OSCR

Single-cell multi-omic integration analysis prioritizes druggable genes and reveals cell-type-specific causal effects in glioblastomagenesis.

Code ↔ Paper

18 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 18 matches · 4 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Method › Bidirectional MR-PheWAS ↔ Main Analyses/16.Bidirectional MR-PheWAS.R, lines 93–181 · score 0.92 · MR PRESSO, Reverse MR, bidirectional MR, PheWAS, IVW, MA
  2. [2] § Result › Integrating brain tissue-specific genetic multi-omics data to identify putatively causal genes for glioma ↔ Main Analyses/5.Enrichment Analyses.R, lines 233–274 · score 0.80 · CDKN2B, CDKN2A, MDM4, NPAS3, SOX8, STMN3
  3. [3] § Result › Investigating cell-type-specific effects ↔ Visualization/Fig4g.R, lines 202–260 · score 0.76 · DNase, H3K4me3, dELS, pELS, CTCF, PLS
  4. [4] § Result › Investigating cell-type-specific effects ↔ Visualization/Fig4c-e.R, lines 140–223 · score 0.75 · glioblastomagenesis causal genes, inhibitory neurons, excitatory neurons, PP.H4.abf, unduplicated, oligodendrocytes
  5. [5] § Method › CellChat ↔ Main Analyses/11.CellChat.R, lines 117–160 · score 0.74 · parietal lobes, CellChat, brain regions, temporal, frontal, Human
  6. [6] § Method › CT-FM-SNP ↔ Main Analyses/14.CT-FM-SNP.R, lines 90–136 · score 0.72 · CT FM SNP, credible, LDSC, GWAS
  7. [7] § Method › DEG ↔ Main Analyses/6.DEG.R, lines 36–66 · score 0.71 · edgeR, limma, Batch, TMM, TCGA, DEG
  8. [8] § Result › Uncovering the cellular context critical for GBM origins ↔ Main Analyses/10.Pseudotime Analyses.R, lines 7–79 · score 0.68 · CytoTRACE, potency category, potency score, UMAP, totipotent, pseudotime
  9. [9] § Method › Gene prioritization using locus-based methods › Mapgen ↔ Main Analyses/14.CT-FM-SNP.R, lines 10–86 · score 0.65 · LD block, fine mapped, genome wide, mapgen, PIP, SNPs
  10. [10] § Method › Gene prioritization using locus-based methods › Mapgen ↔ Visualization/Fig4h.R, the whole file · a weak match · score 0.65 · LD block, fine mapped, genome wide, mapgen, PIP, SNPs
  11. [11] § Method › Enrichment analyses and PPI ↔ Main Analyses/5.Enrichment Analyses.R, lines 233–274 · score 0.57 · scGSEA, JASMINE, GO, enrichment, tissue, pathway
  12. [12] § Result › Investigating cell-type-specific effects ↔ Visualization/Fig4h.R, the whole file · a weak match · score 0.57 · credible model, fine mapping, rs6964933, PIP, EGFR, LD
  13. [13] § Result › Uncovering the cellular context critical for GBM origins ↔ Main Analyses/10.Pseudotime Analyses.R, lines 236–295 · score 0.56 · oligodendrocyte precursor cells, Malignant cells, variances, Pseudotime, OPCs, astrocytes
  14. [14] § Method › Pseudotime analyses in snRNA-seq ↔ Main Analyses/10.Pseudotime Analyses.R, lines 7–79 · score 0.53 · cytotrace2, UMAP, potency, Pseudotime, seq, GBM
  15. [15] § Result › High EGFR expression related to lower genetic glioblastomagenesis risk ↔ Main Analyses/15.eQTpLot.R, the whole file · a weak match · score 0.52 · eQTpLot, eQTL, congruent, R2, EGFR, LD
  16. [16] § Method › Enrichment analyses and PPI ↔ Visualization/Fig2a.R, the whole file · a weak match · score 0.52 · clusterProfiler, KEGG, GO, Bonferroni, enrichment, seq
  17. [17] § Result › Uncovering the cellular context critical for GBM origins ↔ Main Analyses/11.CellChat.R, lines 437–500 · score 0.51 · oligodendrocyte precursor cells, Malignant cells, OPCs, astrocytes, brain, GBM
  18. [18] § Method › GWAS data ↔ Main Analyses/3.QTL_based_association_analyses(TWAS_PWAS_SMR).R, lines 31–114 · score 0.50 · FinnGen R9, PWAS, TWAS, SMR, QTL, EUR

