OSCR

Uncovering Spatiotemporal and Functional Dynamics of Long Non-coding RNAs During Alzheimer's Progression in the Human Brain at Single-Cell Resolution.

Code ↔ Paper

12 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 12 matches
  1. [1] § Methods › snRNA-seq Data Processing and Quality Control ↔ lncAD_importing.R, lines 226–312 · score 0.91 · SCTransform, FindClusters, FindNeighbors, RunPCA, RunUMAP, regressing
  2. [2] § Methods › Pseudobulk-based Intercellular Gene Expression Profiles Comparison ↔ lncAD_celltype_comparison.R, lines 220–261 · score 0.84 · Variance stabilizing transformation, DESeqDataSetFromMatrix, DESeq2, AggregateExpression, AD stage, Seurat
  3. [3] § Methods › snRNA-seq Data Processing and Quality Control ↔ lncAD_PAGA_prepare.R, lines 47–88 · score 0.80 · FindClusters, FindNeighbors, RunPCA, RunUMAP, Seurat, clustering
  4. [4] § Methods › Genomic Regions Tracking ↔ lncAD_cis_act.R, lines 213–251 · score 0.79 · findOverlaps, GRanges, GenomicFeatures, Overlapping genes, tx, cis
  5. [5] § Methods › Graph-based Intercellular Gene Expression Profiles Comparison ↔ lncAD_PAGA_prepare.R, lines 175–225 · score 0.76 · H5AD, SaveH5Seurat, Scanpy, PAGA, subset, tissue
  6. [6] § Methods › Genomic Regions Tracking ↔ lncAD_custom_plots.R, lines 470–510 · score 0.70 · TxDb, genomic windows, Tracking, Transcript, gene
  7. [7] § Methods › Cell Type Annotation and Quantification ↔ lncAD_intro.R, lines 127–165 · score 0.62 · ScType, Seurat metadata, confidence, scores, clusters, matrix
  8. [8] § Methods › Graph-based Intercellular Gene Expression Profiles Comparison ↔ lncAD_PAGA.py, lines 34–94 · score 0.58 · PAGA graphs, Scanpy, weights, connectivity, Cell
  9. [9] § Methods › Differential Expression Analysis ↔ lncAD_importing.R, lines 226–312 · score 0.58 · PrepSCTFindMarkers, Seurat, filtered, tissue, AD
  10. [10] § Results › Intercellular Expression Reveals Convergent Progression Across Brain Regions ↔ lncAD_intro.R, lines 127–165 · score 0.58 · oligodendrocyte precursor cells, ScType, classified, OPCs, clusters, AD
  11. [11] § Results › Cell-Type-Specific lncRNA Dynamics is Shaped By Tissue Context During AD Progression ↔ lncAD_DEG_plot.R, lines 1507–1552 · score 0.53 · Pan cellular, glial cell, Mixed, Neuronal, neurons, genes
  12. [12] § Methods › Differential Expression Analysis ↔ lncAD_diff_exp.R, lines 82–135 · score 0.50 · FindMarkers, MAST, latent, Pairwise, AD

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 · 312 lines · 8 KB · no license · 2 matches

