OSCR

DNA methylation profiling identifies long-range epigenetic silencing of clustered protocadherins as a key determinant of meningioma progression.

Code ↔ Paper

22 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 22 matches
  1. [1] § Methods › Analyses of progression-free survival ↔ 06_survival.R, lines 875–913 · score 0.82 · integrated discrimination improvement, reclassification improvement, Adjuvant RT, full cohort, NRI, IDI
  2. [2] § Methods › DNA methylation profiling and analysis ↔ 03_discovery_clustering.R, lines 1211–1270 · score 0.81 · unsupervised hierarchical clustering, adjusted rand, probe subset, cluster assignments, matrices, scoring
  3. [3] § Methods › DNA methylation profiling and analysis ↔ 04_machine_learning_prediction.R, lines 251–311 · score 0.79 · highest accuracy, machine learning, Methylation cluster assignment, kappa, ROC, trained
  4. [4] § Methods › DNA methylation profiling and analysis ↔ 12_classifier.R, lines 46–90 · score 0.75 · cross validated, random forest, tuning, ROC, trained, metric
  5. [5] § Results › A DNA hypermethylation signature underlies meningioma progression independent of molecular groups ↔ 02_discovery_benchmarking.R, lines 73–144 · score 0.74 · Kaplan Meier curve, Cox proportional hazard, risk score, regression, PFS, survival
  6. [6] § Results › Long-range epigenetic silencing of clustered protocadherins drives meningioma progression ↔ 08_cell_lines.R, lines 351–405 · score 0.74 · PCDHGC3 overexpressing, Error bars, catenin localisation, BEN MEN, parental, GFP
  7. [7] § Methods › DNA methylation profiling and analysis ↔ 01_QC_preselect.R, lines 358–423 · score 0.74 · removeBatchEffect, DNA isolation, limma, distances, age, multivariate
  8. [8] § Methods › DNA methylation profiling and analysis ↔ 03_discovery_clustering.R, lines 852–894 · score 0.71 · variable probes, integrated risk score, clinically relevant, weighted, overlap, PC1
  9. [9] § Methods › DNA methylation profiling and analysis ↔ 01_QC_preselect.R, lines 83–121 · score 0.65 · pOOBAH, sex chromosomes, Discovery cohort, quality, SeSAMe, filtered
  10. [10] § Results › A DNA hypermethylation signature underlies meningioma progression independent of molecular groups ↔ 06_survival.R, lines 875–913 · score 0.65 · integrated discrimination improvement, reclassification improvement, full cohort, NRI, IDI, survival
  11. [11] § Results › A DNA hypermethylation signature underlies meningioma progression independent of molecular groups ↔ 06_survival.R, lines 1219–1258 · score 0.61 · clinical covariates, continuous signature, cluster signature, splines, nonlinear, variable
  12. [12] § Results › Long-range epigenetic silencing of clustered protocadherins drives meningioma progression ↔ 08_cell_lines.R, lines 351–405 · score 0.60 · catenin localisation, BEN MEN, overexpressed, GFP, cytoplasmic, PCDHGC3
  13. [13] § Methods › Cell culture studies ↔ 08_cell_lines.R, lines 56–111 · score 0.60 · IOMM Lee, BEN MEN, KT21, NCH93, cell
  14. [14] § Methods › Copy number variant analysis ↔ 10_longitudinal_CNV.R, lines 873–915 · score 0.59 · CNV.focal, Cancer Gene, conumee2, Longitudinal
  15. [15] § Results › Long-range epigenetic silencing of clustered protocadherins drives meningioma progression ↔ 08_cell_lines.R, lines 56–111 · score 0.58 · IOMM Lee, BEN MEN, KT21, NCH93, METHlow, METHhigh
  16. [16] § Results › Long-range epigenetic silencing of clustered protocadherins drives meningioma progression ↔ 11_RNAseq.R, lines 86–135 · score 0.57 · log2 fold changes, hypermethylated genes, PCDHA, PCDHB, Volcano, PCDHG
  17. [17] § Results › A DNA hypermethylation signature underlies meningioma progression independent of molecular groups ↔ 06_survival.R, lines 171–209 · score 0.57 · Kaplan Meier curve, HR, Cox, hazard, survival, model
  18. [18] § Methods › Gene expression profiling ↔ 11_RNAseq.R, lines 44–84 · score 0.56 · DESeq2, Gene expression, QC, clusters
  19. [19] § Methods › DNA methylation profiling and analysis ↔ 04_machine_learning_prediction.R, lines 126–201 · score 0.56 · meaningful components, prcomp, weighted, pairwise, predictive, overlap
  20. [20] § Methods › Confocal microscopy ↔ 08_cell_lines.R, lines 474–532 · score 0.56 · IOMM Lee, stained, nuclei, GFP, cytoplasmic, nuclear
  21. [21] § Results › Independent, but convergent effects of CNVs and methylation cluster signature on meningioma progression ↔ 09_discovery_CNV.R, lines 591–637 · score 0.52 · Kruskal Wallis, genome stability, Dunn, METHlow, METHhigh, deletions
  22. [22] § Results › CNVs and DNA methylation shape the evolutionary trajectory of meningioma progression ↔ 07_trajectory_analysis.R, lines 585–633 · score 0.52 · normal meninges, cluster signature, Ellipses, adjustment, Dura, Leptomeninges

