OSCR

Network-based stratification of allele-specific expression reveals patient subgroups in Huntington's disease.

Code ↔ Paper

11 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 11 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § 2 Methods › 2.1 Dataset ↔ scripts/downstream analysis/cluster-analysis/clinical_data_association.R, lines 62–107 · score 0.90 · post mortem interval, RNA integrity, RNA seq, striatal scores, CAG repeats, sex
  2. [2] § 2 Methods › 2.4 Comparative analysis of patient clusters ↔ scripts/downstream analysis/cluster-analysis/clinical_data_association.R, lines 62–107 · score 0.81 · post mortem interval, RNA integrity, CAG repeats, age, death, onset
  3. [3] § 2 Methods › 2.2 Variant calling and allele-specific expression analysis ↔ scripts/fastq-processing/run_gatk_steps.sh, the whole file · a weak match · score 0.69 · SplitNCigarReads, AddOrReplaceReadGroups, sequence, GATK, STAR, BAM
  4. [4] § 3 Results › 3.3.4 Clinical variables association analysis ↔ scripts/downstream analysis/cluster-analysis/clinical_data_association.R, lines 1–60 · score 0.63 · clinical variables, cortical score, striatal score, duration, age, death
  5. [5] § 2 Methods › 2.4 Comparative analysis of patient clusters ↔ scripts/downstream analysis/cluster-analysis/quality-control.R, lines 124–161 · score 0.63 · DESeq2, Batch correction, v1, transformed, protein, filters
  6. [6] § 2 Methods › 2.3 Network-based stratification on allele-specific expression data ↔ scripts/downstream analysis/ASE-pre-processing/ASE_HTT.R, lines 468–533 · score 0.60 · gap statistic, optimal cluster, matrix, ASE, gene
  7. [7] § 2 Methods › 2.3 Network-based stratification on allele-specific expression data ↔ scripts/pyNBS/python-parallel.py, lines 42–99 · score 0.58 · pyNBS, PPI network, iterations, binary, mutation, mapped
  8. [8] § 2 Methods › 2.2 Variant calling and allele-specific expression analysis ↔ scripts/fastq-processing/STAR_create_genome_index.sh, the whole file · a weak match · score 0.58 · genomeGenerate, STAR, FASTA, hg19, VCF, fastq
  9. [9] § 2 Methods › 2.2 Variant calling and allele-specific expression analysis ↔ scripts/downstream analysis/ASE-pre-processing/ASE_HTT.R, lines 231–297 · score 0.56 · population median, median ASE, binomial, mapping
  10. [10] § 3 Results › 3.2 ASE-based stratification informed by protein-protein interactions reveals three clusters ↔ scripts/pyNBS/pyNBS/pyNBS_plotting.py, lines 29–60 · score 0.54 · co cluster frequency, cluster assignment, Patient clusters, heatmap
  11. [11] § 2 Methods › 2.2 Variant calling and allele-specific expression analysis ↔ scripts/fastq-processing/ASEReadCounter.sh, lines 17–46 · score 0.52 · ASEReadCounter, biallelic, GATK, WASP, VCF, BAM

