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Protocol for single-cell epigenetic profiling in human organoids and tumoroids with Epi-CyTOF.

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] § Step-by-step method details › Quantification and statistical analysis with R ↔ scripts/compute_all_gates.R, the whole file · a weak match · score 0.83 · quantile_val, modality_df, gates_df, df_long, unimodal, Quantification
  2. [2] § Before you begin › Innovation ↔ notebooks/Epi-CyTOF_data_analysis.Rmd, lines 9–59 · score 0.51 · epigenetic profiling, human organoid, CyTOF, single cell, technical, mass

Paper

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The authors' code

R · 135 lines · 4.5 KB · MIT · 1 match

  1. #' Compute marker-specific gating thresholds and signal indices
  2. #'
  3. #' This function computes gating thresholds for each marker in a long-format
  4. #' single-cell expression dataset. The gating strategy depends on whether a
  5. #' marker is classified as unimodal or multimodal based on prior modality
  6. #' assessment.
  7. #'
  8. #' For unimodal markers, gates are computed using either a quantile-based
  9. #' approach or a median/standard deviation-based rule. For multimodal markers,
  10. #' density estimation combined with peak detection is used to identify
  11. #' separation points between expression modes.
  12. #'
  13. #' Additionally, a signal index (SI) is calculated to quantify the separation
  14. #' between negative and positive populations.
  15. #'
  16. #' @param df A data.frame in long format containing at least:
  17. #' \describe{
  18. #' \item{marker}{Marker name}
  19. #' \item{expression}{Numeric expression values}
  20. #' }
  21. #'
  22. #' @param modality_df A data.frame containing modality information per marker:
  23. #' \describe{
  24. #' \item{marker}{Marker name}
  25. #' \item{unimodal}{Logical indicating whether the marker is unimodal}
  26. #' }
  27. #'
  28. #' @param lower Numeric lower bound for expression filtering (default: -Inf)
  29. #' @param upper Numeric upper bound for expression filtering (default: Inf)
  30. #' @param bw Bandwidth method for density estimation (default: "nrd0")
  31. #' @param method_unimodal Method for unimodal gating:
  32. #' \code{"quantile"} or \code{"median_sd"}
  33. #' @param quantile_val Quantile threshold used for gating when
  34. #' \code{method_unimodal = "quantile"} (default: 0.05)
  35. #' @param k_sd Number of standard deviations used when
  36. #' \code{method_unimodal = "median_sd"} (default: 2)
  37. #'
  38. #' @return A data.frame with one row per marker containing:
  39. #' \describe{
  40. #' \item{marker}{Marker name}
  41. #' \item{gate}{Estimated gating threshold}
  42. #' \item{SI}{Signal index measuring separation between negative and positive populations}
  43. #' }
  44. #'
  45. #' @details
  46. #' The signal index (SI) is defined as:
  47. #' \deqn{SI = (median(pos) - median(neg)) / (sd(pos) + sd(neg))}
  48. #'
  49. #' Density-based peak detection is performed using the \pkg{pracma} package.
  50. #'
  51. #' @importFrom stats density median sd quantile
  52. #' @importFrom pracma findpeaks
  53. #'
  54. #' @examples
  55. #' \dontrun{
  56. #' gates_df <- compute_all_gates_2(df_long, modality_df)
  57. #' }
  58. #'
  59. #' @export
  60. compute_all_gates <- function(df, modality_df,
  61. lower = -Inf, upper = Inf,
  62. bw = "nrd0",
  63. method_unimodal = c("quantile", "median_sd"),
  64. quantile_val = 0.05,
  65. k_sd = 2) {
  67. method_unimodal <- match.arg(method_unimodal)
  68. markers <- unique(df$marker)
  70. gates_SI <- list()
  72. for (m in markers) {
  74. expr <- df$expression[df$marker == m]
  75. is_unimodal <- modality_df$unimodal[modality_df$marker == m]
  77. gate_val <- NA
  78. SI <- NA
  80. if (length(expr) > 0 && !is.na(is_unimodal) && is_unimodal) {
  82. expr_in_bounds <- expr[expr >= lower & expr <= upper]
  84. if (method_unimodal == "quantile") {
  85. gate_val <- unname(quantile(expr_in_bounds, quantile_val, na.rm = TRUE))
  87. } else if (method_unimodal == "median_sd") {
  88. gate_val <- median(expr_in_bounds, na.rm = TRUE) -
  89. k_sd * sd(expr_in_bounds, na.rm = TRUE)
  90. }
  92. neg <- expr[expr < gate_val]
  93. pos <- expr[expr >= gate_val]
  95. if (length(neg) > 0 && length(pos) > 0) {
  96. SI <- (median(pos) - median(neg)) / (sd(pos) + sd(neg))
  97. }
  99. } else if (length(expr) > 0) {
  101. dens <- density(expr, bw = bw)
  102. inv_y <- max(dens$y) - dens$y
  103. minima <- pracma::findpeaks(inv_y)
  105. if (!is.null(minima)) {
  107. min_idx <- minima[, 2]
  108. peak_idx <- pracma::findpeaks(dens$y)[, 2]
  110. gate_idx <- min_idx[min_idx > min(peak_idx) & min_idx < max(peak_idx)]
  111. gate_val <- if (length(gate_idx) == 0) NA else dens$x[gate_idx]
  113. gate_val <- gate_val[gate_val >= lower & gate_val <= upper]
  114. gate_val <- sort(gate_val)[1]
  116. if (!is.na(gate_val)) {
  118. neg <- expr[expr < gate_val]
  119. pos <- expr[expr >= gate_val]
  121. if (length(neg) > 0 && length(pos) > 0) {
  122. SI <- (median(pos) - median(neg)) / (sd(pos) + sd(neg))
  123. }
  124. }
  125. }
  126. }
  128. gates_SI[[m]] <- list(gate = gate_val, SI = SI)
  129. }
  131. gate_vals <- sapply(gates_SI, function(x) x$gate)
  132. SI_vals <- sapply(gates_SI, function(x) x$SI)
  134. data.frame(marker = markers, gate = gate_vals, SI = SI_vals)
  135. }

