OSCR

TET CpG sequence-context-specific DNA demethylation shapes progression of IDH-mutant gliomas.

Code ↔ Paper

29 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 29 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § STAR★Methods › Method details › Epigenetic clocks ↔ scripts/analysis_EPITOC2.R, lines 45–111 · score 1.00 · HannumG2013, LevineM2018, PCPhenoAge, CBL_common, CBL_specific, Cortex_common
  2. [2] § STAR★Methods › Method details › Epigenetic clocks ↔ scripts/vis_aging_clocks.R, lines 609–674 · score 1.00 · HannumG2013, LevineM2018, PCPhenoAge, CBL_common, CBL_specific, Cortex_common
  3. [3] § STAR★Methods › Method details › Sequence contexts ↔ scripts/export_sequence_context.R, lines 1–65 · score 0.90 · Forward_Sequence, AlleleA_ProbeSeq, reverse complement, b5 manifest, file.csv, sequence context
  4. [4] § STAR★Methods › Method details › Sequence contexts ↔ scripts/load_probe_annotations.R, lines 177–243 · score 0.84 · Forward_Sequence, AlleleA_ProbeSeq, b5 manifest, infinium methylationepic, file.csv, stranded
  5. [5] § STAR★Methods › Method details › RepliSeq ↔ scripts/download_wgEncodeUwRepliSeq.sh, the whole file · a weak match · score 0.81 · CrossMap, bigWig, wgEncodeUwRepliSeq, goldenpath, downloaded, hg38
  6. [6] § Results › Molecular mechanism of oligodendroglioma progression ↔ scripts/vis_differential_proteomics.R, lines 1414–1467 · score 0.76 · adaptive immune response, extracellular matrix, MAG, MBP, TMPO, collagen
  7. [7] § STAR★Methods › Method details › Differential methylated position analysis ↔ scripts/vis_aging_clocks.R, lines 517–581 · score 0.75 · eBayes, lmFit, topTable, model.matrix, zero, limma
  8. [8] § STAR★Methods › Method details › Differential methylated position analysis ↔ scripts/analysis_differential.R, lines 393–462 · score 0.74 · eBayes, lmFit, topTable, model.matrix, limma, error
  9. [9] § STAR★Methods › Method details › Gene enrichment ↔ scripts/load_probe_annotations.R, lines 177–243 · score 0.73 · b5 manifest, infinium methylationepic, file.csv, probes annotated, Illumina, gene
  10. [10] § STAR★Methods › Method details › DNA methylation processing and analysis ↔ scripts/analysis_snp_fingerprinting.R, lines 526–615 · score 0.71 · getSnpBeta, RGSet, fingerprinting, distance, minfi, GLASS OD
  11. [11] § STAR★Methods › Method details › DNA methylation processing and analysis ↔ scripts/analysis_dyeCorrection_rate.R, lines 388–451 · score 0.71 · Dye correction, dyeMethod, offset, correlated, probe
  12. [12] § STAR★Methods › Method details › DNA methylation processing and analysis ↔ scripts/analysis_mvalues_all_samples.R, lines 81–126 · score 0.70 · Dye correction, dyeMethod, offset, minfi, correlated, idat
  13. [13] § STAR★Methods › Method details › CGCψ ↔ scripts/analysis_A_IDH_HG__A_IDH_LG_lr__lasso_fit.R, lines 251–326 · score 0.70 · TCGA LGG, idh lg, idh hg, LASSO, glmnet, GLASS NL
  14. [14] § STAR★Methods › Method details › Proteomics ↔ scripts/vis_differential_proteomics.R, lines 925–1007 · score 0.69 · ExonBnd, UCSC_RefGene_Name, TSS200, UTR, TSS1500, proteomics
  15. [15] § STAR★Methods › Method details › CGCψ ↔ scripts/load_TCGA-LGG_OD.R, lines 187–269 · score 0.69 · TCGA LGG, dyeCorr, dyeMethod, offset, probes
  16. [16] § STAR★Methods › Method details › TCGA-LGG (1p/19q codel) ↔ scripts/load_TCGA-LGG_OD.R, lines 1–61 · score 0.67 · TCGA LGG, TCGAbiolinks, days, status, Survival
  17. [17] § STAR★Methods › Method details › RepliSeq ↔ scripts/vis_differential.R, lines 4864–4927 · score 0.64 · wgEncodeUwRepliSeq, outliers, correlation, grades
  18. [18] § Results › DNA demethylation and accelerated epigenetic aging contribute to the axis of progression ↔ scripts/vis_aging_clocks.R, lines 254–305 · score 0.61 · dnaMethyAge, PCHorvathS2018, aging, clocks, tissue, CGC
  19. [19] § Results › WHO grade-specific methylation changes are TET sequence context specific ↔ scripts/load_GLASS-OD_metadata.R, lines 1337–1383 · score 0.60 · cell division, solo WCGW, CpGs, mitotic, DNA
  20. [20] § STAR★Methods › Method details › Gene enrichment ↔ scripts/analysis_DMP_gene_level.R, lines 1028–1075 · score 0.58 · olfactory receptor, GencodeCompV12_NAME, 0–9, gene, probe
  21. [21] § STAR★Methods › Method details › TCGA-LGG (1p/19q codel) ↔ scripts/analysis_survival.R, lines 897–952 · score 0.55 · TCGA LGG, survival event
  22. [22] § Results › CGCψ: Molecular continuous grading of IDH-mutant oligodendrogliomas ↔ scripts/analysis_survival.R, lines 897–952 · score 0.55 · TCGA LGG, hazard ratio, CoxPH, survival, CGC, tumors
  23. [23] § Results › CGCψ: Molecular continuous grading of IDH-mutant oligodendrogliomas ↔ scripts/load_gene_annotations.R, lines 4–42 · score 0.54 · genes annotated, HOXC12, WNT1, HOXA3, HOXA6, HOXA7
  24. [24] § STAR★Methods › Method details › MNP CNS classifier ↔ scripts/vis_cohort_overview.R, lines 385–446 · score 0.54 · v12.8, classifier version, cohort, Quality, MNP, PredictBrain
  25. [25] § Results › WHO grade-specific methylation changes are TET sequence context specific ↔ RNAseq/MethyltransferaseExpression.R, the whole file · a weak match · score 0.51 · DNA methyltransferase, DNMT1, Enzymes, TET1
  26. [26] § STAR★Methods › Method details › Tumor purity ↔ scripts/analysis_chr4_del.R, lines 1–70 · score 0.51 · low purity, v5, arms, CNVP, segment, bins
  27. [27] § Results › CGCψ: Molecular continuous grading of IDH-mutant oligodendrogliomas ↔ scripts/vis_LGC_x_PCA.R, lines 1196–1260 · score 0.51 · NCI Methylscape, high grade classes, IDH, recurrent
  28. [28] § STAR★Methods › Method details › Tumor purity ↔ scripts/analysis_tumor_purities_EPIC.R, lines 241–290 · score 0.50 · Tumor purity, v5, CNVP, bins, PC1, MNP
  29. [29] § Results › Oligosarcomas are an aggressive subtype of oligodendroglioma with lower tumor cell fractions ↔ scripts/vis_oligosarcoma_and_purity.R, lines 1–67 · score 0.50 · tumor cell fraction, dilution, spiking, R3, R2, oligosarcoma

