OSCR

Postmortem brain single-nucleus and bulk gene expression analyses identify shared and distinct abnormalities in bipolar disorder and major depressive disorder.

Code ↔ Paper

7 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 7 matches
  1. [1] § Results › Consistency with results from snRNA-seq of the dlPFC ↔ HBCC_03_cellchat.R, lines 62–122 · score 0.95 · en l6 ct, EN_L6B, lamp5 lhx6, lamp5 reln, pvalb chc, en l2
  2. [2] § Results › Consistency with results from snRNA-seq of the dlPFC ↔ HBCC_01_preprocessing.R, lines 238–299 · score 0.95 · en l6 ct, EN_L6B, lamp5 lhx6, lamp5 reln, pvalb chc, en l2
  3. [3] § Results › Single-nucleus transcriptomic profiling and cell population of case and controls in the OFC ↔ OFCsnRNA_01_preprocessing.R, lines 401–478 · score 0.95 · ExN.L5ET, ExN.L2, ExN.L3, ExN.L6CT, ExN.L6IT, InN.Lamp5
  4. [4] § Results › Transcription factor network analysis reveals key regulatory relationships ↔ OFCsnRNA_05_hdWGCNA_mdd_bd_ExN.R, lines 307–367 · score 0.83 · regulon score, BCL11A, POU6F2, CREM, ETS2, FOXP2
  5. [5] § Results › Co-expression modules associated with translation and mitochondrial ATP production in neurons contribute to BD ↔ OFCsnRNA_05_hdWGCNA_mdd_bd_ExN.R, lines 183–241 · score 0.63 · module trait correlation, biological processes, component, cellular, RNA, Classification
  6. [6] § Materials and methods › Differential abundance (DA) analysis ↔ OFCsnRNA_02_subtype_milo.R, lines 1–82 · score 0.54 · sub clusters, single cell, DA, graphs
  7. [7] § Results › Dysfunction of parvalbumin interneurons and hyperactivity of excitatory neurons in BD and MDD ↔ OFCsnRNA_04_cellchat_bd_mdd.R, lines 61–118 · score 0.51 · ExN.L5, InN.Pvalb, Ch, Ba, signaling, pathways

