OSCR

Mapping the GDF15 arm of the integrated stress response in human cells and tissues.

Code ↔ Paper

23 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 23 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Methods › Tissue groups ↔ ISRGDF15_index/Data Processing Scripts/Tissue_proliferation_index.R, lines 169–231 · score 0.87 · skin lower leg, skeletal muscle, breast mammary, salivary gland, suprapubic, brain
  2. [2] § Methods › Tissue groups ↔ ISRGDF15_index/Main Figure Scripts/Figure_4D_4E.R, lines 126–180 · score 0.86 · heart atrial appendage, skin lower leg, heart left ventricle, colon, suprapubic, esophageal
  3. [3] § Methods › The Gene Tissue Expression (GTEx) dataset ↔ ISRGDF15_index/Main Figure Scripts/Helper Scripts/gtex/Attibutes_Phenos_Merged_plus_COD.R, lines 167–236 · score 0.86 · heart disease, aortic dissection, probable mi, acute mi, DTHFUCOD, DTHCOD
  4. [4] § Methods › The Gene Tissue Expression (GTEx) dataset ↔ ISRGDF15_index/Main Figure Scripts/Helper Scripts/Figure_4D_Base.R, lines 134–179 · score 0.79 · small intestines, kidney cortex, ileum, pancreas, spleen, uterus
  5. [5] § Methods › Senescence and Proliferation Index Scores ↔ ISRGDF15_index/Data/gtex/Age_comparisons/Tissue_Expression_Processing.R, lines 2–43 · score 0.77 · CDKN1A, CDKN2A, TOP2A, proliferative genes, CCND2, senescence
  6. [6] § Results › Linking cause of death and tissue-specific ISR activation ↔ ISRGDF15_index/Main Figure Scripts/Figure_4B.R, lines 1–50 · score 0.76 · brain frontal cortex, heart left ventricle, Emergency Room, hospital inpatient, GDF15 expression, death
  7. [7] § Results › Applying the ISRGDF15 index in human tissues ↔ ISRGDF15_index/Data/gtex/Age_comparisons/Tissue_Expression_Processing.R, lines 2–43 · score 0.76 · CDKN1A, CDKN2A, TOP2A, tissue expressions, CCND2, MKI67
  8. [8] § Results › Applying the ISRGDF15 index in human tissues ↔ ISRGDF15_index/Data/gtex/Age_comparisons/Age_vs_Chose_Comparison.R, lines 2–74 · score 0.75 · CDKN1A, CDKN2A, TOP2A, tissue expressions, CCND2, MKI67
  9. [9] § Methods › Senescence and Proliferation Index Scores ↔ ISRGDF15_index/Data/gtex/Age_comparisons/Age_vs_Chose_Comparison.R, lines 2–74 · score 0.70 · CDKN1A, CDKN2A, TOP2A, CCND2, senescence, MKI67
  10. [10] § Results › Linking cause of death and tissue-specific ISR activation ↔ ISRGDF15_index/Main Figure Scripts/Helper Scripts/Figure_3B_4A_Base.R, lines 814–889 · score 0.70 · brain frontal cortex, heart left ventricle, Emergency Room, hospital inpatient, Spearman, tissue
  11. [11] § Results › Generating the ISRGDF15 index ↔ ISRGDF15_index/Main Figure Scripts/Figure_1G.R, lines 1–73 · score 0.69 · ascending median score, Kruskal Wallis, contact inhibition, Dunn, DEX, Oligo
  12. [12] § Methods › Statistics ↔ ISRGDF15_index/Main Figure Scripts/Figure_1G_HedgesG.R, lines 41–114 · score 0.68 · Wilcoxon rank sum, cohen.d, Kruskal, Hedge, BH, Bonferroni
  13. [13] § Methods › ISR gene list compilation ↔ ISRGDF15_index/Main Figure Scripts/Helper Scripts/Fibroblast_lifespan/GO_vs_AnyGenes_vs_JacksonLabs_ListsofGenes.R, the whole file · a weak match · score 0.66 · GO Consortium, Jackson, IRGM, Igtp, ISR gene, NARS