Paper

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The authors' code

R · 578 lines · 22 KB · no license · 5 matches

  1. library(sesame)
  2. library(IlluminaHumanMethylationEPICv2anno.20a1.hg38)
  3. library(FactoMineR)
  4. library(factoextra)
  5. library(dplyr)
  6. library(ggplot2)
  7. library(here)
  9. ###clone figshare repository including the given folder structure to your local directory
  10. ###populate raw data subfolders with data from either GEO or figshare itself
  11. ###refer to the README.txt file at https://github.com/MerkLab/Meningioma_DNA_methylation_project for further information
  14. ###get data (discovery cohort and cell lines)
  15. idat_dir = here("data", "GSE304093")
  16. setwd(here("data", "general_datasets"))
  17. targets = read.csv(file="targets_discovery_celllines.csv")
  18. betas.celllines = openSesame(idat_dir)
  19. betas.celllines <- betas.celllines[complete.cases(betas.celllines), ]
  21. # tag sex chromosome probes for removal
  22. annoEPICv2 = getAnnotation(IlluminaHumanMethylationEPICv2anno.20a1.hg38)
  24. keep <- !(rownames(betas.celllines) %in% annoEPICv2$Name[annoEPICv2$chr %in%
  25. c("chrX","chrY")])
  26. table(keep)
  27. betas.celllines = betas.celllines[keep,]
  29. #######make PCA analysis
  30. #get top 10k most variable probes as for discovery
  31. #make subset of most variable beta values
  32. bVals.sub = betas.celllines
  33. bVals.sub = as.data.frame(bVals.sub)
  34. bVals.sub$var = apply(bVals.sub,1,var)
  35. bVals.sub <- bVals.sub[order(bVals.sub$var, decreasing = TRUE),]
  36. bVals.sub = bVals.sub[,-242]
  37. bVals.sub.50k = bVals.sub[1:10000,]
  40. ###projecting cell lines in pca space from discovery cohort
  41. ###use PCA function from factormineR that contains active and supplementary individuals
  42. ###discovery samples are used as active individuals
  43. ###those determine the prinicipal components
  44. ###cell lines are used as supplementary components
  45. meth.pca = PCA(t(bVals.sub.10k), ind.sup = 1:10,graph=FALSE)
  46. fviz_eig(meth.pca, addlabels = TRUE)
  48. coord.meth.pca = meth.pca$ind$coord
  49. coord.meth.pca = as.data.frame(coord.meth.pca)
  50. supp.ind.coord = as.data.frame(meth.pca$ind.sup$coord)
  51. coord.meth.pca = rbind(supp.ind.coord, coord.meth.pca)
  52. rownames(targets) = targets$ID
  53. all(rownames(targets)==rownames(coord.meth.pca))
  56. #make pca plots for various conditions for 10k probes
  57. df <- cbind(targets, coord.meth.pca)
  59. setwd(here("results"))
  60. cairo_pdf(filename = "PCA_10k_disc_celllines_PC1-2.pdf", width = 5, height = 5)
  61. ggplot(df, aes(x=Dim.1, y=Dim.2, color = clustering, size = 6, shape = clustering))+
  62. geom_jitter(data = df %>% filter(clustering == "METHlow"), shape=16, size=5, alpha=0.7,show.legend = F)+
  63. geom_jitter(data = df %>% filter(clustering == "METHhigh"), shape=16, size=5, alpha=0.7,show.legend = F)+
  64. geom_jitter(data = df %>% filter(clustering == "BEN_MEN"), shape=25, size=7,fill="black", alpha=0.6,show.legend = F)+
  65. geom_jitter(data = df %>% filter(clustering == "HBL52"), shape=17, size=7, alpha=0.6,show.legend = F)+
  66. geom_jitter(data = df %>% filter(clustering == "IOMM_LEE"), shape=19, size=7, alpha=0.6,show.legend = F)+
  67. geom_jitter(data = df %>% filter(clustering == "KT21"), shape=18, size=9, alpha=0.6,show.legend = F)+
  68. geom_jitter(data = df %>% filter(clustering == "NCH93"), shape=15, size=7, alpha=0.6,show.legend = F)+
  69. scale_color_manual(values=c(BEN_MEN = "black",
  70. IOMM_LEE = "black",
  71. HBL52 = "black",
  72. NCH93 = "black",
  73. KT21 = "black",
  74. METHhigh = "violetred4",
  75. METHlow = "cyan4"))+
  76. scale_shape_manual(values = c(BEN_MEN = 0,
  77. IOMM_LEE = 1,
  78. HBL52 = 2,
  79. NCH93 = 5,
  80. KT21 = 6,
  81. METHhigh = 16,
  82. METHlow = 16)) +
  83. theme_classic()
  84. dev.off()
  86. setwd(here("results"))
  87. cairo_pdf(filename = "PCA_10k_disc_celllines_PC1-3.pdf", width = 5, height = 5)
  88. ggplot(df, aes(x=Dim.1, y=Dim.3, color = clustering, size = 6, shape = clustering))+
  89. geom_jitter(data = df %>% filter(clustering == "METHlow"), shape=16, size=5, alpha=0.7,show.legend = F)+
  90. geom_jitter(data = df %>% filter(clustering == "METHhigh"), shape=16, size=5, alpha=0.7,show.legend = F)+
  91. geom_jitter(data = df %>% filter(clustering == "BEN_MEN"), shape=25, size=7,fill="black", alpha=0.6,show.legend = F)+
  92. geom_jitter(data = df %>% filter(clustering == "HBL52"), shape=17, size=7, alpha=0.6,show.legend = F)+
  93. geom_jitter(data = df %>% filter(clustering == "IOMM_LEE"), shape=19, size=7, alpha=0.6,show.legend = F)+
  94. geom_jitter(data = df %>% filter(clustering == "KT21"), shape=18, size=9, alpha=0.6,show.legend = F)+
  95. geom_jitter(data = df %>% filter(clustering == "NCH93"), shape=15, size=7, alpha=0.6,show.legend = F)+