Paper

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The authors' code

R · 605 lines · 18 KB · no license · 2 matches

  1. setwd("")
  3. library(Seurat)
  4. library(tidyverse)
  5. library(cowplot)
  6. library(patchwork)
  7. library(WGCNA)
  8. library(hdWGCNA)
  10. theme_set(theme_cowplot())
  11. set.seed(12345)
  12. enableWGCNAThreads(nThreads = 8)
  14. seurat_obj <- readRDS("")
  16. seurat_obj <- SetupForWGCNA(seurat_obj, gene_select = "fraction", fraction = 0.05, wgcna_name = "exp_sc")
  18. seurat_obj <- MetacellsByGroups(seurat_obj = seurat_obj, group.by = c("cell_type_1", "Donor"), k = 25, max_shared = 10, ident.group = "cell_type_1")
  20. seurat_obj <- NormalizeMetacells(seurat_obj)
  22. seurat_obj <- SetDatExpr(seurat_obj, group_name = "ExN", group.by = "cell_type_1", assay = 'RNA', layer = 'data')
  24. seurat_obj <- TestSoftPowers(seurat_obj, networkType = 'signed')
  26. plot_list <- PlotSoftPowers(seurat_obj)
  28. wrap_plots(plot_list, ncol=2)
  30. power_table <- GetPowerTable(seurat_obj)
  31. head(power_table)
  33. seurat_obj <- ConstructNetwork(seurat_obj, tom_name = "ExN")
  35. pdf()
  36. PlotDendrogram(seurat_obj, main = 'hdWGCNA Dendrogram')
  37. dev.off()
  39. seurat_obj <- ScaleData(seurat_obj, features = VariableFeatures(seurat_obj))
  41. seurat_obj <- ModuleEigengenes(seurat_obj, group.by.vars = "Donor")
  43. hMEs <- GetMEs(seurat_obj, harmonized = TRUE)
  45. seurat_obj <- ModuleConnectivity(seurat_obj , group.by = "cell_type_1", group_name = "ExN")
  47. seurat_obj <- ResetModuleNames(seurat_obj, new_name = "M")
  49. pdf()
  50. PlotKMEs(seurat_obj, text_size = 3, ncol = 3)
  51. dev.off()
  53. modules <- GetModules(seurat_obj) %>% subset(module != 'grey')
  54. head(modules[,1:6])
  56. hub_df <- GetHubGenes(seurat_obj, n_hubs = 10)
  57. head(hub_df)
  59. saveRDS(seurat_obj, file = "")
  62. library(UCell)
  63. seurat_obj <- ModuleExprScore(seurat_obj, n_genes = 20, method = 'UCell')
  65. plot_list <- ModuleFeaturePlot(seurat_obj, features = "hMEs", order = TRUE)
  67. pdf()
  68. wrap_plots(plot_list, ncol = 4)
  69. dev.off()
  71. plot_list <- ModuleFeaturePlot(seurat_obj, features = 'scores', order = 'shuffle', ucell = TRUE)
  73. pdf()
  74. wrap_plots(plot_list, ncol = 4)
  75. dev.off()
  77. pdf()
  78. ModuleCorrelogram(seurat_obj)
  79. dev.off()
  81. hMEs <- GetMEs(seurat_obj, harmonized = TRUE)
  82. modules <- GetModules(seurat_obj)
  83. mods <- levels(modules$module); mods <- mods[mods != 'grey']
  85. [email hidden] <- cbind([email hidden], hMEs)
  87. p <- DotPlot(seurat_obj, features = mods, group.by = "cell_type_2")
  88. p <- p +
  89. RotatedAxis() +
  90. scale_color_gradient2(high='red', mid='grey95', low='blue')
  91. print(p)
  92. ggsave()
  95. group1 <- [email hidden] %>% subset(cell_type_1 == "ExN" & Classification == "BD") %>% rownames
  96. group2 <- [email hidden] %>% subset(cell_type_1 == "ExN" & Classification == "MDD") %>% rownames
  98. DMEs <- FindDMEs(seurat_obj, barcodes1 = group1, barcodes2 = group2, test.use = 'wilcox', wgcna_name = "exp_sc")
  99. head(DMEs)
  101. PlotDMEsLollipop(seurat_obj, DMEs, wgcna_name = "exp_sc", pvalue = "p_val_adj")
  104. pdf()
  105. PlotDMEsLollipop(seurat_obj, DMEs, wgcna_name = "exp_sc", pvalue = "p_val_adj")
  106. dev.off()
  109. clusters <- c("ExN.L2-3IT", "ExN.L3-5IT", "ExN.L6IT", "ExN.L5-6NP", "ExN.L6CT", "ExN.L5ET",
  110. "InN.Lamp5", "InN.Sncg", "InN.Vip", "InN.Sst", "InN.Pvalb-Ba", "InN.Pvalb-Ch",
  111. "Ast", "Mic", "Oli", "OPC", "End")
  112. clusters <- factor(clusters,
  113. levels = c("ExN.L2-3IT", "ExN.L3-5IT", "ExN.L6IT", "ExN.L5-6NP", "ExN.L6CT", "ExN.L5ET",
  114. "InN.Lamp5", "InN.Sncg", "InN.Vip", "InN.Sst", "InN.Pvalb-Ba", "InN.Pvalb-Ch",
  115. "Ast", "Mic", "Oli", "OPC", "End"))
  117. DMEs <- data.frame()
  119. for(cur_cluster in clusters){
  121. group1 <- [email hidden] %>% subset(cell_type_2 == cur_cluster & Classification == "BD") %>% rownames
  122. group2 <- [email hidden] %>% subset(cell_type_2 == cur_cluster & Classification == "MDD") %>% rownames
