OSCR

Cell type-specific gene regulatory atlas prioritizes drug targets and repurposable medicines in Alzheimer's disease.

Code ↔ Paper

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] § Methods › snATAC-seq data quality control, dimensionality reduction, and clustering ↔ snATAC_QC_Clustering.R, lines 1–45 · score 0.75 · Quality control, ArchR, UMAP, fragment, clustering, TSS
  2. [2] § Methods › Peak calling and annotation ↔ Peak_annotation.R, lines 43–81 · score 0.71 · peakAnnoEnrichment, addMotifAnnotations, motif enrichment, cisBP, peaks
  3. [3] § Methods › snRNA-seq data quality control, dimensionality reduction, and clustering ↔ Processing4DEG.R, lines 65–111 · score 0.65 · qc metrics, detected genes, snRNA, Outliers
  4. [4] § Methods › snRNA-seq data quality control, dimensionality reduction, and clustering ↔ snRNA_QC.py, lines 24–34 · score 0.59 · calculate_qc_metrics, Scanpy, ribosomal, mitochondrial, genes
  5. [5] § Results › Identifying TF–target gene networks involved in AD progression ↔ Peak_annotation.R, lines 177–227 · score 0.55 · Positive TF regulators, TF motifs, genes linked, peaks, score
  6. [6] § Results › Identifying AD progression–associated targets enriched by cCREs with AD GWAS ↔ fun4p2g.r, lines 40–70 · score 0.52 · bp downstream, kb, promoters, upstream, TSS

