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Differential gene regulatory network analysis reveals transcriptional disruption in opioid.

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6 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 6 matches
  1. [1] § Results › Transcription factor-level network disruption in OUD data set (astrocyte) › Functional enrichment and biological relevance of identified TFs ↔ gsea2.R, lines 41 to 120 · score 0.99 · normalized enrichment scores, Post translational Protein, Extracellular Matrix Organization, Elevated Platelet Cytosolic, Platelet Degranulation, Laminin Interactions · Report a mismatch
  2. [2] § Results › Transcription factor-level network disruption in OUD data set (astrocyte) › Functional enrichment and biological relevance of identified TFs ↔ gsea2.R, lines 41 to 120 · score 0.96 · post translational protein, Cellular Senescence, ERK signaling, Cellular Responses, Cell Cycle, Neuronal System · Report a mismatch
  3. [3] § Results › Transcription factor-level network disruption in OUD data set (astrocyte) › Functional enrichment and biological relevance of identified TFs ↔ gsea2.R, lines 1 to 39 · score 0.89 · Interferon Gamma Signaling, Elevated Platelet Cytosolic, Platelet Degranulation, Immune System, Platelet Activation, Cytokine Signaling · Report a mismatch
  4. [4] § Results › Simulation studies ↔ simulation_plot.R, lines 1 to 46 · score 0.67 · PCA score, Local_U, Global_F, Global_U, simulation, distance · Report a mismatch
  5. [5] § Materials and methods › Application to real datasets › Statistical testing on TF activity matrices ↔ simulation_plot.R, lines 1 to 46 · score 0.60 · Local_U, Global_F, Global_U, score, permutation, PCA · Report a mismatch
  6. [6] § Results › Transcription factor-level network disruption in OUD data set (astrocyte) › Dissecting TF–Target relationships by differential expression categories ↔ gene_expression.R, lines 1 to 88 · score 0.53 · gene expression, STAT5A, SERPINA3, Violin, SOCS3, ATF3 · Report a mismatch

