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)$mist_pars)
## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.274121 -0.471209030 0.3623838 0.32488405 0.01915854
## ENSMUSG00000000003 1.586202 1.098734578 3.6199140 -2.16249453 -2.85873841
## ENSMUSG00000000028 1.283939 -0.009938242 0.0985196 0.04219455 -0.01007775
## ENSMUSG00000000037 1.028149 -4.158435838 11.4899159 -4.41165171 -2.95152100
## ENSMUSG00000000049 1.034583 -0.067620173 0.1188776 0.06162854 0.04176091
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 6.400119 15.139993 3.660511 1.897404
## ENSMUSG00000000003 24.135670 5.753880 5.109565 8.969364
## ENSMUSG00000000028 7.763001 7.708252 3.298996 2.195743
## ENSMUSG00000000037 8.499263 12.026079 7.168603 2.234785
## ENSMUSG00000000049 6.585597 8.304051 3.195952 1.305927
dmSingle
# Perform differential methylation analysis for the single-group
Dat_sce <- dmSingle(Dat_sce)
# View the top genomic features with drastic methylation changes
head(rowData(Dat_sce)$mist_int)
## ENSMUSG00000000037 ENSMUSG00000000003 ENSMUSG00000000001 ENSMUSG00000000049
## 0.059105215 0.032480115 0.011632077 0.007170808
## ENSMUSG00000000028
## 0.005079249
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)$mist_pars, n = 3)
## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.255329 -0.61298767 0.4802841 0.40831766 0.01009127
## ENSMUSG00000000003 1.640251 1.26996570 3.2325638 -2.28401050 -2.52146072
## ENSMUSG00000000028 1.289953 -0.01726036 0.1085411 0.04558792 -0.01854391
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.814364 15.969483 3.412765 2.090464
## ENSMUSG00000000003 24.772595 4.201358 5.057342 8.930926
## ENSMUSG00000000028 7.877231 7.610079 3.156014 2.147512
head(rowData(Dat_sce_g2)$mist_pars, n = 3)
## Beta_0 Beta_1 Beta_2 Beta_3 Beta_4
## ENSMUSG00000000001 1.9278939 -1.5452466 9.308607 -10.045490 2.1314787
## ENSMUSG00000000003 -0.8139109 -3.4412179 9.716602 -5.515422 -0.7456901
## ENSMUSG00000000028 2.3330925 -0.8325805 3.312083 -2.793488 0.4666256
## Sigma2_1 Sigma2_2 Sigma2_3 Sigma2_4
## ENSMUSG00000000001 5.663777 6.153624 3.628892 1.512319
## ENSMUSG00000000003 6.430052 10.509734 4.259788 2.938772
## ENSMUSG00000000028 11.316478 5.586455 3.158478 3.060604
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
## 0.048052450 0.044883921 0.026302083 0.010398669
## ENSMUSG00000000028
## 0.003538486
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 beta (2025-04-02 r88102)
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## other attached packages:
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## [3] SummarizedExperiment_1.39.0 Biobase_2.69.0
## [5] GenomicRanges_1.61.0 GenomeInfoDb_1.45.0
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## [13] mist_1.1.0 BiocStyle_2.37.0
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