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

R · 272 lines · 8.9 KB · no license · 2 matches

  1. #####################################################################
  2. # 0. Load libarary and archR proj
  3. #####################################################################
  5. library(ArchR)
  6. library(Seurat)
  7. library(dplyr)
  8. library(tidyr)
  9. library(ComplexHeatmap)
  10. library(BSgenome.Hsapiens.UCSC.hg38)
  11. set.seed(1)
  12. addArchRThreads(threads = 32)
  13. addArchRGenome("hg38")
  15. # Define the input path
  16. atac_dir = "NotAD_out"
  18. ##########################################################################################
  19. # 1. Load Previously Prepared ArchR project
  20. ##########################################################################################
  22. atac_proj3 <- loadArchRProject(atac_dir, force=TRUE)
  24. print(colnames(getCellColData(atac_proj3)))
  25. table(atac_proj3$NamedClust)
  27. getAvailableMatrices(atac_proj3)
  30. ##########################################################################################
  31. # 2. Identifying Marker Peaks with ArchR
  32. ##########################################################################################
  33. markersPeaks <- getMarkerFeatures(
  34. ArchRProj = atac_proj3,
  35. useMatrix = "PeakMatrix",
  36. groupBy = "NamedClust",
  37. bias = c("TSSEnrichment", "log10(nFrags)"),
  38. testMethod = "wilcoxon"
  39. )
  41. markersPeaks
  43. ## get markers and save it
  44. markerList_all <- getMarkers(markersPeaks)
  45. markerList_all
  46. saveRDS(markerList_all, file = "./marker_peak_all.rds")
  48. markerList_sig <- getMarkers(markersPeaks, cutOff = "FDR <= 0.05 & Log2FC >= 0.5")
  49. markerList_sig
  50. saveRDS(markerList_sig, file = "./marker_peak_sig.rds")
  52. ##########################################################################################
  53. # 3. Motif Enrichment
  54. ##########################################################################################
  55. atac_proj3 <- addMotifAnnotations(ArchRProj = atac_proj3, motifSet = "cisbp", name = "Motif",force=TRUE)
  57. ##########################################################################################
  58. # 3.1 Motif Enrichment in Marker Peaks
  60. enrichMotifs <- peakAnnoEnrichment(
  61. seMarker = markersPeaks,
  62. ArchRProj = atac_proj3,
  63. peakAnnotation = "Motif",
  64. cutOff = "FDR <= 0.05 & Log2FC >= 0.5"
  65. )
  67. enrichMotifs
  69. # Plot a different style heatmap
  70. plot_mat <- plotEnrichHeatmap(enrichMotifs[,column_order], n=5, transpose=FALSE, cutOff=10, returnMatrix=TRUE)
  72. # Extract top 5 enriched genes for each column
  73. top_genes <- lapply(1:ncol(plot_mat), function(i) {
  74. top_indices <- order(plot_mat[, i], decreasing = TRUE)[1:5] # Get indices of top 5 values
  75. return(rownames(plot_mat)[top_indices]) # Return gene names
  76. })
  78. # Create a new matrix with the selected genes in the specific order
  79. new_mat <- do.call(rbind, lapply(1:length(top_genes), function(i) {
  80. plot_mat[top_genes[[i]], , drop = FALSE] # Subsetting and maintaining as matrix
  81. }))
  83. # Adjust column and row names if needed
  84. colnames(new_mat) <- colnames(plot_mat)
  86. # Modify row names by removing characters after "_"
  87. rownames(new_mat) <- unlist(top_genes) # Flattening the list to set row names
  88. rownames(new_mat) <- sub("_.*", "", rownames(new_mat))
  90. saveRDS(new_mat, file = "marker_Topmotif.rds")
  92. library(ComplexHeatmap)
  93. library(circlize)
  94. # Assuming `enrichMotifs` is a matrix or can be converted to one
  95. pdf("Plots/Motifs-Enriched-Marker-Heatmap_Advanced.pdf",width = 6, height = 8)
  96. Heatmap(new_mat,
  97. col = colorRampPalette(c("#E6E7E8", "#3A97FF", "#8816A7","#000436"))(100),
  98. cluster_rows = FALSE, # Disables clustering of rows
  99. cluster_columns = FALSE,
  100. show_row_names = TRUE,
  101. show_column_names = TRUE,
  102. name = "Norm.Enrichment -log10(P-adj)[0-Max]", # Sets the legend title
  103. row_names_side = "left", # Puts row names on the left
  104. border = TRUE, # Adds a border around the heatmap
  105. #use_raster = FALSE
  106. )
  107. dev.off()
  109. ##########################################################################################
  110. # 5. ChromVAR Deviations
  111. ##########################################################################################
  113. if("Motif" %ni% names(atac_proj3@peakAnnotation)){
  114. atac_proj3 <- addMotifAnnotations(ArchRProj = atac_proj3, motifSet = "cisbp", name = "Motif",force = TRUE)
  115. }
  117. ## background peaks
  118. atac_proj3 <- addBgdPeaks(atac_proj3)
  120. ## compute per-cell deviations accross all of motif annotations
  121. atac_proj3 <- addDeviationsMatrix(
  122. ArchRProj = atac_proj3,
  123. peakAnnotation = "Motif",
  124. force = TRUE
  125. )
  127. ## access these deviations, set plot = TRUE, return a ggplot object
  128. plotVarDev <- getVarDeviations(atac_proj3, name = "MotifMatrix", plot = TRUE)
