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mist: a hierarchical Bayesian framework for detecting differential DNA methylation dynamics in single-cell data.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Results › Overview of mist ↔ R/estiParam.R, the whole file · a weak match · score 0.62 · pseudotime vector, single cell DNA, genomic features, hierarchical Bayesian, matrix, mist
  2. [2] § Results › mist in parameter estimation ↔ R/Fig2b_d.R, lines 106–159 · score 0.52 · squared error, relative bias, Boxplots, outliers, Figure 2, GAM

Paper

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The authors' code

R · 96 lines · 4.3 KB · other · 1 match

  1. #' Parameter Estimation With mist
  2. #'
  3. #' This function performs the Gibbs sampling procedure based on hierarchical Bayesian modeling
  4. #' to produce the parameters required for differential methylation analysis.
  5. #'
  6. #' @param Dat_sce A `SingleCellExperiment` object containing the single-cell DNA methylation level.
  7. #' Methylation levels should be stored as an assay, with genomic feature (gene) names in rownames
  8. #' and cells in colnames.
  9. #' @param Dat_name A character string specifying the name of the assay to extract the methylation level data.
  10. #' @param ptime_name A character string specifying the name of the column in `colData` containing the pseudotime vector.
  11. #' @param BPPARAM A `BiocParallelParam` object specifying the parallel backend for computations, as used in `bplapply()`. Defaults to `MulticoreParam()` for parallel processing.
  12. #' @param verbose A logical value indicating whether to print progress messages to the console.
  13. #' Defaults to \code{TRUE}. Set to \code{FALSE} to suppress messages.
  14. #'
  15. #' @return The updated sce object with A numeric matrix of estimated parameters for all genomic features in the rowData, including:
  16. #' - \eqn{\beta_0} to \eqn{\beta_4}: Estimated coefficients for the polynomial of degree 4.
  17. #' - \eqn{\sigma^2_1} to \eqn{\sigma^2_4}: Estimated variances for each stage along the pseudotime.
  18. #'
  19. #' @import MCMCpack BiocParallel car mvtnorm SummarizedExperiment SingleCellExperiment BiocGenerics
  20. #' @importFrom stats pgamma poly qgamma rnorm runif
  21. #' @export
  22. #'
  23. #' @examples
  24. #' library(SingleCellExperiment)
  25. #' data <- readRDS(system.file("extdata", "group1_sampleData_sce.rds", package = "mist"))
  26. #' Dat_sce_new <- estiParam(
  27. #' Dat_sce = data,
  28. #' Dat_name = "Methy_level_group1",
  29. #' ptime_name = "pseudotime"
  30. #' )
  31. estiParam <- function(Dat_sce,
  32. Dat_name,
  33. ptime_name,
  34. BPPARAM = MulticoreParam(),
  35. verbose = TRUE) {
  36. ######## 1. Input Validation
  37. # Check if Dat_sce is a SingleCellExperiment object
  38. if (!methods::is(Dat_sce, "SingleCellExperiment")) {
  39. stop("Dat_sce must be a SingleCellExperiment object.",
  40. call. = TRUE, domain = NULL)
  41. }
  43. # Check if Dat_name is provided
  44. if (is.null(Dat_name)) {
  45. stop("Missing Dat_name: Specify the assay name to extract data.",
  46. call. = TRUE, domain = NULL)
  47. }
  49. # Check if ptime_name is provided
  50. if (is.null(ptime_name)) {
  51. stop("Missing ptime_name: Specify the column name in colData for pseudotime.",
  52. call. = TRUE, domain = NULL)
  53. }
  55. # Extract the assay and pseudotime data
  56. scDNAm_mat <- assay(Dat_sce, Dat_name)
  57. ptime <- colData(Dat_sce)[[ptime_name]]
  59. # Check if the number of cells (columns) matches the length of pseudotime
  60. if (ncol(scDNAm_mat) != length(ptime)) {
  61. stop("The number of cells in the data matrix and the pseudotime vector must match.",
  62. call. = TRUE, domain = NULL)
  63. }
  65. ###### 2. Normalize pseudotime to 0 - 1
  66. ptime <- ptime[is.finite(ptime) & !is.na(ptime)]
  67. ptime_all <- c(ptime / max(ptime))
  68. if (verbose) message("Pseudotime cleaning and normalization to [0, 1] completed.")
  70. ###### 3. Remove Genomic Features Containing Only 0/1 Values in All Timepoints
  71. rmRes <- BiocParallel::bplapply(seq_len(nrow(scDNAm_mat)), rmBad, dat_ori = scDNAm_mat, ptime_all = ptime_all,
  72. BPPARAM = BPPARAM)
  73. rmIndex <- which(unlist(rmRes) == 1)
  74. scDNAm_mat_clean <- scDNAm_mat[!seq_len(nrow(scDNAm_mat)) %in% rmIndex, ]
  75. if (verbose) message("Removal of genomic features with too many 0/1 values completed.")
  77. ###### 4. Parameter Estimation using Gibbs Sampling
  78. beta_sigma_list <- BiocParallel::bplapply(seq_len(nrow(scDNAm_mat_clean)), run_bayesian_estimation,
  79. dat_ready = scDNAm_mat_clean, ptime_all = ptime_all,
  80. BPPARAM = BPPARAM)
  82. # Assign names to the parameters
  83. name_vector <- c("Beta_0", "Beta_1", "Beta_2", "Beta_3", "Beta_4", "Sigma2_1", "Sigma2_2", "Sigma2_3", "Sigma2_4")
  84. beta_sigma_list <- lapply(beta_sigma_list, function(x) {
  85. names(x) <- name_vector
  86. return(x)
  87. })
  89. # Assign genomic feature names to the list
  90. names(beta_sigma_list) <- rownames(scDNAm_mat_clean)
  92. rowData(Dat_sce)$mist_pars <- do.call(rbind, beta_sigma_list)
  94. # Return the final sce object
  95. return(Dat_sce)
  96. }