  124. cur_DMEs <- FindDMEs(seurat_obj, barcodes1 = group1, barcodes2 = group2, test.use = 'wilcox', pseudocount.use = 0.01, wgcna_name = "exp_sc")
  126. cur_DMEs$cluster <- cur_cluster
  128. DMEs <- rbind(DMEs, cur_DMEs)
  129. }
  131. modules <- GetModules(seurat_obj)
  132. mods <- levels(modules$module); mods <- mods[mods != 'grey']
  134. plot_df <- DMEs
  136. plot_df$module <- factor(as.character(plot_df$module), levels=mods)
  138. plot_df$cluster <- factor(plot_df$cluster,
  139. levels = c("ExN.L2-3IT", "ExN.L3-5IT", "ExN.L6IT", "ExN.L5-6NP", "ExN.L6CT", "ExN.L5ET",
  140. "InN.Lamp5", "InN.Sncg", "InN.Vip", "InN.Sst", "InN.Pvalb-Ba", "InN.Pvalb-Ch",
  141. "Ast", "Mic", "Oli", "OPC", "End"))
  143. maxval <- 2; minval <- -2
  144. plot_df$avg_log2FC <- ifelse(plot_df$avg_log2FC > maxval, maxval, plot_df$avg_log2FC)
  145. plot_df$avg_log2FC <- ifelse(plot_df$avg_log2FC < minval, minval, plot_df$avg_log2FC)
  147. plot_df$Significance <- gtools::stars.pval(plot_df$p_val_adj)
  149. plot_df$textcolor <- ifelse(abs(plot_df$avg_log2FC) > 1, "white", "black")
  151. p <- plot_df %>%
  152. ggplot(aes(y=cluster, x=module, fill=avg_log2FC)) +
  153. geom_tile()
  155. p <- p +
  156. geom_text(label=plot_df$Significance, color=plot_df$textcolor)
  158. p <- p +
  159. scale_fill_gradient2(low = "dodgerblue4", mid = "gray90", high = "brown") +
  160. RotatedAxis() +
  161. theme(
  162. panel.border = element_rect(fill=NA, color='black', size=1),
  163. axis.line.x = element_blank(),
  164. axis.line.y = element_blank(),
  165. plot.margin = margin(0,0,0,0)
  166. ) + xlab('') + ylab('') +
  167. coord_equal()
  169. print(p)
  170. ggsave()
  173. cur_traits <- c("Classification", "Age", "Sex", "PMI", "Brain.pH", "RIN")
  175. seurat_obj <- ModuleTraitCorrelation(
  176. seurat_obj,
  177. traits = cur_traits,
  178. group.by = "cell_type_2"
  179. )
  181. mt_cor <- GetModuleTraitCorrelation(seurat_obj)
  183. pdf()
  184. PlotModuleTraitCorrelation(
  185. seurat_obj, label = 'fdr', label_symbol = 'stars',
  186. text_size = 2, text_digits = 2,
  187. text_color = "black", high_color = "gold3", mid_color = "gray90", low_color = "skyblue3",
  188. plot_max = 0.4, combine=TRUE)
  189. dev.off()
  192. library(enrichR)
  193. library(GeneOverlap)
  195. theme_set(theme_cowplot())
  196. set.seed(12345)
  198. dbs <- c('GO_Biological_Process_2023','GO_Cellular_Component_2023','GO_Molecular_Function_2023')
  200. seurat_obj <- RunEnrichr(seurat_obj, dbs=dbs, max_genes = Inf)
  202. enrich_df <- GetEnrichrTable(seurat_obj)
  204. head(enrich_df)
  206. EnrichrBarPlot(seurat_obj, outdir = "enrichr_plots", n_terms = 10, plot_size = c(5,7), logscale=TRUE)
  208. pdf()
  209. EnrichrDotPlot(seurat_obj,
  210. mods = "all", database = "GO_Biological_Process_2023", n_terms = 4, term_size = 8, p_adj = FALSE
  211. ) + scale_color_stepsn(colors=rev(viridis::magma(256)))
  212. dev.off()
  215. seurat_ref <- readRDS("")
  217. seurat_query <- readRDS("")
  218. seurat_query <- subset(seurat_query, Classification == "CTRL")
  220. seurat_query <- ProjectModules(seurat_obj = seurat_query, seurat_ref = seurat_ref, wgcna_name = "exp_sc", wgcna_name_proj = "projected", assay = "RNA")
  222. seurat_query <- MetacellsByGroups(seurat_obj = seurat_query, group.by = c("cell_type_1", "Donor"), k = 25, max_shared = 10, ident.group = "cell_type_1")
  223. seurat_query <- NormalizeMetacells(seurat_query)
  225. seurat_ref <- SetDatExpr(seurat_ref, group_name = "ExN", group.by = "cell_type_1")
  227. seurat_query <- SetDatExpr(seurat_query, group_name = "ExN", group.by = "cell_type_1")
  229. seurat_query <- ModulePreservation(seurat_query, seurat_ref = seurat_ref, name = "CTRL_ExN", verbose = 3, n_permutations = 200)
  231. saveRDS(seurat_query, file = "")
  233. plot_list <- PlotModulePreservation(seurat_query, name = "CTRL_ExN", statistics = "summary")
  234. pdf()
  235. wrap_plots(plot_list, ncol=2)
  236. dev.off()
  238. plot_list <- PlotModulePreservation(seurat_query, name = "CTRL_ExN", statistics = "rank")
  239. pdf()
  240. wrap_plots(plot_list, ncol=2)
  241. dev.off()