  96. scale_color_manual(values=c(BEN_MEN = "black",
  97. IOMM_LEE = "black",
  98. HBL52 = "black",
  99. NCH93 = "black",
  100. KT21 = "black",
  101. METHhigh = "violetred4",
  102. METHlow = "cyan4"))+
  103. scale_shape_manual(values = c(BEN_MEN = 0,
  104. IOMM_LEE = 1,
  105. HBL52 = 2,
  106. NCH93 = 5,
  107. KT21 = 6,
  108. METHhigh = 16,
  109. METHlow = 16)) +
  110. theme_classic()
  111. dev.off()
  114. ####make boxplot to show hypermethylated METHhigh probes in discovery and cell lines
  115. #get hyper probes only and subset beta values
  116. setwd(here("data", "08_cell_lines"))
  117. hyper_probes = read.csv(file="probes_hyper_cluster_disc.csv", header = T)
  118. hyper_probes = hyper_probes$Probe_ID
  119. bVals = as.data.frame(betas.celllines)
  120. bVals.hyper.probes = subset(bVals, rownames(bVals) %in% hyper_probes)
  121. all(colnames(bVals.hyper.probes) == targets$Basename)
  122. bVals.hyper.probes = bVals.hyper.probes[,match(targets$Basename, colnames(bVals.hyper.probes))]
  123. all(colnames(bVals.hyper.probes) == targets$Basename)
  124. colnames(bVals.hyper.probes) = targets$clustering
  125. setwd(here("results"))
  126. write.csv(bVals.hyper.probes, file="hyper_probes_beta_discovery_cellines.csv")
  129. #do the plot, with only single averages for the cell lines as horizontal lines
  130. setwd(here("data", "08_cell_lines"))
  131. avg_beta_hyper_probes_disc = read.csv(file="avg_hyper_probes.csv", header = T)
  133. setwd(here("results"))
  134. cairo_pdf(filename = "Hyper_probes_box_disc_cl_avg.pdf", width = 3.2, height = 7)
  135. ggplot(avg_beta_hyper_probes_disc,
  136. aes(x=factor(cluster, levels = c("METHlow", "METHhigh")), y=betas))+
  137. geom_point(position = position_jitter(), alpha=0.95, aes(color=betas))+
  138. geom_boxplot(outlier.shape = NA, alpha=0.5)+
  139. scale_y_continuous(breaks = seq(0,1,0.25), limits=c(0,1))+
  140. scale_color_gradientn(colours = c("navy","dodgerblue3","indianred1","red3"), limits=c(0,1))+
  141. geom_hline(yintercept=0.4808, linetype="dotted", color = "#A56D90", size=2)+
  142. geom_hline(yintercept=0.2751, linetype="dotted", color = "#1257BA", size=2)+
  143. geom_hline(yintercept=0.7595, linetype="dotted", color = "#F25050", size=2)+
  144. geom_hline(yintercept=0.7464, linetype="dotted", color = "#F45353", size=2)+
  145. geom_hline(yintercept=0.6986, linetype="dotted", color = "#FD6666", size=2)+
  146. theme_classic()+
  147. theme(legend.position = "none")
  148. dev.off()
  152. #visualize genomic regions and get probe IDs and their beta values
  153. #after getting the probes for regions, re-do heatmap using pheatmap to have the same color range
  154. all(colnames(bVals) == targets$Basename)
  155. bVals = bVals[,match(targets$Basename, colnames(bVals))]
  156. colnames(bVals) = targets$ID
  157. bVals_cl = bVals[,1:10]
  159. # extract cell line names (remove _A / _B)
  160. cell_lines <- sub("_[AB]$", "", colnames(bVals_cl))
  162. # average replicates
  163. bVals_merged <- sapply(unique(cell_lines), function(cl) {
  164. rowMeans(bVals_cl[, cell_lines == cl, drop = FALSE])
  165. })
  167. # keep row names
  168. bVals_merged <- as.data.frame(bVals_merged)
  169. rownames(bVals_merged) <- rownames(bVals_cl)
  171. #order from benign to malignant
  172. bVals_merged = bVals_merged[,c(1,2,3,5,4)]
  174. #PCDHA@ cluster
  175. PCDHA_probes = visualizeRegion("chr5",140786136,141012344,bVals_merged, draw = F)
  176. setwd(here("results"))
  177. write.csv(PCDHA_probes,file ="Cell_lines_PCDHA_probes_betas.csv")
  179. setwd(here("results"))
  180. cairo_pdf(filename = "PCDHA_betas_cell_lines.pdf", width = 12, height = 3)
  181. pheatmap(t(PCDHA_probes), color = colorRampPalette(c("navy","dodgerblue3","indianred1","red3"))(100),
  182. cluster_rows=FALSE, cluster_cols=FALSE,show_colnames = F, border_color = NA)
  183. dev.off()
  186. #PCDHB@ cluster
  187. PCDHB_probes = visualizeRegion("chr5",141051394,141248234,bVals_merged, draw = F)
  188. setwd(here("results"))
  189. write.csv(PCDHB_probes,file ="Cell_lines_PCDHB_probes_betas.csv")
  191. setwd(here("results"))
  192. cairo_pdf(filename = "PCDHB_betas_cell_lines.pdf", width = 12, height = 3)
  193. pheatmap(t(PCDHB_probes), color = colorRampPalette(c("navy","dodgerblue3","indianred1","red3"))(100),
  194. cluster_rows=FALSE, cluster_cols=FALSE,show_colnames = F, border_color = NA)
  195. dev.off()
  197. #PCDHG@ cluster
  198. PCDHG_probes = visualizeRegion("chr5",141330685,141512979,bVals_merged, draw = F)
  199. setwd(here("results"))
  200. write.csv(PCDHG_probes,file ="Cell_lines_PCDHG_probes_betas.csv")
  202. setwd(here("results"))
  203. cairo_pdf(filename = "PCDHG_betas_cell_lines.pdf", width = 12, height = 3)
