OSCR

A multi-center cross-platform single-cell multimodal atlas of the mouse cerebral cortex.

Code ↔ Paper

19 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 19 matches
  1. [1] § Methods › Gene activity scoring of scATAC-seq data ↔ ATAC-code.R, lines 56–103 · score 0.94 · gene activity matrix, FindTransferAnchors, TransferData, NormalizeData, RunPCA, ScaleData
  2. [2] § Methods › scATAC-seq data processing ↔ ATAC-code.R, lines 1–54 · score 0.94 · peak region fragments, blacklist ratio, FindTopFeatures, min.cutoff, Quality control, SVD
  3. [3] § Methods › Chromatin co-accessibility analysis ↔ ATAC-code.R, lines 443–483 · score 0.89 · generate_ccans, run_cicero, sample_num, co accessibility, target genes, genome
  4. [4] § Methods › RNA-seq data preprocessing and integration ↔ RNA-code.R, lines 48–90 · score 0.89 · LogNormalize, scale.factor, FindVariableFeatures, ScaleData, log normalization, vst
  5. [5] § Methods › RNA-seq data preprocessing and integration ↔ RNA-code.R, lines 92–134 · score 0.81 · min.dist, FindClusters, FindNeighbors, RunUMAP, resolution, dims
  6. [6] § Data Records ↔ RNA-code.R, lines 1–46 · score 0.76 · nCount_RNA, HB_percent, nFeature_RNA, percent.mt, quality control, genes
  7. [7] § Methods › Identification of cell-type-specific regulatory regions ↔ ATAC-code.R, lines 338–391 · score 0.76 · ClosestFeature, CoveragePlot, FindAllMarkers, LR, threshold, assay
  8. [8] § Methods › scATAC-seq cell-type annotation ↔ ATAC-code.R, lines 56–103 · score 0.76 · FindTransferAnchors, TransferData, scRNA, query, prediction, LSI
  9. [9] § Methods › RNA-seq data preprocessing and integration ↔ ATAC-code.R, lines 1–54 · score 0.73 · FindClusters, FindNeighbors, RunUMAP, algorithm, resolution, dims
  10. [10] § Technical Validation ↔ RNA-code.R, lines 1–46 · score 0.71 · nCount_RNA, HB_percent, nFeature_RNA, percent.mt, Quality control, genes
  11. [11] § Data Records ↔ ATAC-code.R, lines 566–603 · score 0.67 · nCount_peaks, orig.ident, TSS enrichment, pct, atac
  12. [12] § Methods › Differential expression and enrichment analysis ↔ RNA-code.R, lines 136–183 · score 0.67 · clusterProfiler, GO terms, db, simplification, mm, enrichment
  13. [13] § Technical Validation ↔ ATAC-code.R, lines 240–272 · score 0.60 · microglial cells, inhibitory neurons, excitatory neurons, astrocytes, oligodendrocytes
  14. [14] § Methods › RNA-seq data preprocessing and integration ↔ RNA-code.R, lines 48–90 · score 0.59 · RunPCA, Harmony integration, Elbow, variable, RNA, cell
  15. [15] § Technical Validation ↔ RNA-code.R, lines 244–300 · score 0.57 · inhibitory neurons, excitatory neurons, divergence, evolutionary, humans, species
  16. [16] § Technical Validation ↔ ATAC-code.R, lines 240–272 · score 0.55 · oligodendrocyte precursor cells, excitatory neurons, probability, inhibitory, astrocytes
  17. [17] § Methods › scATAC-seq cell-type annotation ↔ ATAC-code.R, lines 206–238 · score 0.55 · probability matrix, annotation transition, row, Dropsn, Dropsc
  18. [18] § Technical Validation ↔ RNA-code.R, lines 244–300 · score 0.53 · inhibitory neurons, excitatory neurons, classic, species, astrocytes, oligodendrocytes
  19. [19] § Methods › Cell type annotation and cross-dataset cross-validation ↔ RNA-code.R, lines 136–183 · score 0.51 · AggregateExpression, CPM, enriched, assay, matrices, RNA

