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Differential sex-dependent responses of circulating steroid hormones and cortical gene expression in a traumatic brain injury model.

Code ↔ Paper

8 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 8 matches
  1. [1] § MATERIALS AND METHODS › Determining biological enrichment of tissue remodeling and the immune system during TBI using RNA expression data ↔ 4_functionalEnrichmentAnalysis.py, lines 66 to 114 · score 0.85 · GO_Biological_Process_2021, GO_Cellular_Component_2025, functional enrichment, GO terms, cutoff, enrichr · Report a mismatch
  2. [2] § MATERIALS AND METHODS › Differential gene expression analysis ↔ 2_edgeR_differentialGeneExpression.R, lines 82 to 120 · score 0.68 · quasi likelihood, QL, gene expression, cutoff, batch, log2 · Report a mismatch
  3. [3] § RESULTS › Different sex hormones are correlated with similar immune responses in male and female mice ↔ 5a_SemanticSimilarity_Immune.R, lines 1 to 59 · score 0.66 · innate immune, immune processes, enriched GO, BP terms, biological processes, semantic · Report a mismatch
  4. [4] § RESULTS › Characterizing the early innate immune response to cortical injury ↔ 5a_SemanticSimilarity_Immune.R, lines 1 to 59 · score 0.62 · innate immune processes, functionally enriched GO, BP terms, Biological Process, semantic, DEGs · Report a mismatch
  5. [5] § MATERIALS AND METHODS › Correlating cortical gene expression with steroid hormones using differentially expressed eigengenes ↔ 3_normalizeRNAseqData.R, lines 45 to 106 · score 0.60 · DESeq2, contralateral sample, vst, Batch, ipsilateral, mouse · Report a mismatch
  6. [6] § RESULTS › Characterization of tissue remodeling biological processes and cellular components after TBI ↔ 5b_SemanticSimilarity_TissueRemodeling.R, lines 60 to 111 · score 0.60 · tissue remodeling biological, Cellular Components, BP term, matrix, semantic, CC · Report a mismatch
  7. [7] § MATERIALS AND METHODS › Experimental design and statistical analysis ↔ 4_functionalEnrichmentAnalysis.py, lines 66 to 114 · score 0.57 · Functional enrichment, GO terms, Enrichr, overlap, scores, FDR · Report a mismatch
  8. [8] § MATERIALS AND METHODS › Determining biological enrichment of tissue remodeling and the immune system during TBI using RNA expression data ↔ 5b_SemanticSimilarity_TissueRemodeling.R, lines 1 to 58 · score 0.56 · tissue remodeling, enriched GO, cellular component, score, semantic, immune · Report a mismatch