  204. pheatmap(t(PCDHG_probes), color = colorRampPalette(c("navy","dodgerblue3","indianred1","red3"))(100),
  205. cluster_rows=FALSE, cluster_cols=FALSE,show_colnames = F, border_color = NA)
  206. dev.off()
  210. ###boxplot for PCDH clusters
  211. #get data
  212. setwd(here("data", "08_cell_lines"))
  213. data_boxplot_PCDH = read.csv(file="data_longrange_cluster.csv")
  214. str(data_boxplot_PCDH)
  215. data_boxplot_PCDH$cell_line = factor(data_boxplot_PCDH$cell_line, levels = c("H","B","N","K","I"))
  217. # grouped boxplot PCDH clusters
  218. setwd(here("results"))
  219. cairo_pdf(filename = "Boxplot_Celllines_PCDH_clusters.pdf", width = 5, height = 5)
  220. ggplot(data_boxplot_PCDH, aes(x=cluster, y=values, fill=cell_line)) +
  221. geom_boxplot(outliers = F)+
  222. scale_fill_manual(values=c("#1257BA","#A56D90","#FD6666","#F45353","#F25050"))+
  223. theme_classic()
  224. dev.off()
  229. ###work on FACS data
  230. ###parental meningioma cells MFI and percentage b-catenin cyto + nuclear
  232. #get MFI data for parental cells
  233. setwd(here("data", "08_cell_lines"))
  234. parental_MFI = read.csv(file="Parental_MFI.csv", header = T)
  235. parental_MFI$line = factor(parental_MFI$line, levels = c("HBL", "BEN", "NCH", "Lee", "KT"))
  236. parental_MFI$group = factor(parental_MFI$group, levels = c("cytplasmic", "nuclear"))
  237. str(parental_MFI)
  239. #plot with datapoints
  240. # Define the manual order of your samples
  241. desired_order <- c("HBL", "BEN", "NCH", "Lee", "KT") # replace with your order
  243. # Set factor levels manually
  244. parental_MFI$line <- factor(parental_MFI$line, levels = desired_order)
  246. # Create numeric x-axis for spacing
  247. parental_MFI$x_pos <- as.numeric(parental_MFI$line) * 1.5 # increase multiplier for more spacing
  249. # Define consistent dodge for groups
  250. dodge <- position_dodge(width = 0.6)
  252. # Plot
  253. setwd(here("results"))
  254. cairo_pdf(filename = "Parental_MFI_points.pdf", width = 3, height = 7)
  255. ggplot(parental_MFI, aes(x = x_pos, y = MFI_FC, color = group)) +
  256. # Raw points with slight jitter
  257. geom_point(
  258. position = position_jitterdodge(
  259. jitter.width = 0.12,
  260. dodge.width = dodge$width
  261. ),
  262. size = 4,
  263. alpha = 0.8
  264. ) +
  265. # Error bars on top of mean
  266. geom_errorbar(
  267. stat = "summary",
  268. fun.data = mean_se,
  269. position = dodge,
  270. width = 0.5,
  271. linewidth = 1.4
  272. ) +
  273. # Mean points
  274. geom_point(
  275. stat = "summary",
  276. fun = mean,
  277. position = dodge,
  278. size = 3
  279. ) +
  280. scale_color_manual(values = c("magenta2")) +
  282. #add horizontal line
  283. geom_hline(yintercept = 1, linetype = "dashed", color = "deepskyblue1", size=2)+
  285. # Map numeric x back to labels with manual order
  286. scale_x_continuous(
  287. breaks = 1:length(desired_order) * 1.5,
  288. labels = desired_order
  289. ) +
  290. scale_y_continuous(limits = c(0, NA)) +
  291. theme_classic() +
  292. theme(legend.position = "none")
  293. dev.off()
  296. #get percentage data for parental cells
  297. setwd(here("data", "08_cell_lines"))
  298. parental_percent = read.csv(file="Parental_percentage.csv", header = T)
  299. parental_percent$line = factor(parental_percent$line, levels = c("HBL", "BEN", "NCH", "Lee", "KT"))
  300. parental_percent$group = factor(parental_percent$group, levels = c("cytplasmic", "nuclear"))
  301. str(parental_percent)
  303. #plot with datapoints shown
  304. desired_order <- c("HBL", "BEN", "NCH", "Lee", "KT")
  306. # Set factor levels manually
  307. parental_percent$line <- factor(parental_percent$line, levels = desired_order)
  309. # Create numeric x-axis for spacing
  310. parental_percent$x_pos <- as.numeric(parental_percent$line) * 1.5
  312. # Define dodge for groups
  313. dodge <- position_dodge(width = 0.6)
  315. setwd(here("results"))
  316. cairo_pdf(filename = "Parental_percent_points.pdf", width = 4, height = 7)
  317. ggplot(parental_percent, aes(x = x_pos, y = MFI_FC, color = group)) +
  318. # Raw points with slight jitter
  319. geom_point(
  320. position = position_jitterdodge(jitter.width = 0.1, dodge.width = dodge$width),
  321. size = 4,
  322. alpha = 0.8
  323. ) +
  324. # Error bars
  325. geom_errorbar(
  326. stat = "summary",
  327. fun.data = mean_se,
  328. position = dodge,
  329. width = 0.5,
  330. linewidth = 1.4
  331. ) +
  332. # Mean points
  333. geom_point(
  334. stat = "summary",
  335. fun = mean,
  336. position = dodge,
  337. size = 3
  338. ) +
  339. scale_color_manual(values = c("deepskyblue1", "magenta2")) +
  340. # Map numeric x back to labels in the correct order
  341. scale_x_continuous(
  342. breaks = 1:length(desired_order) * 1.5, # <- fixed
  343. labels = desired_order # <- fixed
  344. ) +