Paper

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The authors' code

R · 213 lines · 6.4 KB · MIT · 3 matches

  1. # ===============================
  2. # Clinical plots + 3-cluster association tests
  3. # ===============================
  5. library(dplyr)
  6. library(tidyr)
  7. library(ggplot2)
  8. library(purrr)
  10. # -------------------------------
  11. # 1) Input: metadata table
  12. # -------------------------------
  13. # Assumes you have a data.frame called `metadata`
  14. # with a cluster column called `groups` (values "1","2","3")
  15. # and the clinical variables as numeric columns.
  18. # set working dir to the folder containing this script
  19. setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
  21. # paths relative to the script directory
  22. data_dir <- file.path(getwd(), "Data")
  23. out_dir <- file.path(getwd(), "output")
  25. # create output directory if missing
  26. if (!dir.exists(out_dir)) dir.create(out_dir, recursive = TRUE)
  28. # (optional) stop if Data folder is missing
  29. if (!dir.exists(data_dir)) stop("Missing Data folder: ", data_dir)
  32. ##import clinical data
  33. clinical_data <- read.delim(file.path(out_dir, "cleaned-metadata.txt"), sep ="\t")
  38. clinical_data$cluster <- factor(clinical_data$cluster, levels = c("1","2","3"))
  40. # Example variable list (replace with your real column names)
  41. clinical_vars_names <- c(
  42. "cluster",
  43. "age_of_death",
  44. "sex",
  45. "pmi",
  46. "RIN",
  47. "age_of_onset",
  48. "cag",
  49. "Duration",
  50. "h.v_cortical_score",
  51. "h.v_striatal_score",
  52. "vonsattel_grade",
  53. "HDSubject.grade",
  54. "HDSubject.striatal_score",
  55. "HDSubject.cortical_score",
  56. "HDSubject.cag_adj_onset",
  57. "HDSubject.cag_adj_striatal_score",
  58. "HDSubject.cag_adj_cortical_score",
  59. "mRNASeq.RIN"
  60. )
  62. # Keep only variables that exist
  63. clinical_data_sub <- clinical_data[, clinical_vars_names[clinical_vars_names %in% colnames(clinical_data)], drop = FALSE]
  65. clinical_data_sub$cluster <- factor(clinical_data$cluster, levels = c("1","2","3"))
  66. clinical_data_sub$sex <- factor(clinical_data$sex, levels = c("M"))
  67. clinical_data_sub$vonsattel_grade <- factor(clinical_data$vonsattel_grade, levels = c("3", "4"))
  68. clinical_data_sub$HDSubject.grade <- factor(clinical_data$HDSubject.grade, levels = c("3", "4"))
  70. # Numeric variables only (exclude cluster)
  71. num_vars <- names(clinical_data_sub)[sapply(clinical_data_sub, is.numeric)]
  72. num_vars <- setdiff(num_vars, "cluster")
  74. # If you want to drop duplicate versions, uncomment and edit:
  75. num_vars <- setdiff(num_vars, c("HDSubject.striatal_score", "HDSubject.cortical_score", "mRNASeq.RIN"))
  77. clinical_long <- clinical_data_sub %>%
  78. dplyr::select(cluster, dplyr::all_of(num_vars)) %>%
  79. tidyr::pivot_longer(
  80. cols = -cluster,
  81. names_to = "variable",
  82. values_to = "value"
  83. ) %>%
  84. dplyr::filter(!is.na(value))
  86. # Pretty facet labels (edit as needed)
  87. pretty_labels <- c(
  88. age_of_death = "Age of Death",
  89. pmi = "Post-mortem Interval",
  90. RIN = "RIN",
  91. mRNASeq.RIN = "RNA Integrity Number",
  92. age_of_onset = "Age of Onset",
  93. cag = "CAG repeats",
  94. Duration = "Disease Duration",
  95. h.v_cortical_score = "Cortical score",
  96. h.v_striatal_score = "Striatal score",
  97. HDSubject.striatal_score = "Striatal score (HDSubject)",
  98. HDSubject.cortical_score = "Cortical score (HDSubject)",
  99. HDSubject.cag_adj_onset = "CAG adjusted onset",
  100. HDSubject.cag_adj_striatal_score = "CAG adjusted striatal score",
  101. HDSubject.cag_adj_cortical_score = "CAG adjusted cortical score"
