Contents

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 1.239793 -0.812292554  0.7905570  0.36113628 -0.05642390
## ENSMUSG00000000003 1.636478  1.507091937  3.5987541 -2.59831691 -2.89088729
## ENSMUSG00000000028 1.308739  0.005595154  0.1052620  0.02305620 -0.02521009
## ENSMUSG00000000037 1.006877 -4.894850046 13.0707335 -4.61242636 -3.62477282
## ENSMUSG00000000049 1.005655 -0.158609861  0.3103474 -0.08733549  0.13228932
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.184032 15.183924 3.114117 1.826032
## ENSMUSG00000000003 25.416840  2.596218 6.030290 8.890647
## ENSMUSG00000000028  8.170899  6.888983 3.502161 2.283669
## ENSMUSG00000000037  8.567505 14.261554 6.471687 2.197505
## ENSMUSG00000000049  5.350651  8.705501 2.954788 1.314802

3 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 
##        0.068134864        0.033241657        0.016583384        0.009446557 
## ENSMUSG00000000028 
##        0.004667178

4 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)$mist_pars_group1, n = 3)
##                      Beta_0      Beta_1   Beta_2      Beta_3       Beta_4
## ENSMUSG00000000001 1.232429 -0.89294484 1.090386  0.38436298 -0.320267435
## ENSMUSG00000000003 1.616777  1.87283400 2.299257 -2.47445437 -1.995626860
## ENSMUSG00000000028 1.273690 -0.01300002 0.134100  0.04248286 -0.003700711
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.301442 13.851255 3.004876 1.865224
## ENSMUSG00000000003 25.768155  3.428453 6.035565 8.754384
## ENSMUSG00000000028  7.520042  8.172288 3.303499 2.762588
head(rowData(Dat_sce)$mist_pars_group2, n = 3)
##                        Beta_0     Beta_1   Beta_2     Beta_3     Beta_4
## ENSMUSG00000000001  1.8935580 -0.9014650 5.934080 -4.8783653 -0.3131641
## ENSMUSG00000000003 -0.8394897 -1.5851271 4.290100 -1.3204273 -1.3365637
## ENSMUSG00000000028  2.3327407 -0.6291666 2.476819 -0.6818228 -1.0461705
##                     Sigma2_1  Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001  5.616122  6.646827 3.938307 1.303361
## ENSMUSG00000000003  6.661643 10.441203 4.594972 2.874717
## ENSMUSG00000000028 10.943867  4.978352 3.707316 3.413476

7 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 
##         0.04206112         0.03358577         0.02480387         0.01174847 
## ENSMUSG00000000028 
##         0.00482716

7.1 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) (2024-10-26 r87273 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=C                          
## [2] LC_CTYPE=English_United States.utf8   
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.utf8    
## 
## 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_3.5.1               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_1.19.1       matrixStats_1.5.0          
## [13] mist_0.99.17                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_1.3-3            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          snow_0.4-4              
## [58] glue_1.8.0               codetools_0.2-20         gtable_0.3.6            
## [61] BiocIO_1.17.1            UCSC.utils_1.3.1         munsell_0.5.1           
## [64] tibble_3.2.1             pillar_1.10.1            htmltools_0.5.8.1       
## [67] quantreg_5.99.1          GenomeInfoDbData_1.2.13  R6_2.5.1                
## [70] evaluate_1.0.3           lattice_0.22-6           Rsamtools_2.23.1        
## [73] bslib_0.8.0              MatrixModels_0.5-3       Rcpp_1.0.14             
## [76] coda_0.19-4.1            SparseArray_1.7.3        xfun_0.50               
## [79] pkgconfig_2.0.3