Paper

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The authors' code

Python · 138 lines · 8.5 KB · Apache-2.0 · 2 matches

  1. ###############################################################
  2. ## 4_functionalEnrichmentAnalysis.py ##
  3. ## ______ ______ __ __ ##
  4. ## /\ __ \ /\ ___\ /\ \/\ \ ##
  5. ## \ \ __ \ \ \___ \ \ \ \_\ \ ##
  6. ## \ \_\ \_\ \/\_____\ \ \_____\ ##
  7. ## \/_/\/_/ \/_____/ \/_____/ ##
  8. ## @Developed by: Stabenfeldt and Plaisier Labs ##
  9. ## (https://plaisierlab.engineering.asu.edu/) ##
  10. ## Arizona State University ##
  11. ## 242 ISTB1, 550 E Orange St ##
  12. ## Tempe, AZ 85281 ##
  13. ## @github: https://github.com/plaisier-lab/sex_specific_tbi ##
  14. ## @Author: Amberlyn Simmons, Sierra Wilferd ##
  15. ## @License: <> ##
  16. ## ##
  17. ## If this program is used in your analysis please ##
  18. ## mention who built it. Thanks. :-) ##
  19. ###############################################################
  20. ## Purpose: to identify functionally enriched gene ontology biological process and cellular component terms from differentially expressed RNAseq data.
  22. #############
  23. ## imports ##
  24. #############
  25. import numpy as np
  26. import pandas as pd
  27. from sklearn.decomposition import PCA
  28. from scipy import stats
  29. from scipy.stats import pearsonr
  30. from statsmodels.stats.multitest import multipletests
  31. import gseapy as gp
  32. ## used to convert ensembl IDs to gene IDs
  33. import mygene
  34. mg = mygene.MyGeneInfo()
  35. import seaborn as sns
  36. from matplotlib.backends.backend_pdf import PdfPages
  37. import matplotlib.pyplot as plt
  38. import matplotlib as mpl
  40. ##################
  41. ## Declare data ##
  42. ##################
  43. ## SW DEGs
  44. filepath1 = '../Data/2b_Final_DEGs_SW/DEGs/'
  45. conditions1 = ['injured24_female', 'injured72_female', 'injured24_male', 'injured72_male']
  46. addNaive = ['naive_male_upregulated', 'naive_male_downregulated', 'naive_female_upregulated', 'naive_female_downregulated']
  47. date = '07012025'
  48. saveOut1 = '../Data/4_FunctionalEnrichmentAnalysis/FEAresults_SW/'
  50. ####################################
  51. ## Functional Enrichment Analysis ##
  52. ####################################
  53. # merge dictionaries
  54. build_translator_BP = pd.DataFrame(columns = ['Human readable'])
  55. build_translator_CC = pd.DataFrame(columns = ['Human readable'])
  57. # set up enrichment - USE MOUSE!!!!
  58. countDEG = pd.DataFrame(columns = [i + '_upregulated' for i in conditions1] + [i + '_downregulated' for i in conditions1], index = ['HowMany', 'Direction'])
  59. overlapDEG = pd.DataFrame(columns = [i + '_upregulated' for i in conditions1] + [i + '_downregulated' for i in conditions1], index = ['HowMany', 'Direction'])
  61. for naive in addNaive:
  62. countDEG.loc['HowMany', naive] = int(0)
  63. overlapDEG.loc['HowMany', naive] = int(0)
  64. #countDEG.loc['Direction', naive] = naive.split('_')[2]
  66. for condition1 in conditions1:
  67. print(condition1)
  68. allDEGs = pd.read_csv(filepath1 + condition1 + '_DEGlist_symb_' + date + '.csv', index_col = 0)
  69. # significant genes
  70. upDEGs = allDEGs.loc[[i for i in allDEGs.index if allDEGs.loc[i, 'logFC'] >= 1 and allDEGs.loc[i, 'FDR'] <= 0.1]]
  71. downDEGs = allDEGs.loc[[i for i in allDEGs.index if allDEGs.loc[i, 'logFC'] <= -1 and allDEGs.loc[i, 'FDR'] <= 0.1]]
  72. # save out how many DEGs there are
  73. countDEG.loc['HowMany', condition1 + '_upregulated'] = len(upDEGs.index)
  74. countDEG.loc['HowMany', condition1 + '_downregulated'] = len(downDEGs.index)
  75. countDEG.loc['Direction', condition1 + '_upregulated'] = 'upregulated'
  76. countDEG.loc['Direction', condition1 + '_downregulated'] = 'downregulated'
  77. # Less code in a for loop
  78. dictDEGs = {'upregulated': upDEGs, 'downregulated':downDEGs}
  79. for direction1 in dictDEGs.keys():
  80. # Translate
  81. mapped = list(mg.querymany(list(dictDEGs[direction1].index), scopes='ensembl.gene', species=10090, as_dataframe=True)['symbol'].dropna())
  82. # Run enrichment
  83. enrichment_BP = gp.enrichr(gene_list=mapped, gene_sets=['GO_Biological_Process_2021'], organism='Mouse', outdir= saveOut1 + condition1 + '_' + direction1 + '_FEA_BP', cutoff=0.05, no_plot=True)
