OSCR

Genomic sequence evolution underlying human neocortical interareal diversification.

Code ↔ Paper

27 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 27 matches · 6 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Electrophysiological recordings and analysis ↔ 10. Electrophysiology and Morphology/01. Electrophysiology/2. features_extraction(allen extracted).ipynb, lines 35–107 · score 0.91 · upstroke downstroke ratio, ISI cv, spike threshold, trough, adaptation, amplitude
  2. [2] § Methods › Electrophysiological recordings and analysis ↔ 10. Electrophysiology and Morphology/01. Electrophysiology/1. features_extraction(manual extracted feature).ipynb, lines 113–230 · score 0.90 · AHP latency, AHP Amp, DepoR, RepoR, half width, phase
  3. [3] § Methods › Electrophysiological and morphological classification ↔ 10. Electrophysiology and Morphology/01. Electrophysiology/2. features_extraction(allen extracted).ipynb, lines 35–107 · score 0.86 · upstroke downstroke ratio, ISI cv, trough, adaptation, amplitude, spike
  4. [4] § Methods › Immunohistochemical analysis ↔ 10. Electrophysiology and Morphology/02. Morphology/2. neuro_morphology_features.ipynb, lines 343–383 · score 0.82 · dendritic field area, soma surface area, exiting soma, morphological feature, reconstructed, ratio
  5. [5] § Methods › Immunohistochemical analysis ↔ 10. Electrophysiology and Morphology/02. Morphology/3. neuro_morphology_ring_quantification.ipynb, lines 131–159 · score 0.82 · dendritic field area, soma surface area, exiting soma, morphological feature, rings, reconstructed
  6. [6] § Methods › Cross-species comparison of inter-areal heterogeneity ↔ 06.integration/inte.coembed.R, lines 91–162 · score 0.76 · SCTransform, IntegrateData, SelectIntegrationFeatures, FindIntegrationAnchors, UMAP, Seurat
  7. [7] § Methods › Cross-species comparison of inter-areal heterogeneity ↔ 06.integration/inte.coembed.gene.R, lines 91–162 · score 0.76 · SCTransform, IntegrateData, SelectIntegrationFeatures, FindIntegrationAnchors, UMAP, Seurat
  8. [8] § Results › Cellular diversity, molecular specialization, and functional variation across human neocortical areas ↔ 10. Electrophysiology and Morphology/01. Electrophysiology/1. features_extraction(manual extracted feature).ipynb, lines 113–230 · score 0.69 · RepoR, half width, phase, voltage, amplitude, AHP
  9. [9] § Methods › Identification of reproducible ATAC peaks ↔ 06. Peak Calling/9. call_macs2_pool.sh, the whole file · a weak match · score 0.67 · MACS2, peak calling, SPMR, extsize, nomodel, dup
  10. [10] § Methods › Identification of reproducible ATAC peaks ↔ 06. Peak Calling/8. call_macs2.sh, the whole file · a weak match · score 0.66 · MACS2, peak calling, SPMR, extsize, nomodel, dup
  11. [11] § Results › Cellular diversity, molecular specialization, and functional variation across human neocortical areas ↔ 10. Electrophysiology and Morphology/02. Morphology/2. neuro_morphology_features.ipynb, lines 476–503 · score 0.64 · dendritic field area, soma surface area, biotin, morphology, reconstructed, electrophysiological