Paper

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The authors' code

R · 347 lines · 18 KB · no license · 3 matches

  1. pacman::p_load(CytoTRACE2, Seurat, ggplot2, gridExtra, tidyverse, patchwork, ggpubr, sceasy, reticulate, monocle3)
  2. ### CytoTRACE2----
  3. setwd("D:/single_eqtl")
  4. tempdir()
  5. tempfile()
  6. tempdir <- function() "D:\\rtemp"
  7. unlockBinding("tempdir", baseenv())
  8. utils::assignInNamespace("tempdir", tempdir, ns="base", envir=baseenv())
  9. assign("tempdir", tempdir, baseenv())
  10. lockBinding("tempdir", baseenv())
  11. A<-readRDS("GBM_core.rds")
  12. A <- cytotrace2(A,
  13. is_seurat = TRUE,
  14. slot_type = "counts",
  15. species = 'human',
  16. seed = 1234)
  17. annotation <- data.frame(phenotype = [email hidden]$annotation_level_4) %>%
  18. set_rownames(., colnames(A))
  19. gc()
  20. A$Phenotype <- A$annotation_level_4
  22. message("Creating plots.")
  23. plot_list <- list()
  24. labels <- c("Differentiated", "Unipotent", "Oligopotent",
  25. "Multipotent", "Pluripotent", "Totipotent")
  26. colors <- c("#9E0142", "#F46D43", "#FEE08B", "#E6F598",
  27. "#66C2A5", "#5E4FA2")
  28. x_limits <- range(A@reductions$[email hidden][,
  29. 1], na.rm = TRUE)
  30. y_limits <- range(A@reductions$[email hidden][,
  31. 2], na.rm = TRUE)
  32. [email hidden][["CytoTRACE2_Score_clipped"]] <- 5.5 -
  33. 6 * [email hidden][["CytoTRACE2_Score"]]
  34. [email hidden][["CytoTRACE2_Score_clipped"]] <- -pmax(pmin([email hidden][["CytoTRACE2_Score_clipped"]],
  35. 5), 0)
  36. potency_score_umap <- FeaturePlot(A, "CytoTRACE2_Score_clipped") +
  37. scale_colour_gradientn(colours = rev(colors),
  38. na.value = "transparent", labels = c(labels),
  39. limits = c(-5, 0), name = "Potency score \n",
  40. guide = guide_colorbar(frame.colour = "black",
  41. ticks.colour = "black")) + xlab("UMAP1") +
  42. ylab("UMAP2") + ggtitle("CytoTRACE 2") + theme(legend.text = element_text(size = 10),
  43. legend.title = element_text(size = 12), axis.text = element_text(size = 12),
  44. axis.title = element_text(size = 12), plot.title = element_text(size = 12,
  45. face = "bold", hjust = 0.5, margin = margin(b = 20))) +
  46. theme(aspect.ratio = 1) + coord_cartesian(xlim = x_limits,
  47. ylim = y_limits)
  48. plot_list <- c(plot_list, setNames(list(potency_score_umap),
  49. paste("CytoTRACE2_UMAP")))
  50. potency_category_umap <- DimPlot(A, reduction = "umap",
  51. group.by = "CytoTRACE2_Potency", label = FALSE) +
  52. scale_color_manual(values = colors, name = "Potency category",
  53. breaks = rev(c("Differentiated", "Unipotent",
  54. "Oligopotent", "Multipotent", "Pluripotent",
  55. "Totipotent"))) + xlab("UMAP1") + ylab("UMAP2") +
  56. ggtitle("CytoTRACE 2") + theme(legend.text = element_text(size = 10),
  57. legend.title = element_text(size = 12), axis.text = element_text(size = 12),
  58. axis.title = element_text(size = 12), plot.title = element_text(size = 12,
  59. face = "bold", hjust = 0.5, margin = margin(b = 20))) +
  60. theme(aspect.ratio = 1) + coord_cartesian(xlim = x_limits,
  61. ylim = y_limits)
