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BrainEnrich: Revealing Biological Insights for Imaging-Derived Phenotypes Through Transcriptomic Enrichment.

Code ↔ Paper

4 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 4 matches
  1. [1] § Methods › BrainEnrich Analytical Framework › Association Methods ↔ 2024_NI_Imaging_Transcriptomics/misc/find_odd_brain.R, lines 1–57 · score 0.59 · pls1w, brain map, Pearson, imaging transcriptomics, permutation, weights
  2. [2] § Methods › Simulation Studies › Type 1 Error Simulations ↔ 2024_NI_Imaging_Transcriptomics/functions/vacc_function.R, lines 2–80 · score 0.58 · resampled gene, SynGO, spin, GS, correlation, scores
  3. [3] § Methods › BrainEnrich Analytical Framework › Group‐Level Enrichment Analysis ↔ 2024_NI_Imaging_Transcriptomics/functions/vacc_function.R, lines 2–80 · score 0.58 · aggregate gene, resampling genes, spinning brain, LOO, imaging transcriptomics, GS
  4. [4] § Methods › Simulation Studies › Simulation Data and Setup ↔ 2024_NI_Imaging_Transcriptomics/data/BrainInfo/perm_id_weights/generate_permID_MoranW.R, lines 22–110 · score 0.53 · right hemisphere, left hemisphere, temporal, transformed, matched, transcriptional

Paper

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The authors' code

R · 113 lines · 4.8 KB · no license · 2 matches

  1. vacc_function<-function(
  2. data_path='F:/Google Drive/post-doc/vitural_histology_revisit/revision_code/data',
  3. atlas=c('desikan',
  4. 'schaefer100',
  5. 'schaefer200'),
  6. rdonor=c('r0.2',
  7. 'r0.4',
  8. 'r0.6'),
  9. gs_type=c('MF',
  10. 'Sim',
  11. 'SynGO'),
  12. brain_type=c('sample_data',
  13. 'sim_nospatial',
  14. 'sim_spatial0.03',
  15. 'sim_spatial0.02',
  16. 'sim_spatial0.01',
  17. 'real_brain'),
  18. null_type=c('spin_brain','random_gene','random_gene_coexp', 'random_gene_subset','spin_random_mixed'),
  19. cor_type=c('pearson','spearman','loo','pls1','pls1w'),
  20. sampled_geneSetList=NULL, # any constrained sampled_geneSetList for random_gene?
  21. slrum_idx=1){
  22. # print all input parameters
  23. cat('==========\n')
  24. cat('input parameters for vacc fun:\n')
  25. cat(sprintf('data_path: %s\n',data_path))
  26. cat(sprintf('atlas: %s\n',atlas))
  27. cat(sprintf('rdonor: %s\n',rdonor))
  28. cat(sprintf('gs_type: %s\n',gs_type))
  29. cat(sprintf('brain_type: %s\n',brain_type))
  30. cat(sprintf('null_type: %s\n',null_type))
  31. cat(sprintf('cor_type: %s\n',cor_type))
  32. cat(sprintf('is null sampled_geneSetList: %s\n',is.null(sampled_geneSetList)))
  33. cat(sprintf('slrum_idx: %s\n',slrum_idx))
  34. cat('==========\n')
  35. brain_data=load_BrainDat(data_path=sprintf('%s/BrainDat',data_path),
  36. atlas=atlas,
  37. type=brain_type,
  38. col_idx=slrum_idx)
  39. gene_data=load_GeneExp(data_path=sprintf('%s/GeneExp',data_path),
  40. atlas=atlas,
  41. rdonor=rdonor)
  42. geneSetList=load_GeneSets(data_path=sprintf('%s/GeneSets',data_path),
  43. atlas=atlas,
  44. rdonor=rdonor,
  45. gs_type=gs_type)
  46. geneList.true=corr_brain_gene(gene_data,brain_data,method=cor_type)
  47. if (null_type=='random_gene') {
  48. geneList.null = resampling_geneList(geneList.true)
  49. } else if (null_type=='spin_brain'){
  50. perm.id = load_permid(data_path=sprintf('%s/BrainInfo/perm_id_weights',data_path),
  51. atlas=atlas,
  52. type='spin_brain')
  53. null_brain_data = generate_null_brain_data(brain_data, perm.id)
  54. geneList.null = corr_brain_gene(gene_data, null_brain_data,method=cor_type)
  55. } else if (null_type=='spin_random_mixed'){
  56. perm.id = load_permid(data_path=sprintf('%s/BrainInfo/perm_id_weights',data_path),
  57. atlas=atlas,
  58. type='spin_brain')
  59. null_brain_data = generate_null_brain_data(brain_data, perm.id)
  60. geneList.tmp = corr_brain_gene(gene_data, null_brain_data,method=cor_type)
  61. geneList.null = apply(geneList.tmp,2,sample)
  62. row.names(geneList.null)=row.names(geneList.tmp)
  63. colnames(geneList.null)=paste0('null_',c(1:5000))
  64. attr(geneList.null,'is_fisherz')=attr(geneList.tmp,'is_fisherz')
  65. attr(geneList.null,'n.region')=attr(geneList.tmp,'n.region')
  66. }
  67. cat('==========\n')
  68. cat('start testing \n')
  69. res=list()
  70. for (method2test in c('mean','median','meanabs','meansqr','maxmean','sig_n','ks_orig','ks_weighted')){
  71. if (cor_type %in% c('pls1','loo','pls1w')& method2test=='sig_n'){ # skip sig_n for pls1 and loo
  72. next
  73. }
  74. cat(paste0(method2test,'....\n'))
  75. score.true=aggregate_geneSetList(geneSetList, geneList.true,method=method2test)
  76. # caculate null stats
  77. if (null_type %in% c('random_gene','spin_brain','spin_random_mixed')){ # resampling without constrains
  78. score.null=aggregate_geneSetList(geneSetList, geneList.null,method=method2test)
  79. } else { # resampling with constrains
  80. if (is.null(sampled_geneSetList)){
  81. stop('sampled_geneSetList is required for random_gene_coexp and random_gene_subset')
  82. }
  83. score.null=aggregate_geneSetList_with_constrain(geneSetList = geneSetList,
  84. sampled_geneSetList = sampled_geneSetList,
  85. geneList = geneList.true,
  86. method=method2test)
  87. }
  88. # caculate p value based on true and null stats
  89. if (method2test %in% c('ks_orig','ks_weighted')){
  90. pvals=caculate_pvals(score.true,score.null,method='split_pos_neg')
  91. } else {
  92. pvals=caculate_pvals(score.true,score.null,method='standard')
  93. }
  94. res[[method2test]][['pvals']]=pvals
  95. rm(score.true,score.null,pvals)
  96. }
  97. # format res to a data.frame
  98. df.list=list()
  99. for (var2extract in c('pvals')){
  100. df.list[[var2extract]]= as.data.frame(lapply(res, function(x){do.call(rbind,x[[var2extract]])}))
  101. }
  102. df2save=do.call(cbind,df.list) %>% tibble::rownames_to_column("geneSet") %>% mutate(sim_brain=slrum_idx)
  103. return(df2save)
  104. }

