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Screening of cell-type-specific meta-programs for drug repurposing in Alzheimer's disease.

Code ↔ Paper

7 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 7 matches
  1. [1] § Materials and methods › Collection and processing of AD sc/snRNA-seq datasets › Cell type annotation and harmonization ↔ Figure1.r, lines 1–80 · score 0.85 · CSF1R, Endothelial cells, Excitatory neurons, AQP4, CD74, FLT1
  2. [2] § Results › Spatial transcriptomics reveals cellular organization heterogeneity in AD ↔ Figure4.r, lines 466–549 · score 0.81 · OPC.m_STON2, microglia.m_C1QA, excitatory neurons, spatial transcriptomics, gja1, inhibitory neurons
  3. [3] § Materials and methods › Collection and processing of AD sc/snRNA-seq datasets › Quality control and data processing ↔ SpatialDeconvolution.r, lines 50–99 · score 0.65 · highly variable genes, Seurat, mt, ribosomal, cells
  4. [4] § Results › Spatial transcriptomics reveals cellular organization heterogeneity in AD ↔ Figure4.r, lines 1–55 · score 0.57 · neighboring cell, MP activity, Spatial transcriptome, Violin, correlations, Heatmap
  5. [5] § Materials and methods › Algorithm for MP identification › Step 2: Cross-sample integration to define MPs ↔ Figure2.r, lines 120–186 · score 0.56 · gene co occurrence, Jaccard, filtered, clustering, modules
  6. [6] § Materials and methods › Collection and processing of AD sc/snRNA-seq datasets › Cell type annotation and harmonization ↔ Figure2.r, lines 228–283 · score 0.52 · brain cell, Excitatory neurons, Inhibitory neurons, Endothelial, Astrocytes, OPCs
  7. [7] § Results › Single-cell atlas of AD high-pathology samples ↔ Figure1.r, lines 1–80 · score 0.52 · endothelial cells, excitatory neurons, inhibitory neurons, GSE157827, astrocytes, OPCs