  14. [14] § Results › Linking cause of death and tissue-specific ISR activation ↔ ISRGDF15_index/Main Figure Scripts/Figure_4D_4E.R, lines 126–180 · score 0.66 · atrial appendage, left ventricle, transverse, colon, esophageal, skin
  15. [15] § Methods › ISR gene list compilation ↔ ISRGDF15_index/Data Processing Scripts/Figure_1A_Process_Data.R, lines 1–34 · score 0.63 · GO Consortium, IRGM, Igtp, Jackson, ISR gene, NARS
  16. [16] § Methods › Statistics ↔ ISRGDF15_index/Main Figure Scripts/Helper Scripts/Figure_4E_Base.R, lines 135–220 · score 0.62 · Wilcoxon rank sum, cohen.d, Prism, Hedge
  17. [17] § Results › Generating the ISRGDF15 index ↔ ISRGDF15_index/Main Figure Scripts/Figure_3D.R, lines 242–305 · score 0.60 · mitoNUITs, contact inhibition, mutations, oligomycin, DEX, SURF1
  18. [18] § Results › Generating the ISRGDF15 index ↔ ISRGDF15_index/Data Processing Scripts/Figure_1G_Process_Data.R, lines 42–88 · score 0.60 · mitoNUITs, contact inhibition, mutations, oligomycin, DEX, SURF1
  19. [19] § Results › Generating the ISRGDF15 index ↔ ISRGDF15_index/Main Figure Scripts/Figure_3D.R, lines 242–305 · score 0.55 · mitoNUITs, contact inhibition, DG, mutations, oligomycin, DEX
  20. [20] § Results › Experimental validation of the ISRGDF15 index ↔ ISRGDF15_index/Main Figure Scripts/Helper Scripts/Figure_2F_Base.R, lines 425–476 · score 0.55 · dimerizable PERK, arsenite, thapsigargin, untreated, parental, UT
  21. [21] § Results › Experimental validation of the ISRGDF15 index ↔ ISRGDF15_index/Main Figure Scripts/Figure_2F.R, lines 54–116 · score 0.53 · dimerizable PERK, arsenite, thapsigargin, untreated, parental, UT
  22. [22] § Results › Linking cause of death and tissue-specific ISR activation ↔ ISRGDF15_index/Main Figure Scripts/Helper Scripts/Figure_4D_Base.R, lines 1–48 · score 0.52 · median proliferation score, Proliferative tissues, deathplace, split, ISR
  23. [23] § Results › Linking cause of death and tissue-specific ISR activation ↔ ISRGDF15_index/Main Figure Scripts/Figure_4B.R, lines 139–234 · score 0.51 · emergency room, hospital inpatient, HI, death, tissue, score

Paper

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The authors' code

R · 519 lines · 22 KB · no license · 2 matches

  1. # ============================================================================
  2. # Figures 4D & 4E: Journal-Compliant Dot Plots with Individual Donor Points
  3. # Journal requirement: show individual data points behind tissue-level summaries
  4. # ============================================================================
  5. # Generates FOUR plots (two tissue sets × two analytes):
  6. # [ISR] Figure_4D_4E_ISR_all_tissues.png
  7. # [ISR sig] Figure_4D_4E_ISR_significant_tissues.png
  8. # [Proliferation] Figure_4D_4E_Proliferation_all_tissues.png
  9. # [Prolif sig] Figure_4D_4E_Proliferation_significant_tissues.png
  10. #
  11. # ISR (Factor1) scores come from All_Tissue_data_DTHPLCE.csv (per-donor).