  345. scale_y_continuous(limits = c(0, NA)) +
  346. theme_classic() +
  347. theme(legend.position = "none")
  348. dev.off()
  351. ###investigate changes of PCDHGC3 overexpressiion on b-catenin localisation
  353. #####BEN-MEN-1 analysis with GFP or PCDHGC3 overexpression
  354. #get MFI data
  355. #get MFI data for GFP cells
  356. setwd(here("data", "08_cell_lines"))
  357. GFP_MFI = read.csv(file="GFP_MFI_BEN.csv", header = T)
  358. GFP_MFI$line = factor(GFP_MFI$line, levels = c("BEN_parental","BEN_GFP", "BEN_C3"))
  359. GFP_MFI$group = factor(GFP_MFI$group, levels = c("cytoplasmic", "nuclear"))
  360. str(GFP_MFI)
  362. #make plot with datapoints
  363. # Define manual order of samples
  364. desired_order <- c("BEN_parental", "BEN_GFP", "BEN_C3")
  366. # Set factor levels manually
  367. GFP_MFI$line <- factor(GFP_MFI$line, levels = desired_order)
  369. # Create numeric x-axis for spacing
  370. GFP_MFI$x_pos <- as.numeric(GFP_MFI$line) * 1.5 # adjust multiplier for more spacing
  372. # Define consistent dodge for groups
  373. dodge <- position_dodge(width = 0.6)
  375. # Plot
  376. setwd(here("results"))
  377. cairo_pdf(filename = "GFP_MFI_points_BEN.pdf", width = 4, height = 5)
  378. ggplot(GFP_MFI, aes(x = x_pos, y = MFI_FC, color = group)) +
  379. # Raw data points with slight jitter
  380. geom_point(
  381. position = position_jitterdodge(
  382. jitter.width = 0.12,
  383. dodge.width = dodge$width
  384. ),
  385. size = 6,
  386. alpha = 0.8
  387. ) +
  388. # Error bars for mean ± SE
  389. geom_errorbar(
  390. stat = "summary",
  391. fun.data = mean_se,
  392. position = dodge,
  393. width = 0.5,
  394. linewidth = 1.4
  395. ) +
  396. # Mean points
  397. geom_point(
  398. stat = "summary",
  399. fun = mean,
  400. position = dodge,
  401. size = 3
  402. ) +
  403. scale_color_manual(values = c("magenta2")) +
  404. scale_y_continuous(limits = c(0, 1),
  405. breaks = seq(0, 2.5, by = 0.5)) +
  407. #add horizontal line
  408. geom_hline(yintercept = 1, linetype = "dashed", color = "deepskyblue1", size=2)+
  410. # Map numeric x back to labels in the manual order
  411. scale_x_continuous(
  412. breaks = 1:length(desired_order) * 1.5,
  413. labels = desired_order
  414. ) +
  415. theme_classic() +
  416. theme(legend.position = "none")
  417. dev.off()
  420. #####IOMM-Lee analysis with GFP or PCDHGC3 overexpression
  421. #get MFI data
  422. #get MFI data for GFP cells
  423. setwd(here("data", "08_cell_lines"))
  424. GFP_MFI = read.csv(file="GFP_MFI_Lee.csv", header = T)
  425. GFP_MFI$line = factor(GFP_MFI$line, levels = c("Lee_parental","Lee_GFP", "Lee_C3"))
  426. GFP_MFI$group = factor(GFP_MFI$group, levels = c("cytplasmic", "nuclear"))
  427. str(GFP_MFI)
  429. #make plot with datapoints
  430. # Define manual order of samples
  431. desired_order <- c("Lee_parental", "Lee_GFP", "Lee_C3")
  433. # Set factor levels manually
  434. GFP_MFI$line <- factor(GFP_MFI$line, levels = desired_order)
  436. # Create numeric x-axis for spacing
  437. GFP_MFI$x_pos <- as.numeric(GFP_MFI$line) * 1.5 # adjust multiplier for more spacing
  439. # Define consistent dodge for groups
  440. dodge <- position_dodge(width = 0.6)
  442. # Plot
  443. setwd(here("results"))
  444. cairo_pdf(filename = "GFP_MFI_points.pdf", width = 5, height = 5)
  445. ggplot(GFP_MFI, aes(x = x_pos, y = MFI_FC, color = group)) +
  446. # Raw data points with slight jitter
  447. geom_point(
  448. position = position_jitterdodge(
  449. jitter.width = 0.12,
  450. dodge.width = dodge$width
  451. ),
  452. size = 6,
  453. alpha = 0.8
  454. ) +
  455. # Error bars for mean ± SE
  456. geom_errorbar(
  457. stat = "summary",
  458. fun.data = mean_se,
  459. position = dodge,
  460. width = 0.5,
  461. linewidth = 1.4
  462. ) +
  463. # Mean points
  464. geom_point(
  465. stat = "summary",
  466. fun = mean,
  467. position = dodge,
  468. size = 3
  469. ) +
  470. scale_color_manual(values = c("magenta2")) +
  471. scale_y_continuous(limits = c(0, NA),
  472. breaks = seq(0, 2.5, by = 0.5)) +
  474. #add horizontal line
  475. geom_hline(yintercept = 1, linetype = "dashed",color = "deepskyblue1", size=2)+
  477. # Map numeric x back to labels in the manual order
  478. scale_x_continuous(
  479. breaks = 1:length(desired_order) * 1.5,
  480. labels = desired_order
  481. ) +
  482. theme_classic() +
  483. theme(legend.position = "none")
  484. dev.off()
  487. ###analyses b-catenin cytoplasmic and nuclear from ICC stains
  489. ##############make boxplot of total b-catenin intensity
  490. ###IOMM-LEE
  491. setwd(here("data", "08_cell_lines"))
  492. Lee_total = read.csv(file="Bcatenin_ICC_quartiles_total_Lee.csv")
  493. Lee_total$MainGroup <- factor(Lee_total$MainGroup, levels = c("Lee-GFP", "Lee-C3"))
  495. setwd(here("results"))