Paper

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The authors' code

R · 603 lines · 18 KB · CC-BY-4.0 · 10 matches

  1. #Quality Control----
  2. brain <- NucleosomeSignal(object = brain)
  3. head(brain)
  4. brain$nucleosome_group <- ifelse(brain$nucleosome_signal > 4, 'NS > 4', 'NS < 4')
  5. FragmentHistogram(object = brain, group.by = 'nucleosome_group', region = 'chr1-1-10000000')
  7. brain <- TSSEnrichment(brain)
  8. head(brain)
  9. brain$pct_reads_in_peaks <- brain$peak_region_fragments / brain$passed_filters * 100
  10. brain$blacklist_ratio <- brain$blacklist_region_fragments / brain$peak_region_fragments
  11. VlnPlot(
  12. object = brain,
  13. features = c('pct_reads_in_peaks', 'peak_region_fragments',
  14. 'TSS.enrichment', 'blacklist_ratio', 'nucleosome_signal'),
  15. pt.size = 0.1,
  16. ncol = 5
  17. )
  19. brain <- subset(
  20. x = brain,
  21. subset =
  22. peak_region_fragments > 3000 &
  23. peak_region_fragments < 100000 &
  24. pct_reads_in_peaks > 40 &
  25. blacklist_ratio < 0.025
  26. )
  28. #Normalization----
  29. brain <- RunTFIDF(brain)
  30. brain <- FindTopFeatures(brain, min.cutoff = 'q0')
  31. brain <- RunSVD(object = brain)
  32. pdf("LSI_component.pdf")
  33. DepthCor(brain)
  34. dev.off()
  36. #Clustering----
  37. brain <- RunUMAP(
  38. object = brain,
  39. reduction = 'lsi',
  40. dims = 2:30
  41. )
  42. brain <- FindNeighbors(
  43. object = brain,
  44. reduction = 'lsi',
  45. dims = 2:30
  46. )
  47. brain <- FindClusters(
  48. object = brain,
  49. algorithm = 3,
  50. resolution = 1.2,
  51. verbose = FALSE
  52. )
  54. DimPlot(object = brain, label = TRUE) + NoLegend()
  56. #Create Gene Activity Matrix----
  57. DefaultAssay(brain) <- "peaks"
  58. gene.activities <- GeneActivity(brain, features = VariableFeatures(pbmc.rna))
  59. brain[['RNA']] <- CreateAssayObject(counts = gene.activities)
  60. DefaultAssay(brain) <- 'RNA'
  61. brain <- NormalizeData(
  62. object = brain,
  63. assay = 'RNA',
  64. )
  65. brain <- FindVariableFeatures(brain, selection.method = "vst", nfeatures = 2000)
  66. brain <- ScaleData(brain, features = rownames(brain))
  67. brain <- RunPCA(brain)
  69. FeaturePlot(
  70. object = brain,
  71. features = c('Sst','Pvalb',"Gad2","Neurod6","Rorb","Syt6"),
  72. pt.size = 0.1,
  73. max.cutoff = 'q95',
  74. ncol = 3
  75. )
  77. #scRNA-seq Annotation----
  78. setwd("~/R/cortex_total/dropsc")
  79. setwd("~/R/cortex_total/ATAC")
  80. pbmc.rna <- readRDS("merge_scRNAlist.rds")
  81. table(pbmc.rna$sub.cluster)
  82. p1 <- DimPlot(pbmc.rna, group.by = "sub.cluster", label = TRUE) + NoLegend() + ggtitle("RNA")
  83. p2 <- DimPlot(brain, group.by = "orig.ident", label = FALSE) + NoLegend() + ggtitle("ATAC")
  84. p1 + p2
  86. DefaultAssay(brain) <- "RNA"
  87. transfer.anchors <- FindTransferAnchors(reference = pbmc.rna,
  88. query = brain,
  89. reference.reduction = "pca",
  90. dims = 1:30)
  91. predicted.labels <- TransferData(
  92. anchorset = transfer.anchors,
  93. refdata = pbmc.rna$sub.cluster,
  94. weight.reduction = brain[["lsi"]],
  95. dims = 2:30
  96. )
  98. brain <- AddMetaData(object = brain, metadata = predicted.labels)
  99. table(brain$predicted.id)
  101. brain$'dropsc' <- brain$predicted.id
  102. head(brain)
  103. table(brain$'10xsc', brain$dropsn, brain$'10xsn')
  105. #Filtering----
  106. Idents(brain) = "dropsc"