  1. ############################################################
  2. # Install required packages (run once if not installed)
  3. ############################################################
  4. install.packages("Seurat")
  5. install.packages("dplyr")
  6. install.packages("Matrix")
  7. install.packages("stringr")
  8. install.packages("hdf5r")
  9. install.packages("Tidyomics")
  11. ############################################################
  12. # Load required libraries
  13. ############################################################
  14. library(Seurat)
  15. library(dplyr)
  16. library(Matrix)
  17. library(stringr)
  18. library(hdf5r)
  19. library(Tidyomics)
  21. ############################################################
  22. # NOTE: Download GEO dataset externally (bash example)
  23. # wget "https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE147528&format=file" -O GSE147528_all_files.tar
  24. # tar -xvf GSE147528_all_files.tar
  25. ############################################################
  29. ############################################################
  30. # Import dataset GSE147528 (10X HDF5 format)
  31. ############################################################
  33. # Define path to dataset directory
  34. data_dir <- "/home/allanca/AD_studies/GSE147528"
  36. # Define sample table mapping GSM IDs to sample names
  37. sample_table <- read.table(text = "
  38. GSM4432635 SFG2
  39. GSM4432636 SFG1
  40. GSM4432637 SFG3
  41. GSM4432638 SFG4
  42. GSM4432639 SFG6
  43. GSM4432640 SFG7
  44. GSM4432641 SFG5
  45. GSM4432642 SFG9
  46. GSM4432643 SFG8
  47. GSM4432644 SFG10
  48. GSM4432645 EC2
  49. GSM4432646 EC1
  50. GSM4432647 EC3
  51. GSM4432648 EC4
  52. GSM4432649 EC6
  53. GSM4432650 EC7
  54. GSM4432651 EC5
  55. GSM4432652 EC9
  56. GSM4432653 EC8
  57. GSM4432654 EC10
  58. ", col.names = c("GSM", "Sample"))
  60. # Initialize list to store Seurat objects
  61. seurat_list <- list()
  63. # Loop through samples and read each H5 matrix
  64. for (i in seq_len(nrow(sample_table))) {
  66. # Extract GSM ID and sample name
  67. gsm <- sample_table$GSM[i]
  68. sample <- sample_table$Sample[i]
  70. # Build file path
  71. file_path <- file.path(data_dir, paste0(gsm, "_", sample, "_raw_gene_bc_matrices_h5.h5"))
  73. # Read 10X HDF5 file
  74. data <- Read10X_h5(file_path)
  76. # Create Seurat object
  77. seurat_obj <- CreateSeuratObject(counts = data, project = sample, min.cells = 3, min.features = 200)
  79. # Add sample metadata
  80. seurat_obj$sample_id <- sample
  82. # Store object in list
  83. seurat_list[[sample]] <- seurat_obj
  84. }
  86. # Merge all Seurat objects into one
  87. combined <- merge(seurat_list[[1]], y = seurat_list[-1], add.cell.ids = names(seurat_list), project = "GSE147528")
  89. # Extract metadata
  90. metadados <- combined[[]]
  92. # Inspect metadata
  93. View(metadados)
  95. # Count cells per sample
  96. table(metadados$orig.ident)
  100. ############################################################
  101. # Import dataset GSE157827 (Matrix Market format)
  102. ############################################################
  104. # Define data directory
  105. data_dir <- "/home/allanca/AD_studies/GSE157827"
  107. # List matrix files
  108. matrix_files <- list.files(data_dir, pattern = "_matrix.mtx.gz$", full.names = TRUE)
  110. # Initialize list
  111. seurat_list <- list()
  113. # Loop through each sample matrix
  114. for (matrix_file in matrix_files) {
  116. # Extract prefix without suffix
  117. prefix <- sub("_matrix.mtx.gz", "", matrix_file)
  119. # Extract sample name (AD or NC)
  120. sample <- str_extract(prefix, "(AD|NC)[0-9]+")
  122. # Build associated file paths
  123. barcodes_file <- paste0(prefix, "_barcodes.tsv.gz")
  124. features_file <- paste0(prefix, "_features.tsv.gz")
  126. # Read matrix and annotation files
  127. mat <- readMM(matrix_file)
  128. features <- read.delim(features_file, header = FALSE)
  129. barcodes <- read.delim(barcodes_file, header = FALSE)
  131. # Assign gene and cell names
  132. rownames(mat) <- make.unique(features$V2)
  133. colnames(mat) <- barcodes$V1
  135. # Create Seurat object
  136. seu <- CreateSeuratObject(counts = mat, project = sample, min.cells = 3, min.features = 200)
  138. # Add metadata
  139. seu$orig.ident <- sample
  140. seu$sample_id <- sample
  142. # Store object
  143. seurat_list[[sample]] <- seu
  144. }
  146. # Merge all samples
  147. seu_combined <- merge(x = seurat_list[[1]], y = seurat_list[-1])
  149. # Inspect sample distribution
  150. table(seu_combined$orig.ident)
  152. # Extract metadata
  153. metadados <- seu_combined[[]]