  12. [12] § Results › Cellular diversity, molecular specialization, and functional variation across human neocortical areas ↔ 10. Electrophysiology and Morphology/02. Morphology/3. neuro_morphology_ring_quantification.ipynb, lines 131–159 · score 0.64 · dendritic field area, soma surface area, biotin, morphology, reconstructed, electrophysiological
  13. [13] § Methods › Cell clustering using denoised snATAC-seq data ↔ bin/snapATAC.match2rna.mouseBrain.R, lines 195–258 · score 0.64 · snATAC, ATAC seq, snRNA, UMAP, neighbor, resolutions
  14. [14] § Methods › Enrichment analysis for AUPs ↔ 09. AUP Enrichment/1-2. make_TEpeak_bool.R, the whole file · a weak match · score 0.63 · ChIPpeakAnno, findOverlapsOfPeaks, hg38, AUPs, enrichment, TE
  15. [15] § Methods › Constructing networks of pathways enriched with AUGs ↔ bin/run.enrichR.R, the whole file · a weak match · score 0.62 · biological process, cellular component, GO, molecular, enriched
  16. [16] § Methods › Constructing networks of pathways enriched with AUGs ↔ bin/run.rGREAT.R, the whole file · a weak match · score 0.61 · biological process, cellular component, GO, molecular, enrichment
  17. [17] § Methods › Enrichment analysis for AUPs ↔ 09. AUP Enrichment/2. PeakAnnot_enrich.R, lines 60–95 · score 0.61 · fisher.test, p.adjust, BH, AUPs, enrichment, peaks
  18. [18] § Methods › Analysis of the transcriptome of transposable elements ↔ 06.integration/inte.parseRNA.R, lines 147–194 · score 0.61 · FindVariableFeatures, RNA seq, variable genes, selection
  19. [19] § Methods › Mapping subtypes to cortical layers of the published human snRNA-seq data ↔ bin/snapATAC.match2rna.mouseBrain.R, lines 135–193 · score 0.57 · TransferData, prediction score, anchor, Brain, meta
  20. [20] § Methods › Differential chromatin accessibility analysis between neocortical areas ↔ 08. DAP/my_runDAP.py, the whole file · a weak match · score 0.56 · rank genes, Scanpy, DAPs, Wilcoxon, filtered
  21. [21] § Methods › Mapping subtypes to cortical layers of the published human snRNA-seq data ↔ 06.integration/inte.coembed.R, lines 91–162 · score 0.56 · SelectIntegrationFeatures, FindIntegrationAnchors, meta, row, Seurat, score
  22. [22] § Methods › Construction of hierarchical cell taxonomy based on transcriptome ↔ bin/snapATAC.match2rna.mouseBrain.R, lines 135–193 · score 0.55 · TransferData, prediction score, anchor, Brain
  23. [23] § Methods › Synteny analysis and peak evolutionary age determination ↔ 05.LDSC_analysis/run_gwas.sh, lines 1–11 · score 0.54 · liftOver, human genome, UCSC, mapped
  24. [24] § Methods › Denoising of single-nucleus ATAC-seq data ↔ 06. Peak Calling/count_atac.R, lines 41–97 · score 0.54 · FeatureMatrix, ATAC peaks, chromatin, cell
  25. [25] § Methods › Mapping cells to spatial transcriptome data ↔ 04. Spatial Deconvolution/01. Nucleus_segmentation/pipline_segment.py, lines 95–119 · score 0.52 · nucleus segmentation, pretrained, versatile, stardist, model
  26. [26] § Methods › Cell clustering using denoised snATAC-seq data ↔ 06.integration/inte.parseRNA.R, lines 45–83 · score 0.51 · dimension reduction, GABAergic, UMAP, neighbor, resolutions, Clustering
  27. [27] § Methods › Cross-species comparison of inter-areal heterogeneity ↔ bin/snapATAC.match2rna.mouseBrain.R, lines 195–258 · score 0.50 · snRNA, RNA seq, TransferData, Brain, match, cells