  107. brain <- subset(brain, idents = setdiff(unique(Idents(brain)), c("Pericytes","endothelial cells")))
  109. #Save Data----
  110. setwd("~/R/cortex_total/ATAC/filtered_analysis")
  111. setwd("~/R/cortex_total/ATAC")
  112. brain <- readRDS("brain.rds")
  113. saveRDS(brain, "brain.rds")
  115. #Annotation Comparison Plot----
  116. data <- data.frame(
  117. dropsn = brain$dropsn,
  118. dropsc = brain$dropsc,
  119. X10xsc = brain$`10xsc`,
  120. X10xsn = brain$`10xsn`,
  121. brain@reductions$[email hidden]
  122. )
  123. head(data)
  124. library(ggplot2)
  125. library(ggforce)
  126. library(dplyr)
  127. library(tidyr)
  128. library(patchwork)
  129. library(ggsci)
  131. methods <- c("dropsn","dropsc","X10xsc","X10xsn")
  132. all_celltypes <- unique(unlist(data[c("dropsn", "dropsc", "X10xsc", "X10xsn")]))
  133. color_palette <- viridis::viridis_pal(option = "plasma")(length(all_celltypes))
  134. names(color_palette) <- all_celltypes
  136. plots <- list()
  137. for (method in methods) {
  138. method_data <- data %>%
  139. mutate(cell_id = rownames(data)) %>%
  140. select(umap_1, umap_2, celltype = !!method, cell_id)
  142. method_centroids <- method_data %>%
  143. group_by(celltype) %>%
  144. summarise(
  145. centroid_x = median(umap_1),
  146. centroid_y = median(umap_2),
  147. .groups = "drop"
  148. )
  150. other_annotations <- data %>%
  151. mutate(cell_id = rownames(data)) %>%
  152. select(cell_id, all_of(methods)) %>%
  153. pivot_longer(cols = -cell_id, names_to = "annotation_method", values_to = "annotated_celltype") %>%
  154. filter(annotation_method != method)
  156. conflict_data <- method_data %>%
  157. left_join(other_annotations, by = "cell_id") %>%
  158. filter(celltype != annotated_celltype)
  160. sample_size_per_group <- conflict_data %>%
  161. group_by(annotated_celltype) %>%
  162. summarise(n = n(), .groups = "drop") %>%
  163. mutate(sample_n = pmin(100, n))
  165. connection_data <- conflict_data %>%
  166. group_by(annotated_celltype) %>%
  167. sample_n(size = min(100, n()), replace = FALSE) %>%
  168. ungroup() %>%
  169. left_join(method_centroids, by = c("annotated_celltype" = "celltype"))
  171. p <- ggplot() +
  172. geom_point(
  173. data = method_data,
  174. aes(x = umap_1, y = umap_2, color = celltype),
  175. size = 0.4, alpha = 0.6
  176. ) +
  177. geom_segment(
  178. data = connection_data,
  179. aes(x = umap_1, y = umap_2,
  180. xend = centroid_x, yend = centroid_y,
  181. color = annotated_celltype),
  182. linewidth = 0.3, alpha = 0.5
  183. ) +
  184. geom_point(
  185. data = method_centroids,
  186. aes(x = centroid_x, y = centroid_y, fill = celltype),
  187. shape = 23, size = 4, color = "black", stroke = 0.8
  188. ) +
  189. scale_color_manual(values = color_palette, name = "Celltype") +
  190. scale_fill_manual(values = color_palette, name = "Celltype") +
  191. labs(title = paste("Method:", method)) +
  192. theme_void() +
  193. theme(
  194. plot.title = element_text(hjust = 0.5, face = "bold")
  195. )
  196. plots[[method]] <- p
  197. }
  199. pdf("annotation_comparison.pdf", width = 16, height = 12)
  200. combined_plot <- plots[[1]] + plots[[2]] + plots[[3]] + plots[[4]] +
  201. plot_layout(ncol = 2, guides = "collect") +
  202. plot_annotation(theme = theme(plot.title = element_text(hjust = 0.5, face = "bold")))
  203. print(combined_plot)
  204. dev.off()