  155. table(metadados$orig.ident)
  159. ############################################################
  160. # Subset only male samples
  161. ############################################################
  163. male_samples <- c(
  164. "AD1","AD2","AD5","AD6","AD8","AD10","AD20","AD21",
  165. "NC3","NC7","NC11","NC12","NC16","NC17"
  166. )
  168. # Subset Seurat object
  169. seu_male <- subset(seu_combined, subset = orig.ident %in% male_samples)
  173. ############################################################
  174. # Merge both datasets (GSE147528 + GSE157827)
  175. ############################################################
  177. combined_all <- merge(
  178. x = combined,
  179. y = seu_male,
  180. add.cell.ids = c("SFG_EC", "PFC"),
  181. project = "RNA"
  182. )
  184. # Extract metadata
  185. metadatas <- combined_all[[]]
  189. ############################################################
  190. # Calculate mitochondrial gene percentage
  191. ############################################################
  193. combined_all <- PercentageFeatureSet(combined_all, pattern = "^MT-", col.name = "percent.mt")
  195. # Log2 transform mitochondrial percentage
  196. combined_all$percent.mt.log2 <- log(combined_all$percent.mt)
  198. # Generate violin plot
  199. p <- VlnPlot(combined_all, features = "percent.mt")
  201. # Save plot
  202. ggsave("percent_mt_violinplot.png", plot = p, width = 6, height = 4)
  204. pdf("percent_mt_violinplot.pdf", width = 6, height = 4)
  205. print(p)
  206. dev.off()
  208. # Save raw merged object
  209. saveRDS(combined_all, "combined_all.rds")
  213. ############################################################
  214. # Filter low-quality cells
  215. ############################################################
  217. combined_all_filt <- subset(
  218. combined_all,
  219. subset = nFeature_RNA > 200 &
  220. nFeature_RNA < 2500 &
  221. percent.mt < 20
  222. )
  226. ############################################################
  227. # Reload saved object if necessary
  228. ############################################################
  230. combined_all <- readRDS("combined_all.rds")
  234. ############################################################
  235. # Standard Seurat processing pipeline
  236. ############################################################
  238. combined_all_f <- SCTransform(combined_all_filt, vars.to.regress = "percent.mt")
  240. combined_all_f <- RunPCA(combined_all_f)
  242. combined_all_f <- RunUMAP(combined_all_f, dims = 1:20)
  244. combined_all_f <- FindNeighbors(combined_all_f, dims = 1:20, verbose = FALSE)
  246. combined_all_f <- FindClusters(combined_all_f, verbose = FALSE, resolution = seq(0,1,0.1))
  250. ############################################################
  251. # Metadata processing
  252. ############################################################
  254. # Extract metadata
  255. checkMeta <- combined_all_f[[]]
  257. # Extract tissue name from sample ID
  258. checkMeta$tissue <- gsub("[^A-Za-z]", "", checkMeta$orig.ident)
  260. # Combine AD and NC labels
  261. checkMeta$tissue <- ifelse(checkMeta$tissue %in% c("AD", "NC"), "AD_NC", checkMeta$tissue)
  263. # Assign tissue annotation to Seurat object
  264. combined_all_f$tissue <- checkMeta$tissue
  266. # Inspect metadata
  267. visualizeee <- combined_all_f[[]]
  269. # Save processed object
  270. write_rds(combined_all_f, "Allan_alz_combined_data.rds")
  274. ############################################################
  275. # Visualization
  276. ############################################################
  278. # UMAP split by tissue
  279. DimPlot(combined_all_f, split.by = "tissue", group.by = "SCT_snn_res.0.1")
  281. # Dopaminergic neuron markers
  282. dopaminergic_markers <- c(
  283. "TH",
  284. "SLC6A3",
  285. "DDC"
  286. )
  288. # Feature plot
  289. FeaturePlot(combined_all_f, split.by = "tissue", features = dopaminergic_markers)
  293. ############################################################
  294. # Differential expression analysis
  295. ############################################################
  297. # Prepare SCT object for marker detection
  298. combined_all_f <- PrepSCTFindMarkers(combined_all_f)
  300. # Save processed object
  301. write_rds(combined_all_f, "Allan_alz_combined_data.rds")
  303. # Find markers for cluster 11 vs all other clusters
  304. res_c11_vs_all <- FindMarkers(
  305. combined_all_f,
  306. ident.1 = "11",
  307. group.by = "SCT_snn_res.0.1",
  308. logfc.threshold = 0
  309. )
  311. # Export results
  312. write.csv(res_c11_vs_all, "Tables/DE_cluster11vsall.csv")