Paper

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The authors' code

R · 873 lines · 32 KB · no license · 3 matches

  1. #!/usr/bin/env R
  4. # load ----
  7. source('scripts/load_constants.R')
  8. source('scripts/load_functions.R')
  9. source('scripts/load_palette.R')
  10. source('scripts/load_themes.R')
  13. if(!exists('glass_od.metadata.array_samples')) {
  14. source('scripts/load_GLASS-OD_metadata.R')
  15. }
  18. # if(!exists('data.mvalues.probes')) {
  19. # source('scripts/load_mvalues_hq_samples.R')
  20. # }
  21. #
  24. library(ggplot2)
  27. # corr + forest mainly PCs ----
  28. ## load data ----
  31. plt <- glass_od.metadata.array_samples |>
  32. filter_GLASS_OD_idats(CONST_N_GLASS_OD_INCLUDED_SAMPLES) |>
  33. dplyr::mutate(`age_at_diagnosis_days` = as.Date(patient_diagnosis_date) - as.Date(patient_birth_date)) |>
  34. #dplyr::filter(!is.na(age_at_diagnosis_days)) |>
  35. #dplyr::mutate(age_at_diagnosis_days = age_at_diagnosis_days - min(na.omit(age_at_diagnosis_days))) |>
  36. #dplyr::mutate(age_at_diagnosis_days = as.numeric(age_at_diagnosis_days)) |>
  37. #dplyr::mutate(time_tissue_in_ffpe = ifelse(isolation_material == "ffpe", time_between_resection_and_array, 0)) |>
  39. dplyr::rename_with(~ gsub("array_","",.x)) |>
  40. dplyr::rename_with(~ gsub("dnaMethyAge__","dnaMethyAge: ",.x)) |>
  42. dplyr::select(resection_id,
  44. PC1,
  45. PC2,
  46. PC3,
  47. PC4,
  48. PC5,
  49. PC6,
  51. `dnaMethyAge: PCHorvathS2018`,
  52. `dnaMethyAge: PCHannumG2013`,
  53. percentage.detP.signi,
  54. `qc_SPECIFICITY_I_GT_Mismatch_6_(PM)_Red_smaller_NA_0.5`,
  55. GLASS_NL_g2_g3_sig, A_IDH_HG__A_IDH_LG_lr__lasso_fit,
  57. ) |>
  58. tibble::column_to_rownames('resection_id') |>
  60. dplyr::mutate(`percentage.detP.signi` = log(percentage.detP.signi)) |>
  61. dplyr::rename(`log(% det-P)` = percentage.detP.signi) |>
  63. dplyr::rename(`GLASS-NL p-r signature` = GLASS_NL_g2_g3_sig) |>
  64. dplyr::rename(`CGC[Ac]` = `A_IDH_HG__A_IDH_LG_lr__lasso_fit`) |>
  65. dplyr::rename(`QC: Spec I GT MM 6` = `qc_SPECIFICITY_I_GT_Mismatch_6_(PM)_Red_smaller_NA_0.5`)
  70. ## corrplot ----
  73. # <3
  74. ggcorrplot(plt, abs=T) +
  75. labs(caption=paste0("n=",nrow(plt)," samples"),
  76. subtitle=format_subtitle("correlation PCs"))
  78. ggsave("output/figures/vis_aging_clocks__PC_clustering.pdf", height = 1.9, width=4)
  83. ## stats grade ----
  86. data <- glass_od.metadata.array_samples |>
  87. filter_GLASS_OD_idats(CONST_N_GLASS_OD_INCLUDED_SAMPLES) |>
  88. filter_first_G2_and_last_G3(154) |>
  90. dplyr::group_by(patient_id) |>
  91. dplyr::mutate(is.paired = dplyr::n() == 2) |>
  92. dplyr::ungroup() |>
  94. dplyr::mutate(patient = as.factor(paste0("p",ifelse(is.paired,patient_id,"_remainder")))) |>
  95. assertr::verify(resection_tumor_grade %in% c(2,3)) |>
  96. dplyr::mutate(gr.status = factor(ifelse(resection_tumor_grade == 2, "Grade2", "Grade3"), levels=c("Grade2", "Grade3"))) |>
  98. (function(.) {
  99. print(dim(.))
  100. assertthat::assert_that(nrow(.) == 154)
  101. return(.)
  102. })() |>
  104. dplyr::select(resection_id, gr.status, patient) |>
  105. dplyr::left_join(
  106. plt |>
  107. tibble::rownames_to_column('resection_id'),
  108. by=c('resection_id'='resection_id')
  109. ) |>
  110. tibble::column_to_rownames('resection_id')
  113. n <- nrow(data)
  116. design <- model.matrix(~~patient + gr.status, data=data)
  117. fit <- limma::lmFit(data |> dplyr::mutate(patient = NULL) |> dplyr::mutate(gr.status = NULL) |> t() |> as.data.frame()
  118. , design)
  119. fit <- limma::eBayes(fit, trend=T)
  120. stats <- limma::topTable(fit,
  121. n=nrow(data),
  122. coef="gr.statusGrade3",
  123. sort.by = "none",
  124. confint=T,
  125. adjust.method="fdr") |>
  126. tibble::rownames_to_column('covar')
  129. order = data.frame(covar = c(
  130. "PC6",
  131. "PC4",
  132. "PC5",
  133. "log(% det-P)",
  134. "PC1",
  135. "QC: Spec I GT MM 6" ,
  136. "GLASS-NL p-r signature" ,
  137. "PC2" ,
  138. "CGC[Ac]" ,
  139. "dnaMethyAge: PCHannumG2013" ,
  140. "dnaMethyAge: PCHorvathS2018",
  141. "PC3"
  142. )) |>
  143. dplyr::mutate(rank = 1:dplyr::n())
  146. stats <- stats |>
  147. dplyr::left_join(order, by=c('covar'='covar'), suffix=c('',''))
  152. stats.all <- rbind(
  153. stats |>
  154. dplyr::mutate(logFC = 0) |>
  155. dplyr::mutate(t = 0) |>
  156. dplyr::mutate(type = "zero")
  157. ,
  158. stats |>
  159. dplyr::mutate(type = "datapoint")
  160. ,
  161. stats |>
  162. dplyr::mutate(logFC = CI.L) |>
  163. dplyr::mutate(type = "CI")
  164. ,
  165. stats |>
  166. dplyr::mutate(logFC = CI.R) |>
  167. dplyr::mutate(type = "CI")
  168. )
  173. ggplot(stats.all, aes(x = t, y=reorder(covar, -rank), label=paste0("q=",format.pval(adj.P.Val,nsmall=3, digits = 1)), group=covar)) +
  174. geom_line(data=subset(stats.all, type %in% c("datapoint", "zero")), lwd=theme_nature_lwd) +
  175. #geom_line(data=subset(stats.all, type %in% c("CI")),lwd=theme_nature_lwd*3) +
  176. geom_point(data=subset(stats.all, type == "datapoint"), size=theme_nature_size/3) +
  177. geom_text(data=subset(stats.all, type %in% c("datapoint")), size=theme_nature_size, family=theme_nature_font_family) +
  178. #ggrepel::geom_text_repel(
  179. # data=subset(stats.all, type %in% c("datapoint")), size=theme_nature_size, nudge_y = 0, family=theme_nature_font_family) +
  180. labs(x="t WHO grade 2 - 3",
  181. y = NULL,
  182. caption = paste0("First Grade 2 and last Grade 3: n=",n, " samples")) +
  183. theme_nature
  186. ggsave("output/figures/vis_aging_clocks__PC_clustering_limma.pdf", width=3.5, height=2.028)
  192. # corr + forest with QC & clocks ----
  193. ## corrplot ----
  196. plt <- glass_od.metadata.array_samples |>
  197. filter_GLASS_OD_idats(CONST_N_GLASS_OD_INCLUDED_SAMPLES) |>
  198. dplyr::mutate(`age_at_diagnosis_days` = as.Date(patient_diagnosis_date) - as.Date(patient_birth_date)) |>
  199. dplyr::filter(!is.na(age_at_diagnosis_days)) |>
  200. dplyr::mutate(age_at_diagnosis_days = age_at_diagnosis_days - min(na.omit(age_at_diagnosis_days))) |>
  201. dplyr::mutate(age_at_diagnosis_days = as.numeric(age_at_diagnosis_days)) |>
  202. dplyr::mutate(time_tissue_in_ffpe = ifelse(isolation_material == "ffpe", time_between_resection_and_array, 0)) |>
  203. dplyr::select(resection_id,
  205. array_epiTOC2_tnsc, array_epiTOC2_hypoSC, contains("array_dnaMethyAge"), array_RepliTali,
  206. array_percentage.detP.signi, array_PC1, `array_qc_SPECIFICITY_I_GT_Mismatch_6_(PM)_Red_smaller_NA_0.5`,
  207. time_tissue_in_ffpe,
  208. array_GLASS_NL_g2_g3_sig,array_PC2, array_A_IDH_HG__A_IDH_LG_lr__lasso_fit,
  210. array_PC3,
  211. age_at_diagnosis_days,
  212. array_PC4,
  213. array_PC5,
  214. array_PC6,
  216. array_median.overall.methylation
  217. ) |>