compute_all_gates.R at commit 139f966, under MIT · at the source

Overview

Authors: Ezgi Senoglu1, Vivian Mittné2, Sevina Dietz1, Anne Eugster1, Sebastian Thieme3, Franziska Baenke2,4,5, Mareike Albert1, Claudia Peitzsch1
ORCID iDs: Vivian Mittné, Franziska Baenke, Mareike Albert, Claudia Peitzsch
  1. Center for Regenerative Therapies Dresden (CRTD), TUD Dresden University of Technology, 01307 Dresden, Germany
  2. Department of Visceral, Thoracic and Vascular Surgery, Medical Faculty and University Hospital Carl Gustav Carus, Dresden University of Technology, 01307 Dresden, Germany
  3. Department of Pediatrics, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, 01307 Dresden, Germany
  4. National Center for Tumor Diseases (NCT/UCC), 01037 Dresden, a partnership between DKFZ, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, and Helmholtz-Zentrum Dresden-Rossendorf (HZDR), 01328 Dresden, Germany
  5. German Cancer Consortium (DKTK), Partner Site Dresden, 01307 Dresden, and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
Institutions: Center for Regenerative Therapies Dresden (Germany); Technische Universität Dresden (Germany); University Hospital Carl Gustav Carus (Germany); Helmholtz-Zentrum Dresden-Rossendorf (Germany); National Center for Tumor Diseases (Germany); Nationales Centrum für Tumorerkrankungen Dresden (Germany)
Journal: STAR protocols, volume 7, issue 3, article 104695
Dates: published online 31 July 2026; in print July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.xpro.2026.104695 · PMID 42541720 · PMCID PMC13453457 · OpenAlex W7171968201
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), other condition (population)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: Bioinformatics, cell biology, single cell, mass cytometry, developmental biology, cancer, Genomics, Immunology, molecular biology, Neuroscience, organoids
Topic: Cancer Cells and Metastasis (Oncology, Medicine), according to OpenAlex
Funding: CyTOF; CRTD; TU Dresden; RRID (SCR_027145); Ezio Bonifacio; German Center for Diabetes Research; Deutsche Forschungsgemeinschaft (DFG) (FZT 111); Federal Ministry for Research; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (01EW2208); Sächsisches Staatsministerium für Wissenschaft und Kunst; Stanford University; Center for Regenerative Therapies TU Dresden; Emmy Noether Programme (2231/1-1, SPP EPIADAPT 563285578, AL 2231/4-1, 2231/3-1); German Centers for Health Research; DZG
Citations: not cited yet (Europe PMC); 55 references in the paper
Research resources: RRID:AB_10695148, RRID:AB_10839410, RRID:AB_1950414, Rabbit polyclonal anti-H3R2me2a-163Dy RRID:AB_2313773, RRID:AB_2564878, RRID:AB_2564914, RRID:AB_2614987, RRID:AB_2615074, RRID:AB_2650523, RRID:AB_2650525, Rabbit polyclonal anti-H3ac-112Cd RRID:AB_2687871, RRID:AB_2715573, RRID:AB_2793285, RRID:AB_2793302, RRID:AB_2793635, RRID:AB_2793668, RRID:AB_2793725, RRID:AB_2798355, RRID:AB_2820126, RRID:AB_2888928, RRID:AB_2938640, RRID:AB_2943175, RRID:AB_3086680, RRID:AB_309864, RRID:AB_3289584, RRID:AB_3291566, RRID:AB_3309383, RRID:AB_3313966, RRID:AB_3333979, Rabbit polyclonal anti-HOP-155Gd RRID:AB_3423623, RRID:AB_3614254, RRID:AB_3657748, RRID:AB_882455, Human: HPSI0114i-kolf_2 iPSC RRID:CVCL_AE29, Human: CRTDi004-A iPSC RRID:CVCL_YR23

Abstract

In this protocol, we present Epi-CyTOF, a cytometry by time of flight (CyTOF) approach for single-cell epigenome profiling in cortical organoids as a model system for human brain development. We describe steps for in silico panel design, antibody conjugation and testing, sample preparation and staining, and high-dimensional data analysis. Additionally, we provide recommendations for applying Epi-CyTOF to other model systems based on our experience with gastroesophageal tumoroids.