  206. #Probability Matrix----
  207. calculate_annotation_transition_matrix <- function(data) {
  208. methods <- c("dropsn", "dropsc", "X10xsc", "X10xsn")
  209. all_celltypes <- unique(unlist(data[methods]))
  211. transition_matrix <- matrix(0, nrow = length(all_celltypes), ncol = length(all_celltypes))
  212. rownames(transition_matrix) <- all_celltypes
  213. colnames(transition_matrix) <- all_celltypes
  215. data$true_celltype <- apply(data[methods], 1, function(x) {
  216. tbl <- table(x)
  217. names(tbl)[which.max(tbl)]
  218. })
  220. for (i in 1:nrow(data)) {
  221. true_type <- data$true_celltype[i]
  222. annotations <- unlist(data[i, methods])
  223. annotation_counts <- table(annotations)
  225. for (annotated_type in names(annotation_counts)) {
  226. if (annotated_type %in% all_celltypes) {
  227. transition_matrix[true_type, annotated_type] <- transition_matrix[true_type, annotated_type] + annotation_counts[annotated_type]
  228. }
  229. }
  230. }
  232. probability_matrix <- transition_matrix / rowSums(transition_matrix)
  234. return(list(
  235. count_matrix = transition_matrix,
  236. probability_matrix = probability_matrix
  237. ))
  238. }
  240. transition_results <- calculate_annotation_transition_matrix(data)
  241. transition_results[[2]]
  243. library(ComplexHeatmap)
  244. library(circlize)
  246. prob_matrix <- transition_results[[2]]
  247. new_order <- c("Excitatory neurons", "Inhibitory neurons",
  248. "Astrocytes", "Oligodendrocytes", "Oligodendrocyte Precursor Cells",
  249. "Microglial cells")
  250. prob_matrix <- prob_matrix[new_order, new_order]
  252. col_fun <- colorRamp2(c(0, 0.5, 1), viridis::viridis_pal(option = "plasma")(3))
  254. pdf("probability_heatmap.pdf")
  255. Heatmap(prob_matrix,
  256. name = "probability",
  257. col = col_fun,
  258. cluster_rows = F,
  259. cluster_columns = F,
  260. row_names_side = "left",
  261. column_names_side = "top",
  262. row_names_gp = gpar(fontsize = 10),
  263. column_names_gp = gpar(fontsize = 10),
  264. cell_fun = function(j, i, x, y, width, height, fill) {
  265. if(prob_matrix[i, j] >= 0.01) {
  266. grid.text(sprintf("%.1f%%", prob_matrix[i, j] * 100), x, y,
  267. gp = gpar(fontsize = 8))
  268. }
  269. },
  270. heatmap_legend_param = list(legend_height = unit(4, "cm")),
  271. column_title_side = "bottom")
  272. dev.off()
  274. #UMAP Visualization----
  275. library(Seurat)
  276. library(tidyverse)
  277. library(ggrepel)
  278. library(scattermore)
  280. pbmc <- brain
  281. head([email hidden])
  282. dim(pbmc)
  283. umap = pbmc@reductions$[email hidden] %>%
  284. as.data.frame() %>%
  285. cbind(cell_type = [email hidden]$final_annotation)
  287. umap$UMAP_1 <- umap$umap_1
  288. umap$UMAP_2 <- umap$umap_2
  289. allcolour = viridis::viridis_pal(option = "plasma")(6)
  290. p <- ggplot(umap, aes(x = UMAP_1, y = UMAP_2, color = cell_type)) +
  291. geom_point(size = 0.1, alpha = 1) + scale_color_manual(values = allcolour)
  293. p2 <- p +
  294. theme(panel.grid.major = element_blank(),
  295. panel.grid.minor = element_blank(),
  296. panel.border = element_blank(),
  297. axis.title = element_blank(),
  298. axis.text = element_blank(),
  299. axis.ticks = element_blank(),
  300. panel.background = element_rect(fill = 'white'),
  301. plot.background = element_rect(fill = "white"))
  303. p3 <- p2 +
  304. theme(
  305. legend.title = element_blank(),