Paper

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The authors' code

R · 260 lines · 9.6 KB · MIT · 4 matches

  1. #!/usr/bin/env Rscript
  3. suppressPackageStartupMessages(library("argparse"))
  5. # create parser object
  6. parser <- ArgumentParser()
  8. # specify our desired options
  9. # by default ArgumentParser will add an help option
  10. parser$add_argument("-i", "--input", required=TRUE, help="load snap RData")
  11. parser$add_argument("-s", "--seurat", required=TRUE, help="load seurat RData")
  12. #parser$add_argument("--cpu", default = 4, help="# of cpus [default %(default)s]")
  13. parser$add_argument("-o", "--output", required=TRUE, help="output file prefix")
  14. # get command line options, if help option encountered print help and exit,
  15. # otherwise if options not found on command line then set defaults,
  16. args <- parser$parse_args()
  18. suppressPackageStartupMessages(library("SnapATAC"))
  19. suppressPackageStartupMessages(library("Seurat"))
  20. suppressPackageStartupMessages(library("GenomicRanges"));
  21. suppressPackageStartupMessages(library("RColorBrewer"))
  22. suppressPackageStartupMessages(library("data.table"))
  23. suppressPackageStartupMessages(library("ggplot2"))
  24. suppressPackageStartupMessages(library("plyr"))
  25. #library("future")
  26. library("Matrix")
  27. library("tictoc")
  29. snapF = args$input
  30. seuratF = args$seurat
  31. #cpus = as.numeric(args$cpu)
  32. outF = args$output
  34. #----------------------
  35. # load rna seurat
  36. rna.se <- readRDS(seuratF)
  38. #-------------------
  39. # downsample to 200
  40. idx.ls <- list()
  41. for(l in unique(rna.se$ClusterName)){
  42. idx <- which(rna.se$ClusterName == l)
  43. if(length(idx)>200){
  44. set.seed(2020)
  45. idx.dn <- sample(idx, 200)
  46. }else{
  47. print(length(idx))
  48. set.seed(2020)
  49. idx.dn <- idx
  50. }
  51. idx.ls[[l]] <- idx.dn
  52. }
  53. idx.ls <- sort(unlist(idx.ls))
  55. rna.se <- rna.se[, idx.ls]
  57. rna.se <- NormalizeData(rna.se)
  58. rna.se <- FindVariableFeatures(rna.se)
  59. all.genes <- rownames(rna.se)
  60. rna.se <- ScaleData(rna.se, features = all.genes)
  61. rna.se <- RunPCA(rna.se)
  62. #ElbowPlot(rna.subset.se)
  63. rna.se <- FindNeighbors(rna.se, dims = 1:20)
  64. rna.se <- FindClusters(rna.se, resolution = 0.5)
  65. rna.se <- RunUMAP(rna.se, dims = 1:20)
  67. #DimPlot(rna.subset.se, reduction = "umap")
  68. #DimPlot(rna.subset.se, reduction = "umap", group.by = "ClusterName")
  69. fwrite(rna.se[[]], paste(outF, "rna.seObj.meta.tsv", sep="."), sep="\t", quote=F, col.names=T, row.names=F)
  70. saveRDS(rna.se, file=paste(outF, "rna.seObj.rds", sep="."))
  73. #-------------------------
  74. # load atac and convert to Seurat Obj
  75. x.sp <- readRDS(snapF)
  76. x.sp@metaData$cluster <- x.sp@metaData$L3cluster
  78. #-------------------
  79. # downsample to 200
  80. idx.ls <- list()
  81. for(l in unique(x.sp@metaData$cluster)){
  82. idx <- which(x.sp@metaData$cluster == l)
  83. if(length(idx)>200){
  84. set.seed(2020)
  85. idx.dn <- sample(idx, 200)
  86. }else{
  87. print(length(idx))
  88. set.seed(2020)
  89. idx.dn <- idx
  90. }
  91. idx.ls[[l]] <- idx.dn
  92. }
  93. idx.ls <- sort(unlist(idx.ls))
  95. x.sp <- x.sp[idx.ls, ]
  98. pca.use = x.sp@smat@dmat;
  99. eigs.dims <- 1:ncol(pca.use)
  100. pca.use = pca.use[,eigs.dims]
  102. metaData.use = x.sp@metaData
  103. data.use = t(x.sp@pmat)
  104. peak.use = as.data.frame(x.sp@peak);
  105. gmat.use = t(x.sp@gmat);
  107. rownames(x = data.use) = peak.use$name;
  108. colnames(x = data.use) = paste(x.sp@sample, x.sp@barcode, sep=".");