  129. plotVarDev
  130. plotPDF(plotVarDev, name = "Variable-Motif-Deviation-Scores", width = 5, height = 5, ArchRProj = atac_proj3, addDOC = TRUE)
  132. # Save project
  133. saveArchRProject(atac_proj3)
  135. ##########################################################################################
  136. # 8. Peak2GeneLinkage
  137. ##########################################################################################
  139. atac_proj3 <- addPeak2GeneLinks(
  140. ArchRProj = atac_proj3,
  141. reducedDims = "IterativeLSI"
  142. )
  144. p2g <- getPeak2GeneLinks(
  145. ArchRProj = atac_proj3,
  146. corCutOff = 0.45,
  147. resolution = 1,
  148. returnLoops = FALSE
  149. )
  151. p2g
  153. markerGenes <- c(
  154. "SPIB", "SPI1", "BCL11A", "BCL11B", "SPIC",
  155. "CTCF","CTCFL","SOX13","SOX9","SOX4",
  156. "NFIC","NFIX","NFIB","NFIA","ZFX",
  157. "ZBTB7A","ZNF148","PATZ1",
  158. "TCF12","NHLH2","ASCL2","ASCL1","TFAP4",
  159. "JUNB", "JUN","FOSL2","FOS", "JUND"
  160. )
  162. p <- plotBrowserTrack(
  163. ArchRProj = atac_proj3,
  164. groupBy = "NamedClust",
  165. geneSymbol = markerGenes,
  166. upstream = 50000,
  167. downstream = 50000,
  168. loops = getPeak2GeneLinks(atac_proj3)
  169. )
  171. plotPDF(plotList = p,
  172. name = "Plot-Tracks-Marker-Genes-with-Peak2GeneLinks.pdf",
  173. ArchRProj = atac_proj3,
  174. addDOC = FALSE, width = 5, height = 5)
  177. ## Plotting a heatmap of peak-to-gene links
  178. p <- plotPeak2GeneHeatmap(ArchRProj = atac_proj3, groupBy = "NamedClust")
  179. p
  180. ggsave("Plots/PlotPeak2GeneHeatmap.pdf")
  182. ##########################################################################################
  183. # 9. Identification of Positive TF-Regulators
  184. ##########################################################################################
  185. seGroupMotif <- getGroupSE(ArchRProj = atac_proj3, useMatrix = "MotifMatrix", groupBy = "NamedClust")
  186. seGroupMotif
  187. seZ <- seGroupMotif[rowData(seGroupMotif)$seqnames=="z",]
  189. rowData(seZ)$maxDelta <- lapply(seq_len(ncol(seZ)), function(x){
  190. rowMaxs(assay(seZ) - assay(seZ)[,x])
  191. }) %>% Reduce("cbind", .) %>% rowMaxs
  194. corGSM_MM <- correlateMatrices(
  195. ArchRProj = atac_proj3,
  196. useMatrix1 = "GeneScoreMatrix",
  197. useMatrix2 = "MotifMatrix",
  198. reducedDims = "IterativeLSI"
  199. )
  201. corGSM_MM
  203. corGSM_MM$maxDelta <- rowData(seZ)[match(corGSM_MM$MotifMatrix_name, rowData(seZ)$name), "maxDelta"]
  205. corGSM_MM <- corGSM_MM[order(abs(corGSM_MM$cor), decreasing = TRUE), ]
  206. corGSM_MM <- corGSM_MM[which(!duplicated(gsub("\\-.*","",corGSM_MM[,"MotifMatrix_name"]))), ]
  207. corGSM_MM$TFRegulator <- "NO"
  208. corGSM_MM$TFRegulator[which(corGSM_MM$cor > 0.5 & corGSM_MM$padj < 0.01 & corGSM_MM$maxDelta > quantile(corGSM_MM$maxDelta, 0.75))] <- "YES"
  209. sort(corGSM_MM[corGSM_MM$TFRegulator=="YES",1])
  211. p <- ggplot(data.frame(corGSM_MM), aes(cor, maxDelta, color = TFRegulator)) +
  212. geom_point() +
  213. theme_ArchR() +
  214. geom_vline(xintercept = 0, lty = "dashed") +
  215. scale_color_manual(values = c("NO"="darkgrey", "YES"="firebrick3")) +
  216. xlab("Correlation To Gene Score") +
  217. ylab("Max TF Motif Delta") +
  218. scale_y_continuous(
  219. expand = c(0,0),
  220. limits = c(0, max(corGSM_MM$maxDelta)*1.05)
  221. )
  223. p
  226. library(ggplot2)
  227. library(ggrepel) # Ensure ggrepel is loaded
  229. # Your existing plot code
  230. p <- ggplot(data.frame(corGSM_MM), aes(cor, maxDelta, color = TFRegulator)) +
  231. geom_point() +
  232. theme_ArchR() +
  233. geom_vline(xintercept = 0, lty = "dashed") +
  234. scale_color_manual(values = c("NO"="darkgrey", "YES"="firebrick3")) +
  235. xlab("Correlation To Gene Score") +
  236. ylab("Max TF Motif Delta") +
  237. scale_y_continuous(
  238. expand = c(0,0),
  239. limits = c(0, max(corGSM_MM$maxDelta)*1.05)
  240. ) +
  241. geom_text(data = subset(data.frame(corGSM_MM), GeneScoreMatrix_name %in% markerGenes & TFRegulator == "YES"),
  242. aes(label = GeneScoreMatrix_name), # Use your specific label column here
  243. vjust = -1) # Adjust text positioning
  246. ggsave("Plots/Pos_TF_corGSM.pdf")
  249. p <- ggplot(data.frame(corGSM_MM), aes(cor, maxDelta, color = TFRegulator)) +
  250. geom_point() +
  251. theme_ArchR() +
  252. geom_vline(xintercept = 0, lty = "dashed") +
  253. scale_color_manual(values = c("NO"="darkgrey", "YES"="firebrick3")) +
  254. xlab("Correlation To Gene Score") +
  255. ylab("Max TF Motif Delta") +
  256. scale_y_continuous(
  257. expand = c(0,0),
  258. limits = c(0, max(corGSM_MM$maxDelta)*1.05)
  259. )
  261. # Filtering and sorting for the text labels
  262. top_labels <- as.data.frame(corGSM_MM) %>%
  263. filter(TFRegulator == "YES") %>%
  264. arrange(desc(cor)) %>%
  265. head(5)
  267. p + geom_text(data = top_labels,
  268. aes(label = GeneScoreMatrix_name), # Use your specific label column here
  269. vjust = -1) # Adjust text positioning
  272. ggsave("Plots/Pos_TF_corGSM2.pdf")