  218. tibble::column_to_rownames('resection_id') |>
  219. dplyr::filter(!is.na(time_tissue_in_ffpe)) |>
  221. dplyr::mutate(array_GLASS_OD_g2_g3_sig2 = NULL) |>
  222. dplyr::mutate(array_GLASS_OD_prim_rec_sig2 = NULL) |>
  223. dplyr::mutate(array_GLASS_OD_g2_g3_sig3 = NULL) |>
  224. dplyr::mutate(array_GLASS_OD_prim_rec_sig3 = NULL) |>
  225. dplyr::mutate(array_GLASS_OD_g2_g3_sig4 = NULL) |>
  226. dplyr::mutate(array_GLASS_OD_prim_rec_sig4 = NULL) |>
  229. dplyr::mutate(array_PC3 = -1 * array_PC3) |>
  230. dplyr::mutate(array_percentage.detP.signi = log(array_percentage.detP.signi)) |>
  232. dplyr::mutate(`CGC[Ac]` = -1 * array_A_IDH_HG__A_IDH_LG_lr__lasso_fit, array_A_IDH_HG__A_IDH_LG_lr__lasso_fit = NULL) |>
  233. dplyr::mutate(`-1 * array_dnaMethyAge__ZhangY2017` = -1 * array_dnaMethyAge__ZhangY2017 , array_dnaMethyAge__ZhangY2017 = NULL) |>
  234. dplyr::mutate(`-1 * array_epiTOC2_hypoSC` = -1 * array_epiTOC2_hypoSC, array_epiTOC2_hypoSC = NULL) |> # seems at inversed scale
  235. dplyr::mutate(`-1 * array_dnaMethyAge__LuA2019` = -1 * array_dnaMethyAge__LuA2019, array_dnaMethyAge__LuA2019 = NULL) |> # seems at inversed scale
  236. dplyr::mutate(`-1 * QC: SPECIFICITY I GT MM 6` = -1 * `array_qc_SPECIFICITY_I_GT_Mismatch_6_(PM)_Red_smaller_NA_0.5`, `array_qc_SPECIFICITY_I_GT_Mismatch_6_(PM)_Red_smaller_NA_0.5`=NULL) |>
  237. dplyr::select(-array_dnaMethyAge__YangZ2016) |> # epiTOC1, near identical to epiTOC2
  238. dplyr::select(-array_dnaMethyAge__PCHorvathS2013) |> # very similar to its 2018 equivalent
  239. dplyr::mutate(`array_qc_BISULFITE_CONVERSION_I_Beta_I-3_Beta_larger_0.12_0.18` = NULL) |> # contains N/A's
  240. dplyr::mutate(`array_qc_BISULFITE_CONVERSION_I_Beta_I-6_Beta_larger_0.2_0.3` = NULL) |> # contains N/A's
  241. dplyr::rename(`% detP significant probes (log)` = array_percentage.detP.signi) |>
  243. dplyr::mutate(`-1 * CGC[Ac]` = -1 * `CGC[Ac]`, `CGC[Ac]`= NULL) |>
  244. dplyr::mutate(`array_dnaMethyAge__ZhangY2017` = -1 * `-1 * array_dnaMethyAge__ZhangY2017`, `-1 * array_dnaMethyAge__ZhangY2017`=NULL) |>
  245. dplyr::mutate(`-1 * array_GLASS_NL_g2_g3_sig` = -1 * array_GLASS_NL_g2_g3_sig, array_GLASS_NL_g2_g3_sig = NULL) |>
  247. dplyr::mutate(`-1 * array_median.overall.methylation` = -1 * array_median.overall.methylation) |>
  248. dplyr::mutate(array_median.overall.methylation = NULL) |>
  251. dplyr::mutate(`-1 * array_PC2` = -1 * array_PC2, array_PC2 = NULL)
  254. colnames(plt) <- gsub("array_","",colnames(plt))
  258. p1 = ggcorrplot(plt)
  259. p1
  260. ggsave(plot = p1, "output/figures/vis_aging_clocks__ggcorrplot.pdf", width= 8.5 * 0.975 / 2 , height = 4)
  265. order <- c(
  266. "PC5",
  267. "PC4",
  268. "PC6",
  271. "-1 * PC2",
  272. "-1 * GLASS_NL_g2_g3_sig",
  273. "-1 * CGC[Ac]",
  274. "-1 * median.overall.methylation",
  276. "dnaMethyAge__ZhangY2017",
  278. "dnaMethyAge__LuA2023p3",
  279. "dnaMethyAge__LuA2023p2",
  281. "dnaMethyAge__PCPhenoAge",
  282. "dnaMethyAge__PCHorvathS2018",
  283. "dnaMethyAge__PCHannumG2013",
  284. "PC3",
  285. "dnaMethyAge__ShirebyG2020",
  286. "dnaMethyAge__McEwenL2019",
  287. "dnaMethyAge__LuA2023p1",
  288. "-1 * dnaMethyAge__LuA2019",
  289. "dnaMethyAge__Cortex_common",
  290. "dnaMethyAge__CBL_common",
  291. "RepliTali",
  292. "dnaMethyAge__CBL_specific",
  293. "dnaMethyAge__HorvathS2018",
  294. "dnaMethyAge__ZhangQ2019",
  295. "dnaMethyAge__HannumG2013",
  296. "epiTOC2_tnsc",
  297. "dnaMethyAge__LevineM2018",
  298. "age_at_diagnosis_days",
  300. "-1 * epiTOC2_hypoSC",
  301. "PC1",
  302. "% detP significant probes (log)",
  303. "-1 * QC: SPECIFICITY I GT MM 6",
  304. "time_tissue_in_ffpe"
  305. )
  311. ## forests ----
  312. ### grade ----
  315. tmp <- glass_od.metadata.array_samples |>
  316. filter_GLASS_OD_idats(CONST_N_GLASS_OD_INCLUDED_SAMPLES) |>
  317. filter_first_G2_and_last_G3(154) |>
  318. dplyr::mutate(`age_at_diagnosis_days` = as.Date(patient_diagnosis_date) - as.Date(patient_birth_date)) |>
  319. dplyr::filter(!is.na(age_at_diagnosis_days)) |>
  320. dplyr::mutate(age_at_diagnosis_days = age_at_diagnosis_days - min(na.omit(age_at_diagnosis_days))) |>
  321. dplyr::mutate(age_at_diagnosis_days = as.numeric(age_at_diagnosis_days)) |>
  322. dplyr::mutate(time_tissue_in_ffpe = ifelse(isolation_material == "ffpe", time_between_resection_and_array, 0)) |>
  323. dplyr::select(resection_id,
  324. resection_tumor_grade,
  326. array_median.overall.methylation,
  328. array_epiTOC2_tnsc, array_epiTOC2_hypoSC, contains("array_dnaMethyAge"), array_RepliTali,
  329. array_percentage.detP.signi, array_PC1, `array_qc_SPECIFICITY_I_GT_Mismatch_6_(PM)_Red_smaller_NA_0.5`,
  330. time_tissue_in_ffpe,
  331. array_GLASS_NL_g2_g3_sig, array_PC2, array_A_IDH_HG__A_IDH_LG_lr__lasso_fit,
  333. array_PC3,
  334. age_at_diagnosis_days,
  336. array_PC4,
  337. array_PC5,
  338. array_PC6
  339. ) |>
  340. tibble::column_to_rownames('resection_id') |>
  341. dplyr::filter(!is.na(time_tissue_in_ffpe)) |>
  343. dplyr::mutate(array_GLASS_OD_g2_g3_sig2 = NULL) |>
  344. dplyr::mutate(array_GLASS_OD_prim_rec_sig2 = NULL) |>
  345. dplyr::mutate(array_GLASS_OD_g2_g3_sig3 = NULL) |>
  346. dplyr::mutate(array_GLASS_OD_prim_rec_sig3 = NULL) |>
  347. dplyr::mutate(array_GLASS_OD_g2_g3_sig4 = NULL) |>
  348. dplyr::mutate(array_GLASS_OD_prim_rec_sig4 = NULL) |>
  350. dplyr::mutate(`-1 * array_median.overall.methylation` = -1 * array_median.overall.methylation) |>
  351. dplyr::mutate(array_median.overall.methylation = NULL) |>
  354. dplyr::mutate(array_PC3 = -1 * array_PC3) |>
  355. dplyr::mutate(array_percentage.detP.signi = log(array_percentage.detP.signi)) |>
  357. dplyr::mutate(`CGC[Ac]` = -1 * array_A_IDH_HG__A_IDH_LG_lr__lasso_fit, array_A_IDH_HG__A_IDH_LG_lr__lasso_fit = NULL) |>
  358. dplyr::mutate(`-1 * array_dnaMethyAge__ZhangY2017` = -1 * array_dnaMethyAge__ZhangY2017 , array_dnaMethyAge__ZhangY2017 = NULL) |>
  359. dplyr::mutate(`-1 * array_epiTOC2_hypoSC` = -1 * array_epiTOC2_hypoSC, array_epiTOC2_hypoSC = NULL) |> # seems at inversed scale
  360. dplyr::mutate(`-1 * array_dnaMethyAge__LuA2019` = -1 * array_dnaMethyAge__LuA2019, array_dnaMethyAge__LuA2019 = NULL) |> # seems at inversed scale
  361. dplyr::mutate(`-1 * QC: SPECIFICITY I GT MM 6` = -1 * `array_qc_SPECIFICITY_I_GT_Mismatch_6_(PM)_Red_smaller_NA_0.5`, `array_qc_SPECIFICITY_I_GT_Mismatch_6_(PM)_Red_smaller_NA_0.5`=NULL) |>
  362. dplyr::select(-array_dnaMethyAge__YangZ2016) |> # epiTOC1, near identical to epiTOC2
  363. dplyr::select(-array_dnaMethyAge__PCHorvathS2013) |> # very similar to its 2018 equivalent
  364. dplyr::mutate(`array_qc_BISULFITE_CONVERSION_I_Beta_I-3_Beta_larger_0.12_0.18` = NULL) |> # contains N/A's
  365. dplyr::mutate(`array_qc_BISULFITE_CONVERSION_I_Beta_I-6_Beta_larger_0.2_0.3` = NULL) |> # contains N/A's