Paper

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The authors' code

R · 114 lines · 3.6 KB · no license · 2 matches

  1. library(Seurat)
  2. library(ggplot2)
  5. rm(list=ls())
  6. GSE157827 <- readRDS("./GSE157827.rds")
  8. umapz<-as.data.frame(GSE157827@reductions$[email hidden])
  9. umapz$celltype<[email hidden]$cellAssign
  12. ann_colors=list(c("astrocytes"="#3bb2d0","endothelial"="#3887be", "excitatory_neurons"="#f9886c","inhibitory_neurons"="#e55e5e","microglia"="#8a8acb","oligodendrocytes"="#56b881","OPC"="#41afa5"))
  15. p1=ggplot(umapz,aes(UMAP_1,UMAP_2,color=celltype))+
  16. geom_point(size=0.5)+
  17. scale_color_manual(values = c("astrocytes"="#3bb2d0","endothelial"="#3887be", "excitatory_neurons"="#f9886c","inhibitory_neurons"="#e55e5e","microglia"="#8a8acb","oligodendrocytes"="#56b881","OPC"="#41afa5"))+
  18. theme_bw()
  19. pdf("./figure1/Fig1A.pdf",12,9)
  20. print(p1)
  21. dev.off()
  25. marker<-read.table("./celltypeMarker.txt",sep = "\t",header = T,row.names = 1)
  27. meanCell<-marker;meanCell [meanCell==1]<-0
  28. PercentCell<-meanCell
  30. for (i in unique(GSE157827$cellAssign)) {
  31. data<-subset(GSE157827,cellAssign==i)
  33. dataz<-as.data.frame(data@assays$RNA@counts[intersect(rownames(data),rownames(meanCell)),])
  34. aa<-apply(dataz,1,mean)
  36. bb<-apply(dataz,1,function(x){length(which(x!=0))/length(x)})
  37. meanCell[names(aa),i]<-aa
  38. PercentCell[names(bb),i]<-bb
  40. }
  42. PercentCell[PercentCell<0.5]<-0
  43. meanCell[PercentCell==0]<-0
  46. for (i in 1:dim(marker)[2]) {
  48. rownames(marker)[which(marker[,i]==1)]<-paste0(colnames(marker)[i],"_",rownames(marker)[which(marker[,i]==1)])
  49. }
  52. rownames(meanCell)<-rownames(marker)
  53. rownames(PercentCell)<-rownames(marker)
  55. library(reshape2)
  57. meanCell1<-melt(as.matrix(meanCell))
  58. PercentCell1<-melt(as.matrix(PercentCell))
  59. meanCell1$percent<-PercentCell1$value
  61. meanCell1<-meanCell1[-which(meanCell1[,3]==0),]
  62. # excitatory neurons (marked by NRGN), inhibitory neurons (GAD1),
  63. # astrocytes (AQP4), oligodendrocytes (MBP), microglia (CSF1R and CD74),
  64. # oligodendrocyte progenitor cells (VCAN), endothelial cells (FLT1),
  65. #
  67. celltype<-c( "astrocytes","endothelial", "excitatory_neurons", "inhibitory_neurons" , "microglia" , "oligodendrocytes" ,"OPC" )
  68. meanCell1$cellorder<-match(meanCell1[,2],celltype)
  69. meanCell1<-meanCell1[order(meanCell1$cellorder),]
  71. meanCell1[which(meanCell1$value>8),"value"]<-8
  73. p1=ggplot(meanCell1,aes(factor(Var2, levels=unique(Var2)[1:7]),factor(Var1, levels=unique(Var1)[c(1:7,9:19,8)]),fill=value))+
  74. geom_point(size=meanCell1$percent*10, shape = 21, colour = "black")+
  75. scale_fill_gradient2(low ="#f9f2bb",mid ="#ea9847", high ="#8e1c2b")+
  76. theme_bw()+
  77. theme(axis.text.x = element_text(angle = 45,hjust = 1))
  78. pdf("。/figure1/Fig1B.pdf",6,6.5)
  79. print(p1)
  80. dev.off()
  83. ###
  85. rm(list = ls())
  87. zz<-list.files()
  88. zz<-zz[grep("^zzF",zz)]
  90. barz<-as.data.frame(matrix(0,9,7))
  91. rownames(barz)<-substr(zz,4,nchar(zz)-4)
  92. colnames(barz)<-c( "astrocytes","endothelial", "excitatory_neurons", "inhibitory_neurons" , "microglia" , "oligodendrocytes" ,"OPC" )
  94. i<-zz[1]
  95. j=1
  96. for (i in zz) {
  97. data<-readRDS(i)
  98. asdd<-as.data.frame(table([email hidden]$cellAssign))
  100. barz[j,]<-asdd[match(colnames(barz),asdd[,1]),2]
  101. j=j+1
  102. }
  104. barzz<-melt(as.matrix(barz))
  105. barzz<-na.omit(barzz)
  106. pdf("./figure1/F1C.pdf",8.4,5.7)
  108. ggplot(data = barzz, mapping = aes(x = Var1, y = value, fill = Var2)) +
  109. geom_bar(stat = 'identity', position = 'stack',color="black")+
  110. scale_fill_manual(values = c("astrocytes"="#3bb2d0","endothelial"="#3887be", "excitatory_neurons"="#f9886c","inhibitory_neurons"="#e55e5e","microglia"="#8a8acb","oligodendrocytes"="#56b881","OPC"="#41afa5"))+
  111. theme( axis.text.x = element_text(angle = 90,vjust = 0.85,hjust = 0.75) ##就是这里
  112. )+
  113. theme_bw()
  114. dev.off()

Figure1.r at commit 84d6435, no license · at the source

Overview

Authors: Chunlong Zhang1, Yuxi Zhang1, Zhiyi Wu1, Yuting Zhang1, Fei Xue1, Qinglong Tan1, Xiaoling Zhong1, Yu Zhang1, Ziyan Zhao1, Yunyi Peng1, Hongping Chen2, Feng Li1, Yunpeng Zhang1
ORCID iDs: Chunlong Zhang, Feng Li, Yunpeng Zhang
  1. College of Bioinformatics Science and Technology, Harbin Medical University, No. 157 Baojian Road, Nangang District, Harbin 150081, China
  2. Department of Neurology, The First Affiliated Hospital of Harbin Medical University, No. 23 Youzheng Street, Nangang District, Harbin 150081, China
Institutions: Harbin Medical University (China); First Affiliated Hospital of Harbin Medical University (China)
Journal: Briefings in bioinformatics, volume 27, issue 4, article bbag411
Dates: received 23 April 2026; accepted 1 July 2026; published online 27 July 2026; in print July 2026
Type: Case report · Language: English
License: CC BY
Identifiers: DOI 10.1093/bib/bbag411 · PMID 42506875 · PMCID PMC13403181 · OpenAlex W7171429719
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Graphs
Keywords: Alzheimer’s disease, meta-program, single cell/nucleus RNA sequencing, multi-omics
MeSH: Alzheimer Disease*, Brain*, Drug Repositioning*, Spatial Transcriptomics*, Algorithms, Atlases as Topic, Female, Gene Expression Profiling, Humans, Male, Microglia, Protein Interaction Maps, Single-Cell Gene Expression Analysis, Transcription, Genetic, Unsupervised Machine Learning (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Natural Science Foundation of Heilongjiang Province (LH2023C062); National Science and Technology Major Program (2024ZD0530500); National Natural Science Foundation of China (62472131)
Citations: not cited yet (Europe PMC); 43 references in the paper