Peak_annotation.R at commit baf2d2b, no license · at the source

Overview

Authors: Yunxiao Ren1,2, Ming Hu3,4, Yang E. Li5, Andrew A. Pieper6,7,8,9,10,11, Jeffrey Cummings12, Feixiong Cheng1,2,4,13
13 affiliations
  1. Cleveland Clinic Genome Center, Cleveland Clinic Research, Cleveland Clinic, Cleveland, Ohio 44195, USA
  2. Department of Genomic Sciences and Systems Biology, Cleveland Clinic Research, Cleveland Clinic, Cleveland, Ohio 44195, USA
  3. Department of Quantitative Health Sciences, Cleveland Clinic Research, Cleveland Clinic, Cleveland, Ohio 44195, USA
  4. Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio 44195, USA
  5. Department of Neurosurgery and Genetics, Washington University School of Medicine, St. Louis, Missouri 63110, USA
  6. Department of Psychiatry, Case Western Reserve University, Cleveland, Ohio 44106, USA
  7. Brain Health Medicines Center, Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, Ohio 44106, USA
  8. Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, Ohio 44106, USA
  9. Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland, Ohio 44106, USA
  10. Department of Pathology, Case Western Reserve University, School of Medicine, Cleveland, Ohio 44106, USA
  11. Department of Neurosciences, Case Western Reserve University, School of Medicine, Cleveland, Ohio 44106, USA
  12. Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, Kirk Kerkorian School of Medicine, University of Nevada–Las Vegas, Las Vegas, Nevada 89154, USA
  13. Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
Institutions: Cleveland Clinic (United States); Cleveland Clinic Lerner College of Medicine (United States); Case Western Reserve University (United States); Washington University in St. Louis (United States); University Hospitals of Cleveland (United States); University Hospitals Cleveland Medical Center (United States); Louis Stokes Cleveland VA Medical Center (United States); University of Nevada, Las Vegas (United States); Case Comprehensive Cancer Center
Journal: Genome research, volume 36, issue 3, pages 645-659
Dates: received 14 January 2025; accepted 15 January 2026; published online 2 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1101/gr.280436.125 · PMID 41565468 · PMCID PMC12951949 · OpenAlex W7125430809
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing
MeSH: Alzheimer Disease*, Drug Repositioning*, Gene Regulatory Networks*, Gene Expression Regulation, Genome-Wide Association Study, Humans, Transcription Factors (* major topic)
Journal subjects: Resource
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Institute on Aging (U01AG073323, R01AG066707, R01AG084250, R01AG076448, R01AG082118, R01AG092462, R01AG092591, RF1AG082211, R33AG083003, R21AG083003, R35AG71476, R25AG083721-01); NINDS (RF1NS133812, RO1NS139383); Alzheimer’s Association (ALZDISCOVERY-1051936); Brockman Foundation (19PABH134580006); NIGMS (P20GM109025)
Citations: not cited yet (Europe PMC); 88 references in the paper