  102. )
  104. clinical_long <- clinical_long %>%
  105. dplyr::mutate(
  106. variable_label = dplyr::recode(variable, !!!pretty_labels, .default = variable)
  107. )
  109. # -----------------------------
  110. # 2) Kruskal-Wallis + epsilon²
  111. # -----------------------------
  112. eps2_kw <- function(H, n, k) {
  113. e <- (as.numeric(H) - k + 1) / (n - k)
  114. ifelse(is.finite(e) & e > 0, e, 0)
  115. }
  117. min_n_per_group <- 3 # set to 3 if missingness is low
  119. kw_results <- clinical_long %>%
  120. dplyr::group_by(variable, variable_label) %>%
  121. dplyr::group_modify(~{
  122. d <- .x
  123. tab <- table(d$cluster)
  125. if (length(tab) < 3 || any(tab < min_n_per_group)) {
  126. return(dplyr::tibble(
  127. H = NA_real_, p.value = NA_real_, epsilon2 = NA_real_,
  128. n_total = nrow(d),
  129. n_c1 = as.integer(ifelse(is.na(tab["1"]), 0, tab["1"])),
  130. n_c2 = as.integer(ifelse(is.na(tab["2"]), 0, tab["2"])),
  131. n_c3 = as.integer(ifelse(is.na(tab["3"]), 0, tab["3"]))
  132. ))
  133. }
  135. kt <- stats::kruskal.test(value ~ cluster, data = d)
  136. H <- unname(kt$statistic)
  137. n <- nrow(d)
  138. k <- length(tab)
  140. dplyr::tibble(
  141. H = H,
  142. p.value = kt$p.value,
  143. epsilon2 = eps2_kw(H, n, k),
  144. n_total = n,
  145. n_c1 = as.integer(unname(tab["1"])),
  146. n_c2 = as.integer(unname(tab["2"])),
  147. n_c3 = as.integer(unname(tab["3"]))
  148. )
  149. }) %>%
  150. dplyr::ungroup() %>%
  151. dplyr::mutate(padj_BH = p.adjust(p.value, method = "BH")) %>%
  152. dplyr::arrange(padj_BH, p.value)
  154. # Optional: inspect results
  155. print(kw_results)
  157. # -----------------------------
  158. # 3) Annotation labels for plot (NO FDR shown)
  159. # -----------------------------
  160. p_labels <- clinical_long %>%
  161. dplyr::group_by(variable, variable_label) %>%
  162. dplyr::summarise(
  163. y_pos = max(value, na.rm = TRUE) + 0.10 * diff(range(value, na.rm = TRUE)),
  164. .groups = "drop"
  165. ) %>%
  166. dplyr::left_join(
  167. kw_results %>% dplyr::select(variable, p.value, epsilon2), # <- no padj_BH
  168. by = "variable"
  169. ) %>%
  170. dplyr::mutate(
  171. label = ifelse(
  172. is.na(p.value),
  173. "KW p = NA",
  174. paste0("p = ", signif(p.value, 2),
  175. "\nε² = ", signif(epsilon2, 2))
  176. )
  177. )
  179. # -----------------------------
  180. # 4) Plot (3 clusters)
  181. # -----------------------------
  182. p <- ggplot(clinical_long, aes(x = cluster, y = value, fill = cluster)) +
  183. geom_violin(trim = FALSE, alpha = 0.9, color = "black") +
  184. geom_boxplot(width = 0.15, outlier.shape = NA, fill = "white", color = "black") +
  185. geom_jitter(width = 0.08, size = 1.2, alpha = 0.7) +
  186. geom_text(
  187. data = p_labels,
  188. aes(x = 2, y = y_pos, label = label),
  189. inherit.aes = FALSE,
  190. size = 3
  191. ) +
  192. facet_wrap(~ variable_label, scales = "free_y", ncol = 4) +
  193. scale_fill_manual(values = c("1" = "#F8766D", "2" = "#00BA38", "3" = "#00BFC4")) +
  194. labs(x = "Cluster", y = NULL) +
  195. theme_bw(base_size = 11) +
  196. theme(
  197. strip.text = element_text(face = "bold"),
  198. legend.position = "none",
  199. panel.grid.minor = element_blank()
  200. )
  202. png("clinical_data.png", width = 1500, height = 1000, res = 120)
  203. p
  204. dev.off()
  206. # Example: sex vs cluster
  207. tab_sex <- table(clinical_data_sub$cluster, clinical_data_sub$HDSubject.grade)
  208. fisher.test(tab_sex)
  210. ggplot(clinical_data_sub, aes(x = cluster, fill = vonsattel_grade)) +
  211. geom_bar(position = "fill") +
  212. labs(y = "Proportion", x = "Cluster", title = "Sex distribution by cluster") +
  213. theme_bw()