  244. library(Seurat)
  245. library(tidyverse)
  246. library(cowplot)
  247. library(patchwork)
  248. library(magrittr)
  249. library(WGCNA)
  250. library(hdWGCNA)
  251. library(igraph)
  252. library(motifmatchr)
  253. library(TFBSTools)
  254. library(EnsDb.Hsapiens.v86)
  255. library(BSgenome.Hsapiens.UCSC.hg38)
  256. library(GenomicRanges)
  257. library(xgboost)
  260. theme_set(theme_cowplot())
  261. set.seed(12345)
  263. seurat_obj <- readRDS("")
  265. library(JASPAR2024)
  266. library(RSQLite)
  267. library(EnsDb.Hsapiens.v86)
  269. JASPAR2024 <- JASPAR2024()
  271. sq24 <- RSQLite::dbConnect(RSQLite::SQLite(), db(JASPAR2024))
  273. pfm_core <- TFBSTools::getMatrixSet(x = sq24, opts = list(collection = "CORE", tax_group = 'vertebrates', all_versions = FALSE))
  275. seurat_obj <- MotifScan(seurat_obj, species_genome = 'hg38', pfm = pfm_core, EnsDb = EnsDb.Hsapiens.v86)
  277. motif_df <- GetMotifs(seurat_obj)
  279. tf_genes <- unique(motif_df$gene_name)
  280. modules <- GetModules(seurat_obj)
  281. nongrey_genes <- subset(modules, module != 'grey') %>% .$gene_name
  282. genes_use <- c(tf_genes, nongrey_genes)
  284. seurat_obj <- SetWGCNAGenes(seurat_obj, genes_use)
  285. seurat_obj <- SetDatExpr(seurat_obj, group_name = "ExN", group.by = "cell_type_1")
  287. model_params <- list(objective = 'reg:squarederror', max_depth = 1, eta = 0.1, nthread = 24, alpha = 0.5)
  289. seurat_obj <- ConstructTFNetwork(seurat_obj, model_params)
  291. seurat_obj <- AssignTFRegulons(seurat_obj, strategy = "A", reg_thresh = 0.01, n_tfs = 10)
  293. saveRDS(seurat_obj, file = "")
  296. tf_regulons <- GetTFRegulons(seurat_obj)
  298. hub_df <- GetHubGenes(seurat_obj, n_hubs = 10) %>%
  299. subset(gene_name %in% tf_regulons$tf)
  301. Idents(seurat_obj) <- seurat_obj$cell_type_1
  302. marker_tfs <- FindAllMarkers(
  303. seurat_obj,
  304. features = unique(tf_regulons$tf)
  305. )
  307. top_tfs <- marker_tfs %>% subset(cluster == "ExN") %>%
  308. slice_max(n = 80, order_by = avg_log2FC)
  310. intersect(top_tfs$gene, hub_df$gene_name)
  313. tf_for_plot <- c("FOXP2", "RORB", "CUX2", "POU6F2", "PKNOX2", "BCL11A",
  314. "BHLHE40", "ETS2", "CREM", "E2F3", "ZNF675", "PPARG")
  316. for (tf in tf_for_plot) {
  317. p <- RegulonBarPlot(seurat_obj, selected_tf = tf, cutoff = 0.25)
  318. print(p)
  319. ggsave()
  320. }
  323. seurat_obj <- RegulonScores(seurat_obj, target_type = 'positive', cor_thresh = 0.05, ncores = 16)
  325. seurat_obj <- RegulonScores(seurat_obj, target_type = 'negative', cor_thresh = -0.05, ncores = 16)
  327. pos_regulon_scores <- GetRegulonScores(seurat_obj, target_type='positive')
  328. neg_regulon_scores <- GetRegulonScores(seurat_obj, target_type='negative')
  330. tf_for_plot <- c("FOXP2", "RORB", "CUX2", "POU6F2", "PKNOX2", "BCL11A",
  331. "BHLHE40", "ETS2", "CREM", "E2F3", "ZNF675", "PPARG")
  333. for (tf in tf_for_plot) {
  334. cur_tf <- tf
  336. seurat_obj$pos_regulon_score <- pos_regulon_scores[,cur_tf]
  337. seurat_obj$neg_regulon_score <- neg_regulon_scores[,cur_tf]
  339. p1 <- FeaturePlot(seurat_obj, feature = cur_tf) + umap_theme()
  340. p2 <- FeaturePlot(seurat_obj, feature = 'pos_regulon_score', cols = c('lightgrey', 'red')) + umap_theme()
  341. p3 <- FeaturePlot(seurat_obj, feature = 'neg_regulon_score', cols = c('lightgrey', 'seagreen')) + umap_theme()
  343. p <- p1 | p2 | p3
  344. print(p)
  345. ggsave()
  346. }
  349. tf_for_plot <- c("FOXP2", "RORB", "CUX2", "POU6F2", "PKNOX2", "BCL11A",
  350. "BHLHE40", "ETS2", "CREM", "E2F3", "ZNF675", "PPARG")
  352. for (tf in tf_for_plot) {
  353. cur_tf <- tf
  355. p1 <- TFNetworkPlot(
  356. seurat_obj, selected_tfs = cur_tf,
  357. target_type = 'positive',
  358. label_TFs = 1, depth = 2, cutoff = 0.05
  359. ) + ggtitle(paste0("Positive targets of ", cur_tf)) +
  360. theme(plot.title = element_text(hjust = 0.5))
  362. p2 <- TFNetworkPlot(
  363. seurat_obj, selected_tfs = cur_tf,
  364. target_type = 'negative',
  365. label_TFs = 1, depth = 2, cutoff = 0.05
  366. ) + ggtitle(paste0("Negative targets of ", cur_tf)) +