  62. plot_list <- c(plot_list, setNames(list(potency_category_umap),
  63. paste("CytoTRACE2_Potency_UMAP")))
  64. rel_order_umap <- FeaturePlot(A, "CytoTRACE2_Relative") +
  65. scale_colour_gradientn(colours = (c("#000004FF",
  66. "#3B0F70FF", "#8C2981FF", "#DE4968FF", "#FE9F6DFF",
  67. "#FCFDBFFF")), na.value = "transparent", limits = c(0,
  68. 1), breaks = seq(0, 1, by = 0.2), labels = c("0.0 (More diff.)",
  69. "0.2", "0.4", "0.6", "0.8", "1.0 (Less diff.)"),
  70. name = "Relative\norder \n", guide = guide_colorbar(frame.colour = "black",
  71. ticks.colour = "black")) + ggtitle("CytoTRACE 2") +
  72. xlab("UMAP1") + ylab("UMAP2") + theme(legend.text = element_text(size = 10),
  73. legend.title = element_text(size = 12), axis.text = element_text(size = 12),
  74. axis.title = element_text(size = 12), plot.title = element_text(size = 12,
  75. face = "bold", hjust = 0.5, margin = margin(b = 20))) +
  76. theme(aspect.ratio = 1) + coord_cartesian(xlim = x_limits,
  77. ylim = y_limits)
  78. plot_list <- c(plot_list, setNames(list(rel_order_umap),
  79. paste("CytoTRACE2_Relative_UMAP")))
  81. phenotype_umap <- DimPlot(A, reduction = "umap",
  82. group.by = "Phenotype", label = FALSE) + xlab("UMAP1") +
  83. ylab("UMAP2") + ggtitle("Phenotypes") + theme(legend.text = element_text(size = 8),
  84. legend.title = element_text(size = 12), axis.text = element_text(size = 10),
  85. axis.title = element_text(size = 10), plot.title = element_text(size = 12,
  86. face = "bold", hjust = 0.5, margin = margin(b = 20))) +
  87. theme(aspect.ratio = 1) + coord_cartesian(xlim = x_limits,
  88. ylim = y_limits)
  89. plot_list <- c(plot_list, setNames(list(phenotype_umap),
  90. paste("Phenotype_UMAP")))
  91. mtd <- [email hidden][c("Phenotype", "CytoTRACE2_Score")]
  92. medians <- mtd %>% group_by(Phenotype) %>% summarise(CytoTRACE2_median_per_pheno = median(CytoTRACE2_Score,
  93. na.rm = TRUE)) %>% arrange(desc(CytoTRACE2_median_per_pheno))
  94. phenotypes <- unique(mtd$Phenotype)
  95. medians <- data.frame(Phenotype = character(),
  96. CytoTRACE2_median_per_pheno = numeric(),
  97. stringsAsFactors = FALSE)
  99. for (phenotype in phenotypes) {
  100. subset_mtd <- mtd[mtd$Phenotype == phenotype, ]
  101. median_score <- median(subset_mtd$CytoTRACE2_Score, na.rm = TRUE)
  103. medians <- rbind(medians, data.frame(Phenotype = phenotype,
  104. CytoTRACE2_median_per_pheno = median_score))
  105. }
  107. medians <- medians[order(-medians$CytoTRACE2_median_per_pheno), ]
  110. mtd <- mtd %>% inner_join(medians, by = "Phenotype")
  112. mtd$Phenotype <- factor(mtd$Phenotype, levels = medians$Phenotype)
  115. potencyBoxplot_byPheno <- ggplot(mtd[!is.na(mtd$Phenotype), ], aes(x = Phenotype, y = CytoTRACE2_Score)) +
  116. geom_boxplot(aes(fill = CytoTRACE2_Score), width = 0.8, alpha = 0.5, outlier.shape = NA) +
  117. geom_jitter(aes(color = CytoTRACE2_Score), width = 0.05, height = 0, alpha = 0.5, shape = 21, stroke = 0.1, size = 1) +
  118. theme_classic() +
  119. scale_y_continuous(breaks = seq(0, 1, by = 0.2)) +
  120. scale_fill_gradientn(colors = rev(colors)) +
  121. scale_color_gradientn(colors = rev(colors)) +