Paper

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The authors' code

R · 121 lines · 4.2 KB · no license · 3 matches

  1. # ===== Libraries =====
  2. library(tidyverse)
  3. library(forcats)
  4. library(ggtext)
  6. # ===== Load your GSEA results =====
  7. gsea <- read_csv("~/Desktop/Lab/celloracle/scortch/gsea_results_reactome_astrocyte/gseapy.gene_set.prerank.report.csv")
  9. # Filter significant rows and handle Gene %
  10. gsea_sig <- gsea %>%
  11. mutate(Gene_percent = as.numeric(gsub("%", "", `Gene %`))) %>%
  12. filter(`FDR q-val` < 0.1, !is.na(NES))
  14. # ===== Selected 25 pathways =====
  15. selected_terms <- c(
  16. "Signaling by NOTCH",
  17. "Signaling by ERBB4",
  18. "Nuclear Events (Kinase and Transcription Factor Activation)",
  19. "Response to Elevated Platelet Cytosolic Ca2+",
  20. "Platelet Degranulation",
  21. "Post-translational Protein Phosphorylation",
  22. "Laminin Interactions",
  23. "Extracellular Matrix Organization",
  24. "Integrin Cell Surface Interactions",
  25. "Cell Cycle, Mitotic",
  26. "Cytokine Signaling in Immune System",
  27. "Neuronal System",
  28. "Signaling by NTRKs",
  29. "Stimuli-sensing Channels",
  30. "Fc Epsilon Receptor (FCERI) Signaling",
  31. "Cellular Responses to Stress",
  32. "Cellular Senescence",
  33. "Immune System",
  34. "Regulation of IGF Transport and Uptake by Insulin-like Growth Factor Binding Proteins (IGFBPs)",
  35. "Interferon Gamma Signaling",
  36. "Platelet Activation, Signaling and Aggregation",
  37. "Signaling by Interleukins",
  38. "Interleukin-4 and Interleukin-13 Signaling"
  39. )
  41. # ===== Filter and simplify labels =====
  42. plot_data <- gsea_sig %>%
  43. filter(Term %in% selected_terms) %>%
  44. mutate(
  45. Term_simple = Term,
  46. Term_simple = str_replace_all(
  47. Term_simple,
  48. "Regulation of IGF Transport and Uptake by Insulin-like Growth Factor Binding Proteins \\(IGFBPs\\)",
  49. "IGF Transport & Uptake (IGFBPs)"
  50. )
  51. )
  53. # ===== Assign categories =====
  54. plot_data <- plot_data %>%
  55. mutate(Category = case_when(
  56. Term %in% c(
  57. "Signaling by NOTCH","Signaling by ERBB4","Nuclear Events (Kinase and Transcription Factor Activation)",
  58. "Post-translational Protein Phosphorylation","Cellular Responses to Stress","Cellular Senescence",
  59. "Signaling by NTRKs","Stimuli-sensing Channels","Neuronal System","Cell Cycle, Mitotic"
  60. ) ~ "MAPK/ERK Signaling",
  61. Term %in% c(
  62. "Cytokine Signaling in Immune System","Immune System","Signaling by Interleukins",
  63. "Interleukin-4 and Interleukin-13 Signaling","Interferon Gamma Signaling",
  64. "Fc Epsilon Receptor (FCERI) Signaling","Platelet Activation, Signaling and Aggregation",
  65. "Platelet Degranulation","Response to Elevated Platelet Cytosolic Ca2+"
  66. ) ~ "Cytokine Signaling",
  67. Term %in% c(
  68. "Laminin Interactions","Extracellular Matrix Organization",
  69. "Integrin Cell Surface Interactions",
  70. "Regulation of IGF Transport and Uptake by Insulin-like Growth Factor Binding Proteins (IGFBPs)"
  71. ) ~ "ECM Remodeling",
  72. TRUE ~ "Other"
  73. ))
  75. # ===== Category colors for labels =====
  76. cat_colors <- c(
  77. "MAPK/ERK Signaling" = "#E41A1C",
  78. "Cytokine Signaling" = "#377EB8",
  79. "ECM Remodeling" = "#4DAF4A",
  80. "Other" = "#984EA3"
  81. )
  83. # ===== Build colored term labels =====
  84. plot_data <- plot_data %>%
  85. mutate(
  86. Term_colored = paste0("<span style='color:", cat_colors[Category], "'>", Term_simple, "</span>"),
  87. # custom ordering: sort by Category order, then Term within category
  88. Category = factor(Category, levels = c("MAPK/ERK Signaling", "Cytokine Signaling", "ECM Remodeling", "Other"))
  89. ) %>%
  90. arrange(Category, Term_simple) %>%
  91. mutate(
  92. Term_colored = factor(Term_colored, levels = unique(Term_colored)),
  93. FDR_adj = `FDR q-val` + 1e-3,
  94. neglogFDR = pmin(-log10(FDR_adj), 1.5) # cap at 10
  95. )
  97. # ===== Plot =====
  98. png("selected_reactome_pathways_termcolored_sorted.png", res = 300, width = 2500, height = 2000)
  99. ggplot(plot_data, aes(
  100. x = NES,
  101. y = Term_colored,
  102. size = Gene_percent,
  103. color = neglogFDR
  104. )) +
  105. geom_point() +
  106. scale_size(range = c(3, 9)) +
  107. scale_color_gradient(low = "blue", high = "red",name = "-log(p_adj)") + # no legend
  108. labs(
  109. #title = "Selected Reactome Pathway Enrichment",
  110. x = "Normalized Enrichment Score (NES)",
  111. y = NULL,
  112. size = "Gene % Affected"
  113. ) +
  114. theme_minimal(base_size = 14) +
  115. theme(
  116. panel.grid.major.y = element_blank(),
  117. axis.text.y = element_markdown(size = 10),
  118. legend.position = "right"
  119. )
  120. dev.off()

gsea2.R at commit fa01466, no license · at the source

Overview

Authors: Jianlan Ren1, Xu Wang2, Feiyang Luan1, Yeqing Chen1, Le Gao1, Wenzhe Ho2, Zhi Wei1
ORCID iDs: Jianlan Ren, Wenzhe Ho, Zhi Wei
  1. Department of Computer Science, New Jersey Institute of Technology, Newark, New Jersey, 07102, United States
  2. Department of Pathology and Laboratory Medicine, Temple University Lewis Katz School of Medicine, Philadelphia, Pennsylvania, 19140, United States
Institutions: New Jersey Institute of Technology (United States); Temple University (United States)
Journal: NAR genomics and bioinformatics, volume 8, issue 2, article lqag031
Dates: received 1 December 2025; accepted 23 February 2026; published online 27 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/nargab/lqag031 · PMID 42064781 · PMCID PMC13126120 · OpenAlex W7141703947
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: human (organism), pain (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity
MeSH: Analgesics, Opioid*, Brain*, Gene Regulatory Networks*, Transcription Factors*, Astrocytes, Gene Expression Regulation, Humans, Single-Cell Gene Expression Analysis, Statistics, Nonparametric (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIGMS NIH HHS (R35 GM158529); NIDA NIH HHS (R01 DA058536); National Institutes of Health (R35GM158529, P01CA281867, R15HG012087, UM1TR004789); NHGRI NIH HHS (R15 HG012087)
Citations: not cited yet (Europe PMC); 49 references in the paper