  109. colnames(x = gmat.use) = paste(x.sp@sample, x.sp@barcode, sep=".");
  110. rownames(x = pca.use) = paste(x.sp@sample, x.sp@barcode, sep=".");
  111. rownames(metaData.use) = paste(x.sp@sample, x.sp@barcode, sep=".");
  112. colnames(x = pca.use) <- paste0("pca_", eigs.dims);
  114. atac.se <- CreateSeuratObject(counts = data.use, assay = "ATAC");
  115. atac.se[["ACTIVITY"]] <- CreateAssayObject(counts = gmat.use);
  116. atac.se <- AddMetaData(atac.se, metadata = metaData.use);
  117. atac.se$tech <- "atac"
  118. DefaultAssay(atac.se) <- "ACTIVITY"
  120. atac.se[["pca"]] <- new(Class = "DimReduc", cell.embeddings = pca.use,
  121. feature.loadings = matrix(0,0,0), feature.loadings.projected = matrix(0,0,0),
  122. assay.used ="ATAC", stdev = rep(1,length(eigs.dims)),
  123. key ="pca_", jackstraw = new(Class = "JackStrawData"), misc = list())
  125. atac.se <- NormalizeData(atac.se)
  126. atac.se <- FindVariableFeatures(object = atac.se, assay="ACTIVITY");
  128. all.genes <- rownames(atac.se)
  129. atac.se <- ScaleData(atac.se, features = all.genes)
  130. #atac.se <- RunPCA(atac.se, verbose = FALSE)
  131. atac.se <- RunUMAP(atac.se, dims = 1:20)
  132. #atac.se <- FindNeighbors(atac.se, dims = 1:20)
  133. #atac.se <- FindClusters(atac.se, resolution = 0.5)
  135. #DimPlot(atac.se, reduction = "umap")
  136. #DimPlot(atac.se, reduction = "umap", group.by="L3cluster")
  139. # set parallel
  140. #plan("multiprocess", workers = cpus)
  142. #---------------------------
  143. # identify variable genes from RNA
  144. variable.genes = VariableFeatures(object = rna.se);
  146. #--------------------------
  147. # transfer anchors
  148. rna2atac.transfer.anchors <- FindTransferAnchors(
  149. reference = rna.se,
  150. query = atac.se,
  151. features = variable.genes,
  152. reference.assay = "RNA",
  153. query.assay = "ACTIVITY",
  154. reduction = "cca"
  155. );
  157. #------------------------------------
  158. # transfer label from RNA to ATAC
  159. rna2atac.predictions <- TransferData(
  160. anchorset = rna2atac.transfer.anchors,
  161. refdata = rna.se$ClusterName,
  162. weight.reduction = atac.se[["pca"]],
  163. dims = 1:20
  164. )
  166. rna2atac.predictions.df <- data.frame(rna2atac.predicted.id = rna2atac.predictions$predicted.id, rna2atac.predict.max.score = apply(rna2atac.predictions[,-1], 1, max))
  167. rownames(rna2atac.predictions.df) <- colnames(atac.se)
  168. atac.se <- AddMetaData(atac.se, metadata = rna2atac.predictions.df)
  170. outfname = paste(outF,"rna2atac.predictScore.hist.pdf", sep=".")
  171. pdf(outfname)
  172. hist(
  173. rna2atac.predictions.df$rna2atac.predict.max.score,
  174. xlab="prediction score",
  175. col="lightblue",
  176. xlim=c(0, 1),
  177. main="rna2atac prediction"
  178. );
  179. abline(v=0.5, col="red", lwd=2, lty=2);
  180. dev.off()
  182. refdata <- GetAssayData(
  183. object = rna.se,
  184. assay = "RNA",
  185. slot = "data"
  186. );
  188. rna2atac.imputation <- TransferData(
  189. anchorset = rna2atac.transfer.anchors,
  190. refdata = refdata,
  191. weight.reduction = atac.se[["pca"]],
  192. dims = 1:20
  193. );
  195. # add imputate gmat to x.sp
  196. atac.se[["RNA"]] <- CreateAssayObject(counts = rna2atac.imputation@data);
  198. fwrite(atac.se[[]], paste(outF, "atac.seObj.meta.tsv", sep="."), sep="\t", quote=F, col.names=T, row.names=F)
  199. saveRDS(atac.se, file=paste(outF, "atac.seObj.rds", sep="."))
  201. #------------------------------------------
  202. # plot original embed with transfered label
  203. pdf(paste(outF,"origEmbed.transferLabel.pdf", sep="."))
  204. # transfer RNA label to ATAC