  496. cairo_pdf(filename = "Boxplots_ICC_Bcatenin_total_3.pdf", width = 5, height = 7)
  497. ggplot(Lee_total, aes(x = MainGroup, y = Value, fill=MainGroup)) +
  498. geom_jitter(aes(color = MainGroup),position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.8),
  499. size = 1.8, alpha = 0.6) +
  500. geom_boxplot(position = position_dodge(width = 0.8), width = 0.6, outlier.shape = NA) +
  501. scale_fill_manual(values = c("Lee-GFP" = "violetred4", "Lee-C3" = "steelblue4")) +
  502. scale_color_manual(values = c("Lee-GFP" = "violetred4", "Lee-C3" = "steelblue4")) +
  503. labs(title = "Boxplot title", x = "Main Group", y = "Value") +
  504. theme_classic()
  505. dev.off()
  508. ##############make boxplot of b-catenin ratio nucleus to cytoplasm
  509. setwd(here("data", "08_cell_lines"))
  510. Lee_ratio = read.csv(file="Bcatenin_ICC_quartiles_ratios_Lee.csv")
  511. Lee_ratio$MainGroup <- factor(Lee_ratio$MainGroup, levels = c("Lee-GFP", "Lee-C3"))
  513. setwd(here("results"))
  514. cairo_pdf(filename = "Boxplots_ICC_Bcatenin_ratios_new.pdf", width = 7, height = 7)
  515. ggplot(Lee_ratio, aes(x = MainGroup, y = Value, fill=SubGroup)) +
  516. geom_jitter(aes(color = SubGroup),position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.8),
  517. size = 1.8, alpha = 0.6) +
  518. geom_boxplot(position = position_dodge(width = 0.8), width = 0.6, outlier.shape = NA) +
  519. scale_fill_manual(values = c("Q1" = "cyan2", "Q2" = "turquoise4",
  520. "Q3" = "violetred2", "Q4" = "darkmagenta")) +
  521. scale_color_manual(values = c("Q1" = "cyan2", "Q2" = "turquoise4",
  522. "Q3" = "violetred2", "Q4" = "darkmagenta")) +
  523. geom_hline(yintercept = 0.88, linetype = "dashed", color = "cyan2",linewidth = 1.25)+
  524. geom_hline(yintercept = 1.13, linetype = "dashed", color = "turquoise4",linewidth = 1.25)+
  525. geom_hline(yintercept = 1.46, linetype = "dashed", color = "violetred2",linewidth = 1.25)+
  526. geom_hline(yintercept = 2.15, linetype = "dashed", color = "darkmagenta",linewidth = 1.25)+
  527. labs(title = "Boxplot title", x = "Main Group", y = "Value") +
  528. theme_classic()
  529. dev.off()
  531. wilcox.test(Value ~ MainGroup, data = Lee_total)
  532. wilcox.test(Value ~ MainGroup, data = Lee_ratio)
  536. #do tha same for BEN-MEN-1
  537. setwd(here("data", "08_cell_lines"))
  538. BEN_total = read.csv(file="Bcatenin_ICC_quartiles_total_BEN.csv")
  539. BEN_total$MainGroup <- factor(BEN_total$MainGroup, levels = c("BEN_GFP", "BEN_C3"))
  541. setwd(here("results"))
  542. cairo_pdf(filename = "Boxplots_ICC_Bcatenin_total_BEN.pdf", width = 5, height = 7)
  543. ggplot(BEN_total, aes(x = MainGroup, y = Value, fill=MainGroup)) +
  544. geom_jitter(aes(color = MainGroup),position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.8),
  545. size = 1.8, alpha = 0.6) +
  546. geom_boxplot(position = position_dodge(width = 0.8), width = 0.6, outlier.shape = NA) +
  547. scale_fill_manual(values = c("BEN_GFP" = "violetred4", "BEN_C3" = "steelblue4")) +
  548. scale_color_manual(values = c("BEN_GFP" = "violetred4", "BEN_C3" = "steelblue4")) +
  549. labs(title = "Boxplot title", x = "Main Group", y = "Value") +
  550. theme_classic()
  551. dev.off()
  553. ##############make boxplot of b-catenin ratio nucleus to cytoplasm
  554. setwd(here("data", "08_cell_lines"))
  555. BEN_ratio = read.csv(file="Bcatenin_ICC_quartiles_ratios_BEN.csv")
  556. BEN_ratio$MainGroup <- factor(BEN_ratio$MainGroup, levels = c("BEN_GFP", "BEN_C3"))
  558. setwd(here("results"))
  559. cairo_pdf(filename = "Boxplots_ICC_Bcatenin_ratios_BEN.pdf", width = 7, height = 7)
  560. ggplot(BEN_ratio, aes(x = MainGroup, y = Value, fill=SubGroup)) +
  561. geom_jitter(aes(color = SubGroup),position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.8),
  562. size = 1.8, alpha = 0.6) +
  563. geom_boxplot(position = position_dodge(width = 0.8), width = 0.6, outlier.shape = NA) +
  564. scale_fill_manual(values = c("Q1" = "cyan2", "Q2" = "turquoise4",
  565. "Q3" = "violetred2", "Q4" = "darkmagenta")) +
  566. scale_color_manual(values = c("Q1" = "cyan2", "Q2" = "turquoise4",
  567. "Q3" = "violetred2", "Q4" = "darkmagenta")) +
  568. geom_hline(yintercept = 0.12538226, linetype = "dashed", color = "cyan2",linewidth = 1.25)+
  569. geom_hline(yintercept = 0.37512742, linetype = "dashed", color = "turquoise4",linewidth = 1.25)+
  570. geom_hline(yintercept = 0.62487258, linetype = "dashed", color = "violetred2",linewidth = 1.25)+
  571. geom_hline(yintercept = 0.87512742, linetype = "dashed", color = "darkmagenta",linewidth = 1.25)+
  572. labs(title = "Boxplot title", x = "Main Group", y = "Value") +
  573. theme_classic()
  574. dev.off()
  576. wilcox.test(Value ~ MainGroup, data = BEN_total)
  577. wilcox.test(Value ~ MainGroup, data = BEN_ratio)