For complete details on the use and execution of this protocol, please refer to Ditzer et al .1

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

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

Stacheltier/EPI-CyTOF

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 139f966968412ee930ad19077a72b98d51bf29e0, 30 April 2026
Languages: C/C++ (340), R (216), Shell (2), C (2), JavaScript (1)
Size: 2,742 files, 561 scripts
Software Heritage: not archived
Found in: the text, “Quantification and statistical analysis with R”
Holds: README, environment (renv.lock, renv/library/windows/R-4.5/x86_64-w64-mingw32/askpass/DESCRIPTION, renv/library/windows/R-4.5/x86_64-w64-mingw32/assorthead/DESCRIPTION, renv/library/windows/R-4.5/x86_64-w64-mingw32/backports/DESCRIPTION, renv/library/windows/R-4.5/x86_64-w64-mingw32/Biobase/DESCRIPTION, renv/library/windows/R-4.5/x86_64-w64-mingw32/Cairo/DESCRIPTION, renv/library/windows/R-4.5/x86_64-w64-mingw32/cli/DESCRIPTION, renv/library/windows/R-4.5/x86_64-w64-mingw32/colorRamps/DESCRIPTION, renv/library/windows/R-4.5/x86_64-w64-mingw32/diptest/DESCRIPTION, renv/library/windows/R-4.5/x86_64-w64-mingw32/doParallel/DESCRIPTION, renv/library/windows/R-4.5/x86_64-w64-mingw32/flowCore/DESCRIPTION, renv/library/windows/R-4.5/x86_64-w64-mingw32/FNN/DESCRIPTION), tests, documentation, 57 notebooks
Not found: license file, CITATION.cff, continuous integration
Tools: ggplot2 (18 files), tidyverse (8 files), broom (2 files), lme4 (2 files), patchwork (2 files), survival (2 files), UMAP (2 files), caret (1 file), SingleCellExperiment (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
562 files
At the source: github.com/Stacheltier/EPI-CyTOF

ParkerICI/premessa

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 68b42bb984637d0f3ad6a0ecc83e9278994afc85, 6 September 2022
Languages: R (15), JavaScript (3)
Size: 63 files, 18 scripts
Software Heritage: not archived
Found in: the text, “De-barcoding of pooled samples after measurement”
Holds: README, license file, environment (DESCRIPTION), documentation
Not found: CITATION.cff, tests, continuous integration
Tools: ggplot2 (4 files), reshape2 (2 files), data.table (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
20 files
At the source: github.com/ParkerICI/premessa.git

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 579 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);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data and code availability

The Epi-CyTOF data for the cortical organoids are available in the FlowRepository: FR-FCM-Z8DC. The data analysis workflow is available within the paper-specific GitHub repository (https://github.com/Stacheltier/EPI-CyTOF).55

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 11 keywords, 15 funders, 45 references, 35 RRIDs.

Cite

This paper

Senoglu, E., Mittné, V., Dietz, S., Eugster, A., Thieme, S., Baenke, F., Albert, M., & Peitzsch, C. (2026). Protocol for single-cell epigenetic profiling in human organoids and tumoroids with Epi-CyTOF. STAR protocols, 7(3), 104695. https://doi.org/10.1016/j.xpro.2026.104695

BibTeX

@article{senoglu2026protocol,
author = {Senoglu, Ezgi and Mittné, Vivian and Dietz, Sevina and Eugster, Anne and Thieme, Sebastian and Baenke, Franziska and Albert, Mareike and Peitzsch, Claudia},
title = {{Protocol for single-cell epigenetic profiling in human organoids and tumoroids with Epi-CyTOF}},
journal = {STAR protocols},
year = {2026},
month = jul,
volume = {7},
number = {3},
pages = {104695},
publisher = {Elsevier},
issn = {2666-1667},
doi = {10.1016/j.xpro.2026.104695},
url = {https://doi.org/10.1016/j.xpro.2026.104695},
pmid = {42541720},
pmcid = {PMC13453457}
}

RIS

TY - JOUR
AU - Senoglu, Ezgi
AU - Mittné, Vivian
AU - Dietz, Sevina
AU - Eugster, Anne
AU - Thieme, Sebastian
AU - Baenke, Franziska
AU - Albert, Mareike
AU - Peitzsch, Claudia
TI - Protocol for single-cell epigenetic profiling in human organoids and tumoroids with Epi-CyTOF
T2 - STAR protocols
J2 - STAR Protoc
PY - 2026
DA - 2026/07/31
VL - 7
IS - 3
SP - 104695
SN - 2666-1667
PB - Elsevier
DO - 10.1016/j.xpro.2026.104695
UR - https://doi.org/10.1016/j.xpro.2026.104695
LA - en
ER -

CSL-JSON

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