  205. DimPlot(atac.se, group.by = "rna2atac.predicted.id", label = TRUE, repel = TRUE, reduction="umap") + ggtitle("scRNA-seq cells: match to snATAC-seq") + NoLegend()
  206. DimPlot(atac.se, group.by = "rna2atac.predicted.id", label = FALSE, repel = FALSE, reduction="umap") + ggtitle("scRNA-seq cells: match to snATAC-seq") + NoLegend()
  207. DimPlot(atac.se, reduction = "umap", group.by="L3cluster", label = TRUE, repel = TRUE) + ggtitle("snATAC-seq cells") + NoLegend()
  208. DimPlot(atac.se, reduction = "umap", group.by="L3cluster", label = FALSE, repel = FALSE) + ggtitle("snATAC-seq cells") + NoLegend()
  209. DimPlot(rna.se, group.by = "ClusterName", label = TRUE, repel = TRUE, reduction="umap") + ggtitle("scRNA-seq cells") + NoLegend()
  210. DimPlot(rna.se, group.by = "ClusterName", label = FALSE, repel = FALSE, reduction="umap") + ggtitle("scRNA-seq cells") + NoLegend()
  211. dev.off()
  213. ##################################
  214. ## co-embedding
  215. ##################################
  216. coembed <- merge(x = rna.se, y = atac.se)
  217. rm(rna.se)
  218. rm(atac.se)
  220. # Finally, we run PCA and UMAP on this combined object, to visualize the co-embedding of both
  221. # datasets
  222. coembed <- ScaleData(coembed, features = variable.genes, do.scale = FALSE)
  223. coembed <- RunPCA(coembed, features = variable.genes, verbose = FALSE)
  224. coembed <- RunUMAP(coembed, dims = 1:20)
  225. coembed <- FindNeighbors(coembed, dims = 1:20)
  226. coembed <- FindClusters(coembed, resolution = 0.5)
  228. coembedF <- coembed[[]]
  229. idents=Idents(coembed)
  230. coembedF <- data.frame(coembedF, coembed.idents=idents)
  231. fwrite(coembedF, paste(outF, "coembed.seObj.meta.tsv", sep="."), sep="\t", quote=F, col.names=T, row.names=T)
  232. saveRDS(coembed, file=paste(outF,"coembed.rds", sep="."))
  234. coor_umap <- coembed@reductions$[email hidden]
  235. meta_coembed <- coembed[[c("ClusterName","cluster", "L3Color")]]
  236. n <- length(unique(meta_coembed$ClusterName[!is.na(meta_coembed$ClusterName)]))
  237. cluster_color <- colorRampPalette(brewer.pal(8, "Set1"))(n)
  238. color_map <- setNames(cluster_color, unique(meta_coembed$ClusterName[!is.na(meta_coembed$ClusterName)]))
  239. meta_coembed$cluster_color <- color_map[match(meta_coembed$ClusterName, names(color_map))]
  241. meta_coembed <- cbind(meta_coembed, coor_umap)
  242. meta_coembed[is.na(meta_coembed$L3Color), "L3Color"] <- "#e6e6e6"
  243. meta_coembed[is.na(meta_coembed$cluster_color), "cluster_color"] <- "#e6e6e6"
  245. pdf(paste(outF, "coembed.pdf", sep="."), width=6, height = 6)
  246. DimPlot(coembed, group.by = "tech", cols=c("red", "black"))
  248. cols_map <- setNames(unique(meta_coembed$cluster_color), unique(meta_coembed$ClusterName))
  249. DimPlot(coembed, group.by = "ClusterName", label = FALSE, repel = FALSE, reduction="umap", cols=cols_map) + ggtitle("coembed: scRNA") + NoLegend()
  250. DimPlot(rev(coembed), group.by = "ClusterName", label = TRUE, repel = TRUE, reduction="umap", cols=cols_map) + ggtitle("coembed: snRNA") + NoLegend()
  252. cols_map <- setNames(unique(meta_coembed$L3Color), unique(meta_coembed$cluster))
  253. DimPlot(coembed, group.by = "cluster", label = FALSE, repel = FALSE, reduction="umap", cols=cols_map) + ggtitle("coembed: snATAC") + NoLegend()
  254. DimPlot(rev(coembed), group.by = "cluster", label = TRUE, repel = TRUE, reduction="umap", cols=cols_map) + ggtitle("coembed: snATAC") + NoLegend()
  256. plot(rev(meta_coembed$UMAP_1), rev(meta_coembed$UMAP_2), pch=19, cex=0.2, col=rev(meta_coembed$cluster_color))
  257. plot(meta_coembed$UMAP_1, meta_coembed$UMAP_2, pch=19, cex=0.2, col=meta_coembed$L3Color)
  258. dev.off()