lncAD_importing.R at commit f5f55d4, no license · at the source

Overview

Authors: Allan de Carvalho1,2, Izabela Mamede3, Leonardo Sanches1, Guilherme Juvenal1, Fernanda Tibolla Viero1, Gloria Regina Franco3, Yong Tang4,5, Eduardo Moraes Reis1, Henning Ulrich1
ORCID iDs: Allan de Carvalho, Izabela Mamede, Leonardo Sanches, Guilherme Juvenal, Fernanda Tibolla Viero, Gloria Regina Franco, Yong Tang, Eduardo Moraes Reis, Henning Ulrich
  1. Department of Biochemistry, Institute of Chemistry, University of São Paulo, São Paulo, SP Brazil
  2. Department of Biochemistry and Molecular Biology, Federal University of Viçosa, Viçosa, MG Brazil
  3. Department of Biochemistry, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, MG Brazil
  4. International Joint Research Centre On Purinergic Signalling, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137 China
  5. School of Health and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137 China
Institutions: Universidade Federal de Viçosa (Brazil); Universidade de São Paulo (Brazil); Universidade Federal de Minas Gerais (Brazil); Chengdu University of Traditional Chinese Medicine (China)
Journal: Molecular neurobiology, volume 63, issue 1, article 599
Dates: received 7 January 2026; accepted 10 April 2026; published online 30 April 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1007/s12035-026-05859-z · PMID 42060014 · PMCID PMC13132975 · OpenAlex W7158944487
Open access: hybrid, 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, Connectivity, Machine learning
Keywords: SnRNA-seq, Long noncoding RNA, Cis-acting RNA, Neurodegeneration, Neural circuitry, Cerebral cortex
MeSH: Alzheimer Disease*, Brain*, Disease Progression*, RNA, Long Noncoding*, Single-Cell Analysis*, Gene Expression Regulation, Humans, Single-Cell Gene Expression Analysis (* major topic)
Topic: Cancer-related molecular mechanisms research (Cancer Research, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Conselho Nacional de Desenvolvimento Científico e Tecnológico (308012/2021-6); Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil (2024-2972); Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Finance Code 001); Fundação de Amparo à Pesquisa do Estado de São Paulo (2018/07366-4)
Citations: not cited yet (Europe PMC); 81 references in the paper

Abstract

Increasing prevalence of Alzheimer’s disease, driven by population aging, highlights the need to investigate its underlying molecular mechanisms. Within this context, long non-coding RNAs (lncRNAs) have emerged as a key regulatory layer. To advance the understanding of lncRNA dysregulation and function during Alzheimer’s disease progression, we reanalyzed publicly available single-nucleus RNA sequencing (snRNA-seq) datasets. The selected transcriptomic datasets were integrated and subjected to differential expression and genomic co-localization correlation analyses to infer putative cis-regulatory mechanisms. Our results reveal conserved cell-type composition and a shared transcriptional trajectory across brain regions during Alzheimer’s disease progression. In contrast, lncRNAs displayed marked cell-type and context specificity and formed coordinated expression patterns with neighboring genes within defined chromatin contexts. These associations suggest potential cis-regulatory roles and implicate lncRNAs in processes such as synaptic plasticity and maladaptive oligodendrocyte differentiation linked to myelin dysfunction. While these findings are primarily hypothesis-generating, they provide a cross-regional framework and a prioritized set of candidate lncRNAs for future functional investigation in Alzheimer’s disease.

