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PVT inhibition mitigates neuropathology and cognitive deficits in chronic cerebral hypoperfusion.

Code ↔ Paper

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  1. [1] § STAR★Methods › Method details › Weighted gene co-expression network analysis ↔ WGCNA_Analysis.R, lines 1–61 · score 0.55 · soft thresholding, TOM, cutting, WGCNA, power, network

Paper

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The authors' code

R · 94 lines · 3.1 KB · no license · 1 match

  1. library(WGCNA)
  2. library(openxlsx)
  3. library(readxl)
  4. library(dplyr)
  6. options(stringsAsFactors = FALSE)
  7. enableWGCNAThreads()
  9. df <- read_excel("result.xlsx")
  10. femData_clean <- na.omit(df)
  12. datExpr0 <- as.data.frame(t(femData_clean[, -c(1)]))
  13. names(datExpr0) <- femData_clean[[1]]
  14. rownames(datExpr0) <- colnames(femData_clean[, -c(1)])
  16. gsg <- goodSamplesGenes(datExpr0, verbose = 3)
  17. if (!gsg$allOK) {
  18. datExpr0 <- datExpr0[gsg$goodSamples, gsg$goodGenes]
  19. }
  21. sample_names <- rownames(datExpr0)
  22. group_vector <- ifelse(grepl("^group_1", sample_names), "group1",
  23. ifelse(grepl("^group_2", sample_names), "group2", "Unknown"))
  25. traitData <- data.frame(
  26. sample = sample_names,
  27. Group = group_vector,
  28. ADG = rnorm(length(sample_names), 1.1, 0.3)
  29. )
  30. traitData$Group_numeric <- ifelse(traitData$Group == "group2", 1, 0)
  32. Samples <- rownames(datExpr0)
  33. traitRows <- match(Samples, traitData$sample)
  34. datTraits <- traitData[traitRows, c("ADG", "Group_numeric")]
  35. rownames(datTraits) <- traitData[traitRows, 1]
  37. powers <- c(c(1:10), seq(from = 12, to = 30, by = 2))
  38. sft <- pickSoftThreshold(datExpr0, powerVector = powers, verbose = 5, networkType = "signed")
  40. sft_index <- which(abs(sft$fitIndices[, 3]) > 0.8)
  41. if (length(sft_index) > 0) {
  42. chosen_power <- sft$fitIndices[min(sft_index), 1]
  43. } else {
  44. chosen_power <- sft$fitIndices[which.max(abs(sft$fitIndices[, 3])), 1]
  45. }
  47. net <- blockwiseModules(datExpr0, power = chosen_power,
  48. TOMType = "signed",
  49. networkType = "signed",
  50. minModuleSize = 15,
  51. reassignThreshold = 0,
  52. mergeCutHeight = 0.25,
  53. numericLabels = TRUE,
  54. pamRespectsDendro = FALSE,
  55. saveTOMs = TRUE,
  56. saveTOMFileBase = "BTOM",
  57. verbose = 3)
  59. moduleLabels <- net$colors
  60. moduleColors <- labels2colors(net$colors)
  61. MEs <- net$MEs
  63. nGenes <- ncol(datExpr0)
  64. nSamples <- nrow(datExpr0)
  66. MEs0 <- moduleEigengenes(datExpr0, moduleColors)$eigengenes
  67. MEs <- orderMEs(MEs0)
  68. moduleTraitCor <- cor(MEs, datTraits, use = "p")
  69. moduleTraitPvalue <- corPvalueStudent(moduleTraitCor, nSamples)
  71. trait_of_interest <- "Group_numeric"
  72. trait_data <- as.data.frame(datTraits[, trait_of_interest])
  73. names(trait_data) <- trait_of_interest
  75. GS <- as.data.frame(cor(datExpr0, trait_data, use = "p"))
  76. colnames(GS) <- paste("GS", trait_of_interest, sep=".")
  78. modNames <- substring(names(MEs), 3)
  79. MM <- as.data.frame(cor(datExpr0, MEs, use = "p"))
  80. colnames(MM) <- paste("MM", modNames, sep=".")
  82. gene_module_mm_gs <- data.frame(
  83. BrainRegion = colnames(datExpr0),
  84. Module = moduleColors
  85. )
  87. for(mod in unique(moduleColors)) {
  88. if(mod == "grey") next
  89. module_genes <- (moduleColors == mod)
  90. gene_module_mm_gs[module_genes, "MM"] <- MM[module_genes, paste0("MM.", mod)]
  91. }
  93. gene_module_mm_gs$GS <- GS[, paste0("GS.", trait_of_interest)]
  94. write.csv(gene_module_mm_gs, "All_BrainRegions_MM_GS.csv", row.names = FALSE)