snapATAC.match2rna.mouseBrain.R at commit 2b62147, under MIT · at the source

Overview

Authors: Wei He1, Weizhen Hou1, Chengyong Jiang1,2, Xiuxiu Zhang1, Yaoyi Wang1, Yuqiu Zhou1, Kevin T. Gobeske3, Shaojie Ma3, Jiacheng Fan1, Haiyang Wang1, Peilong Li1, Wenbin Xie1,2, Biao Yan1,2, Nenad Sestan3,4, Liang Chen1,5, Jiayi Zhang1,2, Ying Zhu1
  1. Department of Neurosurgery, Huashan Hospital, State Key Laboratory of Brain Function and Disorders, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University,Shanghai, 200032 China
  2. Institute for Medical and Engineering Innovation, Eye & ENT Hospital, Fudan University,Shanghai, 200031 China
  3. Department of Neuroscience, Yale School of Medicine,New Haven, CT 06520 USA
  4. Departments of Psychiatry, Genetics and Comparative Medicine, Wu Tsai Institute, Program in Cellular Neuroscience, Neurodegeneration and Repair, Yale Child Study Center, and Kavli Institute for Neuroscience, Yale School of Medicine,New Haven, CT 06510 USA
  5. Tianqiao and Chrissy Chen Institute Clinical Translational Research Center, Shanghai, 200040 China
Institutions: Frontiers Center for Brain Science of the Ministry of Education (China); Fudan University (China); Yale University (United States)
Journal: Genome biology, volume 27, issue 1, article 269
Dates: received 21 December 2024; accepted 25 June 2026; published online 29 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s13059-026-04177-w · PMID 42374471 · PMCID PMC13501727 · OpenAlex W7166524253
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), non-human primate (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Connectivity, Machine learning, Single-unit activity, calcium imaging
MeSH: Evolution, Molecular*, Neocortex*, Animals, DNA Transposable Elements, Genome, Human, Genomics, Humans, Macaca, Mice, Neurons, Transcription Factors, Transcriptome (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Key R&D Program of China (2023YFF1204802); STI2030-Major Projects (2021ZD0200100); National Natural Science Foundation of China (32570650, 82272116, 82588301); Shanghai Science and Technology Commission Program (24JS2810100); Shanghai Municipal Science and Technology Key Project (20Z11900100); MOST of China (2022ZD0208604); Key Research and Development Program of Ningxia (2022BEG02046); Sanming Project of Medicine in Shenzhen (SZSM202011015); he National Natural Science Foundation of China (T2325008)
Citations: not cited yet (Europe PMC); 118 references in the paper
Research resources: RRID:SCR_002001

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 27 matches between paragraphs and lines of code.