  366. dplyr::rename(`% detP significant probes (log)` = array_percentage.detP.signi) |>
  368. dplyr::mutate(resection_tumor_grade = factor(paste0("Grade",resection_tumor_grade), levels=c("Grade2","Grade3"))) |>
  370. dplyr::mutate(`-1 * CGC[Ac]` = -1 * `CGC[Ac]`, `CGC[Ac]`= NULL) |>
  371. dplyr::mutate(`array_dnaMethyAge__ZhangY2017` = -1 * `-1 * array_dnaMethyAge__ZhangY2017`, `-1 * array_dnaMethyAge__ZhangY2017`=NULL) |>
  372. dplyr::mutate(`-1 * array_GLASS_NL_g2_g3_sig` = -1 * array_GLASS_NL_g2_g3_sig, array_GLASS_NL_g2_g3_sig = NULL) |>
  373. dplyr::mutate(`-1 * array_PC2` = -1 * array_PC2, array_PC2 = NULL)
  375. colnames(tmp) <- gsub("array_","",colnames(tmp))
  378. data <- tmp |>
  379. dplyr::mutate(`resection_tumor_grade` = NULL) |>
  380. dplyr::mutate(array_PC1 = NULL) |>
  381. dplyr::mutate(PC1 = NULL) |>
  382. as.data.frame() |>
  383. scale(center=T, scale=T) |>
  384. t() |>
  385. as.data.frame()
  388. design <- model.matrix(~factor(resection_tumor_grade), data=tmp) # no correcion, you can' time in ffpe for quality
  389. #design <- model.matrix(~age_at_diagnosis_days + factor(resection_tumor_grade), data=tmp)
  390. #design <- model.matrix(~factor(resection_tumor_grade), data=tmp)
  391. fit <- limma::lmFit(data, design)
  392. fit <- limma::eBayes(fit, trend=T)
  393. stats <- limma::topTable(fit,
  394. n=nrow(data),
  395. coef="factor(resection_tumor_grade)Grade3",
  396. sort.by = "none",
  397. confint=T,
  398. adjust.method="fdr") |>
  399. tibble::rownames_to_column('covar')
  402. stats = rbind(stats,
  404. data.frame(covar=c('array_PC1'), logFC=0, CI.L=0, CI.R=0, AveExpr=0,t=0,P.Value=NA, adj.P.Val=NA,B=NA)
  406. ) |>
  407. dplyr::mutate(covar = gsub("array_", "", covar))
  410. plt <- data.frame(covar = order) |>
  411. dplyr::mutate(y = dplyr::n():1) |>
  412. assertr::verify(covar %in% stats$covar) |>
  413. assertr::verify(stats$covar %in% covar) |>
  414. dplyr::left_join(stats, by=c('covar'='covar'))
  417. plt <- rbind(
  418. plt |>
  419. dplyr::mutate(logFC = 0) |>
  420. dplyr::mutate(t = 0) |>
  421. dplyr::mutate(type = "zero")
  422. ,
  423. plt |>
  424. dplyr::mutate(type = "datapoint")
  425. ,
  426. plt |>
  427. dplyr::mutate(logFC = CI.L) |>
  428. dplyr::mutate(type = "CI")
  429. ,
  430. plt |>
  431. dplyr::mutate(logFC = CI.R) |>
  432. dplyr::mutate(type = "CI")
  433. ) |>
  434. dplyr::mutate(stars = gtools::stars.pval(adj.P.Val))
  437. #order
  440. ggplot(plt, aes(x = t, y=reorder(covar, y), label=stars, group=covar)) +
  441. geom_line(data=subset(plt, type %in% c("datapoint", "zero")),lwd=theme_nature_lwd) +
  442. #geom_line(data=subset(plt, type %in% c("CI")),lwd=theme_nature_lwd*3) +
  443. geom_point(data=subset(plt, type == "datapoint"), size=theme_nature_size/3) +
  444. geom_text(data=subset(plt, type %in% c("datapoint")), x = 7.5, size=theme_nature_size, family=theme_nature_font_family) +
  445. xlim(-5,7.7) +
  446. labs(y = NULL, x="t statistic WHO Grade 2 vs. Grade 3") +
  447. theme_nature
  450. ggsave("output/figures/vis_aging_clocks__forest__grade.pdf", width=2.75,height = 2.665)
  453. # https://support.bioconductor.org/p/37524/
  454. # https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/forest.html
  455. # https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/metareg.html
  458. ### prim-rec ----
  461. tmp <- glass_od.metadata.array_samples |>
  462. filter_GLASS_OD_idats(CONST_N_GLASS_OD_INCLUDED_SAMPLES) |>
  463. filter_primaries_and_last_recurrences(179) |>
  464. dplyr::mutate(`age_at_diagnosis_days` = as.Date(patient_diagnosis_date) - as.Date(patient_birth_date)) |>
  465. dplyr::filter(!is.na(age_at_diagnosis_days)) |>
  466. dplyr::mutate(age_at_diagnosis_days = age_at_diagnosis_days - min(na.omit(age_at_diagnosis_days))) |>
  467. dplyr::mutate(age_at_diagnosis_days = as.numeric(age_at_diagnosis_days)) |>
  468. dplyr::mutate(time_tissue_in_ffpe = ifelse(isolation_material == "ffpe", time_between_resection_and_array, 0)) |>
  469. dplyr::select(resection_id,
  470. resection_number,
  472. array_median.overall.methylation,
  474. array_epiTOC2_tnsc, array_epiTOC2_hypoSC, contains("array_dnaMethyAge"), array_RepliTali,
  475. array_percentage.detP.signi, array_PC1, `array_qc_SPECIFICITY_I_GT_Mismatch_6_(PM)_Red_smaller_NA_0.5`,
  476. time_tissue_in_ffpe,
  477. array_GLASS_NL_g2_g3_sig, array_PC2, array_A_IDH_HG__A_IDH_LG_lr__lasso_fit,
  479. array_PC3,
  480. age_at_diagnosis_days,
  482. array_PC4,
  483. array_PC5,
  484. array_PC6
  485. ) |>
  486. tibble::column_to_rownames('resection_id') |>
  487. dplyr::filter(!is.na(time_tissue_in_ffpe)) |>
  489. dplyr::mutate(array_GLASS_OD_g2_g3_sig2 = NULL) |>
  490. dplyr::mutate(array_GLASS_OD_prim_rec_sig2 = NULL) |>
  491. dplyr::mutate(array_GLASS_OD_g2_g3_sig3 = NULL) |>
  492. dplyr::mutate(array_GLASS_OD_prim_rec_sig3 = NULL) |>
  493. dplyr::mutate(array_GLASS_OD_g2_g3_sig4 = NULL) |>
  494. dplyr::mutate(array_GLASS_OD_prim_rec_sig4 = NULL) |>
  497. dplyr::mutate(`-1 * array_median.overall.methylation` = -1 * array_median.overall.methylation) |>
  498. dplyr::mutate(array_median.overall.methylation = NULL) |>
  500. dplyr::mutate(array_PC3 = -1 * array_PC3) |>
  501. dplyr::mutate(array_percentage.detP.signi = log(array_percentage.detP.signi)) |>
  503. dplyr::mutate(`CGC[Ac]` = -1 * array_A_IDH_HG__A_IDH_LG_lr__lasso_fit, array_A_IDH_HG__A_IDH_LG_lr__lasso_fit = NULL) |>
  504. dplyr::mutate(`-1 * array_dnaMethyAge__ZhangY2017` = -1 * array_dnaMethyAge__ZhangY2017 , array_dnaMethyAge__ZhangY2017 = NULL) |>
  505. dplyr::mutate(`-1 * array_epiTOC2_hypoSC` = -1 * array_epiTOC2_hypoSC, array_epiTOC2_hypoSC = NULL) |> # seems at inversed scale
  506. dplyr::mutate(`-1 * array_dnaMethyAge__LuA2019` = -1 * array_dnaMethyAge__LuA2019, array_dnaMethyAge__LuA2019 = NULL) |> # seems at inversed scale
  507. dplyr::mutate(`-1 * QC: SPECIFICITY I GT MM 6` = -1 * `array_qc_SPECIFICITY_I_GT_Mismatch_6_(PM)_Red_smaller_NA_0.5`, `array_qc_SPECIFICITY_I_GT_Mismatch_6_(PM)_Red_smaller_NA_0.5`=NULL) |>
  508. dplyr::select(-array_dnaMethyAge__YangZ2016) |> # epiTOC1, near identical to epiTOC2
  509. dplyr::select(-array_dnaMethyAge__PCHorvathS2013) |> # very similar to its 2018 equivalent
  510. dplyr::mutate(`array_qc_BISULFITE_CONVERSION_I_Beta_I-3_Beta_larger_0.12_0.18` = NULL) |> # contains N/A's
  511. dplyr::mutate(`array_qc_BISULFITE_CONVERSION_I_Beta_I-6_Beta_larger_0.2_0.3` = NULL) |> # contains N/A's
  512. dplyr::rename(`% detP significant probes (log)` = array_percentage.detP.signi) |>
  514. dplyr::mutate(resection_prim_rec = factor(ifelse(resection_number == 1,"primary", "recurrent"), levels=c("primary","recurrent"))) |>
  515. dplyr::mutate(resection_number = NULL) |>
  517. dplyr::mutate(`-1 * CGC[Ac]` = -1 * `CGC[Ac]`, `CGC[Ac]`= NULL) |>