Supplementary Information: The online version contains supplementary material available at 10.1007/s12035-026-05859-z.

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 12 matches between paragraphs and lines of code.

Allan-bqi/lncRNAs-AD

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: f5f55d4cd681916f70f98bddb84f0f5551cdbfb2, 21 March 2026
Languages: R (17), Python (1)
Size: 19 files, 18 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Seurat (17 files), tidyverse (17 files), circlize (16 files), ComplexHeatmap (16 files), ggplot2 (16 files), ggpubr (16 files), igraph (16 files), patchwork (16 files), pheatmap (16 files), rstatix (16 files), anndata (1 file), DESeq2 (1 file), Matplotlib (1 file), NumPy (1 file), pandas (1 file), reticulate (1 file), Scanpy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
19 files
At the source: github.com/Allan-bqi/lncRNAs-AD

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;
  • 18 scripts, each with its path and the digest of its content;
  • 12 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

Data Availability

Transcriptome data is available at Gene Omnibus (GEO) repository from NCBI, under their original IDs: GSE157827 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE157827) (PFC data) and GSE147528 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE147528) (EC and SFG data). Data generated from the analysis presented here is all available as supplementary material. The entire code to reproduce these analyses is available at: [https://github.com/Allan-bqi/lncRNAs-AD].

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 6 keywords, 8 MeSH terms, 4 funders, 81 references.

Cite

This paper

de Carvalho, A., Mamede, I., Sanches, L., Juvenal, G., Viero, F. T., Franco, G. R., Tang, Y., Reis, E. M., & Ulrich, H. (2026). Uncovering Spatiotemporal and Functional Dynamics of Long Non-coding RNAs During Alzheimer's Progression in the Human Brain at Single-Cell Resolution. Molecular neurobiology, 63(1), 599. https://doi.org/10.1007/s12035-026-05859-z

BibTeX

@article{decarvalho2026uncovering,
author = {de Carvalho, Allan and Mamede, Izabela and Sanches, Leonardo and Juvenal, Guilherme and Viero, Fernanda Tibolla and Franco, Gloria Regina and Tang, Yong and Reis, Eduardo Moraes and Ulrich, Henning},
title = {{Uncovering Spatiotemporal and Functional Dynamics of Long Non-coding RNAs During Alzheimer's Progression in the Human Brain at Single-Cell Resolution}},
journal = {Molecular neurobiology},
year = {2026},
month = apr,
volume = {63},
number = {1},
pages = {599},
publisher = {Springer Science+Business Media},
issn = {0893-7648},
doi = {10.1007/s12035-026-05859-z},
url = {https://doi.org/10.1007/s12035-026-05859-z},
pmid = {42060014},
pmcid = {PMC13132975}
}

RIS

TY - JOUR
AU - de Carvalho, Allan
AU - Mamede, Izabela
AU - Sanches, Leonardo
AU - Juvenal, Guilherme
AU - Viero, Fernanda Tibolla
AU - Franco, Gloria Regina
AU - Tang, Yong
AU - Reis, Eduardo Moraes
AU - Ulrich, Henning
TI - Uncovering Spatiotemporal and Functional Dynamics of Long Non-coding RNAs During Alzheimer's Progression in the Human Brain at Single-Cell Resolution
T2 - Molecular neurobiology
J2 - Mol Neurobiol
PY - 2026
DA - 2026/04/30
VL - 63
IS - 1
SP - 599
SN - 0893-7648
PB - Springer Science+Business Media
DO - 10.1007/s12035-026-05859-z
UR - https://doi.org/10.1007/s12035-026-05859-z
LA - en
ER -