  306. legend.key = element_rect(fill = 'white'),
  307. legend.text = element_text(size = 20),
  308. legend.key.size = unit(1, 'cm')
  309. ) +
  310. guides(color = guide_legend(override.aes = list(size = 5)))
  312. p4 <- p3 +
  313. geom_segment(aes(x = min(umap$UMAP_1), y = min(umap$UMAP_2),
  314. xend = min(umap$UMAP_1) + 3, yend = min(umap$UMAP_2)),
  315. colour = "black", size = 1, arrow = arrow(length = unit(0.3, "cm"))) +
  316. geom_segment(aes(x = min(umap$UMAP_1), y = min(umap$UMAP_2),
  317. xend = min(umap$UMAP_1), yend = min(umap$UMAP_2) + 3),
  318. colour = "black", size = 1, arrow = arrow(length = unit(0.3, "cm"))) +
  319. annotate("text", x = min(umap$UMAP_1) + 1.5, y = min(umap$UMAP_2) - 1, label = "UMAP_1",
  320. color = "black", size = 3, fontface = "bold") +
  321. annotate("text", x = min(umap$UMAP_1) - 1, y = min(umap$UMAP_2) + 1.5, label = "UMAP_2",
  322. color = "black", size = 3, fontface = "bold", angle = 90) +
  323. theme(legend.position = "none")
  325. cell_type_med <- aggregate(cbind(UMAP_1, UMAP_2) ~ cell_type,
  326. data = umap,
  327. FUN = median)
  329. p5 <- p3 +
  330. geom_label_repel(aes(label = cell_type), fontface = "bold", data = cell_type_med, size = 2,
  331. point.padding = unit(0.5, "lines")) +
  332. theme(legend.position = "none")
  334. pdf("umap_cell.pdf", width = 5, height = 5)
  335. p5
  336. dev.off()
  338. #Specific Gene Peak Visualization----
  339. library(ArchR)
  340. library(reshape2)
  341. head(brain)
  342. Idents(brain) <- "final_annotation"
  343. idents.plot <- names(which(table(Idents(brain)) > 50))
  344. brain <- SortIdents(brain)
  345. DefaultAssay(brain) <- "peaks"
  346. pdf("gene_peaks.pdf")
  347. CoveragePlot(
  348. object = brain,
  349. region = c("Neurod6", "Gad2"),
  350. idents = idents.plot,
  351. extend.upstream = 1000,
  352. extend.downstream = 1000,
  353. ncol = 1)
  354. dev.off()
  356. #Differentially Accessible Regions Analysis----
  357. DefaultAssay(brain) <- "peaks"
  358. Idents(brain) <- "predicted.id"
  359. cellType_DARs <- FindAllMarkers(
  360. object = brain,
  361. test.use = "LR",
  362. logfc.threshold = 0.25,
  363. min.pct = 0.25,
  364. verbose = FALSE
  365. )
  367. head(cellType_DARs)
  368. cellType_DARs <- cellType_DARs %>%
  369. dplyr::filter(p_val_adj <= 0.05 | avg_log2FC > 0.8) %>%
  370. dplyr::group_by(cluster) %>%
  371. dplyr::top_n(n = 100, wt = avg_log2FC) %>%
  372. dplyr::ungroup()
  374. cellType_DARs$peak_id <- rownames(cellType_DARs)
  375. cf <- ClosestFeature(
  376. object = brain,
  377. regions = cellType_DARs$peak_id
  378. )
  380. cellType_DARs <- cellType_DARs %>%
  381. left_join(
  382. dplyr::select(as.data.frame(cf), peak_id = query_region, gene_name, gene_biotype, type, distance),
  383. by = "peak_id"
  384. )
  386. cat("Significant DARs by cell type:", nrow(cellType_DARs), "\n")
  388. #Gene Average Accessibility Heatmap----
  389. library(ggh4x)
  390. mouse_genes <- unique(cellType_DARs$gene_name)[1]
  391. object <- brain
  393. Stack_CoveragePlot <- function(object, mouse_genes, cols, extend = 1000) {
  394. gene_peaks <- cellType_DARs %>%
  395. dplyr::filter(gene_name %in% mouse_genes) %>%
  396. dplyr::group_by(gene_name) %>%
  397. top_n(1, wt = avg_log2FC) %>%
  398. pull(peak_id)
  400. p_list <- lapply(gene_peaks, function(p) {
  401. CoveragePlot(
  402. object = object,
  403. region = p,