  12. # Proliferation scores (sum of MKI67 + RRM2 + TOP2A TPM per donor) are
  13. # computed on first run from the GTEx TPM file, then cached to:
  14. # Data/gtex/Processed/proliferation_per_donor.csv
  15. #
  16. # X-axis: tissues, ordered by descending mean score (each plot uses its own metric)
  17. # Dots: individual donors, translucent and jittered
  18. # Line: crossbar at tissue mean, opaque
  19. # ============================================================================
  21. library(here)
  22. library(tidyverse)
  23. library(ggplot2)
  24. library(purrr)
  26. # ============================================================================
  27. # SOURCE FIGURE 4D SCRIPT TO GET data_sig (significant tissues)
  28. # ============================================================================
  29. message("\n=== Sourcing Figure 4D script to get data_sig ===")
  30. source(here("Main Figure Scripts", "Helper Scripts", "Figure_4D_Base.R"), local = TRUE)
  32. # Re-define output directory AFTER sourcing (source script clears environment)
  33. out_dir <- here("Results", "Figures", "Figure_4D_4E")
  34. if (!dir.exists(out_dir)) dir.create(out_dir, recursive = TRUE)
  36. # data_sig: tissue-level summary with Tissue, Avg_Factor1, proliferation_score,
  37. # Significant, deathplace_comparison, Proliferative_Group
  38. # Standardise tissue names to lowercase
  39. data_sig <- data_sig %>% mutate(Tissue = tolower(Tissue))
  40. sig_tissues <- unique(data_sig$Tissue)
  42. message("Significant tissues in data_sig: ", length(sig_tissues))
  44. # ============================================================================
  45. # LOAD PER-DONOR ISR (FACTOR1) SCORES
  46. # ============================================================================
  47. # Build the per-donor / per-tissue long-format dataframe by reading the
  48. # per-tissue Plot_Data_<tissue>.csv files written by
  49. # Figure_3B_4A_Tissue_Scatter.R. Helper applies NO DTHPLCE filtering; this
  50. # script's main donor_isr below uses every donor (no DTHPLCE restriction),
  51. # while the supplemental Figure 4E DTHPLCE breakdown filters internally.
  52. message("\n=== Loading per-donor ISR scores ===")
  53. source(here("Main Figure Scripts", "Helper Scripts",
  54. "Load_All_Tissue_DTHPLCE.R"))
  55. all_tissue_data <- load_all_tissue_dthplce()
  57. donor_isr <- all_tissue_data %>%
  58. filter(fa_vs_gdf15 == "Factor1") %>%
  59. select(SAMPID, Tissue, value) %>%
  60. rename(score = value) %>%
  61. mutate(Tissue = tolower(Tissue))
  63. message("Per-donor ISR rows loaded: ", nrow(donor_isr))
  64. message("Unique tissues in ISR data: ", n_distinct(donor_isr$Tissue))
  66. # ============================================================================
  67. # LOAD OR COMPUTE PER-DONOR PROLIFERATION SCORES
  68. # Cached after first run to avoid re-reading the 2.5 GB GTEx TPM file.
  69. # ============================================================================
  70. per_donor_prolif_file <- here("Data", "gtex", "Processed", "proliferation_per_donor.csv")
  72. if (file.exists(per_donor_prolif_file)) {
  74. message("\n=== Loading cached per-donor proliferation scores ===")
  75. donor_prolif <- read.csv(per_donor_prolif_file)
  76. message("Rows loaded: ", nrow(donor_prolif))
  78. # Info: raw sums of 0 will become -Inf after log2 at plot time.