estiParam.R at commit f0061d2, under other · at the source

Overview

Authors: Daoyu Duan1, Wenjing Ma2, Wen Tang3, Hao Wu4, Liangliang Zhang1, Hao Feng3
ORCID iDs: Daoyu Duan, Liangliang Zhang, Hao Feng
  1. Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH USA
  2. Department of Biostatistics, University of Michigan, Ann Arbor, MI USA
  3. Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, TX USA
  4. Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA USA
Institutions: Case Western Reserve University (United States); University of Michigan (United States); The University of Texas Health Science Center at Houston (United States); Emory University (United States)
Journal: Nature communications, volume 17, issue 1, article 3835
Dates: received 21 April 2025; accepted 20 February 2026; published online 12 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-70523-y · PMID 41820383 · PMCID PMC13121613 · OpenAlex W7135045748
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism)
Methods: Statistics
Keywords: Statistical methods, Computational models, Epigenomics, DNA methylation
MeSH: DNA Methylation*, Single-Cell Analysis*, Animals, Bayes Theorem, Brain, Embryonic Development, Epigenesis, Genetic, Genomics, Humans, Mice, Sequence Analysis, DNA (* major topic)
Topic: Epigenetics and DNA Methylation (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIGMS NIH HHS (R35 GM154862); U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) (R35GM154862)
Citations: not cited yet (Europe PMC); 67 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

Zenodo 18451940

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Statistics and reproducibility”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
At the source:

dxd429/mist

License: other
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: f0061d291f375a63876f71dec3ff2cc03eeeaeb2, 31 January 2026
Languages: R (9)
Size: 27 files, 9 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (DESCRIPTION), tests, documentation, 1 notebook
Not found: CITATION.cff, continuous integration
Tools: ggplot2 (2 files), SingleCellExperiment (2 files), car (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
12 files
At the source: github.com/dxd429/mist

dxd429/mistdata

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 944f151c56a44b66fb8e2e9eceda0a8b9b65735a, 1 February 2026
Languages: R (33)
Size: 210 files, 33 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: mgcv (26 files), ggplot2 (6 files), SingleCellExperiment (5 files), tidyverse (4 files), car (2 files), igraph (2 files), Monocle 3 (2 files), pheatmap (2 files), Seurat (2 files), ComplexHeatmap (1 file), data.table (1 file), pROC (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
34 files
At the source: github.com/dxd429/mistData/tree/mist_1.01

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it points to the authors' code: dxd429/mist, Zenodo 18451940

Read it in the paper: doi.org/10.1038/s41467-026-70523-y.

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  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 42 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

Datasets cited

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-70523-y.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 4 keywords, 11 MeSH terms, 2 funders, 66 references.

Cite

This paper

Duan, D., Ma, W., Tang, W., Wu, H., Zhang, L., & Feng, H. (2026). mist: a hierarchical Bayesian framework for detecting differential DNA methylation dynamics in single-cell data. Nature communications, 17(1), 3835. https://doi.org/10.1038/s41467-026-70523-y

BibTeX

@article{duan2026mist,
author = {Duan, Daoyu and Ma, Wenjing and Tang, Wen and Wu, Hao and Zhang, Liangliang and Feng, Hao},
title = {{mist: a hierarchical Bayesian framework for detecting differential DNA methylation dynamics in single-cell data}},
journal = {Nature communications},
year = {2026},
month = mar,
volume = {17},
number = {1},
pages = {3835},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-70523-y},
url = {https://doi.org/10.1038/s41467-026-70523-y},
pmid = {41820383},
pmcid = {PMC13121613}
}

RIS

TY - JOUR
AU - Duan, Daoyu
AU - Ma, Wenjing
AU - Tang, Wen
AU - Wu, Hao
AU - Zhang, Liangliang
AU - Feng, Hao
TI - mist: a hierarchical Bayesian framework for detecting differential DNA methylation dynamics in single-cell data
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/03/12
VL - 17
IS - 1
SP - 3835
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-70523-y
UR - https://doi.org/10.1038/s41467-026-70523-y
LA - en
ER -

CSL-JSON

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