  404. extend.upstream = extend,
  405. extend.downstream = extend,
  406. group.by = "predicted.id",
  407. cols = cols,
  408. verbose = FALSE,
  409. annotation = FALSE,
  410. peaks = FALSE
  411. )[[1]]$data %>%
  412. mutate(peak = p)
  413. })
  414. df <- do.call(rbind, p_list)
  415. df <- df %>%
  416. left_join(
  417. dplyr::select(cellType_DARs, peak = peak_id, gene_name),
  418. by = "peak"
  419. )
  421. pdf("stack_coverage.pdf")
  422. ggplot(df, aes(x = position, y = coverage, fill = assay_group)) +
  423. geom_area(show.legend = TRUE) +
  424. geom_vline(xintercept = 0, linetype = "dashed", color = "red", alpha = 0.7) +
  425. facet_grid2(assay_group ~ gene_name, switch = "y", scales = "free_x", independent = "x") +
  426. theme_bw() +
  427. theme(
  428. panel.spacing.x = unit(0.2, "mm"),
  429. panel.spacing.y = unit(-0.1, "mm"),
  430. axis.text.x = element_text(size = 6),
  431. axis.text.y = element_blank(),
  432. axis.title = element_blank(),
  433. panel.grid = element_blank(),
  434. strip.background = element_blank(),
  435. strip.text.y.left = element_text(angle = 0, hjust = 1, size = 7),
  436. legend.position = "bottom"
  437. ) +
  438. scale_fill_manual(values = cols, name = "Mouse Cell Type") +
  439. labs(caption = "Red dashed line: TSS")
  440. dev.off()
  441. }
  443. mouse_target_genes <- c("Cd3d", "Thy1", "Cd74", "Mitf", "Pecam1", "Tpm1", "Flt4", "Krt5")
  444. Stack_CoveragePlot(
  445. object = brain,
  446. mouse_genes = mouse_target_genes,
  447. cols = cols,
  448. extend = 1000
  449. )
  451. #Co-accessibility Analysis----
  452. setwd("~/R/cortex_total/ATAC/coaccessibility_analysis")
  453. library(Signac)
  454. library(Seurat)
  455. library(SeuratWrappers)
  456. library(ggplot2)
  457. library(patchwork)
  458. library(cicero)
  459. library(monocle3)
  461. save_monocle_objects(cds = cicero_cds,
  462. directory_path = "coaccessibility_analysis/",
  463. comment = "Cicero analysis intermediate results")
  464. cicero_cds <- load_monocle_objects(directory_path = "coaccessibility_analysis/")
  466. bone <- readRDS("brain.rds")
  467. DefaultAssay(bone) <- "peaks"
  468. bone.cds <- as.cell_data_set(x = bone)
  469. umap_coords <- reducedDims(bone.cds)$UMAP
  470. cicero_cds <- make_cicero_cds(bone.cds, reduced_coordinates = umap_coords)
  472. class(cicero_cds)
  473. genome <- seqlengths(bone)
  474. genome.df <- data.frame("chr" = names(genome), "length" = genome)
  475. conns <- run_cicero(cicero_cds, genomic_coords = genome.df, sample_num = 100)
  476. saveRDS(conns, "conns.rds")
  478. conns <- readRDS("conns.rds")
  479. ccans <- generate_ccans(conns)
  480. head(ccans)
  481. links <- ConnectionsToLinks(conns = conns, ccans = ccans)
  482. Links(bone) <- links
  483. saveRDS(bone, "~/R/cortex_total/ATAC/filtered_analysis/brain.rds")
  485. #Network Visualization----
  486. library(igraph)
  487. library(ggplot2)
  488. library(ggraph)
  489. library(tidygraph)
  491. group <- unique(ccans[, c(2, 3)])
  492. network_data <- group[, c("CCAN", "gene")]
  493. edges <- data.frame()
  494. unique_ccans <- unique(network_data$CCAN)
  496. for (ccan in unique_ccans) {
  497. genes_in_ccan <- network_data$gene[network_data$CCAN == ccan]
  498. if (length(genes_in_ccan) > 1) {
  499. gene_pairs <- t(combn(genes_in_ccan, 2))
  500. edges <- rbind(edges, data.frame(
  501. from = gene_pairs[, 1],