  79. n_zero <- sum(donor_prolif$avg_prolif == 0, na.rm = TRUE)
  80. if (n_zero > 0) {
  81. message(" Note: ", n_zero, " samples have raw sum = 0 (all 3 genes = 0 TPM)")
  82. }
  84. } else {
  86. message("\n=== Computing per-donor proliferation scores from GTEx TPM ===")
  87. message(" (This only runs once; results are cached for future use)")
  89. gtex_tpm_file <- here("Data", "gtex", "GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_tpm.gct.gz")
  90. annotations_file <- here("Data", "gtex", "Insert_GTEx_SampleAttributes_and_SubjectPhenotypes_Here",
  91. "GTEx_Analysis_2017-06-05_v8_Annotations_GTEx_Analysis_2017-06-05_v8_Annotations_SampleAttributesDS.tsv")
  93. if (!file.exists(gtex_tpm_file)) {
  94. stop(paste0(
  95. "\n============================================================\n",
  96. "ERROR: GTEx TPM file not found.\n",
  97. "Expected: ", gtex_tpm_file, "\n",
  98. "Per-donor proliferation scores cannot be computed without it.\n",
  99. "============================================================\n"
  100. ))
  101. }
  102. if (!file.exists(annotations_file)) {
  103. stop(paste0(
  104. "\n============================================================\n",
  105. "ERROR: GTEx sample annotations file not found.\n",
  106. "Expected: ", annotations_file, "\n",
  107. "============================================================\n"
  108. ))
  109. }
  111. message(" Reading GTEx TPM file (may take several minutes)...")
  112. gtex <- read.delim(gtex_tpm_file, skip = 2)
  113. exprs <- gtex %>%
  114. dplyr::select(-Name) %>%
  115. rename(Gene = Description)
  117. message(" Filtering to proliferation genes: MKI67, RRM2, TOP2A ...")
  118. exprs_prolif_genes <- exprs %>%
  119. as.data.frame() %>%
  120. filter(Gene %in% c("MKI67", "RRM2", "TOP2A"))
  122. if (nrow(exprs_prolif_genes) < 3) {
  123. stop("ERROR: Fewer than 3 proliferation genes found in GTEx TPM file.")
  124. }
  126. # Transpose: samples as rows, genes as columns
  127. exprs_prolif_t <- exprs_prolif_genes %>%
  128. column_to_rownames("Gene") %>%
  129. t() %>%
  130. as.data.frame() %>%
  131. rownames_to_column("X")
  133. # Sum raw TPM of 3 genes per sample (NO log2 — raw sums cached; log2 at plot time)
  134. exprs_prolif_summed <- exprs_prolif_t %>%
  135. pivot_longer(cols = c("MKI67", "RRM2", "TOP2A"), names_to = "Gene", values_to = "TPM") %>%
  136. group_by(X) %>%
  137. summarise(sum_TPM = sum(TPM, na.rm = TRUE), .groups = "drop") %>%
  138. mutate(avg_prolif = sum_TPM)
  140. # Load sample annotations
  141. message(" Reading sample annotations...")
  142. ann <- read_tsv(annotations_file, show_col_types = FALSE) %>%
  143. filter(SMAFRZE == "RNASEQ") %>%
  144. mutate(SUBJID = substring(SAMPID, 1, 10)) %>%
  145. mutate(SUBJID = case_when(
  146. substr(SUBJID, nchar(SUBJID), nchar(SUBJID)) == "-" ~ substr(SUBJID, 1, nchar(SUBJID) - 1),
  147. TRUE ~ SUBJID
  148. ))
  150. # Normalise SAMPID format to match TPM column names
  151. ann <- ann %>%
  152. mutate(X = gsub("\\-", ".", SAMPID)) %>%
  153. select(X, SMTSD, SUBJID, SMRIN)
  155. # Join and apply RIN filter (matching Tissue_proliferation_index.R)
  156. donor_prolif_long <- exprs_prolif_summed %>%
  157. inner_join(ann, by = "X") %>%
  158. filter(SMRIN >= 5.5) %>%
  159. select(SAMPID = X, SUBJID, Tissue_raw = SMTSD, avg_prolif) %>%
  160. distinct()
  162. # Standardise tissue names (same transformations as Tissue_proliferation_index.R)
  163. donor_prolif_long <- donor_prolif_long %>%