  122. scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  123. labs(x = "Phenotype", y = "Potency score") +
  124. ggtitle("Developmental potential by phenotype") +
  125. theme(legend.position = "none",
  126. axis.text = element_text(size = 8),
  127. axis.title = element_text(size = 12),
  128. legend.text = element_text(size = 12),
  129. plot.title = element_text(size = 12, face = "bold", hjust = 0.5, margin = margin(b = 20)),
  130. aspect.ratio = 0.8)
  131. dev.off()
  133. potencyBoxplot_byPheno <- ggplot(mtd[!is.na(mtd$Phenotype),
  134. ], aes(x = Phenotype, y = CytoTRACE2_Score)) +
  135. geom_boxplot(aes(fill = CytoTRACE2_median_per_pheno),
  136. width = 0.8, alpha = 0.5, outlier.shape = NA) +
  137. geom_jitter(aes(fill = CytoTRACE2_median_per_pheno),
  138. width = 0.05, height = 0, alpha = 0.5, shape = 21,
  139. stroke = 0.1, size = 1) + theme_classic() +
  140. scale_y_continuous(breaks = seq(0, 1, by = 0.2),
  141. limits = c(0, 1), sec.axis = sec_axis(trans = ~.,
  142. breaks = seq(0, 1, by = 1/12), labels = c("",
  143. "Differentiated", "", "Unipotent", "",
  144. "Oligopotent", "", "Multipotent", "",
  145. "Pluripotent", "", "Totipotent", ""))) +
  146. scale_fill_gradientn(colors = rev(colors),
  147. breaks = c(0, 0.2, 0.4, 0.6, 0.8, 1), limits = c(0,
  148. 1), labels = c(labels)) + scale_color_gradientn(colors = rev(colors),
  149. breaks = c(0, 0.2, 0.4, 0.6, 0.8, 1), limits = c(0,
  150. 1), labels = c(labels)) + scale_x_discrete(labels = function(x) str_wrap(x,
  151. width = 10)) + labs(x = "Phenotype", y = "Potency score") +
  152. ggtitle("Developmental potential by phenotype") +
  153. theme(legend.position = "None", axis.text = element_text(size = 8),
  154. axis.title = element_text(size = 12), legend.text = element_text(size = 12),
  155. plot.title = element_text(size = 12, face = "bold",
  156. hjust = 0.5, margin = margin(b = 20)),
  157. axis.ticks.y.right = element_line(color = c("black",
  158. NA, "black", NA, "black", NA, "black",
  159. NA, "black", NA, "black", NA, "black")),
  160. aspect.ratio = 0.8, axis.ticks.length.y.right = unit(0.3,
  161. "cm"))
  162. plot_list <- c(plot_list, setNames(list(potencyBoxplot_byPheno),
  163. paste("CytoTRACE2_Boxplot_byPheno")))
  166. p1 <- plot_list$CytoTRACE2_UMAP
  167. p2 <- plot_list$CytoTRACE2_Potency_UMAP
  168. p3 <- plot_list$CytoTRACE2_Relative_UMAP
  169. p4 <- plot_list$CytoTRACE2_Boxplot_byPheno
  170. p5 <- plot_list$Phenotype_UMAP
  171. p5
  172. (p1+p2+p3+p5) + plot_layout(ncol = 2)
  174. gc()
  175. p4
  176. p1 <- ggboxplot([email hidden], x = "cell", y = "CytoTRACE2_Score",
  177. width = 0.6, color = "black", fill = "annotation_level_4",
  178. palette = "npg", xlab = F, bxp.errorbar = T, bxp.errorbar.width = 0.5,
  179. size = 1, outlier.shape = NA, legend = "right") +
  180. theme(axis.text.x = element_text(angle = 45, hjust = 1))
  181. p1
  183. ### Vector----
  184. cds<-readRDS("GBM_core.rds")
  185. VEC = cds@int_colData$reducedDims$UMAP
  186. colnames(VEC) = c('UMAP_1','UMAP_2')
  187. pbmc <- CreateSeuratObject(counts = as.matrix (cds@assays@data[["counts"]]), project = "GBM", min.cells = 0, min.features = 0)