  367. theme(plot.title = element_text(hjust = 0.5))
  369. p3 <- TFNetworkPlot(
  370. seurat_obj, selected_tfs = cur_tf,
  371. target_type = 'both',
  372. label_TFs = 1, depth = 2, cutoff = 0.05
  373. ) + ggtitle(paste0("Pos & Neg targets of ", cur_tf)) +
  374. theme(plot.title = element_text(hjust = 0.5))
  376. p <- p1 | p2 | p3
  377. print(p)
  378. ggsave()
  379. }
  382. group1 <- [email hidden] %>% subset(cell_type_1 == "ExN" & Classification == "BD") %>% rownames
  383. group2 <- [email hidden] %>% subset(cell_type_1 == "ExN" & Classification == "MDD") %>% rownames
  385. dregs <- FindDifferentialRegulons(seurat_obj, barcodes1 = group1, barcodes2 = group2)
  387. p <- PlotDifferentialRegulons(seurat_obj, dregs)
  388. print(p)
  389. ggsave()
  392. seurat_obj <- RunModuleUMAP(seurat_obj, n_hubs = 10, n_neighbors = 15, min_dist = 0.1)
  394. tf_for_plot <- c("FOXP2", "RORB", "CUX2", "POU6F2", "PKNOX2", "BCL11A",
  395. "BHLHE40", "ETS2", "CREM", "E2F3", "ZNF675", "PPARG")
  397. for (tf in tf_for_plot) {
  398. cur_tf <- tf
  400. modules <- GetModules(seurat_obj)
  401. umap_df <- GetModuleUMAP(seurat_obj)
  403. mods <- levels(modules$module)
  404. mod_colors <- dplyr::select(modules, c(module, color)) %>%
  405. distinct %>% arrange(module) %>% .$color
  406. cp <- mod_colors; names(cp) <- mods
  408. hub_df <- GetHubGenes(seurat_obj, n_hubs=10)
  410. tf_net <- GetTFNetwork(seurat_obj)
  411. tf_regulons <- GetTFRegulons(seurat_obj) %>%
  412. subset(gene %in% umap_df$gene & tf %in% umap_df$gene)
  413. all(tf_regulons$gene %in% umap_df$gene)
  415. cur_network <- GetTFTargetGenes(
  416. seurat_obj,
  417. selected_tfs=cur_tf,
  418. depth=2,
  419. target_type='both'
  420. ) %>% subset(gene %in% umap_df$gene & tf %in% umap_df$gene)
  422. gene_depths <- cur_network %>%
  423. group_by(gene) %>%
  424. slice_min(n=1, order_by=depth) %>%
  425. dplyr::select(gene, depth) %>% distinct()
  427. cur_network <- cur_network %>%
  428. dplyr::rename(c(source=tf, target=gene))
  430. cur_network <- subset(cur_network, target %in% unique(tf_net$tf) | target %in% hub_df$gene_name)
  432. graph <- tidygraph::as_tbl_graph(cur_network) %>%
  433. tidygraph::activate(nodes) %>%
  434. mutate(degree = centrality_degree())
  436. tf_degrees <- table(tf_regulons$tf)
  437. tmp <- tf_degrees[names(V(graph))]; tmp <- tmp[!is.na(tmp)]
  438. V(graph)[names(tmp)]$degree <- as.numeric(tmp)
  440. V(graph)$gene_type <- ifelse(names(V(graph)) %in% unique(tf_regulons$tf), 'TF', 'Gene')
  441. V(graph)$gene_type <- ifelse(names(V(graph)) == cur_tf, 'selected', V(graph)$gene_type)
  443. umap_layout <- umap_df[names(V(graph)),] %>% dplyr::rename(c(x=UMAP1, y = UMAP2, name=gene))
  444. rownames(umap_layout) <- 1:nrow(umap_layout)
  445. lay <- create_layout(graph, umap_layout)
  447. gene_depths <- subset(gene_depths, gene %in% lay$name)
  448. tmp <- dplyr::left_join(lay, gene_depths, by = c('name' = 'gene'))
  449. lay$depth <- tmp$depth
  450. lay$depth <- ifelse(lay$name %in% cur_tf, 0, lay$depth)
  451. lay$depth <- factor(lay$depth, levels=0:max(as.numeric(lay$depth)))
  453. cur_shapes <- c(23, 24, 25); names(cur_shapes) <- levels(lay$depth)
  455. label_tfs <- subset(cur_network, target %in% tf_regulons$tf) %>% .$target %>% unique
  456. lay$lab <- ifelse(lay$name %in% c(cur_tf, label_tfs), lay$name, NA)
  458. p <- ggraph(lay)
  460. p <- p + geom_point(inherit.aes=FALSE, data=umap_df, aes(x=UMAP1, y=UMAP2), color=umap_df$color, alpha=0.3, size=2)
  462. p <- p + geom_edge_fan(
  463. aes(color=Cor, alpha=abs(Cor)),
  464. arrow = arrow(length = unit(2, 'mm'), type='closed'),
  465. end_cap = circle(3, 'mm')
  466. )
  468. p <- p + geom_node_point(
  469. data=subset(lay, gene_type == 'Gene'), aes(fill=module), shape=21, color='black', size=2
  470. )
  472. p <- p + geom_node_point(
  473. data=subset(lay, gene_type == 'TF'),
  474. aes(fill=module, size=degree, shape=depth), color='black'
  475. )
  477. p <- p + geom_node_label(
  478. aes(label=lab), repel=TRUE, max.overlaps=Inf,
  479. fontface='italic', color='black'
  480. )