CSL-JSON

{
"id": "10.1007/s12035-026-05859-z",
"type": "article-journal",
"title": "Uncovering Spatiotemporal and Functional Dynamics of Long Non-coding RNAs During Alzheimer's Progression in the Human Brain at Single-Cell Resolution",
"container-title": "Molecular neurobiology",
"author": [
{
"family": "de Carvalho",
"given": "Allan"
},
{
"family": "Mamede",
"given": "Izabela"
},
{
"family": "Sanches",
"given": "Leonardo"
},
{
"family": "Juvenal",
"given": "Guilherme"
},
{
"family": "Viero",
"given": "Fernanda Tibolla"
},
{
"family": "Franco",
"given": "Gloria Regina"
},
{
"family": "Tang",
"given": "Yong"
},
{
"family": "Reis",
"given": "Eduardo Moraes"
},
{
"family": "Ulrich",
"given": "Henning"
}
],
"container-title-short": "Mol Neurobiol",
"volume": "63",
"issue": "1",
"page": "599",
"DOI": "10.1007/s12035-026-05859-z",
"PMID": "42060014",
"PMCID": "PMC13132975",
"ISSN": "0893-7648",
"publisher": "Springer Science+Business Media",
"URL": "https://doi.org/10.1007/s12035-026-05859-z",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
30
]
]
}
}

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/s41586-026-10629-x [code]
Whole-genome duplication shaped cell-type evolution in the vertebrate brain.
Journal: Nature
In common: reticulate, rstatix, anndata, 13 other tools, genetics / omics, cellular / molecular, 2 references
[2] doi:10.1038/s41467-026-73007-1 [code]
Single-nucleus epigenomic dysregulation unmasks genetic risk-associated neurodegenerative glia states.
Journal: Nature communications
In common: anndata, circlize, Scanpy, 9 other tools, Alzheimer's / dementia, genetics / omics, cellular / molecular, 6 references
[3] 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: anndata, igraph, circlize, 12 other tools, genetics / omics, cellular / molecular, 3 references
[4] 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: reticulate, rstatix, anndata, 11 other tools, genetics / omics, cellular / molecular, 3 references
[5] doi:10.1002/imt2.70163 [code]
Spatial multi-omics unveils sphingolipid metabolic reprogramming within the retinal pathological niche.
Journal: iMeta
In common: rstatix, anndata, igraph, 13 other tools, genetics / omics, cellular / molecular
[6] doi:10.1186/s13059-026-04177-w [code]
Genomic sequence evolution underlying human neocortical interareal diversification.
Journal: Genome biology
In common: reticulate, rstatix, anndata, 12 other tools, genetics / omics, cellular / molecular, 1 reference
[7] doi:10.1038/s41586-026-10214-2 [code]
Multidimensional profiling of heterogeneity in supratentorial ependymomas.
Journal: Nature
In common: reticulate, anndata, igraph, 13 other tools, genetics / omics
[8] doi:10.1126/sciadv.aeg3223 [code]
The extreme diversity of retinal amacrine cells has deep evolutionary roots.
Journal: Science advances
In common: reticulate, rstatix, anndata, 12 other tools, genetics / omics, cellular / molecular
[9] doi:10.1038/s41467-026-71803-3 [code]
Charting the transition from in vitro gliogenesis to the in vivo maturation of human glial progenitor cells transplanted into the hypomyelinated mouse brain.
Journal: Nature communications
In common: reticulate, anndata, DESeq2, 10 other tools, genetics / omics, cellular / molecular, 3 references
[10] doi:10.1093/bioinformatics/btag592 [code]
Network-based stratification of allele-specific expression reveals patient subgroups in Huntington's disease.
Journal: Bioinformatics (Oxford, England)
In common: reticulate, rstatix, igraph, 11 other tools, genetics / omics, 2 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.