  188. rm(list = setdiff(ls(), c("VEC","pbmc")))
  189. gc()
  190. pbmc <- NormalizeData(pbmc, normalization.method = "LogNormalize", scale.factor = 10000)
  191. pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 5000)
  192. all.genes <- rownames(pbmc)
  193. pbmc <- ScaleData(pbmc, features = all.genes)
  194. pbmc <- RunPCA(pbmc, features = VariableFeatures(object = pbmc),npcs = 150)
  195. PCA = pbmc@reductions$[email hidden]
  196. source('Vector.R')
  197. ##https://github.com/jumphone/Vector
  198. OUT=vector.buildGrid(VEC, N=30,SHOW=TRUE)
  199. OUT=vector.buildNet(OUT, CUT=1, SHOW=TRUE)
  200. OUT=vector.getValue(OUT, PCA, SHOW=TRUE)
  201. OUT=vector.gridValue(OUT,SHOW=TRUE)
  202. OUT=vector.autoCenter(OUT,UP=0.9,SHOW=TRUE)
  203. pdf("vecto_all.pdf", width = 10, height = 8)
  204. vector.drawArrow(OUT,P=0.9,SHOW=TRUE, COL=OUT$COL, SHOW.SUMMIT=TRUE)
  205. dev.off()
  206. ### PAGA----
  207. A <- readRDS("GBM_core.rds")
  208. Idents(object = A) <- "annotation_level_2"
  209. A<-subset(x = A, idents = c("Glial-Neuronal","Differentiated-like","Stem-like","Vascular"))
  210. A <- NormalizeData(A, normalization.method = "LogNormalize", scale.factor = 10000)
  211. A <- FindVariableFeatures(A, selection.method = "vst", nfeatures = 2000)
  212. A <- ScaleData(A, features = rownames(A))
  213. A <- RunPCA(A, features = VariableFeatures(object = A))
  214. A@assays$[email hidden] <- as.matrix(0)
  215. gc()
  216. sceasy::convertFormat(A, from="seurat", to="anndata",
  217. outFile='GBM_core_select.h5ad')
  218. sc <- import("scanpy")
  220. adata_DS1 <- sc$read("GBM_core_select.h5ad")
  221. obsm_keys <- adata_DS1$obsm_keys()
  222. sc$pp$neighbors(adata_DS1, n_neighbors = 20L, use_rep = 'X_pca')
  224. sc$tl$paga(adata_DS1, groups='Cell_type_level_3')
  225. plt <- import("matplotlib")
  226. plt$use("Agg", force = TRUE)
  227. plt$rcParams[["figure.figsize"]] <- list(8, 8)
  229. save_dir <- "figures"
  230. sc$pl$paga(adata_DS1,
  231. color = 'Cell_type_level_3',
  232. fontsize = 7,
  233. frameon = FALSE,
  234. save = "DS1_paga_SELECT_cell.pdf")
  236. ### OPC------
  237. A <- readRDS("GBM_core.rds")
  238. Idents(object = A) <- "cell_type"
  239. rm(list = setdiff(ls(), c("A")))
  240. gc()
  241. A<-subset(x = A, idents = c("malignant cell","oligodendrocyte precursor cell"))
  242. data <- GetAssayData(A, assay = 'RNA', slot = 'counts')
  243. cell_metadata <- [email hidden]
  245. gene_annotation <- data.frame(gene_short_name = rownames(data))
  246. rownames(gene_annotation) <- rownames(data)
  247. cds <- new_cell_data_set(data,
  248. cell_metadata = cell_metadata,
  249. gene_metadata = gene_annotation)
  250. rm(list = setdiff(ls(), c("cds","gene_annotation","A")))
  252. gc()
  253. cds <- preprocess_cds(cds, num_dim = 12)
  254. plot_pc_variance_explained(cds)
  255. cds <- reduce_dimension(cds,preprocess_method = "PCA")
  256. cds <- cluster_cells(cds)
  257. cds.embed <- cds@int_colData$reducedDims$UMAP
  258. int.embed <- Embeddings(A, reduction = "umap")
  259. int.embed <- int.embed[rownames(cds.embed),]