  518. dplyr::mutate(`array_dnaMethyAge__ZhangY2017` = -1 * `-1 * array_dnaMethyAge__ZhangY2017`, `-1 * array_dnaMethyAge__ZhangY2017`=NULL) |>
  519. dplyr::mutate(`-1 * array_GLASS_NL_g2_g3_sig` = -1 * array_GLASS_NL_g2_g3_sig, array_GLASS_NL_g2_g3_sig = NULL) |>
  520. dplyr::mutate(`-1 * array_PC2` = -1 * array_PC2, array_PC2 = NULL)
  522. colnames(tmp) <- gsub("array_","",colnames(tmp))
  525. data <- tmp |>
  526. dplyr::mutate(`resection_prim_rec` = NULL) |>
  527. dplyr::mutate(array_PC1 = NULL) |>
  528. dplyr::mutate(PC1 = NULL) |>
  529. as.data.frame() |>
  530. scale(center=T, scale=T) |>
  531. t() |>
  532. as.data.frame()
  535. design <- model.matrix(~ factor(resection_prim_rec), data=tmp)
  536. #design <- model.matrix(~age_at_diagnosis_days + factor(resection_tumor_grade), data=tmp)
  537. #design <- model.matrix(~factor(resection_tumor_grade), data=tmp)
  538. fit <- limma::lmFit(data, design)
  539. fit <- limma::eBayes(fit, trend=T)
  540. stats <- limma::topTable(fit,
  541. n=nrow(data),
  542. coef="factor(resection_prim_rec)recurrent",
  543. sort.by = "none",
  544. confint=T,
  545. adjust.method="fdr") |>
  546. tibble::rownames_to_column('covar')
  549. stats = rbind(stats,
  551. data.frame(covar=c('array_PC1'), logFC=0, CI.L=0, CI.R=0, AveExpr=0,t=0,P.Value=NA, adj.P.Val=NA,B=NA)
  553. ) |>
  554. dplyr::mutate(covar = gsub("array_", "", covar))
  557. plt <- data.frame(covar = order) |>
  558. dplyr::mutate(y = dplyr::n():1) |>
  559. assertr::verify(covar %in% stats$covar) |>
  560. assertr::verify(stats$covar %in% covar) |>
  561. dplyr::left_join(stats, by=c('covar'='covar'))
  564. plt <- rbind(
  565. plt |>
  566. dplyr::mutate(logFC = 0) |>
  567. dplyr::mutate(t = 0) |>
  568. dplyr::mutate(type = "zero")
  569. ,
  570. plt |>
  571. dplyr::mutate(type = "datapoint")
  572. ,
  573. plt |>
  574. dplyr::mutate(logFC = CI.L) |>
  575. dplyr::mutate(type = "CI")
  576. ,
  577. plt |>
  578. dplyr::mutate(logFC = CI.R) |>
  579. dplyr::mutate(type = "CI")
  580. ) |>
  581. dplyr::mutate(stars = gtools::stars.pval(adj.P.Val))
  584. #order
  587. ggplot(plt, aes(x = t, y=reorder(covar, y), label=stars, group=covar)) +
  588. geom_line(data=subset(plt, type %in% c("datapoint", "zero")),lwd=theme_nature_lwd) +
  589. #geom_line(data=subset(plt, type %in% c("CI")),lwd=theme_nature_lwd*3) +
  590. geom_point(data=subset(plt, type == "datapoint"), size=theme_nature_size/3) +
  591. geom_text(data=subset(plt, type %in% c("datapoint")), x = 7.5, size=theme_nature_size, family=theme_nature_font_family) +
  592. xlim(-5, 7.7) +
  593. labs(y = NULL, x="t statistic primary vs. recurrent") +
  594. theme_nature
  597. ggsave("output/figures/vis_aging_clocks__forest__prim_rec.pdf", width=2.75,height = 2.665)
  600. # https://support.bioconductor.org/p/37524/
  601. # https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/forest.html
  602. # https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/metareg.html
  607. # DMP time_in_tissue_ffpe
  609. # plot order:
  610. plt.order <- data.frame(name = c(
  611. "dnaMethyAge__ZhangY2017",
  612. "CGC[Ac]",
  613. "GLASS_NL_g2_g3_sig",
  614. "PC2",
  615. "time_tissue_in_ffpe",
  616. "QC: SPECIFICITY I GT MM 6",
  617. "detP significant probes (log)",
  618. "PC1",
  619. "epiTOC2_hypoSC",
  620. "dnaMethyAge__LuA2023p2",
  621. "dnaMethyAge__LuA2023p3",
  622. "dnaMethyAge__LuA2019",
  623. "dnaMethyAge__CBL_specific",
  624. "RepliTali",
  625. "epiTOC2_tnsc",
  626. "dnaMethyAge__HannumG2013",
  627. "dnaMethyAge__CBL_common",
  628. "dnaMethyAge__Cortex_common",
  629. "dnaMethyAge__McEwenL2019",
  630. "dnaMethyAge__ZhangQ2019",
  631. "dnaMethyAge__HorvathS2018",
  632. "dnaMethyAge__LevineM2018",
  633. "dnaMethyAge__LuA2023p1",
  634. "PC3",
  635. "dnaMethyAge__ShirebyG2020",
  636. "dnaMethyAge__PCHorvathS2018",
  637. "dnaMethyAge__PCHannumG2013",
  638. "dnaMethyAge__PCPhenoAge"
  639. )) |>
  640. dplyr::mutate(order = 1:dplyr::n()) |>
  641. dplyr::mutate(colname = dplyr::recode(name,
  642. `PC1` = "DMP__PCs__pp_nc__PC1_adj.P.Val",
  643. `PC2` = "DMP__PCs__pp_nc__PC2_adj.P.Val",
  644. `PC3` = "DMP__PCs__pp_nc__PC3_adj.P.Val",
  645. `detP significant probes (log)` = "DMP__pct_detP_signi__pp_nc__adj.P.Val",
  646. `CGC[Ac]` = "DMP__AcCGAP__pp_nc__adj.P.Val",
  647. `GLASS_NL_g2_g3_sig` = "DMP__GLASS_NL_signature__pp_nc__adj.P.Val",
  648. `RepliTali` = "DMP__RepliTali__up_nc__adj.P.Val",
  650. `QC: SPECIFICITY I GT MM 6` = "DMP__mnp_qc_SPECIFICITY_I_GT_Mismatch_6_PM_Red_smaller_NA_0_5__pp_nc__adj.P.Val",
  651. `epiTOC2_hypoSC` = "DMP__epiTOC2_hypoSC__up_nc__adj.P.Val",
  652. `epiTOC2_tnsc` = "DMP__epiTOC2_tnsc__up_nc__adj.P.Val",
  654. `dnaMethyAge__CBL_common` = "DMP__dnaMethyAge__CBL_common__up_nc__adj.P.Val",
  655. `dnaMethyAge__CBL_specific` = "DMP__dnaMethyAge__CBL_specific__up_nc__adj.P.Val",
  656. `dnaMethyAge__Cortex_common` = "DMP__dnaMethyAge__Cortex_common__up_nc__adj.P.Val",
  657. `dnaMethyAge__HannumG2013` = "DMP__dnaMethyAge__HannumG2013__up_nc__adj.P.Val",
  658. `dnaMethyAge__HorvathS2018` = "DMP__dnaMethyAge__HorvathS2018__up_nc__adj.P.Val",
  659. `dnaMethyAge__LevineM2018` = "DMP__dnaMethyAge__LevineM2018__up_nc__adj.P.Val",
  660. `dnaMethyAge__LuA2019` = "DMP__dnaMethyAge__LuA2019__up_nc__adj.P.Val",
  661. `dnaMethyAge__LuA2023p1` = "DMP__dnaMethyAge__LuA2023p1__up_nc__adj.P.Val",
  662. `dnaMethyAge__LuA2023p2` = "DMP__dnaMethyAge__LuA2023p2__up_nc__adj.P.Val",
  663. `dnaMethyAge__LuA2023p3` = "DMP__dnaMethyAge__LuA2023p3__up_nc__adj.P.Val",
  664. `dnaMethyAge__McEwenL2019` = "DMP__dnaMethyAge__McEwenL2019__up_nc__adj.P.Val",
  665. `dnaMethyAge__PCHannumG2013` = "DMP__dnaMethyAge__PCHannumG2013__up_nc__adj.P.Val",
  666. `dnaMethyAge__PCHorvathS2013` = "DMP__dnaMethyAge__PCHorvathS2013__up_nc__adj.P.Val",
  667. `dnaMethyAge__PCHorvathS2018` = "DMP__dnaMethyAge__PCHorvathS2018__up_nc__adj.P.Val",
  668. `dnaMethyAge__PCPhenoAge` = "DMP__dnaMethyAge__PCPhenoAge__up_nc__adj.P.Val",
  669. `dnaMethyAge__ShirebyG2020` = "DMP__dnaMethyAge__ShirebyG2020__up_nc__adj.P.Val",
  670. `dnaMethyAge__YangZ2016` = "DMP__dnaMethyAge__YangZ2016__up_nc__adj.P.Val",
  671. `dnaMethyAge__ZhangQ2019` = "DMP__dnaMethyAge__ZhangQ2019__up_nc__adj.P.Val",
  672. `dnaMethyAge__ZhangY2017` = "DMP__dnaMethyAge__ZhangY2017__up_nc__adj.P.Val",
  673. `time_tissue_in_ffpe` = "DMP__FFPE_decay_time__pp_nc__adj.P.Val"
  674. ))
  679. plt <- data.mvalues.probes |>
  680. (function(.) {
  681. print(dim(.))
  682. assertthat::assert_that(nrow(.) == CONST_N_PROBES_UNMASKED)
  683. return(.)
  684. })() |>
  685. dplyr::filter(detP_good_probe & grepl("^cg", probe_id)) |>
  686. (function(.) {
  687. print(dim(.))
  688. assertthat::assert_that(nrow(.) == CONST_N_PROBES_UNMASKED_AND_DETP)
  689. return(.)