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder driven by complex cellular changes. To identify transcriptional signatures involved in AD pathology, we first analyzed nine single-cell/nucleus RNA-seq datasets from 67 high-pathology cases (Braak stages V/VI), generating an atlas of 363 243 cells. Using a multi-sample integration strategy, we identified 51 cross-sample meta-programs (MPs) across seven cell types that collectively capture key AD-related processes, including synaptic dysfunction and neuroinflammation. By screening bulk transcriptomes from 1208 ad and 725 normal samples, we found that 26 MPs (13 pathogenic and 13 protective) displayed differential activity in AD, characterized by up-regulation of microglia MPs and down-regulated of neuron MPs. Furthermore, spatial transcriptomics revealed that these MPs form spatially coherent communities associated with distinct cellular neighborhoods. Finally, we performed an integrative drug repurposing screen to identify candidate drugs predicted to regulate pathogenic MPs. In conclusion, we conducted an integrated multi-omics study to identify AD cell-type-specific MPs, and this framework can be applied to deconvolve cellular heterogeneity and screen candidate drugs for AD.

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 7 matches between paragraphs and lines of code.

Zhangyx-q/AD_MPcode

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 84d64354b8f19f20ce8f46f9cbadcb98a2a5dced, 8 June 2026
Languages: R (8)
Size: 9 files, 8 scripts
Software Heritage: not checked
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (7 files), ggplot2 (6 files), tidyverse (5 files), pheatmap (4 files), reshape2 (3 files), ggpubr (2 files), circlize (1 file), Harmony (1 file), SingleCellExperiment (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
9 files
At the source: github.com/Zhangyx-q/AD_MPcode

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

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;
  • 8 scripts, each with its path and the digest of its content;
  • 7 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

No dataset and no data link were found in the paper.

Data availability

AD sc/snRNA-seq, bulk, and spatial transcriptome datasets were all obtained from GEO database. The information of these transcriptome datasets were provided in Supplementary Table. The code for cell-type-specific MP identification and spatial transcriptome analyses have been made available on a GitHub repository (https://github.com/Zhangyx-q/AD_MPcode).

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, 13 authors, 4 keywords, 15 MeSH terms, 3 funders, 43 references.

Cite

This paper

Zhang, C., Zhang, Y., Wu, Z., Zhang, Y., Xue, F., Tan, Q., Zhong, X., Zhang, Y., Zhao, Z., Peng, Y., Chen, H., Li, F., & Zhang, Y. (2026). Screening of cell-type-specific meta-programs for drug repurposing in Alzheimer's disease. Briefings in bioinformatics, 27(4), bbag411. https://doi.org/10.1093/bib/bbag411

BibTeX

@article{zhang2026screening,
author = {Zhang, Chunlong and Zhang, Yuxi and Wu, Zhiyi and Zhang, Yuting and Xue, Fei and Tan, Qinglong and Zhong, Xiaoling and Zhang, Yu and Zhao, Ziyan and Peng, Yunyi and Chen, Hongping and Li, Feng and Zhang, Yunpeng},
title = {{Screening of cell-type-specific meta-programs for drug repurposing in Alzheimer's disease}},
journal = {Briefings in bioinformatics},
year = {2026},
month = jul,
volume = {27},
number = {4},
pages = {bbag411},
publisher = {Oxford University Press},
issn = {1467-5463},
doi = {10.1093/bib/bbag411},
url = {https://doi.org/10.1093/bib/bbag411},
pmid = {42506875},
pmcid = {PMC13403181}
}

RIS

TY - JOUR
AU - Zhang, Chunlong
AU - Zhang, Yuxi
AU - Wu, Zhiyi
AU - Zhang, Yuting
AU - Xue, Fei
AU - Tan, Qinglong
AU - Zhong, Xiaoling
AU - Zhang, Yu
AU - Zhao, Ziyan
AU - Peng, Yunyi
AU - Chen, Hongping
AU - Li, Feng
AU - Zhang, Yunpeng
TI - Screening of cell-type-specific meta-programs for drug repurposing in Alzheimer's disease
T2 - Briefings in bioinformatics
J2 - Brief Bioinform
PY - 2026
DA - 2026/07/01
VL - 27
IS - 4
SP - bbag411
SN - 1467-5463
PB - Oxford University Press
DO - 10.1093/bib/bbag411
UR - https://doi.org/10.1093/bib/bbag411
LA - en
ER -

CSL-JSON

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