Abstract

Alzheimer's disease (AD) is a complex and poorly understood neurodegenerative disorder that lacks sufficiently effective treatments. Computational and integrative analyses that leverage multiomic data provide a promising strategy to uncover disease mechanisms and identify therapeutic opportunities. Here, we develop a cell type–specific regulatory atlas of the human middle temporal gyrus via leveraging single-nucleus RNA-seq (1,197,032 nuclei) and ATAC-seq (740,875 nuclei) data sets from 84 donors across four stages of AD neuropathological change (ADNC). We observe differential gene expression for six major cell types intensified at severe ADNC. Integrating peak-to-gene linkages and motif enrichment analyses, we reconstruct transcription factor (TF)–target gene networks across six major brain cell types. By integrating genome-wide association study (GWAS) loci with cell type–specific cis-regulatory DNA elements (CREs), we pinpoint 141 ADNC-associated genes. Using gene set enrichment analysis (GSEA) and network proximity analysis, we further identify nine candidate repurposable drugs that were associated with these ADNC-related genes. In summary, this cell type–specific multiomic atlas provides a comprehensive resource for mechanistic understanding, target prioritization, and therapeutic hypothesis generation in AD and AD-related dementia if broadly applied.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

ChengF-Lab/AD-digitaltwins

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: baf2d2be68b69974489ad2272572436f0b5a34ad, 16 January 2026
Languages: R (8), Python (1)
Size: 20 files, 9 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (6 files), ComplexHeatmap (4 files), ggplot2 (3 files), Seurat (3 files), SingleCellExperiment (3 files), cowplot (2 files), data.table (2 files), limma (2 files), circlize (1 file), Matplotlib (1 file), NumPy (1 file), pandas (1 file), Scanpy (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
10 files
At the source: github.com/ChengF-Lab/AD-digitaltwins

Code availability

All source code and custom scripts used in this study are available at GitHub (https://github.com/ChengF-Lab/AD-digitaltwins) and as Supplemental Code.

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

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;
  • 9 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);
  • 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.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 7 MeSH terms, 5 funders, 87 references.

Cite

This paper

Ren, Y., Hu, M., Li, Y. E., Pieper, A. A., Cummings, J., & Cheng, F. (2026). Cell type-specific gene regulatory atlas prioritizes drug targets and repurposable medicines in Alzheimer's disease. Genome research, 36(3), 645-659. https://doi.org/10.1101/gr.280436.125

BibTeX

@article{ren2026cell,
author = {Ren, Yunxiao and Hu, Ming and Li, Yang E. and Pieper, Andrew A. and Cummings, Jeffrey and Cheng, Feixiong},
title = {{Cell type-specific gene regulatory atlas prioritizes drug targets and repurposable medicines in Alzheimer's disease}},
journal = {Genome research},
year = {2026},
month = mar,
volume = {36},
number = {3},
pages = {645--659},
publisher = {Cold Spring Harbor Laboratory Press},
issn = {1088-9051},
doi = {10.1101/gr.280436.125},
url = {https://doi.org/10.1101/gr.280436.125},
pmid = {41565468},
pmcid = {PMC12951949}
}

RIS

TY - JOUR
AU - Ren, Yunxiao
AU - Hu, Ming
AU - Li, Yang E.
AU - Pieper, Andrew A.
AU - Cummings, Jeffrey
AU - Cheng, Feixiong
TI - Cell type-specific gene regulatory atlas prioritizes drug targets and repurposable medicines in Alzheimer's disease
T2 - Genome research
J2 - Genome Res
PY - 2026
DA - 2026/03/02
VL - 36
IS - 3
SP - 645
EP - 659
SN - 1088-9051
PB - Cold Spring Harbor Laboratory Press
DO - 10.1101/gr.280436.125
UR - https://doi.org/10.1101/gr.280436.125
LA - en
ER -