  482. p <- p + scale_edge_colour_gradient2(high='orange2', mid='white', low='dodgerblue') +
  483. scale_colour_manual(values=cp) +
  484. scale_fill_manual(values=cp) +
  485. scale_shape_manual(values=cur_shapes) +
  486. guides(
  487. edge_alpha = "none",
  488. size = "none",
  489. shape = "none",
  490. fill = "none"
  491. )
  492. print(p)
  493. ggsave()
  494. }
  497. library(enrichR)
  499. seurat_obj <- RunEnrichrRegulons(seurat_obj, wait_time=1)
  501. saveRDS(seurat_obj, file = "")
  503. enrich_df <- GetEnrichrRegulonTable(seurat_obj)
  505. tf_for_plot <- c("FOXP2", "RORB", "CUX2", "POU6F2", "PKNOX2", "BCL11A",
  506. "BHLHE40", "ETS2", "CREM", "E2F3", "ZNF675", "PPARG")
  508. for (tf in tf_for_plot) {
  509. cur_tf <- tf
  511. plot_df <- subset(enrich_df, tf == cur_tf & P.value < 0.05)
  512. table(plot_df$target_type)
  514. p1 <- plot_df %>%
  515. subset(target_type == 'negative') %>%
  516. slice_max(n=10, order_by=Combined.Score) %>%
  517. mutate(Term = stringr::str_replace(Term, " \\s*\\([^\\)]+\\)", "")) %>% head(10) %>%
  518. ggplot(aes(x=-log(Combined.Score), y=reorder(Term, Combined.Score)))+
  519. geom_bar(stat='identity', position='identity', fill = "dodgerblue", alpha = 0.5) +
  520. geom_text(aes(label=Term), x=-.1, color='black', size=3.5, hjust='right') +
  521. xlab('log(Enrichment)') +
  522. scale_x_continuous(expand = c(0, 0), limits = c(NA, 0)) +
  523. ggtitle('Negatively correlated target genes') +
  524. theme(
  525. panel.grid.major=element_blank(),
  526. panel.grid.minor=element_blank(),
  527. legend.title = element_blank(),
  528. axis.ticks.y=element_blank(),
  529. axis.text.y=element_blank(),
  530. axis.line.y=element_blank(),
  531. plot.title = element_text(hjust = 0.5),
  532. axis.title.y = element_blank()
  533. )
  535. p2 <- plot_df %>%
  536. subset(target_type == 'positive') %>%
  537. slice_max(n=10, order_by=Combined.Score) %>%
  538. mutate(Term = stringr::str_replace(Term, " \\s*\\([^\\)]+\\)", "")) %>% head(10) %>%
  539. ggplot(aes(x=log(Combined.Score), y=reorder(Term, Combined.Score)))+
  540. geom_bar(stat='identity', position='identity', fill = "orange2", alpha = 0.5) +
  541. geom_text(aes(label=Term), x=.1, color='black', size=3.5, hjust='left') +
  542. xlab('log(Enrichment)') +
  543. scale_x_continuous(expand = c(0, 0), limits = c(0, NA)) +
  544. ggtitle('Positively correlated target genes') +
  545. theme(
  546. panel.grid.major=element_blank(),
  547. panel.grid.minor=element_blank(),
  548. legend.title = element_blank(),
  549. axis.ticks.y=element_blank(),
  550. axis.text.y=element_blank(),
  551. axis.line.y=element_blank(),
  552. plot.title = element_text(hjust = 0.5),
  553. axis.title.y = element_blank()
  554. )
  556. p <- p1 | p2
  557. print(p)
  558. ggsave()
  559. }
  563. p1 <- ModuleRegulatoryHeatmap(
  564. seurat_obj, feature='delta', dendrogram=FALSE
  565. ) + ggtitle('TFs only')
  566. p2 <- ModuleRegulatoryHeatmap(
  567. seurat_obj, feature='delta', TFs_only=FALSE,
  568. max_val=5, dendrogram=FALSE
  569. ) + ggtitle('All target genes')
  570. p <- p1 | p2
  571. print(p)
  572. ggsave()
  574. p1 <- ModuleRegulatoryHeatmap(
  575. seurat_obj, feature = 'positive', TFs_only = TRUE,
  576. high_color='orange2')
  577. p2 <- ModuleRegulatoryHeatmap(
  578. seurat_obj, feature = 'negative', TFs_only = TRUE,
  579. high_color='dodgerblue')
  580. p <- p1 | p2
  581. print(p)
  582. ggsave()
  584. p1 <- ModuleRegulatoryHeatmap(
  585. seurat_obj, feature = 'positive', TFs_only = FALSE,
  586. high_color='orange2')
  587. p2 <- ModuleRegulatoryHeatmap(
  588. seurat_obj, feature = 'negative', TFs_only = FALSE,
  589. high_color='dodgerblue')
  590. p <- p1 | p2
  591. print(p)
  592. ggsave()
  594. p <- ModuleRegulatoryNetworkPlot(seurat_obj, cutoff=0.5, max_val=1.5)
  595. print(p)
  596. ggsave()
  598. p1 <- ModuleRegulatoryNetworkPlot(
  599. seurat_obj, feature='positive', high_color='orange2')
  600. p2 <- ModuleRegulatoryNetworkPlot(
  601. seurat_obj, feature='negative', high_color='dodgerblue')
  602. p <- p1 | p2
  603. print(p)
  604. ggsave()