  502. to = gene_pairs[, 2],
  503. CCAN = ccan
  504. ))
  505. }
  506. }
  507. edges$weight <- 1
  508. unique_genes <- unique(c(edges$from, edges$to))
  509. nodes <- data.frame(
  510. id = 1:length(unique_genes),
  511. label = unique_genes
  512. )
  513. edges <- edges %>%
  514. left_join(nodes, by = c("from" = "label")) %>%
  515. rename(from_to = id)
  517. edges <- edges %>%
  518. left_join(nodes, by = c("to" = "label")) %>%
  519. rename(to_to = id)
  521. edges <- dplyr::select(edges, from_to, to_to, weight)
  523. net.tidy <- tbl_graph(
  524. nodes = nodes, edges = edges, directed = F
  525. )
  526. net.tidy
  527. net.tidy <- net.tidy %>%
  528. dplyr::filter(group_components() == which.max(table(group_components())))
  530. net_with_centrality <- net.tidy %>%
  531. activate(nodes) %>%
  532. mutate(
  533. degree = centrality_degree(),
  534. betweenness = centrality_betweenness(),
  535. closeness = centrality_closeness(),
  536. pagerank = centrality_pagerank()
  537. )
  539. pdf("network.pdf", width = 8, height = 6)
  540. plasma_colors <- viridis::viridis_pal(option = "plasma")(6)
  542. ggraph(net_with_centrality, layout = "fr") +
  543. geom_edge_link(width = 0.3,
  544. color = "grey70",
  545. alpha = 0.6, show.legend = FALSE) +
  546. geom_node_point(aes(size = betweenness, color = betweenness),
  547. alpha = 0.8) +
  548. geom_node_text(aes(label = ifelse(betweenness > quantile(betweenness, 0.8), label, "")),
  549. repel = TRUE, size = 4) +
  550. scale_size_continuous(range = c(3, 10), name = "Betweenness") +
  551. scale_color_gradientn(colors = plasma_colors, name = "Betweenness") +
  552. theme_void() +
  553. theme(plot.margin = margin(20, 20, 20, 20))
  555. dev.off()
  557. important_genes <- net_with_centrality %>%
  558. as_tibble() %>%
  559. dplyr::filter(betweenness > quantile(betweenness, 0.8)) %>%
  560. dplyr::select(label, degree, betweenness, closeness, pagerank) %>%
  561. arrange(desc(betweenness))
  562. print(important_genes$label)
  563. head(important_genes)
  564. saveRDS(important_genes, "important_genes.rds")
  566. #Quality Control Metrics----
  567. head(brain)
  568. library(ggplot2)
  569. library(tidyr)
  570. library(dplyr)
  572. atac_data_long <- [email hidden] %>%
  573. dplyr::select(orig.ident, nCount_peaks, TSS.enrichment, pct_reads_in_peaks) %>%
  574. pivot_longer(
  575. cols = c(nCount_peaks, TSS.enrichment, pct_reads_in_peaks),
  576. names_to = "metric",
  577. values_to = "value"
  578. )
  580. metric_labels <- c(
  581. "nCount_peaks" = "nCount_peaks",
  582. "TSS.enrichment" = "TSS.enrichment",
  583. "pct_reads_in_peaks" = "FRiP"
  584. )
  586. pdf("ATAC_QC_metrics.pdf", width = 8, height = 3)
  587. p1 <- ggplot(atac_data_long, aes(x = orig.ident, y = value, fill = orig.ident)) +
  588. geom_boxplot(outlier.size = 0.5, alpha = 0.9) +
  589. facet_wrap(~ metric, scales = "free_y", ncol = 3,
  590. labeller = labeller(metric = metric_labels)) +
  591. scale_fill_manual(values = colorRampPalette(c("#5F9EA0"))(12)) +
  592. labs(x = NULL, y = NULL) +
  593. theme_bw() +
  594. theme(
  595. axis.text.x = element_text(angle = 45, hjust = 1),
  596. axis.title.y = element_blank(),
  597. legend.position = "none",
  598. plot.title = element_text(hjust = 0.5),
  599. panel.grid = element_blank(),
  600. strip.text = element_text(size = 12)
  601. )
  602. print(p1)
  603. dev.off()