  260. cds@int_colData$reducedDims$UMAP <- int.embed
  261. plot_cells(cds, reduction_method="UMAP", color_cells_by="Cell_type_level_3")
  262. dev.off()
  263. cds <- learn_graph(cds)
  264. head(colData(cds))
  265. plot_cells(cds,
  266. color_cells_by = "Cell_type_level_3",
  267. label_groups_by_cluster=FALSE,
  268. label_leaves=FALSE,
  269. label_branch_points=TRUE,
  270. group_label_size=4,
  271. cell_size=1.5)
  272. dev.off()
  273. cds = order_cells(cds)
  274. pdf("GBM_core_monocle3_plot_OPC.pdf", width = 10, height = 8)
  276. plot_cells(cds,
  277. color_cells_by = "pseudotime",
  278. label_cell_groups=FALSE,
  279. label_leaves=TRUE,
  280. label_branch_points=TRUE,
  281. graph_label_size=1.5,
  282. group_label_size=4,cell_size=1.5)
  283. dev.off()
  285. saveRDS(cds,file = "GBM_core_monocle3_pseudotime.rds.gz")
  286. rm(list = setdiff(ls(), c("cds")))
  287. gc()
  288. ### AC-----
  289. A <- readRDS("GBM_core.rds")
  290. Idents(object = A) <- "cell_type"
  291. rm(list = setdiff(ls(), c("A")))
  292. gc()
  293. A<-subset(x = A, idents = c("malignant cell","astrocyte"))
  294. data <- GetAssayData(A, assay = 'RNA', slot = 'counts')
  295. cell_metadata <- [email hidden]
  297. gene_annotation <- data.frame(gene_short_name = rownames(data))
  298. rownames(gene_annotation) <- rownames(data)
  299. cds <- new_cell_data_set(data,
  300. cell_metadata = cell_metadata,
  301. gene_metadata = gene_annotation)
  302. rm(list = setdiff(ls(), c("cds","gene_annotation","A")))
  304. gc()
  305. cds <- preprocess_cds(cds, num_dim = 50)
  306. plot_pc_variance_explained(cds)
  307. cds <- reduce_dimension(cds,preprocess_method = "PCA")
  308. cds <- reduce_dimension(cds, reduction_method="tSNE")
  309. cds <- cluster_cells(cds)
  310. cds.embed <- cds@int_colData$reducedDims$UMAP
  311. int.embed <- Embeddings(A, reduction = "umap")
  312. int.embed <- int.embed[rownames(cds.embed),]
  313. cds@int_colData$reducedDims$UMAP <- int.embed
  314. plot_cells(cds, reduction_method="UMAP", color_cells_by="Cell_type_level_3")
  315. dev.off()
  317. cds <- learn_graph(cds)
  318. head(colData(cds))
  319. plot_cells(cds,
  320. color_cells_by = "Cell_type_level_3",
  321. label_groups_by_cluster=FALSE,
  322. label_leaves=FALSE,
  323. label_branch_points=TRUE,
  324. group_label_size=4,
  325. cell_size=1.5)
  326. dev.off()
  327. cds = order_cells(cds)
  328. plot_cells(cds,
  329. color_cells_by = "pseudotime",
  330. label_cell_groups=FALSE,
  331. label_leaves=TRUE,
  332. label_branch_points=TRUE,
  333. graph_label_size=1.5,
  334. group_label_size=4,cell_size=1.5)
  335. dev.off()
  337. saveRDS(cds,file = "GBM_core_monocle3_Astrocyte.rds.gz")
  338. rm(list = setdiff(ls(), c("cds")))
  339. gc()
  341. Track_genes_sig<-c("EGFR")
  342. plot_cells(cds, genes=Track_genes_sig, show_trajectory_graph=FALSE,
  343. label_cell_groups=FALSE, label_leaves=FALSE)
  344. plot_genes_in_pseudotime(cds[Track_genes_sig,],
  345. color_cells_by="Cell_type_level_3",
  346. min_expr=0.5, ncol= 2,cell_size=1.5) +
  347. scale_color_manual(values = c("#5CB85C","#337AB7","#FFFFCC","#D9534F","#F0AD4E"))