  84. enrichment_CC = gp.enrichr(gene_list=mapped, gene_sets=['GO_Cellular_Component_2025'], organism='Mouse', outdir= saveOut1 + condition1 + '_' + direction1 + '_FEA_CC', cutoff=0.05, no_plot=True)
  85. # Make the overlap not a date in excel
  86. enrichment_BP.results['DEGs'] = [i.split('/')[0] for i in enrichment_BP.results['Overlap']]
  87. enrichment_BP.results['GO genes'] = [i.split('/')[1] for i in enrichment_BP.results['Overlap']]
  88. enrichment_BP.results['Overlap'] = [float(i.split('/')[0])/float(i.split('/')[1]) for i in enrichment_BP.results['Overlap']]
  89. enrichment_CC.results['DEGs'] = [i.split('/')[0] for i in enrichment_CC.results['Overlap']]
  90. enrichment_CC.results['GO genes'] = [i.split('/')[1] for i in enrichment_CC.results['Overlap']]
  91. enrichment_CC.results['Overlap'] = [float(i.split('/')[0])/float(i.split('/')[1]) for i in enrichment_CC.results['Overlap']]
  92. # reorder
  93. enrichment_BP.results = enrichment_BP.results[['Gene_set', 'Term', 'DEGs', 'GO genes', 'Overlap', 'P-value', 'Adjusted P-value', 'Old P-value', 'Old Adjusted P-value', 'Odds Ratio', 'Combined Score', 'Genes']]
  94. enrichment_CC.results = enrichment_CC.results[['Gene_set', 'Term', 'DEGs', 'GO genes', 'Overlap', 'P-value', 'Adjusted P-value', 'Old P-value', 'Old Adjusted P-value', 'Odds Ratio', 'Combined Score', 'Genes']]
  95. # Save out data
  96. enrichment_BP.results.to_csv(saveOut1 + 'enrichr_results/BP_' + condition1 + '_' + direction1 + '.csv')
  97. enrichment_CC.results.to_csv(saveOut1 + 'enrichr_results/CC_' + condition1 + '_' + direction1 + '.csv')
  98. # calculate overlap
  99. # identify significant results
  100. enrichment_BP.results.loc[enrichment_BP.results['Adjusted P-value']<=0.05].loc[enrichment_BP.results['Overlap'] >= 0.1].to_csv(saveOut1 + 'enrichr_results/allData_significantGOterms_BP_' + condition1 + '_' + direction1 + '.csv')
  101. enrichment_CC.results.loc[enrichment_CC.results['Adjusted P-value']<=0.05].loc[enrichment_CC.results['Overlap'] >= 0.1].to_csv(saveOut1 + 'enrichr_results/allData_significantGOterms_CC_' + condition1 + '_' + direction1 + '.csv')
  102. #break
  103. # pull out terms only
  104. pd.DataFrame(set(['GO:' + i.split('(GO:')[1].rstrip(')') for i in enrichment_BP.results.loc[enrichment_BP.results['Adjusted P-value']<=0.05].loc[enrichment_BP.results['Overlap'] >= 0.1, 'Term'] if 'GO:' in i]), columns = ['GO terms']).to_csv(saveOut1 + 'enrichr_results/significantGOterms_BP_' + condition1 + '_' + direction1 + '.csv')
  105. pd.DataFrame(set(['GO:' + i.split('(GO:')[1].rstrip(')') for i in enrichment_CC.results.loc[enrichment_CC.results['Adjusted P-value']<=0.05].loc[enrichment_CC.results['Overlap'] >= 0.1, 'Term'] if 'GO:' in i]), columns = ['Term']).to_csv(saveOut1 + 'enrichr_results/significantGOterms_CC_' + condition1 + '_' + direction1 + '.csv')
  106. # build translators of GO terms -- combine with currated terms
  107. for term1 in enrichment_BP.results['Term']:
  108. goTerm = term1.split('(')[1].rstrip(')')
  109. humanReadable_BP = term1.split('(')[0].rstrip(' ')
  110. build_translator_BP.loc[goTerm,'Human readable'] = humanReadable_BP
  111. for term1 in enrichment_CC.results['Term']:
  112. goTerm = term1.split('(')[1].rstrip(')')
  113. humanReadable_CC = term1.split('(')[0].rstrip(' ')
  114. build_translator_CC.loc[goTerm,'Human readable'] = humanReadable_CC
  116. # save out translators
  117. build_translator_BP.to_csv(saveOut1 + 'enrichr_results/translator/GOBP_translator_fromRNAdata.csv', sep=',')
  118. build_translator_CC.to_csv(saveOut1 + 'enrichr_results/translator/GOCC_translator_fromRNAdata.csv', sep=',')
  120. ###################
  121. ## make barplots ##
  122. ###################
  123. pp2 = PdfPages(saveOut1 + 'Figures/Figures_DEGcounts.pdf')
  124. plt.title('DEGs in each condition')
  125. ax = sns.barplot(countDEG.T, x = countDEG.columns, y = 'HowMany', hue = 'Direction')
  126. plt.tick_params(axis='x', labelrotation=90)
  127. for container in ax.containers:
  128. ax.bar_label(container)
  130. plt.tight_layout()
  131. #sns.catplot(countDEG.T, kind="bar", x=countDEG.columns, y = 'HowMany', col = 'Direction', height=4, aspect=.5)
  132. pp2.savefig()
  133. plt.clf()
  134. pp2.close()
  135. plt.close()
  138. # fin