clinical_data_association.R at commit d61affb, under MIT · at the source

Overview

Authors: Daan van Beek1, Aishwarya Iyer1,2, Friederike Ehrhart2, Chris T Evelo2, Theo M de Kok2, Ilja C W Arts1, Michiel E Adriaens1, Martina Kutmon1
ORCID iDs: Daan van Beek, Aishwarya Iyer, Friederike Ehrhart, Chris T Evelo, Theo M de Kok, Ilja C W Arts, Michiel E Adriaens, Martina Kutmon
  1. Maastricht Centre for Systems Biology and Bioinformatics (MaCSBio), Maastricht University, Maastricht, the Netherlands
  2. Department of Translational Genomics, GROW/NUTRIM/MHeNs, Maastricht University, Maastricht, the Netherlands
Institutions: Maastricht University (Netherlands)
Journal: Bioinformatics (Oxford, England), volume 42, issue Suppl 2, article btag592
Dates: published online 21 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/bioinformatics/btag592 · PMID 42635204 · PMCID PMC13501290 · OpenAlex W7204125316
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population)
Methods: Statistics, Machine learning, Connectivity
MeSH: Alleles*, Gene Regulatory Networks*, Huntington Disease*, Algorithms, Gene Expression Profiling, Humans, Protein Interaction Maps, Quantitative Trait Loci (* major topic)
Topic: Genetic Neurodegenerative Diseases (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: European Union's Horizon 2020 Research and Innovation programme; EJP RD COFUND-EJP N° (825575)
Citations: not cited yet (Europe PMC); 42 references in the paper

Abstract

Motivation: Huntington’s disease (HD) exhibits substantial variability in age of onset and disease progression that is not fully explained by CAG repeat length alone. Part of this residual variation is heritable, implicating additional genetic mechanisms. cis-regulatory variation, genetic variants that alter transcription and splicing of nearby genes, represents one such mechanism that can be quantified through allele-specific expression (ASE) analysis. However, methods for integrating ASE profiles into patient stratification frameworks remain underdeveloped, particularly for rare diseases with small cohorts and sparse data.

Results: We adapt a network-based stratification algorithm, originally developed for somatic tumour mutations, to ASE data. By propagating gene-level ASE imbalance profiles through a protein-protein interaction network, we stratified 20 HD patients into three distinct biological patient subgroups. Differential gene expression analysis highlights neuroinflammatory pathways, including microglial activation, immune cell activation, and cytokine regulation, as key sources of inter-patient heterogeneity, while differential ASE analysis implicates proteasomal and ubiquitin-dependent protein catabolic processes, immune activation, and central nervous system development. Intersection of differentially imbalanced and expressed genes identified FAM181B as a candidate gene with potential eQTL-mediated regulation, supported by independent cis-eQTL evidence for rs3780 in the caudate and putamen, the primary HD-affected striatal regions. FAM181B encodes a nuclear protein expressed in neural tissues acting as an interactor of the Hippo pathway TEAD transcription factors, implicating transcriptional regulatory variation as a potential contributor to molecular heterogeneity between patient subgroups. Differences in cortical and striatal neuropathological scores between clusters, even when adjusted for CAG repeat length, provide clinical support for the biological relevance of the identified subgroups.