10.Pseudotime Analyses.R at commit 36d645f, no license · at the source

Overview

Authors: Yu-Feng Huang1, Kun-Long Wang1
ORCID iDs: Yu-Feng Huang
  1. The First Clinical Medical School, Shanxi Medical University, Taiyuan, China
Institutions: Shanxi Medical University (China)
Journal: Journal of translational medicine, volume 24, issue 1, article 940
Dates: received 10 December 2025; accepted 20 April 2026; published online 23 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s12967-026-08266-z · PMID 42177594 · PMCID PMC13386970 · OpenAlex W4411873661
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.06.28.25330486
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions
Keywords: Genome-wide association studies, Single-cell multi-omics, Glioblastomagenesis, Cell-type-specific causal genes
MeSH: Brain Neoplasms*, Carcinogenesis*, Genes, Neoplasm*, Glioblastoma*, Multiomics*, Single-Cell Analysis*, Gene Expression Regulation, Neoplastic, Genome-Wide Association Study, Humans, Organ Specificity, Tumor Microenvironment (* major topic)
Topic: Single-cell and spatial transcriptomics (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Citations: not cited yet (Europe PMC); 117 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 18 matches between paragraphs and lines of code.

1667857557/Glioma-Cell-Type-Specific-Causal-Genes

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 36d645fafc278e95f5009575380ac6072df1e36b, 14 April 2026
Languages: R (28)
Size: 29 files, 28 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggplot2 (21 files), tidyverse (21 files), data.table (16 files), Seurat (6 files), patchwork (5 files), reshape2 (4 files), clusterProfiler (3 files), reticulate (3 files), ComplexHeatmap (2 files), cowplot (2 files), ggpubr (2 files), igraph (2 files), Monocle 3 (2 files), circlize (1 file), edgeR (1 file), limma (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
29 files
At the source: github.com/1667857557/Glioma-Cell-Type-Specific-Causal-Genes

Code availability statement

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Read it in the paper: doi.org/10.1186/s12967-026-08266-z.

Tracing map

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  • 28 scripts, each with its path and the digest of its content;
  • 18 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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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/s12967-026-08266-z.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 4 keywords, 11 MeSH terms, 107 references.

Cite

This paper

Huang, Y.-F., & Wang, K.-L. (2026). Single-cell multi-omic integration analysis prioritizes druggable genes and reveals cell-type-specific causal effects in glioblastomagenesis. Journal of translational medicine, 24(1), 940. https://doi.org/10.1186/s12967-026-08266-z

BibTeX

@article{huang2026single,
author = {Huang, Yu-Feng and Wang, Kun-Long},
title = {{Single-cell multi-omic integration analysis prioritizes druggable genes and reveals cell-type-specific causal effects in glioblastomagenesis}},
journal = {Journal of translational medicine},
year = {2026},
month = may,
volume = {24},
number = {1},
pages = {940},
publisher = {BMC},
issn = {1479-5876},
doi = {10.1186/s12967-026-08266-z},
url = {https://doi.org/10.1186/s12967-026-08266-z},
pmid = {42177594},
pmcid = {PMC13386970}
}

RIS

TY - JOUR
AU - Huang, Yu-Feng
AU - Wang, Kun-Long
TI - Single-cell multi-omic integration analysis prioritizes druggable genes and reveals cell-type-specific causal effects in glioblastomagenesis
T2 - Journal of translational medicine
J2 - J Transl Med
PY - 2026
DA - 2026/05/23
VL - 24
IS - 1
SP - 940
SN - 1479-5876
PB - BMC
DO - 10.1186/s12967-026-08266-z
UR - https://doi.org/10.1186/s12967-026-08266-z
LA - en
ER -

CSL-JSON

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"id": "10.1186/s12967-026-08266-z",
"type": "article-journal",
"title": "Single-cell multi-omic integration analysis prioritizes druggable genes and reveals cell-type-specific causal effects in glioblastomagenesis",
"container-title": "Journal of translational medicine",
"author": [
{
"family": "Huang",
"given": "Yu-Feng"
},
{
"family": "Wang",
"given": "Kun-Long"
}
],
"container-title-short": "J Transl Med",
"volume": "24",
"issue": "1",
"page": "940",
"DOI": "10.1186/s12967-026-08266-z",
"PMID": "42177594",
"PMCID": "PMC13386970",
"ISSN": "1479-5876",
"publisher": "BMC",
"URL": "https://doi.org/10.1186/s12967-026-08266-z",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
23
]
]
}
}

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

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