4_functionalEnrichmentAnalysis.py at commit e1319d6, under Apache-2.0 · at the source

Overview

Authors: Amberlyn Simmons1, Sierra F Wilferd1, Sofia Campagnuolo1, Veronica Pena2, Heather Bimonte-Nelson2, Jason Newbern3, Rachael W Sirianni1,4, Christopher L Plaisier1,5, Sarah E Stabenfeldt1,5
ORCID iDs: Amberlyn Simmons, Sierra F Wilferd, Veronica Pena, Heather Bimonte-Nelson, Jason Newbern, Christopher L Plaisier, Sarah E Stabenfeldt
  1. School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, USA
  2. Psychology, School of Life Sciences, Arizona State University, Tempe, AZ, USA
  3. Neuroscience, School of Life Sciences, ASU, Tempe, AZ, USA
  4. Department of Neurological Surgery, UMass Chan Medical School, Worcester, MA, USA
  5. John Shufeldt School of Medicine and Medical Engineering, ASU, Tempe, AZ, USA
Institutions: Arizona State University (United States); University of Massachusetts Chan Medical School (United States)
Journal: Science advances, volume 12, issue 43, article eaei5171
Dates: received 2 May 2026; accepted 21 September 2026; published online 21 October 2026; in print October 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1126/sciadv.aei5171 · PMID 42905827 · PMCID PMC13685967 · OpenAlex W7223093065
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: mouse (organism), traumatic brain injury (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity, fMRI & imaging
MeSH: Brain Injuries, Traumatic*, Cerebral Cortex*, Gene Expression Regulation*, Gonadal Steroid Hormones*, Sex Characteristics*, Animals, Disease Models, Animal, Estradiol, Female, Male, Mice, Testosterone (* major topic)
Topic: Traumatic Brain Injury and Neurovascular Disturbances (Clinical Neurology, Medicine), according to OpenAlex
Funding: Arizona State University; Achievement Rewards for College Scientists Foundation; National Institutes of Health; National Institute on Aging
Citations: not cited yet (Europe PMC); 66 references in the paper

Abstract

Accumulating evidence supports sex differences in traumatic brain injury (TBI) outcomes. However, the underlying processes that lead to sex differences are not well understood. TBI results in the initiation of molecular and cellular responses that facilitate the progression of neurodegeneration. Little is known about how the circulating hormone profile is altered in response to TBI and whether sex differences in endocrine responses might shape secondary injury pathologies. Using intact male and female mice in a preclinical TBI model, we assessed changes in plasma hormone concentrations and cortical gene expression at 24 and 72 hours after TBI. We demonstrate that males and females exhibit sex-specific alterations in circulating levels of testosterone, androstenedione, estradiol, and dehydroepiandrosterone (DHEA) in response to TBI. We also identified sex differences in the expression of genes that are involved in immune responses and tissue remodeling after injury. Moreover, we report divergent circulating hormone and gene expression correlations between sexes.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 8 matches between paragraphs and lines of code.

Zenodo 21726001

License: Apache-2.0
State: the link answers, verified on 25 October 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data, code, and materials availability:”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (6 files), ggplot2 (4 files), Matplotlib (4 files), NumPy (4 files), pandas (4 files), seaborn (4 files), scikit-learn (3 files), SciPy (3 files), statsmodels (3 files), data.table (2 files), edgeR (2 files), pheatmap (2 files), DESeq2 (1 file), ggpubr (1 file), patchwork (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 25 October 2026: the link answers (HTTP 200)
  • 25 October 2026: the link answers (HTTP 200)
15 files
At the source: dx.doi.org/10.5281/zenodo.21726001

plaisier-lab/sex_specific_tbi

License: Apache-2.0
State: the link answers, verified on 25 October 2026
Evidence: files inventoried
Commit: e1319d67d1da18e92598a66689d1ff8473f512ed, 30 July 2026
Languages: R (8), Python (4), Shell (1)
Size: 17 files, 13 scripts
Software Heritage: not archived
Found in: “Data, code, and materials availability:”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (6 files), ggplot2 (4 files), Matplotlib (4 files), NumPy (4 files), pandas (4 files), seaborn (4 files), scikit-learn (3 files), SciPy (3 files), statsmodels (3 files), data.table (2 files), edgeR (2 files), pheatmap (2 files), DESeq2 (1 file), ggpubr (1 file), patchwork (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 25 October 2026: the link answers
  • 25 October 2026: the link answers
15 files
At the source: github.com/plaisier-lab/sex_specific_tbi/tree/Publication

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

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  • 26 scripts, each with its path and the digest of its content;
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Data

Datasets cited

Data, code, and materials availability

All data and code needed to evaluate and reproduce the results in the paper are present in the paper and/or the Supplementary Materials. This study generated new data and code and can be found in the following public repositories. The RNA-seq, hormone, and metadata for this study have been deposited in the NCBI Gene Expression Omnibus (GEO) database. The data can be found under the series identifier GSE318815 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE318815) and using the link: https://ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE318815. The code used to conduct the studies is available on Zenodo using the DOI: 10.5281/zenodo.21726001 (http://dx.doi.org/10.5281/zenodo.21726001) (https://doi.org/10.5281/zenodo.21726001), which is a freeze of the “Publication” release from the GitHub repository plaisier-lab/sex_specific_tbi (https://github.com/plaisier-lab/sex_specific_tbi/tree/Publication). No new materials were generated in this study.

