616 1.925109
## ENSMUSG00000000003 26.865832 2.977113 7.057809 8.575358
## ENSMUSG00000000028 7.339370 8.278231 3.185380 2.287369head(rowData(Dat_sce)$mist_pars_group2, n = 3)## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.9085656 -1.6787515 9.091969 -8.9702717 1.3991956
## ENSMUSG00000000003 -0.8517436 -1.4344767 3.775552 -1.1026151 -1.1658358
## ENSMUSG00000000028 2.3434307 -0.2404526 1.081644 -0.1793553 -0.4838158
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.645323 5.811149 3.514743 1.389057
## ENSMUSG00000000003 6.552035 12.086588 4.726137 2.906331
## ENSMUSG00000000028 11.480554 4.650397 3.556703 3.5309657 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)## ENSMUSG00000000037 ENSMUSG00000000003 ENSMUSG00000000001 ENSMUSG00000000049
## 0.052257555 0.036105147 0.028515464 0.012440636
## ENSMUSG00000000028
## 0.0021044547.1 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_0.99.13 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