Abstract

Gene regulatory networks (GRNs) inferred from single-cell data offer a powerful lens for dissecting transcriptional regulation across biological conditions. Yet, statistical methods for comparing TF-level binary regulatory matrices—where edges represent the presence or absence of regulation—remain underdeveloped. Here, we introduce and benchmark five complementary statistical tests for group-level comparison of TF-level binary regulatory matrices. These include three global methods—a U-statistic-based dissimilarity test (Global_U), a distance-based pseudo- test (Global_F), and a PCA-based test—as well as two feature-level approaches: a per-feature U-test (Local_U) and Fisher’s exact test. Through extensive simulations spanning sparse, coordinated, balanced, and noisy signal structures, we show that global methods consistently outperform in detecting distributed regulatory shifts, particularly under correlation or noise. Applying this framework to single-nucleus RNA-seq data from human brain donors, we uncover astrocyte-specific regulatory alterations linked to opioid exposure. While each method captures distinct signal types, our results underscore the value of combining global and local tests to enhance sensitivity and interpretability. This unified framework provides a robust statistical foundation for GRN-based comparisons in single-cell studies.

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 6 matches between paragraphs and lines of code.

jianlan0816/U-test-based-GRN

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: fa0146686c28381f2c2e6b1288b9e71f5fce32cd, 12 December 2025
Languages: R (5), Python (2)
Size: 8 files, 7 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (5 files), ggplot2 (3 files), patchwork (3 files), NumPy (2 files), pandas (2 files), Matplotlib (1 file), scikit-learn (1 file), SciPy (1 file), seaborn (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
8 files
At the source: github.com/jianlan0816/U-test-based-GRN

Zenodo 17915951

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: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (5 files), ggplot2 (3 files), patchwork (3 files), NumPy (2 files), pandas (2 files), Matplotlib (1 file), scikit-learn (1 file), SciPy (1 file), seaborn (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
8 files
At the source: doi.org/10.5281/zenodo.17915951

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

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

Datasets cited

Data availability

All analysis scripts are available at https://github.com/jianlan0816/U-test-based-GRN and https://doi.org/10.5281/zenodo.17915951. The snRNA-seq dataset analyzed in this study was obtained from the NCBI Gene Expression Omnibus (GEO) under accession number GSE240457 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE240457).

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

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 9 MeSH terms, 4 funders, 43 references.

Cite

This paper

Ren, J., Wang, X., Luan, F., Chen, Y., Gao, L., Ho, W., & Wei, Z. (2026). Differential gene regulatory network analysis reveals transcriptional disruption in opioid. NAR genomics and bioinformatics, 8(2), lqag031. https://doi.org/10.1093/nargab/lqag031

BibTeX

@article{ren2026differential,
author = {Ren, Jianlan and Wang, Xu and Luan, Feiyang and Chen, Yeqing and Gao, Le and Ho, Wenzhe and Wei, Zhi},
title = {{Differential gene regulatory network analysis reveals transcriptional disruption in opioid}},
journal = {NAR genomics and bioinformatics},
year = {2026},
month = mar,
volume = {8},
number = {2},
pages = {lqag031},
publisher = {Oxford University Press},
issn = {2631-9268},
doi = {10.1093/nargab/lqag031},
url = {https://doi.org/10.1093/nargab/lqag031},
pmid = {42064781},
pmcid = {PMC13126120}
}

RIS

TY - JOUR
AU - Ren, Jianlan
AU - Wang, Xu
AU - Luan, Feiyang
AU - Chen, Yeqing
AU - Gao, Le
AU - Ho, Wenzhe
AU - Wei, Zhi
TI - Differential gene regulatory network analysis reveals transcriptional disruption in opioid
T2 - NAR genomics and bioinformatics
J2 - NAR Genom Bioinform
PY - 2026
DA - 2026/03/27
VL - 8
IS - 2
SP - lqag031
SN - 2631-9268
PB - Oxford University Press
DO - 10.1093/nargab/lqag031
UR - https://doi.org/10.1093/nargab/lqag031
LA - en
ER -

CSL-JSON

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"id": "10.1093/nargab/lqag031",
"type": "article-journal",
"title": "Differential gene regulatory network analysis reveals transcriptional disruption in opioid",
"container-title": "NAR genomics and bioinformatics",
"author": [
{
"family": "Ren",
"given": "Jianlan"
},
{
"family": "Wang",
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},
{
"family": "Luan",
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},
{
"family": "Chen",
"given": "Yeqing"
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"volume": "8",
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"page": "lqag031",
"DOI": "10.1093/nargab/lqag031",
"PMID": "42064781",
"PMCID": "PMC13126120",
"ISSN": "2631-9268",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/nargab/lqag031",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
27
]
]
}
}

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