Reproduced under the paper's license (CC BY-NC), 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, 25 October 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 12 MeSH terms, 4 funders, 63 references.

Cite

This paper

Simmons, A., Wilferd, S. F., Campagnuolo, S., Pena, V., Bimonte-Nelson, H., Newbern, J., Sirianni, R. W., Plaisier, C. L., & Stabenfeldt, S. E. (2026). Differential sex-dependent responses of circulating steroid hormones and cortical gene expression in a traumatic brain injury model. Science advances, 12(43), eaei5171. https://doi.org/10.1126/sciadv.aei5171

BibTeX

@article{simmons2026differential,
author = {Simmons, Amberlyn and Wilferd, Sierra F and Campagnuolo, Sofia and Pena, Veronica and Bimonte-Nelson, Heather and Newbern, Jason and Sirianni, Rachael W and Plaisier, Christopher L and Stabenfeldt, Sarah E},
title = {{Differential sex-dependent responses of circulating steroid hormones and cortical gene expression in a traumatic brain injury model}},
journal = {Science advances},
year = {2026},
month = oct,
volume = {12},
number = {43},
pages = {eaei5171},
publisher = {American Association for the Advancement of Science},
issn = {2375-2548},
doi = {10.1126/sciadv.aei5171},
url = {https://doi.org/10.1126/sciadv.aei5171},
pmid = {42905827},
pmcid = {PMC13685967}
}

RIS

TY - JOUR
AU - Simmons, Amberlyn
AU - Wilferd, Sierra F
AU - Campagnuolo, Sofia
AU - Pena, Veronica
AU - Bimonte-Nelson, Heather
AU - Newbern, Jason
AU - Sirianni, Rachael W
AU - Plaisier, Christopher L
AU - Stabenfeldt, Sarah E
TI - Differential sex-dependent responses of circulating steroid hormones and cortical gene expression in a traumatic brain injury model
T2 - Science advances
J2 - Sci Adv
PY - 2026
DA - 2026/10/21
VL - 12
IS - 43
SP - eaei5171
SN - 2375-2548
PB - American Association for the Advancement of Science
DO - 10.1126/sciadv.aei5171
UR - https://doi.org/10.1126/sciadv.aei5171
LA - en
ER -

CSL-JSON

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If you are an author or a maintainer of this paper's code, you can add this badge. It says what OSCR knows of the paper (its code, its validated tracing map) and links the code to this page. Paste one of these snippets under the README's title. Keep the two marks around it, so that the badge can be replaced in place and is never added twice. OSCR changes nothing in your repository.

Markdown
<!-- oscr-badge:start -->
[![OSCR](https://openscicode.org/badge/doi/10.1126/sciadv.aei5171.svg)](https://openscicode.org/badge/doi/10.1126/sciadv.aei5171)
<!-- oscr-badge:end -->
reStructuredText
.. oscr-badge:start

.. image:: https://openscicode.org/badge/doi/10.1126/sciadv.aei5171.svg
   :target: https://openscicode.org/badge/doi/10.1126/sciadv.aei5171
   :alt: OSCR

.. oscr-badge:end
HTML
<!-- oscr-badge:start -->
<a href="https://openscicode.org/badge/doi/10.1126/sciadv.aei5171"><img src="https://openscicode.org/badge/doi/10.1126/sciadv.aei5171.svg" alt="OSCR"></a>
<!-- oscr-badge:end -->

On GitHub, open the README, choose Edit (the pencil), paste the snippet under the title, then "Commit changes".

If you choose to, you can have the badge kept up to date for you, with oscr badge --write from the command line or with the GitHub Action in your repository's workflows. Every other form and option (a card, AsciiDoc, LaTeX, shields.io) is on the badge page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: code errors and mismatches between the code and the paper about this paper and its code, from readers and its authors, beside the paper and its tracing map.

Report a mismatch between the code and the paper · Report a code error (signed in)

Reproductions: reports from readers who ran the authors' code and did not get the paper's result, what they ran, with which environment, commit and data.

Report a reproduction failure (signed in)

Versions of its code: the releases of its code tied to a version of this paper (the preprint, the accepted manuscript, the version of record…), each with the tracing map versioned with it.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.