OFCsnRNA_05_hdWGCNA_mdd_bd_ExN.R at commit 7f932c5, no license · at the source

Overview

Authors: Rongwei Gao1, Ikuo Otsuka1, Toshiyuki Shirai1, Masao Miyachi1, Kiriko Minami1, Shohei Okada1, Ryo Tsukamoto1, Takaki Tanifuji1, Satoshi Okazaki2, Akitoyo Hishimoto1
ORCID iDs: Rongwei Gao, Ikuo Otsuka, Toshiyuki Shirai
  1. Department of Psychiatry, Kobe University Graduate School of Medicine,Kobe, Japan
  2. Kobe University Inclusive Campus & Healthcare Center,Kobe, Japan
Institutions: Kobe University (Japan)
Journal: Translational psychiatry, volume 16, issue 1, article 442
Dates: received 10 December 2025; accepted 12 June 2026; published online 21 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41398-026-04200-5 · PMID 42324251 · PMCID PMC13529797 · OpenAlex W7165444042
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), histology / microscopy (modality), human (organism), depression (population), bipolar (population), cellular / molecular (subfield)
Methods: Machine learning
Keywords: Comparative genomics, Bipolar disorder, Depression
MeSH: Bipolar Disorder*, Brain*, Major Depressive Disorder*, Prefrontal Cortex*, Female, Gene Expression Profiling, Humans, Neurons (* major topic)
Topic: Bipolar Disorder and Treatment (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: JST Moonshot R&D Program (JPMJMS239F) SENSHIN Medical Research Foundation Smoking Research Foundation; JST SPRING (JPMJSP2148); Japan Society for the Promotion of Science (JP25K19058, JP21K07520 JP24K10710, JP21H02852 JP24K02383); Japan Agency for Medical Research and Development (23dk0307111); JST Moonshot R&D Program (JPMJMS239F) Smoking Research Foundation
Citations: not cited yet (Europe PMC); 69 references in the paper
Research resources: RRID:SCR_021059

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 7 matches between paragraphs and lines of code.