JCVenterInstitute/NSForest

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 4aecd78e7499ed0858889d17d104ea7d9d1ce079, 14 September 2026
Languages: Python (16), Jupyter (3)
Size: 78 files, 19 scripts
Software Heritage: not archived
Found in: the text, “Construction of hierarchical cell taxonomy based”
Holds: README, license file, environment (pyproject.toml, docs/requirements.txt), documentation, 3 notebooks
Not found: CITATION.cff, tests, continuous integration
Tools: pandas (13 files), Scanpy (9 files), Matplotlib (4 files), NumPy (4 files), scikit-learn (2 files), anndata (1 file), Plotly (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
21 files
At the source: github.com/JCVenterInstitute/NSForest

yal054/snATACutils

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 2b62147e1378a6a1935ebc7fb1129320f4eb8754, 24 July 2024
Languages: R (48), Python (8), Shell (3), Perl (1)
Size: 180 files, 60 scripts
Software Heritage: not archived
Found in: “Identification of reproducible ATAC peaks”
Holds: README, license file, 2 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: data.table (29 files), ggplot2 (14 files), UMAP (9 files), Seurat (8 files), NumPy (7 files), pheatmap (7 files), reshape2 (7 files), Matplotlib (5 files), SciPy (5 files), scikit-learn (4 files), pandas (3 files), igraph (2 files), Monocle 3 (2 files), pysam (2 files), tidyverse (2 files), circlize (1 file), ComplexHeatmap (1 file), reticulate (1 file), Snakemake (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
62 files
At the source: github.com/yal054/snATACutils

FduZhuLab/spatial_and_snMultiome

License: MIT
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Evidence: files inventoried
Commit: 320ca72bf9356280f42d9a67fd979fcee79d155b, 7 June 2026
Languages: Shell (34), R (16), Python (10), Jupyter (10)
Size: 91 files, 70 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, 10 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (18 files), pandas (18 files), tidyverse (11 files), Matplotlib (10 files), seaborn (9 files), Seurat (9 files), Scanpy (7 files), scikit-learn (6 files), UMAP (6 files), Pillow (5 files), SciPy (5 files), data.table (3 files), OpenCV (3 files), anndata (2 files), ggplot2 (2 files), ggpubr (2 files), rstatix (2 files), SAMtools (2 files), Squidpy (2 files), AllenSDK (1 file), BEDTools (1 file), reticulate (1 file), TensorFlow (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
72 files
At the source: github.com/FduZhuLab/spatial_and_snMultiome

Zenodo 20581795

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (18 files), pandas (18 files), tidyverse (11 files), Matplotlib (10 files), seaborn (9 files), Seurat (9 files), Scanpy (7 files), scikit-learn (6 files), UMAP (6 files), Pillow (5 files), SciPy (5 files), data.table (3 files), OpenCV (3 files), anndata (2 files), ggplot2 (2 files), ggpubr (2 files), rstatix (2 files), SAMtools (2 files), Squidpy (2 files), AllenSDK (1 file), BEDTools (1 file), reticulate (1 file), TensorFlow (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
72 files
At the source: doi.org/10.5281/zenodo.20581795

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:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 219 scripts, each with its path and the digest of its content;
  • 27 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

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/10.1186/s13059-026-04177-w.

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, 17 authors, 12 MeSH terms, 9 funders, 112 references, 1 RRID.