Availability: All analysis code, Docker containers, and conda environments are available at https://github.com/macsbio/HD-ASE-NBS.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 11 matches between paragraphs and lines of code.

macsbio/HD-ASE-NBS

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: d61affb4d3ed6e5901edbd05b21b5c0118aff994, 19 March 2026
Languages: Shell (14), Python (14), R (10)
Size: 61 files, 38 scripts
Software Heritage: not archived
Found in: “Data availability statement”
Holds: README, license file, environment (docker/Dockerfile.featurecount, docker/Dockerfile.gatk, docker/Dockerfile.pynbs, docker/Dockerfile.r_env, docker/Dockerfile.rseqc, docker/Dockerfile.star, scripts/pyNBS/setup.py), continuous integration
Not found: CITATION.cff, tests, documentation
Tools: pandas (13 files), NumPy (11 files), tidyverse (9 files), ggplot2 (8 files), NetworkX (5 files), DESeq2 (4 files), SAMtools (4 files), SciPy (4 files), circlize (3 files), ComplexHeatmap (3 files), edgeR (3 files), igraph (3 files), limma (3 files), Matplotlib (3 files), pheatmap (3 files), pROC (3 files), reshape2 (3 files), STAR (3 files), BCFtools (2 files), broom (2 files), clusterProfiler (2 files), scikit-learn (2 files), seaborn (2 files), car (1 file), cowplot (1 file), data.table (1 file), emmeans (1 file), FastQC (1 file), ggpubr (1 file), lme4 (1 file), mgcv (1 file), multcomp (1 file), patchwork (1 file), Plotly (1 file), randomForest (1 file), reticulate (1 file), rstatix (1 file), Subread (featureCounts) (1 file), survival (1 file), UMAP (1 file), WGCNA (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
40 files
At the source: github.com/macsbio/HD-ASE-NBS

Availability

All analysis code, Docker containers, and conda environments are available at https://github.com/macsbio/HD-ASE-NBS.

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

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 38 scripts, each with its path and the digest of its content;
  • 11 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

Datasets cited

Data availability statement

The data underlying this article are available in the Gene Expression Omnibus (GEO) repository, under accession number GSE64810 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64810) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64810).

All analysis scripts, Docker containers, and conda environment files supporting the reproducibility of this work are available on GitHub (https://github.com/macsbio/HD-ASE-NBS).

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, 8 MeSH terms, 2 funders, 38 references.

Cite

This paper

van Beek, D., Iyer, A., Ehrhart, F., Evelo, C. T., de Kok, T. M., Arts, I. C. W., Adriaens, M. E., & Kutmon, M. (2026). Network-based stratification of allele-specific expression reveals patient subgroups in Huntington's disease. Bioinformatics (Oxford, England), 42(Suppl 2), btag592. https://doi.org/10.1093/bioinformatics/btag592

BibTeX

@article{vanbeek2026network,
author = {van Beek, Daan and Iyer, Aishwarya and Ehrhart, Friederike and Evelo, Chris T and de Kok, Theo M and Arts, Ilja C W and Adriaens, Michiel E and Kutmon, Martina},
title = {{Network-based stratification of allele-specific expression reveals patient subgroups in Huntington's disease}},
journal = {Bioinformatics (Oxford, England)},
year = {2026},
month = aug,
volume = {42},
number = {Suppl 2},
pages = {btag592},
publisher = {Oxford University Press},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btag592},
url = {https://doi.org/10.1093/bioinformatics/btag592},
pmid = {42635204},
pmcid = {PMC13501290}
}

RIS

TY - JOUR
AU - van Beek, Daan
AU - Iyer, Aishwarya
AU - Ehrhart, Friederike
AU - Evelo, Chris T
AU - de Kok, Theo M
AU - Arts, Ilja C W
AU - Adriaens, Michiel E
AU - Kutmon, Martina
TI - Network-based stratification of allele-specific expression reveals patient subgroups in Huntington's disease
T2 - Bioinformatics (Oxford, England)
J2 - Bioinformatics
PY - 2026
DA - 2026/08/01
VL - 42
IS - Suppl 2
SP - btag592
SN - 1367-4803
PB - Oxford University Press
DO - 10.1093/bioinformatics/btag592
UR - https://doi.org/10.1093/bioinformatics/btag592
LA - en
ER -

CSL-JSON

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