  164. mutate(Tissue = gsub(" - ", "_", Tissue_raw)) %>%
  165. mutate(Tissue = gsub(" ", "_", Tissue)) %>%
  166. mutate(Tissue = gsub("-", "_", Tissue)) %>%
  167. mutate(Tissue = gsub("\\(", "", Tissue)) %>%
  168. mutate(Tissue = gsub("\\)", "", Tissue)) %>%
  169. mutate(Tissue = tolower(Tissue)) %>%
  170. mutate(Tissue = case_when(
  171. Tissue == "brain_frontal_cortex_ba9" ~ "brain_frontal_cortex",
  172. Tissue == "skin_sun_exposed_lower_leg" ~ "skin_lower_leg",
  173. Tissue == "skin_not_sun_exposed_suprapubic" ~ "skin_suprapubic",
  174. Tissue == "colon_sigmoid" ~ "colon_sigmoid",
  175. Tissue == "colon_transverse" ~ "colon_transverse",
  176. Tissue == "esophagus_gastroesophageal_junction" ~ "esophagus_gej",
  177. Tissue == "heart_left_ventricle" ~ "heart_left_ventricle",
  178. Tissue == "heart_atrial_appendage" ~ "heart_atrial_appendage",
  179. TRUE ~ Tissue
  180. ))
  182. # Cache per-donor proliferation scores (RAW sums, not log2).
  183. # log2 is applied at plot time.
  184. donor_prolif <- donor_prolif_long %>%
  185. select(SAMPID, Tissue, avg_prolif)
  187. # Cache for future runs
  188. write.csv(donor_prolif, per_donor_prolif_file, row.names = FALSE)
  189. message(" Cached per-donor proliferation scores to: ", per_donor_prolif_file)
  190. message(" Rows cached: ", nrow(donor_prolif))
  192. }
  194. # ============================================================================
  195. # HARMONISE PROLIFERATION TISSUE NAMES TO MATCH data_sig
  196. # The cached proliferation_per_donor.csv uses GTEx annotation names which
  197. # differ from the ISR analysis names for several tissues.
  198. # ============================================================================
  199. donor_prolif <- donor_prolif %>%
  200. mutate(Tissue = tolower(Tissue)) %>%
  201. mutate(Tissue = case_when(
  202. Tissue == "brain_nucleus_accumbens_basal_ganglia" ~ "brain_nucelus_accumbens_basal_ganglia",
  203. Tissue == "brain_spinal_cord_cervical_c_1" ~ "brain_spinal_cord_cervical_c1",
  204. Tissue == "breast_mammary_tissue" ~ "breast_mammary",
  205. Tissue == "esophagus_gej" ~ "esophagus_gastroesophageal_junction",
  206. Tissue == "esophagus_gastroesophageal_junction" ~ "esophagus_gastroesophageal_junction",
  207. Tissue == "minor_salivary_gland" ~ "salivary_gland",
  208. Tissue == "muscle_skeletal" ~ "skeletal_muscle",
  209. TRUE ~ Tissue
  210. ))
  212. # Verify matching
  213. prolif_tissues_available <- unique(donor_prolif$Tissue)
  214. missing_from_prolif <- setdiff(sig_tissues, prolif_tissues_available)
  215. if (length(missing_from_prolif) > 0) {
  216. message("WARNING: ", length(missing_from_prolif), " significant tissues still missing from proliferation data:")
  217. message(paste(" -", missing_from_prolif, collapse = "\n"))
  218. } else {
  219. message("All ", length(sig_tissues), " significant tissues found in proliferation data.")
  220. }
  222. # ============================================================================
  223. # BUILD TISSUE-LEVEL SUMMARY TABLES (mean per tissue)
  224. # For ordering and crossbar reference
  225. # ============================================================================
  226. tissue_means_isr <- donor_isr %>%
  227. group_by(Tissue) %>%
  228. summarise(mean_score = mean(score, na.rm = TRUE), .groups = "drop")
  230. # Tissue mean for proliferation: avg_prolif contains RAW sums.
  231. # Average raw sums across donors, then apply log2.