  690. })() |>
  691. tibble::tibble() |>
  692. dplyr::select(
  693. `DMP__PCs__pp_nc__PC1_adj.P.Val`,
  694. `DMP__PCs__pp_nc__PC2_adj.P.Val`,
  695. `DMP__PCs__pp_nc__PC3_adj.P.Val`,
  696. `DMP__pct_detP_signi__pp_nc__adj.P.Val`,
  697. `DMP__AcCGAP__pp_nc__adj.P.Val`,
  698. `DMP__GLASS_NL_signature__pp_nc__adj.P.Val`,
  699. `DMP__RepliTali__up_nc__adj.P.Val`,
  701. `DMP__mnp_qc_SPECIFICITY_I_GT_Mismatch_6_PM_Red_smaller_NA_0_5__pp_nc__adj.P.Val`,
  702. `DMP__epiTOC2_hypoSC__up_nc__adj.P.Val`,
  703. `DMP__epiTOC2_tnsc__up_nc__adj.P.Val`,
  705. `DMP__dnaMethyAge__CBL_common__up_nc__adj.P.Val`,
  706. `DMP__dnaMethyAge__CBL_specific__up_nc__adj.P.Val`,
  707. `DMP__dnaMethyAge__Cortex_common__up_nc__adj.P.Val`,
  708. `DMP__dnaMethyAge__HannumG2013__up_nc__adj.P.Val`,
  709. `DMP__dnaMethyAge__HorvathS2018__up_nc__adj.P.Val`,
  710. `DMP__dnaMethyAge__LevineM2018__up_nc__adj.P.Val`,
  711. `DMP__dnaMethyAge__LuA2019__up_nc__adj.P.Val`,
  712. `DMP__dnaMethyAge__LuA2023p1__up_nc__adj.P.Val`,
  713. `DMP__dnaMethyAge__LuA2023p2__up_nc__adj.P.Val`,
  714. `DMP__dnaMethyAge__LuA2023p3__up_nc__adj.P.Val`,
  715. `DMP__dnaMethyAge__McEwenL2019__up_nc__adj.P.Val`,
  716. `DMP__dnaMethyAge__PCHannumG2013__up_nc__adj.P.Val`,
  717. `DMP__dnaMethyAge__PCHorvathS2018__up_nc__adj.P.Val`,
  718. `DMP__dnaMethyAge__PCPhenoAge__up_nc__adj.P.Val`,
  719. `DMP__dnaMethyAge__ShirebyG2020__up_nc__adj.P.Val`,
  720. `DMP__dnaMethyAge__ZhangQ2019__up_nc__adj.P.Val`,
  721. `DMP__dnaMethyAge__ZhangY2017__up_nc__adj.P.Val`,
  722. `DMP__FFPE_decay_time__pp_nc__adj.P.Val`
  723. ) |>
  724. tidyr::pivot_longer(cols = dplyr::everything(), values_to = "adj.P.Val", names_to = "colname") |>
  725. dplyr::mutate(one.min.adj.P.val = 1 - adj.P.Val) |>
  726. dplyr::left_join(plt.order, by=c('colname'='colname')) |>
  727. dplyr::rename(order_y = order) |>
  728. dplyr::group_by(colname) |>
  729. dplyr::mutate(order_x = rank(adj.P.Val)) |>
  730. dplyr::ungroup() |>
  731. dplyr::arrange(order_y, -order_x) |>
  732. dplyr::mutate(facet = factor(name, levels = plt.order |> dplyr::arrange(order) |> dplyr::pull(name))) |>
  733. dplyr::filter(1:dplyr::n() %% 450 == 1)
  737. plt2 <- rbind(plt |>
  738. dplyr::mutate(group = paste0(order_x,colname)),
  739. plt |>
  740. dplyr::mutate(group = paste0(order_x,colname)) |>
  741. dplyr::mutate(adj.P.Val=0,
  742. one.min.adj.P.val=0))
  746. ggplot(plt2, aes(x=order_x, y=one.min.adj.P.val, group=group, col = adj.P.Val < 0.01)) +
  747. facet_grid(rows = vars(facet)) + # , levels=plt.order |> dplyr::arrange(order) |> dplyr::pull(name)
  748. geom_line(lwd=theme_nature_lwd) +
  749. theme_nature +
  750. theme(axis.title.x=element_blank(),
  751. axis.text.x=element_blank(),
  752. axis.ticks.x=element_blank()) +
  753. theme(strip.text.y.right = element_text(angle = 0)) +
  754. coord_cartesian(ylim = c(0,1))
  759. # generic statistics ----
  761. ## prim - rec ----
  762. tmp <- glass_od.metadata.array_samples |>
  763. filter_GLASS_OD_idats(CONST_N_GLASS_OD_INCLUDED_SAMPLES) |>
  764. filter_primaries_and_last_recurrences(179) |>
  766. dplyr::group_by(patient_id) |>
  767. dplyr::mutate(is.paired = dplyr::n() == 2) |>
  768. dplyr::ungroup() |>
  770. dplyr::mutate(patient = as.factor(paste0("p",ifelse(is.paired,patient_id,"remainder")))) |>
  771. dplyr::mutate(pr.status = factor(ifelse(resection_number == 1,"primary","recurrence"),levels=c("primary","recurrence"))) |>
  773. dplyr::mutate(`age_at_diagnosis_days` = as.Date(patient_diagnosis_date) - as.Date(patient_birth_date)) |>
  774. dplyr::filter(!is.na(age_at_diagnosis_days)) |>
  775. dplyr::mutate(age_at_diagnosis_days = age_at_diagnosis_days - min(na.omit(age_at_diagnosis_days))) |>
  776. dplyr::mutate(age_at_diagnosis_days = as.numeric(age_at_diagnosis_days)) |>
  777. dplyr::mutate(time_tissue_in_ffpe = ifelse(isolation_material == "ffpe", time_between_resection_and_array, 0)) |>
  778. dplyr::select(resection_id,
  779. resection_tumor_grade,
  780. patient,
  781. pr.status,
  782. array_PC1,
  783. array_dnaMethyAge__PCHorvathS2018
  784. ) |>
  785. tibble::column_to_rownames('resection_id') |>
  786. dplyr::rename(PCHorvathS2018 = array_dnaMethyAge__PCHorvathS2018)
  789. data <- tmp |>
  790. dplyr::select(PCHorvathS2018) |>
  791. t() |>
  792. as.data.frame()
  795. stopifnot(rownames(tmp) == colnames(data))
  798. design <- model.matrix(~array_PC1 +# factor(patient) +
  799. factor(patient) +
  800. factor(pr.status), data=tmp)
  801. fit <- limma::lmFit(data, design)
  802. fit <- limma::eBayes(fit, trend=T)
  803. stats <- limma::topTable(fit,
  804. n=nrow(data),
  805. coef="factor(pr.status)recurrence",
  806. sort.by = "none",
  807. confint=T,
  808. adjust.method="fdr") |>
  809. tibble::rownames_to_column('covar')
  810. stats
  816. ## WHO grade ----
  819. tmp <- glass_od.metadata.array_samples |>
  820. filter_GLASS_OD_idats(CONST_N_GLASS_OD_INCLUDED_SAMPLES) |>
  821. filter_primaries_and_last_recurrences(180) |>
  823. dplyr::group_by(patient_id) |>
  824. dplyr::mutate(is.paired = dplyr::n() == 2) |>
  825. dplyr::ungroup() |>
  827. dplyr::mutate(patient = as.factor(paste0("p",ifelse(is.paired,patient_id,"remainder")))) |>
  828. dplyr::mutate(gr.status = ifelse(resection_tumor_grade == 2, "Grade2", "Grade3")) |>
  830. dplyr::mutate(`age_at_diagnosis_days` = as.Date(patient_diagnosis_date) - as.Date(patient_birth_date)) |>
  831. dplyr::filter(!is.na(age_at_diagnosis_days)) |>
  832. dplyr::mutate(age_at_diagnosis_days = age_at_diagnosis_days - min(na.omit(age_at_diagnosis_days))) |>
  833. dplyr::mutate(age_at_diagnosis_days = as.numeric(age_at_diagnosis_days)) |>
  834. dplyr::mutate(time_tissue_in_ffpe = ifelse(isolation_material == "ffpe", time_between_resection_and_array, 0)) |>
  835. dplyr::select(resection_id,
  836. resection_tumor_grade,
  837. patient,
  838. gr.status,
  839. array_PC1,
  840. array_PC2,
  841. array_PC3,
  842. array_dnaMethyAge__PCHorvathS2018,
  843. array_A_IDH_HG__A_IDH_LG_lr__lasso_fit
  844. ) |>
  845. tibble::column_to_rownames('resection_id') |>
  846. dplyr::rename(PCHorvathS2018 = array_dnaMethyAge__PCHorvathS2018)
  849. data <- tmp |>
  850. dplyr::select(PCHorvathS2018) |>
  851. #dplyr::select(array_A_IDH_HG__A_IDH_LG_lr__lasso_fit) |>
  852. #dplyr::select(array_PC3) |>
  853. t() |>
  854. as.data.frame()
  857. stopifnot(rownames(tmp) == colnames(data))
  860. design <- model.matrix(~array_PC1 + factor(patient) + factor(gr.status), data=tmp)
  861. fit <- limma::lmFit(data, design)
  862. fit <- limma::eBayes(fit, trend=T)
  863. stats <- limma::topTable(fit,
  864. n=nrow(data),
  865. coef="factor(gr.status)Grade3",
  866. sort.by = "none",
  867. confint=T,
  868. adjust.method="fdr") |>
  869. tibble::rownames_to_column('covar')
  870. stats