vacc_function.R at commit 54c690f, no license · at the source

Overview

Authors: Zhipeng Cao1,2, Dekang Yuan3, Jinmei Qin1, Yujie Wu1, Chenhu Li1, Guilai Zhan1
  1. Shanghai Xuhui Mental Health Center Shanghai China
  2. School of Mental Health Wenzhou Medical University Wenzhou Zhejiang China
  3. Department of Psychiatry University of Vermont College of Medicine Burlington Vermont USA
Journal: Human brain mapping, volume 47, issue 10, article e70605
Dates: received 20 March 2026; accepted 6 July 2026; published online 12 July 2026; in print July 2026
Type: Other · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1002/hbm.70605 · PMID 42438110 · PMCID PMC13357997 · OpenAlex W7168154215
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), depression (population)
Methods: Connectivity, Statistics, Machine learning, Smoothing, state filtering, decompositions, Preprocessing
Keywords: Allen Human Brain Atlas, competitive null model, gene set enrichment, imaging transcriptomics, imaging‐derived phenotypes, individual‐level analysis, self‐contained null model
MeSH: Brain*, Gene Expression Profiling*, Major Depressive Disorder*, Neuroimaging*, Transcriptome*, Atlases as Topic, Humans, Phenotype (* major topic)
Journal subjects: Toolbox
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (32400925); Key Medical Discipline Program of Shanghai Xuhui District (SHXHZDXK202308); Medical Research Project of Shanghai Xuhui District (SHXH202305, SHXH202504, SHXH202401)
Citations: not cited yet (Europe PMC); 76 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 4 matches between paragraphs and lines of code.