Cite

This paper

He, W., Hou, W., Jiang, C., Zhang, X., Wang, Y., Zhou, Y., Gobeske, K. T., Ma, S., Fan, J., Wang, H., Li, P., Xie, W., Yan, B., Sestan, N., Chen, L., Zhang, J., & Zhu, Y. (2026). Genomic sequence evolution underlying human neocortical interareal diversification. Genome biology, 27(1), 269. https://doi.org/10.1186/s13059-026-04177-w

BibTeX

@article{he2026genomic,
author = {He, Wei and Hou, Weizhen and Jiang, Chengyong and Zhang, Xiuxiu and Wang, Yaoyi and Zhou, Yuqiu and Gobeske, Kevin T. and Ma, Shaojie and Fan, Jiacheng and Wang, Haiyang and Li, Peilong and Xie, Wenbin and Yan, Biao and Sestan, Nenad and Chen, Liang and Zhang, Jiayi and Zhu, Ying},
title = {{Genomic sequence evolution underlying human neocortical interareal diversification}},
journal = {Genome biology},
year = {2026},
month = jun,
volume = {27},
number = {1},
pages = {269},
publisher = {BMC},
issn = {1474-7596},
doi = {10.1186/s13059-026-04177-w},
url = {https://doi.org/10.1186/s13059-026-04177-w},
pmid = {42374471},
pmcid = {PMC13501727}
}

RIS

TY - JOUR
AU - He, Wei
AU - Hou, Weizhen
AU - Jiang, Chengyong
AU - Zhang, Xiuxiu
AU - Wang, Yaoyi
AU - Zhou, Yuqiu
AU - Gobeske, Kevin T.
AU - Ma, Shaojie
AU - Fan, Jiacheng
AU - Wang, Haiyang
AU - Li, Peilong
AU - Xie, Wenbin
AU - Yan, Biao
AU - Sestan, Nenad
AU - Chen, Liang
AU - Zhang, Jiayi
AU - Zhu, Ying
TI - Genomic sequence evolution underlying human neocortical interareal diversification
T2 - Genome biology
J2 - Genome Biol
PY - 2026
DA - 2026/06/29
VL - 27
IS - 1
SP - 269
SN - 1474-7596
PB - BMC
DO - 10.1186/s13059-026-04177-w
UR - https://doi.org/10.1186/s13059-026-04177-w
LA - en
ER -

CSL-JSON

{
"id": "10.1186/s13059-026-04177-w",
"type": "article-journal",
"title": "Genomic sequence evolution underlying human neocortical interareal diversification",
"container-title": "Genome biology",
"author": [
{
"family": "He",
"given": "Wei"
},
{
"family": "Hou",
"given": "Weizhen"
},
{
"family": "Jiang",
"given": "Chengyong"
},
{
"family": "Zhang",
"given": "Xiuxiu"
},
{
"family": "Wang",
"given": "Yaoyi"
},
{
"family": "Zhou",
"given": "Yuqiu"
},
{
"family": "Gobeske",
"given": "Kevin T."
},
{
"family": "Ma",
"given": "Shaojie"
},
{
"family": "Fan",
"given": "Jiacheng"
},
{
"family": "Wang",
"given": "Haiyang"
},
{
"family": "Li",
"given": "Peilong"
},
{
"family": "Xie",
"given": "Wenbin"
},
{
"family": "Yan",
"given": "Biao"
},
{
"family": "Sestan",
"given": "Nenad"
},
{
"family": "Chen",
"given": "Liang"
},
{
"family": "Zhang",
"given": "Jiayi"
},
{
"family": "Zhu",
"given": "Ying"
}
],
"container-title-short": "Genome Biol",
"volume": "27",
"issue": "1",
"page": "269",
"DOI": "10.1186/s13059-026-04177-w",
"PMID": "42374471",
"PMCID": "PMC13501727",
"ISSN": "1474-7596",
"publisher": "BMC",
"URL": "https://doi.org/10.1186/s13059-026-04177-w",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
29
]
]
}
}

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

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