08_cell_lines.R at commit d92e2bf, no license · at the source

Overview

Authors: Daniel J Merk1,2, Peter Paßlack1,2, Surender Surender1,2, Foteini Tsiami1,2,3, Lara A Haeusser1,2, Vanessa Arnold1,4,5, Vijayasarathy Sampath-Kumar1,4,5, Lisa Sevenich1,2,4,6, Andrea D Maier7, Tiit Mathiesen7, Marcos Tatagiba8, Hanna Gött8, Jonas Tellermann8, Felix Behling8, Jens Schittenhelm9,10, Hannes Becker1,2,8, Ghazaleh Tabatabai1,2,6,10
ORCID iDs: Daniel J Merk, Peter Paßlack, Lara A Haeusser, Vanessa Arnold, Lisa Sevenich, Andrea D Maier, Jens Schittenhelm, Hannes Becker, Ghazaleh Tabatabai
  1. Department of Neurology and Interdisciplinary Neuro-Oncology, Hertie Institute for Clinical Brain Research, University Hospital Tübingen, Eberhard Karls University Tübingen, Tübingen, Germany
  2. Cluster of Excellence iFIT (EXC 2180) “Image Guided and Functionally Instructed Tumour Therapies”, Eberhard Karls University Tübingen, Tübingen, Germany
  3. Department of Pediatric Oncology, Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Boston, MA USA
  4. M3 Research Center for Malignome, Metabolome, and Microbiome, University Hospital Tübingen, Eberhard Karls University Tübingen, Tübingen, Germany
  5. Graduate Training Center for Neuroscience, Eberhard Karls University Tübingen, Tübingen, Germany
  6. German Consortium for Translational Cancer Research (DKTK), Partner Site Tübingen, German Cancer Research Center (DKFZ), Heidelberg, Germany
  7. Department of Neurosurgery, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
  8. Department of Neurosurgery and Neurotechnology, University Hospital Tübingen, Eberhard Karls University Tübingen, Tübingen, Germany
  9. Institute of Neuropathology, Department of Pathology and Neuropathology, University Hospital Tübingen, Eberhard Karls University Tübingen, Tübingen, Germany
  10. Comprehensive Cancer Center Tübingen Stuttgart, University Hospital Tübingen, Eberhard Karls University Tübingen, Tübingen, Germany
Institutions: Hertie Institute for Clinical Brain Research (Germany); University of Tübingen (Germany); Dana-Farber/Boston Children's Cancer and Blood Disorders Center (United States); Universitätsklinikum Tübingen (Germany); German Cancer Research Center (Germany); Deutsches Konsortium für Translationale Krebsforschung (Germany); Copenhagen University Hospital (Denmark); Rigshospitalet (Denmark)
Journal: Nature communications, volume 17, issue 1, article 9236
Dates: received 14 August 2025; accepted 18 August 2026; published online 29 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-77170-3 · PMID 42668313 · PMCID PMC13526022 · OpenAlex W7204662033
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Evoked potentials
Keywords: CNS cancer, DNA methylation, Cancer epigenetics
MeSH: Cadherins*, DNA Methylation*, Epigenesis, Genetic*, Gene Silencing*, Meningeal Neoplasms*, Meningioma*, Adult, Aged, beta Catenin, Cross-Sectional Studies, Disease Progression, DNA Copy Number Variations, Female, Gene Expression Regulation, Neoplastic, Humans, Longitudinal Studies, Male, Middle Aged (* major topic)
Topic: Meningioma and schwannoma management (Epidemiology, Medicine), according to OpenAlex
Funding: Else Kröner-Fresenius-Stiftung (Else Kroner-Fresenius Foundation) (2019_Kolleg_14)
Citations: not cited yet (Europe PMC); 63 references in the paper

Abstract

Meningioma is the most common primary brain tumour in adults. However, molecular drivers of progression occurring in a subset of meningiomas are poorly understood. We hypothesise that epigenomic variations are causal for the clinical heterogeneity of meningiomas and may be functionally relevant for disease progression. To test this hypothesis, we perform global DNA methylation profiling of a large cross-sectional cohort and a longitudinal cohort of human meningiomas. Our analysis identifies a DNA hypermethylation signature that is correlated with clinical outcomes and enables more accurate prognostication for meningiomas than previous classification systems. Analyses of longitudinal high-grade meningioma samples in comparison to clinically benign meningiomas and normal meningeal tissue show convergent contributions but differing plasticity of copy number variations and DNA hypermethylation along the trajectory of meningioma progression. Systematic analysis of DNA hypermethylation in high-grade meningiomas unravels a tumour suppressive role of clustered protocadherins by restricting β-catenin nuclear localisation, consistent with the association between nuclear β-catenin staining and meningioma progression. Together, our study provides fundamental insights into the molecular mechanisms underlying the heterogeneity of clinically benign and aggressive meningiomas and the microevolutionary adaptation during disease progression.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 22 matches between paragraphs and lines of code.

MerkLabHIH/Meningioma_DNA_methylation_project

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d92e2bfd47de12c85424e61b4f6e5e91f1fa57d9, 27 July 2026
Languages: R (12)
Size: 13 files, 12 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 (10 files), tidyverse (8 files), caret (3 files), ggpubr (3 files), ComplexHeatmap (2 files), emmeans (2 files), lme4 (2 files), lmerTest (2 files), pheatmap (2 files), survival (2 files), broom (1 file), circlize (1 file), data.table (1 file), DESeq2 (1 file), limma (1 file), pROC (1 file), randomForest (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
13 files
At the source: github.com/MerkLabHIH/Meningioma_DNA_methylation_project

Code availability

All code used in this study is publicly available at GitHub (https://github.com/MerkLabHIH/Meningioma_DNA_methylation_project).

Reproduced under the paper's license (CC BY), from the paper cited above.