YuLab-SMU/enrichplot

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 572e6add5507616d9ebd2267669e4a754691e3fa, 27 September 2026
Languages: R (54)
Size: 115 files, 54 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, environment (DESCRIPTION), tests, documentation, 2 notebooks
Not found: license file, CITATION.cff, continuous integration
Tools: ggplot2 (27 files), tidyverse (9 files), igraph (7 files), reshape2 (2 files), clusterProfiler (1 file), cowplot (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
55 files

zh1peng/paper_code

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 54c690f8c151c83a7bdf5ade73fa22de3c99dccf, 7 March 2025
Languages: R (131), MATLAB (62), Python (16), Shell (1), C++ (1)
Size: 595 files, 211 scripts
Software Heritage: archived
Found in: “Data and Code Availability”
Holds: README, 22 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (56 files), ggplot2 (23 files), SPM (20 files), lmerTest (18 files), pandas (13 files), NiBabel (12 files), ggseg (7 files), Statistics and Machine Learning Toolbox (7 files), nlme (7 files), NumPy (7 files), EEGLAB (6 files), SciPy (5 files), JAGS (4 files), netneurotools (4 files), patchwork (4 files), broom (2 files), easystats (2 files), emmeans (2 files), fdr_bh (Benjamini-Hochberg FDR) (2 files), clusterProfiler (1 file), FreeSurfer (1 file), ggpubr (1 file), Matplotlib (1 file), pheatmap (1 file), reticulate (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
212 files

MICA-MNI/ENIGMA

License: BSD-3-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: b08974b55243060cbc1fad12c87048037446e8f7, 15 January 2026
Languages: MATLAB (63), Python (47), Jupyter (5), JavaScript (2)
Size: 1,175 files, 117 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, license file, environment (environment.yml, environment_all_py3.10.yml, environment_windows.yml, requirements.txt, setup.cfg, setup.py, docs/requirements.txt), continuous integration, documentation, 4 notebooks
Not found: CITATION.cff, tests
Tools: NumPy (19 files), SciPy (7 files), Statistics and Machine Learning Toolbox (5 files), pandas (4 files), scikit-learn (4 files), Matplotlib (3 files), NiBabel (3 files), FreeSurfer (2 files), cifti-matlab (1 file), GIfTI library for MATLAB (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
119 files

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

Tracing map

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What the map holds:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 382 scripts, each with its path and the digest of its content;
  • 4 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

Availability statements

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

Read them in the paper: doi.org/10.1002/hbm.70605.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 7 keywords, 8 MeSH terms, 3 funders, 72 references.

Cite

This paper

Cao, Z., Yuan, D., Qin, J., Wu, Y., Li, C., & Zhan, G. (2026). BrainEnrich: Revealing Biological Insights for Imaging-Derived Phenotypes Through Transcriptomic Enrichment. Human brain mapping, 47(10), e70605. https://doi.org/10.1002/hbm.70605

BibTeX

@article{cao2026brainenrich,
author = {Cao, Zhipeng and Yuan, Dekang and Qin, Jinmei and Wu, Yujie and Li, Chenhu and Zhan, Guilai},
title = {{BrainEnrich: Revealing Biological Insights for Imaging-Derived Phenotypes Through Transcriptomic Enrichment}},
journal = {Human brain mapping},
year = {2026},
month = jul,
volume = {47},
number = {10},
pages = {e70605},
publisher = {Wiley},
issn = {1065-9471},
doi = {10.1002/hbm.70605},
url = {https://doi.org/10.1002/hbm.70605},
pmid = {42438110},
pmcid = {PMC13357997}
}

RIS

TY - JOUR
AU - Cao, Zhipeng
AU - Yuan, Dekang
AU - Qin, Jinmei
AU - Wu, Yujie
AU - Li, Chenhu
AU - Zhan, Guilai
TI - BrainEnrich: Revealing Biological Insights for Imaging-Derived Phenotypes Through Transcriptomic Enrichment
T2 - Human brain mapping
J2 - Hum Brain Mapp
PY - 2026
DA - 2026/07/01
VL - 47
IS - 10
SP - e70605
SN - 1065-9471
PB - Wiley
DO - 10.1002/hbm.70605
UR - https://doi.org/10.1002/hbm.70605
LA - en
ER -

CSL-JSON

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"id": "10.1002/hbm.70605",
"type": "article-journal",
"title": "BrainEnrich: Revealing Biological Insights for Imaging-Derived Phenotypes Through Transcriptomic Enrichment",
"container-title": "Human brain mapping",
"author": [
{
"family": "Cao",
"given": "Zhipeng"
},
{
"family": "Yuan",
"given": "Dekang"
},
{
"family": "Qin",
"given": "Jinmei"
},
{
"family": "Wu",
"given": "Yujie"
},
{
"family": "Li",
"given": "Chenhu"
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"given": "Guilai"
}
],
"container-title-short": "Hum Brain Mapp",
"volume": "47",
"issue": "10",
"page": "e70605",
"DOI": "10.1002/hbm.70605",
"PMID": "42438110",
"PMCID": "PMC13357997",
"ISSN": "1065-9471",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/hbm.70605",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
1
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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