CSL-JSON

{
"id": "10.1101/gr.280436.125",
"type": "article-journal",
"title": "Cell type-specific gene regulatory atlas prioritizes drug targets and repurposable medicines in Alzheimer's disease",
"container-title": "Genome research",
"author": [
{
"family": "Ren",
"given": "Yunxiao"
},
{
"family": "Hu",
"given": "Ming"
},
{
"family": "Li",
"given": "Yang E."
},
{
"family": "Pieper",
"given": "Andrew A."
},
{
"family": "Cummings",
"given": "Jeffrey"
},
{
"family": "Cheng",
"given": "Feixiong"
}
],
"container-title-short": "Genome Res",
"volume": "36",
"issue": "3",
"page": "645-659",
"DOI": "10.1101/gr.280436.125",
"PMID": "41565468",
"PMCID": "PMC12951949",
"ISSN": "1088-9051",
"publisher": "Cold Spring Harbor Laboratory Press",
"URL": "https://doi.org/10.1101/gr.280436.125",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
2
]
]
}
}

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

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41467-026-73007-1 [code]
Single-nucleus epigenomic dysregulation unmasks genetic risk-associated neurodegenerative glia states.
Journal: Nature communications
In common: circlize, Scanpy, ComplexHeatmap, 7 other tools, Alzheimer's / dementia, genetics / omics, cellular / molecular, 11 references
[2] doi:10.1016/j.xcrm.2026.102766 [code]
A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma.
Journal: Cell reports. Medicine
In common: SingleCellExperiment, limma, circlize, 12 other tools, genetics / omics, cellular / molecular, 6 references
[3] doi:10.1038/s42003-026-10034-0 [code]
Region- and cell type-specific changes in gene expression in the cerebellum after classical fear conditioning.
Journal: Communications biology
In common: SingleCellExperiment, limma, circlize, 12 other tools, genetics / omics, cellular / molecular, 3 references
[4] doi:10.1038/s41514-026-00391-9 [code]
Region-specific transcriptional signatures of brain aging in the absence of neuropathology at the single-cell level.
Journal: npj aging
In common: circlize, Scanpy, ComplexHeatmap, 10 other tools, genetics / omics, cellular / molecular, 5 references
[5] doi:10.1038/s44318-026-00818-9 [code]
FAM134B-mediated ER-phagy degrades APP and suppresses Alzheimer's disease pathology.
Journal: The EMBO journal
In common: SingleCellExperiment, limma, Scanpy, 10 other tools, Alzheimer's / dementia, cellular / molecular, 3 references
[6] doi:10.1038/s41597-026-06971-4 [code]
Human neuronal differentiation under Aβ exposure: a single-cell transcriptomic and epigenomic dataset.
Journal: Scientific data
In common: limma, circlize, Scanpy, 10 other tools, Alzheimer's / dementia, genetics / omics, 3 references
[7] doi:10.1101/gr.281113.125 [code]
Single-nucleus multiomic profiling of the aging mouse substantia nigra reveals conserved gene alterations linked to Parkinson's disease.
Journal: Genome research
In common: limma, circlize, ComplexHeatmap, 8 other tools, genetics / omics, cellular / molecular, 5 references
[8] doi:10.1002/imt2.70163 [code]
Spatial multi-omics unveils sphingolipid metabolic reprogramming within the retinal pathological niche.
Journal: iMeta
In common: SingleCellExperiment, limma, circlize, 12 other tools, genetics / omics, cellular / molecular
[9] doi:10.1038/s41467-026-75722-1 [code]
Single-nucleus analysis of the adult human olfactory epithelium uncovers shared neurogenesis programs with the brain.
Journal: Nature communications
In common: SingleCellExperiment, circlize, Scanpy, 10 other tools, genetics / omics, 3 references
[10] doi:10.1016/j.cell.2026.05.026 [code]
The critical role of the endogenous immune compartment after CAR T cell therapy in recurrent GBM.
Journal: Cell
In common: SingleCellExperiment, limma, Scanpy, 10 other tools, genetics / omics, 3 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account 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: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

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