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

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

Data

Datasets cited

Data Availability Statement

The raw DNA methylation data and RNAseq data generated in this study have been deposited in the GEO database under accession code GSE304097 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE304097). Publicly available datasets used in this study include DNA methylation array and RNAseq data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE180061 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE183656)19,20. Raw DNA methylation data from the normal meningeal samples30 cannot be deposited in a public or third-party controlled-access repository because the data derive from only two individuals who donated their bodies through the University of Copenhagen Body Donation Programme. Their use is governed by the donor framework, institutional stewardship requirements, and project-specific data-sharing agreements that do not permit onward transfer or repository deposition without separate authorisation. The data are available through a controlled-access procedure administered directly by Andrea Daniela Maier and Tiit Mathiesen from the University of Copenhagen, Denmark. Access may be granted to qualified researchers at recognised academic or non-profit research institutions when the proposed use is compatible with the donor and institutional framework and the applicant agrees to a project-specific Data Use Agreement. Requests should be submitted in writing to and will be acknowledged within five working days and decisions will be provided within 20 working days after receiving all required documentation. Approved access will normally be granted for 12 months and may be renewed following review. Additional preprocessed data of DNA methylation arrays and a folder structure compatible with our submitted code are available on figshare (https://doi.org/10.6084/m9.figshare.32043249)63. The remaining data are available within the Article, Supplementary Information or Source Data file. Source data are provided with this paper.

All code used in this study is publicly available at GitHub (https://github.com/MerkLabHIH/Meningioma_DNA_methylation_project).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 17 authors, 3 keywords, 18 MeSH terms, 1 funder, 63 references.

Cite

This paper

Merk, D. J., Paßlack, P., Surender, S., Tsiami, F., Haeusser, L. A., Arnold, V., Sampath-Kumar, V., Sevenich, L., Maier, A. D., Mathiesen, T., Tatagiba, M., Gött, H., Tellermann, J., Behling, F., Schittenhelm, J., Becker, H., & Tabatabai, G. (2026). DNA methylation profiling identifies long-range epigenetic silencing of clustered protocadherins as a key determinant of meningioma progression. Nature communications, 17(1), 9236. https://doi.org/10.1038/s41467-026-77170-3

BibTeX

@article{merk2026dna,
author = {Merk, Daniel J and Paßlack, Peter and Surender, Surender and Tsiami, Foteini and Haeusser, Lara A and Arnold, Vanessa and Sampath-Kumar, Vijayasarathy and Sevenich, Lisa and Maier, Andrea D and Mathiesen, Tiit and Tatagiba, Marcos and Gött, Hanna and Tellermann, Jonas and Behling, Felix and Schittenhelm, Jens and Becker, Hannes and Tabatabai, Ghazaleh},
title = {{DNA methylation profiling identifies long-range epigenetic silencing of clustered protocadherins as a key determinant of meningioma progression}},
journal = {Nature communications},
year = {2026},
month = aug,
volume = {17},
number = {1},
pages = {9236},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-77170-3},
url = {https://doi.org/10.1038/s41467-026-77170-3},
pmid = {42668313},
pmcid = {PMC13526022}
}

RIS

TY - JOUR
AU - Merk, Daniel J
AU - Paßlack, Peter
AU - Surender, Surender
AU - Tsiami, Foteini
AU - Haeusser, Lara A
AU - Arnold, Vanessa
AU - Sampath-Kumar, Vijayasarathy
AU - Sevenich, Lisa
AU - Maier, Andrea D
AU - Mathiesen, Tiit
AU - Tatagiba, Marcos
AU - Gött, Hanna
AU - Tellermann, Jonas
AU - Behling, Felix
AU - Schittenhelm, Jens
AU - Becker, Hannes
AU - Tabatabai, Ghazaleh
TI - DNA methylation profiling identifies long-range epigenetic silencing of clustered protocadherins as a key determinant of meningioma progression
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/08/29
VL - 17
IS - 1
SP - 9236
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-77170-3
UR - https://doi.org/10.1038/s41467-026-77170-3
LA - en
ER -

CSL-JSON

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"id": "10.1038/s41467-026-77170-3",
"type": "article-journal",
"title": "DNA methylation profiling identifies long-range epigenetic silencing of clustered protocadherins as a key determinant of meningioma progression",
"container-title": "Nature communications",
"author": [
{
"family": "Merk",
"given": "Daniel J"
},
{
"family": "Paßlack",
"given": "Peter"
},
{
"family": "Surender",
"given": "Surender"
},
{
"family": "Tsiami",
"given": "Foteini"
},
{
"family": "Haeusser",
"given": "Lara A"
},
{
"family": "Arnold",
"given": "Vanessa"
},
{
"family": "Sampath-Kumar",
"given": "Vijayasarathy"
},
{
"family": "Sevenich",
"given": "Lisa"
},
{
"family": "Maier",
"given": "Andrea D"
},
{
"family": "Mathiesen",
"given": "Tiit"
},
{
"family": "Tatagiba",
"given": "Marcos"
},
{
"family": "Gött",
"given": "Hanna"
},
{
"family": "Tellermann",
"given": "Jonas"
},
{
"family": "Behling",
"given": "Felix"
},
{
"family": "Schittenhelm",
"given": "Jens"
},
{
"family": "Becker",
"given": "Hannes"
},
{
"family": "Tabatabai",
"given": "Ghazaleh"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "9236",
"DOI": "10.1038/s41467-026-77170-3",
"PMID": "42668313",
"PMCID": "PMC13526022",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-77170-3",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
29
]
]
}
}

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[10] doi:10.1093/neuonc/noag128 [code]
Spatially-resolved single-cell imaging of melanoma brain metastases identifies localized immune patterns predictive of immune checkpoint blockade response.
Journal: Neuro-oncology
In common: pROC, survival, caret, 7 other tools, genetics / omics, other condition

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