PsychiatKobeUniv-singlecell/Code_brain_mood_disorders

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 7f932c51fa43710042e864e17a191cba550aa76a, 30 March 2026
Languages: R (18), Python (3)
Size: 30 files, 21 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 (18 files), ggplot2 (15 files), patchwork (12 files), Seurat (11 files), clusterProfiler (8 files), edgeR (8 files), ComplexHeatmap (5 files), Harmony (2 files), pandas (2 files), Scanpy (2 files), WGCNA (2 files), anndata (1 file), cowplot (1 file), ggpubr (1 file), igraph (1 file), limma (1 file), NumPy (1 file), reshape2 (1 file), rstatix (1 file), SingleCellExperiment (1 file), XGBoost (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
22 files
At the source: github.com/PsychiatKobeUniv-singlecell/Code_brain_mood_disorders

Code availability statement

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Read it in the paper: doi.org/10.1038/s41398-026-04200-5.

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;
  • 21 scripts, each with its path and the digest of its content;
  • 7 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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:

Read it in the paper: doi.org/10.1038/s41398-026-04200-5.

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, 10 authors, 3 keywords, 8 MeSH terms, 5 funders, 67 references, 1 RRID.

Cite

This paper

Gao, R., Otsuka, I., Shirai, T., Miyachi, M., Minami, K., Okada, S., Tsukamoto, R., Tanifuji, T., Okazaki, S., & Hishimoto, A. (2026). Postmortem brain single-nucleus and bulk gene expression analyses identify shared and distinct abnormalities in bipolar disorder and major depressive disorder. Translational psychiatry, 16(1), 442. https://doi.org/10.1038/s41398-026-04200-5

BibTeX

@article{gao2026postmortem,
author = {Gao, Rongwei and Otsuka, Ikuo and Shirai, Toshiyuki and Miyachi, Masao and Minami, Kiriko and Okada, Shohei and Tsukamoto, Ryo and Tanifuji, Takaki and Okazaki, Satoshi and Hishimoto, Akitoyo},
title = {{Postmortem brain single-nucleus and bulk gene expression analyses identify shared and distinct abnormalities in bipolar disorder and major depressive disorder}},
journal = {Translational psychiatry},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {442},
publisher = {Nature Publishing Group},
issn = {2158-3188},
doi = {10.1038/s41398-026-04200-5},
url = {https://doi.org/10.1038/s41398-026-04200-5},
pmid = {42324251},
pmcid = {PMC13529797}
}

RIS

TY - JOUR
AU - Gao, Rongwei
AU - Otsuka, Ikuo
AU - Shirai, Toshiyuki
AU - Miyachi, Masao
AU - Minami, Kiriko
AU - Okada, Shohei
AU - Tsukamoto, Ryo
AU - Tanifuji, Takaki
AU - Okazaki, Satoshi
AU - Hishimoto, Akitoyo
TI - Postmortem brain single-nucleus and bulk gene expression analyses identify shared and distinct abnormalities in bipolar disorder and major depressive disorder
T2 - Translational psychiatry
J2 - Transl Psychiatry
PY - 2026
DA - 2026/06/21
VL - 16
IS - 1
SP - 442
SN - 2158-3188
PB - Nature Publishing Group
DO - 10.1038/s41398-026-04200-5
UR - https://doi.org/10.1038/s41398-026-04200-5
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41398-026-04200-5",
"type": "article-journal",
"title": "Postmortem brain single-nucleus and bulk gene expression analyses identify shared and distinct abnormalities in bipolar disorder and major depressive disorder",
"container-title": "Translational psychiatry",
"author": [
{
"family": "Gao",
"given": "Rongwei"
},
{
"family": "Otsuka",
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{
"family": "Shirai",
"given": "Toshiyuki"
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{
"family": "Miyachi",
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{
"family": "Minami",
"given": "Kiriko"
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{
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{
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"given": "Takaki"
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"volume": "16",
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"page": "442",
"DOI": "10.1038/s41398-026-04200-5",
"PMID": "42324251",
"PMCID": "PMC13529797",
"ISSN": "2158-3188",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41398-026-04200-5",
"language": "en",
"issued": {
"date-parts": [
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21
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]
}
}

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

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