vis_aging_clocks.R at commit 8d13094, no license · at the source

Overview

Authors: Youri Hoogstrate1, Santoesha A. Ghisai1, Levi van Hijfte2, Rania Head1, Iris de Heer1, Marta Padovan3, Maurice de Wit1, Wies R. Vallentgoed1, Angelo Dipasquale4, Maarten M.J. Wijnenga1, Bas Weenink1, Rosa Luning1, Sybren L.N. Maas5,6, Adela Brzobohata7, Michael Weller7, Tobias Weiss7, Maximilian J. Mair8, Anna S. Berghoff8, Adelheid Wöhrer9,10, Albert Jeltsch11
and 14 other authorsJohan A.F. Koekkoek12, Hans M. Hazelbag13, Mathilde C.M. Kouwenhoven14, Yongsoo Kim15, Bart A. Westerman16, Bauke Ylstra15, Anneke M. Niers14, Kevin C. Johnson17, Frederick S. Varn18,19,20, Roel G.W. Verhaak17, Mustafa Khasraw21, Martin J. van den Bent1, Pieter Wesseling15,22, Pim J. French1
ORCID iDs: Youri Hoogstrate, Maarten M.J. Wijnenga, Sybren L.N. Maas, Maximilian J. Mair, Anna S. Berghoff, Albert Jeltsch, Johan A.F. Koekkoek, Bart A. Westerman, Bauke Ylstra, Pim J. French
22 affiliations
  1. Department of Neurology, Erasmus MC Cancer Institute, Erasmus MC, Rotterdam, the Netherlands
  2. Department of Neurosurgery, University Clinic Erlangen, Erlangen, Germany
  3. Department of Oncology, Oncology 1, Veneto Institute of Oncology IOV-IRCCS, 35128 Padua, Italy
  4. RCCS Humanitas Research Hospital, Via Alessandro Manzoni 56, Rozzano, Milan, Italy
  5. Department of Pathology, Erasmus MC Cancer Institute Erasmus MC, Rotterdam, the Netherlands
  6. Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands
  7. Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland
  8. Division of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria
  9. Department of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, Innsbruck, Tyrol, Austria
  10. Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
  11. Institute of Biochemistry and Technical Biochemistry, Department of Biochemistry, University of Stuttgart, Stuttgart, Germany
  12. Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands
  13. Department of Pathology, Haaglanden MC, The Hague, the Netherlands
  14. Department of Neurology, Amsterdam UMC, Amsterdam, the Netherlands
  15. Department of Pathology, Amsterdam UMC, Cancer Center Amsterdam, Amsterdam, the Netherlands
  16. Department of Human Genetics, Amsterdam UMC, Amsterdam, the Netherlands
  17. Department of Neurosurgery, Yale University, New Haven, CT, USA
  18. The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
  19. Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, CT, USA
  20. Institute for Systems Genomics, University of Connecticut, Storrs, CT, USA
  21. Department of Neurosurgery, Duke University, Durham, NC, USA
  22. Princess Máxima Center for Pediatric Oncology, Utrecht, the Netherlands
Institutions: Erasmus MC (Netherlands); Erasmus MC Cancer Institute (Netherlands); Universitätsklinikum Erlangen (Germany); Istituto Oncologico Veneto (Italy); IRCCS Humanitas Research Hospital (Italy); Leiden University Medical Center (Netherlands); University of Zurich (Switzerland); University Hospital Zurich (Switzerland); Medical University of Vienna (Austria); Innsbruck Medical University (Austria); Universität Innsbruck (Austria); University of Stuttgart (Germany); Medisch Centrum Haaglanden (Netherlands); Stichting Haaglanden Medisch Centrum (Netherlands); Amsterdam University Medical Centers (Netherlands); Cancer Center Amsterdam (Netherlands); Yale University (United States); University of Connecticut (United States); The Jackson Laboratory for Genomic Medicine (United States); UConn Health (United States); Duke University (United States); Princess Máxima Center (Netherlands)
Journal: Cell reports. Medicine, volume 7, issue 3, article 102682
Dates: received 9 September 2025; accepted 12 February 2026; published online 17 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.xcrm.2026.102682 · PMID 41850239 · PMCID PMC13006442 · OpenAlex W7138330789
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), other condition (population)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity
Keywords: IDH-mutant glioma, oligodendroglioma, tumor evolution, TET, DNA-methylation, sequence context, continuous grading coefficient
MeSH: Brain Neoplasms*, CpG Islands*, DNA Demethylation*, DNA Methylation*, Glioma*, Isocitrate Dehydrogenase*, Mutation*, Oligodendroglioma*, Disease Progression, Epigenesis, Genetic, Female, Humans (* major topic)
Topic: Glioma Diagnosis and Treatment (Genetics, Medicine), according to OpenAlex
Funding: KWF (2022-4 EXPL/14788); Brain Tumour Charity (GN-000765); Stichting Hanarth Fonds
Citations: cited by 2 papers (Europe PMC); 84 references in the paper
Research resources: MIB1/Ki-67 (Rabbit anti-human) RRID:AB_2631262