ATAC-code.R, under CC-BY-4.0 · at the source

Overview

Authors: Xuefeng Shi1, Zhihui Qi1, Hong Huang2, Zhiming Ye2, Yumin Wu1, Kahei Chan1, Maojin Yao3, Zhongxing Wang1
  1. The First Affiliated Hospital of Sun Yat-sen University, Department of Anesthesia, Guangzhou, Guangdong 510080 China
  2. Department of Thoracic Surgery and Oncology, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, Guangzhou, 510120 China
  3. Brain Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China, Department of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120 China
Institutions: The First Affiliated Hospital, Sun Yat-sen University (China); First Affiliated Hospital of Guangzhou Medical University (China); State Key Laboratory of Respiratory Disease (China); Sun Yat-sen University (China); Sun Yat-sen Memorial Hospital (China)
Journal: Scientific data, volume 13, issue 1, article 1072
Dates: received 24 November 2025; accepted 31 March 2026; published online 11 May 2026
Type: Data paper · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41597-026-07185-4 · PMID 42115229 · PMCID PMC13381758 · OpenAlex W7160844135
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), mouse (organism), methods / tools (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Graphs, Spectral & time-frequency
MeSH: Cerebral Cortex*, Single-Cell Analysis*, Animals, Mice, Sequence Analysis, RNA (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: the Basic and Applied Basic Research Foundation of Guangdong Province (Grant Nos. 2021A1515220042); National Natural Science Foundation of China (Grant Nos.82272224); Guangzhou National Laboratory - State Key Laboratory of Respiratory Disease (Guangzhou Medical University) Joint Funding Project 2024, "Study on the Mechanisms and Intervention Strategies for Small Cell Lung Cancer Development", Project No. GZNL2024B01004 (Grant Nos.GZNL2024B01004); National Natural Science Foundation of China, General Program, "Mechanism of Airway GFAP+ Glial Cells in Promoting Epithelial Barrier Repair Following Viral Infection" (Grant Nos.32470888); Natural Science Foundation of Guangdong Province (Grant Nos. 2022A1515012475)
Citations: cited by 1 paper (Europe PMC); 51 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 19 matches between paragraphs and lines of code.

figshare 30672836

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: R (2)
Size: 8 files, 2 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: circlize (2 files), ComplexHeatmap (2 files), ggplot2 (2 files), patchwork (2 files), Seurat (2 files), tidyverse (2 files), clusterProfiler (1 file), cowplot (1 file), data.table (1 file), edgeR (1 file), Harmony (1 file), igraph (1 file), Monocle 3 (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
2 files
At the source:

Code availability statement

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  • it points to the authors' code: figshare 30672836

Read it in the paper: doi.org/10.1038/s41597-026-07185-4.

Tracing map

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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;
  • 2 scripts, each with its path and the digest of its content;
  • 19 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

Other data links

Data availability statement

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

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41597-026-07185-4.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 5 MeSH terms, 5 funders, 50 references.

Cite

This paper

Shi, X., Qi, Z., Huang, H., Ye, Z., Wu, Y., Chan, K., Yao, M., & Wang, Z. (2026). A multi-center cross-platform single-cell multimodal atlas of the mouse cerebral cortex. Scientific data, 13(1), 1072. https://doi.org/10.1038/s41597-026-07185-4

BibTeX

@article{shi2026multi,
author = {Shi, Xuefeng and Qi, Zhihui and Huang, Hong and Ye, Zhiming and Wu, Yumin and Chan, Kahei and Yao, Maojin and Wang, Zhongxing},
title = {{A multi-center cross-platform single-cell multimodal atlas of the mouse cerebral cortex}},
journal = {Scientific data},
year = {2026},
month = may,
volume = {13},
number = {1},
pages = {1072},
publisher = {Nature Publishing Group},
issn = {2052-4463},
doi = {10.1038/s41597-026-07185-4},
url = {https://doi.org/10.1038/s41597-026-07185-4},
pmid = {42115229},
pmcid = {PMC13381758}
}

RIS

TY - JOUR
AU - Shi, Xuefeng
AU - Qi, Zhihui
AU - Huang, Hong
AU - Ye, Zhiming
AU - Wu, Yumin
AU - Chan, Kahei
AU - Yao, Maojin
AU - Wang, Zhongxing
TI - A multi-center cross-platform single-cell multimodal atlas of the mouse cerebral cortex
T2 - Scientific data
J2 - Sci Data
PY - 2026
DA - 2026/05/11
VL - 13
IS - 1
SP - 1072
SN - 2052-4463
PB - Nature Publishing Group
DO - 10.1038/s41597-026-07185-4
UR - https://doi.org/10.1038/s41597-026-07185-4
LA - en
ER -

CSL-JSON

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"title": "A multi-center cross-platform single-cell multimodal atlas of the mouse cerebral cortex",
"container-title": "Scientific data",
"author": [
{
"family": "Shi",
"given": "Xuefeng"
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{
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"page": "1072",
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"ISSN": "2052-4463",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41597-026-07185-4",
"language": "en",
"issued": {
"date-parts": [
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2026,
5,
11
]
]
}
}

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