WGCNA_Analysis.R at commit f0cca60, no license · at the source

Overview

Authors: Yuying Guan1, Jinghao Ma1, Rui Li1, Jialu Xu1, Taoyue Xu1, Gaifen Li2, Le Sun1
ORCID iDs: Le Sun
  1. Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
  2. Institute of Basic Theory for Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China
Institutions: Capital Medical University (China); Beijing Institute for Brain Disorders (China); China Academy of Chinese Medical Sciences (China)
Journal: iScience, volume 29, issue 8, article 117108
Dates: received 4 January 2026; accepted 22 July 2026; published online 5 August 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.isci.2026.117108 · PMID 42602569 · PMCID PMC13474078 · OpenAlex W7172528902
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), stroke (population), systems (subfield)
Methods: Statistics, Machine learning, Preprocessing, Connectivity, fMRI & imaging, Physiology & signal measures
Keywords: chronic cerebral hypoperfusion, cognitive function, vascular cognitive impairment, paraventricular thalamus, brain-wide activity
Topic: Neurological Disease Mechanisms and Treatments (Neurology, Neuroscience), according to OpenAlex
Funding: National Natural Science Foundation of China (32171021)
Citations: not cited yet (Europe PMC); 63 references in the paper
Research resources: RRID:AB_2722623, Rabbit anti-c-Fos antibody RRID:AB_2891278, EthoVision XT 17 RRID:SCR_000441, R (version 4.4.2) RRID:SCR_001905, GraphPad Prism 8 RRID:SCR_002798, ImageJ RRID:SCR_003070

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.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

SunLab-code/WGCNA-CCH-iScience

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f0cca6060cfcc56e64f163fcc688808985dbcc5b, 1 July 2026
Languages: R (1)
Size: 2 files, 1 script
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (1 file), WGCNA (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2 files
At the source: github.com/SunLab-code/WGCNA-CCH-iScience

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

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  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Code and data availability statement

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

Read it in the paper: doi.org/10.1016/j.isci.2026.117108.

Versions

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Version 3, 28 September 2026

  • Authors: added Le Sun (0000-0001-9399-6772); removed Le Sun

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 5 keywords, 1 funder, 62 references, 6 RRIDs.

Cite

This paper

Guan, Y., Ma, J., Li, R., Xu, J., Xu, T., Li, G., & Sun, L. (2026). PVT inhibition mitigates neuropathology and cognitive deficits in chronic cerebral hypoperfusion. iScience, 29(8), 117108. https://doi.org/10.1016/j.isci.2026.117108

BibTeX

@article{guan2026pvt,
author = {Guan, Yuying and Ma, Jinghao and Li, Rui and Xu, Jialu and Xu, Taoyue and Li, Gaifen and Sun, Le},
title = {{PVT inhibition mitigates neuropathology and cognitive deficits in chronic cerebral hypoperfusion}},
journal = {iScience},
year = {2026},
month = aug,
volume = {29},
number = {8},
pages = {117108},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.117108},
url = {https://doi.org/10.1016/j.isci.2026.117108},
pmid = {42602569},
pmcid = {PMC13474078}
}

RIS

TY - JOUR
AU - Guan, Yuying
AU - Ma, Jinghao
AU - Li, Rui
AU - Xu, Jialu
AU - Xu, Taoyue
AU - Li, Gaifen
AU - Sun, Le
TI - PVT inhibition mitigates neuropathology and cognitive deficits in chronic cerebral hypoperfusion
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/08/05
VL - 29
IS - 8
SP - 117108
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.117108
UR - https://doi.org/10.1016/j.isci.2026.117108
LA - en
ER -

CSL-JSON

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"id": "10.1016/j.isci.2026.117108",
"type": "article-journal",
"title": "PVT inhibition mitigates neuropathology and cognitive deficits in chronic cerebral hypoperfusion",
"container-title": "iScience",
"author": [
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"family": "Guan",
"given": "Yuying"
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{
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"given": "Jinghao"
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{
"family": "Li",
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},
{
"family": "Xu",
"given": "Jialu"
},
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"given": "Le"
}
],
"container-title-short": "iScience",
"volume": "29",
"issue": "8",
"page": "117108",
"DOI": "10.1016/j.isci.2026.117108",
"PMID": "42602569",
"PMCID": "PMC13474078",
"ISSN": "2589-0042",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.isci.2026.117108",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
5
]
]
}
}

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