Abstract

Treatment decisions in IDH-mutant oligodendrogliomas are shaped by tumor aggressiveness, underscoring the need for objective grading of these malignant brain tumors. We collect 302 primary and recurrent resections from oligodendrogliomas and perform Ki-67 staining, proteomics, and DNA methylation profiling. During tumor progression, DNA methylation of oligodendrogliomas changes along a continuum. This continuum is linked to increased epigenetic aging, methylation of transcription factors and Ki-67+ cell density, and large-scale DNA demethylation. Demethylation correlates with CpG flanking sequences preferred by TET enzymes. We confirm these findings in previously profiled astrocytomas, indicating IDH-mutant gliomas progress along a shared epigenetic axis. We develop an objective DNA methylation-based prognostic continuous grading coefficient (CGCψ) that captures these changes and outperforms the World Health Organization (WHO) grading for oligodendrogliomas. Our findings underscore the potential of DNA methylation-based grading to more accurately reflect tumor biology and inform clinical decision-making in IDH-mutant gliomas.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 29 matches between paragraphs and lines of code.

yhoogstrate/idat-tools

License: GPL-3.0
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Evidence: files inventoried
Commit: 668c67c907144d8d2ba6b62a2a06280b8547e4b0, 22 June 2026
Languages: Python (5), Shell (3)
Size: 16 files, 8 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, license file, CITATION.cff, environment (pyproject.toml), tests
Not found: continuous integration, documentation
Tools: NumPy (2 files), pandas (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
10 files
At the source: github.com/yhoogstrate/idat-tools/

yhoogstrate/glass-od

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Commit: 8d130941ab868349272dc7cc3a6377a7af95d980, 8 June 2026
Languages: R (123), Python (6), Shell (3)
Size: 145 files, 132 scripts
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Tools: tidyverse (104 files), ggplot2 (41 files), ggpubr (18 files), patchwork (18 files), limma (9 files), data.table (6 files), reshape2 (3 files), survival (3 files), DESeq2 (2 files), glmnet (2 files), broom (1 file), circlize (1 file), ComplexHeatmap (1 file), Nextflow (1 file), NumPy (1 file), pandas (1 file), pROC (1 file), Seurat (1 file), UMAP (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
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133 files
At the source: github.com/yhoogstrate/glass-od

ErasmusMC-Neuro-Oncology/Continuous_Grading_Classifier

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Commit: ec666713db917d35fb1b01ecd626e038cb9b32d1, 13 April 2026
Languages: R (2)
Size: 8 files, 2 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README
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Tools: data.table (1 file), glmnet (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
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3 files
At the source: github.com/ErasmusMC-Neuro-Oncology/Continuous_Grading_Classifier/

yiluyucheng/dnaMethyAge

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Commit: 0d40c9bb02c7d9a4d91ce01d150a9c0b12edf1f4, 9 April 2026
Languages: R (11)
Size: 49 files, 11 scripts
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12 files
At the source: github.com/yiluyucheng/dnaMethyAge

Zenodo 2632938

License: CC-BY-4.0
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At the source: zenodo.org/records/2632938

Zenodo 7108429

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jamieendicott/nature_comm_2022

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Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
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At the source: github.com/jamieendicott/Nature_Comm_2022/tree/v1.1

The paper's code and data availability statement is in the Data section.

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

Data

Datasets cited

Data and code availability

• DNA methylation data are available in GEO: GSE297733, proteomics data in PRIDE: PXD070222 (https://www.ebi.ac.uk/pride/PXD070222). Supplementary tables have been deposited at https://zenodo.org/records/17711746. • Computer code to analyze all data are available at https://github.com/yhoogstrate/glass-od, for estimating CGCψ at https://github.com/ErasmusMC-Neuro-Oncology/Continuous_Grading_Classifier/, and mixing ∗.idat files at https://github.com/yhoogstrate/idat-tools/. • Any additional information required to reanalyze the data reported in this work is available from the lead contact upon request.

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

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 34 authors, 7 keywords, 12 MeSH terms, 3 funders, 83 references, 1 RRID.

Cite

This paper

Hoogstrate, Y., Ghisai, S. A., van Hijfte, L., Head, R., de Heer, I., Padovan, M., de Wit, M., Vallentgoed, W. R., Dipasquale, A., Wijnenga, M. M., Weenink, B., Luning, R., Maas, S. L., Brzobohata, A., Weller, M., Weiss, T., Mair, M. J., Berghoff, A. S., Wöhrer, A., . . . French, P. J. (2026). TET CpG sequence-context-specific DNA demethylation shapes progression of IDH-mutant gliomas. Cell reports. Medicine, 7(3), 102682. https://doi.org/10.1016/j.xcrm.2026.102682

BibTeX

@article{hoogstrate2026tet,
author = {Hoogstrate, Youri and Ghisai, Santoesha A. and van Hijfte, Levi and Head, Rania and de Heer, Iris and Padovan, Marta and de Wit, Maurice and Vallentgoed, Wies R. and Dipasquale, Angelo and Wijnenga, Maarten M.J. and Weenink, Bas and Luning, Rosa and Maas, Sybren L.N. and Brzobohata, Adela and Weller, Michael and Weiss, Tobias and Mair, Maximilian J. and Berghoff, Anna S. and Wöhrer, Adelheid and Jeltsch, Albert and Koekkoek, Johan A.F. and Hazelbag, Hans M. and Kouwenhoven, Mathilde C.M. and Kim, Yongsoo and Westerman, Bart A. and Ylstra, Bauke and Niers, Anneke M. and Johnson, Kevin C. and Varn, Frederick S. and Verhaak, Roel G.W. and Khasraw, Mustafa and van den Bent, Martin J. and Wesseling, Pieter and French, Pim J.},
title = {{TET CpG sequence-context-specific DNA demethylation shapes progression of IDH-mutant gliomas}},
journal = {Cell reports. Medicine},
year = {2026},
month = mar,
volume = {7},
number = {3},
pages = {102682},
publisher = {Elsevier},
issn = {2666-3791},
doi = {10.1016/j.xcrm.2026.102682},
url = {https://doi.org/10.1016/j.xcrm.2026.102682},
pmid = {41850239},
pmcid = {PMC13006442}
}

RIS

TY - JOUR
AU - Hoogstrate, Youri
AU - Ghisai, Santoesha A.
AU - van Hijfte, Levi
AU - Head, Rania
AU - de Heer, Iris
AU - Padovan, Marta
AU - de Wit, Maurice
AU - Vallentgoed, Wies R.
AU - Dipasquale, Angelo
AU - Wijnenga, Maarten M.J.
AU - Weenink, Bas
AU - Luning, Rosa
AU - Maas, Sybren L.N.
AU - Brzobohata, Adela
AU - Weller, Michael
AU - Weiss, Tobias
AU - Mair, Maximilian J.
AU - Berghoff, Anna S.
AU - Wöhrer, Adelheid
AU - Jeltsch, Albert
AU - Koekkoek, Johan A.F.
AU - Hazelbag, Hans M.
AU - Kouwenhoven, Mathilde C.M.
AU - Kim, Yongsoo
AU - Westerman, Bart A.
AU - Ylstra, Bauke
AU - Niers, Anneke M.
AU - Johnson, Kevin C.
AU - Varn, Frederick S.
AU - Verhaak, Roel G.W.
AU - Khasraw, Mustafa
AU - van den Bent, Martin J.
AU - Wesseling, Pieter
AU - French, Pim J.
TI - TET CpG sequence-context-specific DNA demethylation shapes progression of IDH-mutant gliomas
T2 - Cell reports. Medicine
J2 - Cell Rep Med
PY - 2026
DA - 2026/03/01
VL - 7
IS - 3
SP - 102682
SN - 2666-3791
PB - Elsevier
DO - 10.1016/j.xcrm.2026.102682
UR - https://doi.org/10.1016/j.xcrm.2026.102682
LA - en
ER -

CSL-JSON

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{
"family": "Koekkoek",
"given": "Johan A.F."
},
{
"family": "Hazelbag",
"given": "Hans M."
},
{
"family": "Kouwenhoven",
"given": "Mathilde C.M."
},
{
"family": "Kim",
"given": "Yongsoo"
},
{
"family": "Westerman",
"given": "Bart A."
},
{
"family": "Ylstra",
"given": "Bauke"
},
{
"family": "Niers",
"given": "Anneke M."
},
{
"family": "Johnson",
"given": "Kevin C."
},
{
"family": "Varn",
"given": "Frederick S."
},
{
"family": "Verhaak",
"given": "Roel G.W."
},
{
"family": "Khasraw",
"given": "Mustafa"
},
{
"family": "van den Bent",
"given": "Martin J."
},
{
"family": "Wesseling",
"given": "Pieter"
},
{
"family": "French",
"given": "Pim J."
}
],
"container-title-short": "Cell Rep Med",
"volume": "7",
"issue": "3",
"page": "102682",
"DOI": "10.1016/j.xcrm.2026.102682",
"PMID": "41850239",
"PMCID": "PMC13006442",
"ISSN": "2666-3791",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.xcrm.2026.102682",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
1
]
]
}
}

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

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