  232. tissue_means_prolif <- donor_prolif %>%
  233. group_by(Tissue) %>%
  234. summarise(mean_score = log2(mean(avg_prolif, na.rm = TRUE)), .groups = "drop")
  236. # Per-donor display scores: apply log2 to raw sums for plotting.
  237. # Filter out zero-sum samples (log2(0) = -Inf) to avoid axis issues.
  238. donor_prolif_plot <- donor_prolif %>%
  239. filter(avg_prolif > 0) %>%
  240. mutate(score = log2(avg_prolif))
  242. # ============================================================================
  243. # PLOTTING HELPER
  244. # ============================================================================
  245. make_dot_plot <- function(donor_data, # data.frame: SAMPID, Tissue, score
  246. tissue_means, # data.frame: Tissue, mean_score
  247. title,
  248. ylab_text,
  249. dot_color,
  250. mean_color,
  251. tissues_to_include, # character vector of tissue names (lowercase)
  252. order_by = "desc",
  253. show_mean = TRUE) {
  255. # Restrict to requested tissues
  256. donor_sub <- donor_data %>% filter(Tissue %in% tissues_to_include)
  257. means_sub <- tissue_means %>% filter(Tissue %in% tissues_to_include)
  259. if (nrow(donor_sub) == 0 || nrow(means_sub) == 0) {
  260. warning("No data found for the requested tissues in: ", title)
  261. return(NULL)
  262. }
  264. # Compute y-axis limits from data with 10% padding
  265. y_range <- range(donor_sub$score, na.rm = TRUE)
  266. y_pad <- diff(y_range) * 0.1
  267. y_lim <- c(y_range[1] - y_pad, y_range[2] + y_pad)
  269. # Order tissues by descending mean score
  270. tissue_order <- means_sub %>%
  271. arrange(desc(mean_score)) %>%
  272. pull(Tissue)
  274. donor_sub <- donor_sub %>%
  275. mutate(Tissue = factor(Tissue, levels = tissue_order))
  277. means_sub <- means_sub %>%
  278. mutate(Tissue = factor(Tissue, levels = tissue_order))
  280. p <- ggplot() +
  281. # Individual donor dots — translucent, jittered
  282. geom_jitter(
  283. data = donor_sub,
  284. aes(x = Tissue, y = score),
  285. color = dot_color, alpha = 0.25, size = 1.8,
  286. width = 0.22, height = 0
  287. )
  289. # Optionally add tissue mean crossbar
  290. if (show_mean) {
  291. p <- p +
  292. geom_crossbar(
  293. data = means_sub,
  294. aes(x = Tissue, y = mean_score, ymin = mean_score, ymax = mean_score),
  295. color = mean_color, width = 0.55, linewidth = 0.7
  296. )
  297. }
  299. p <- p +
  300. scale_y_continuous(limits = y_lim, expand = c(0, 0)) +
  301. scale_x_discrete(expand = expansion(add = 0.6)) +
  302. labs(title = title, x = "Tissue", y = ylab_text) +
  303. theme_minimal(base_size = 14) +
  304. theme(
  305. plot.title = element_text(face = "bold"),
  306. axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1, colour = "black"),
  307. axis.text.y = element_text(colour = "black"),
  308. axis.title = element_text(colour = "black"),
  309. panel.grid.major.x = element_blank(),
  310. panel.grid.minor = element_blank(),
  311. panel.grid.major.y = element_line(colour = "grey92"),
  312. plot.margin = margin(t = 10, r = 20, b = 5.5, l = 15)
  313. )
  315. return(p)
  316. }
  319. # ============================================================================
  320. # FIGURE 4E + SUPPLEMENTAL FIGURE 8
  321. # ============================================================================
  322. # Both plots are computed on `data_sig` (significant tissues only — built by
  323. # Figure_4D_Base.R after the median-proliferation split). Each is a
  324. # two-group dot plot of Avg_Factor1 (per-tissue ISR^GDF15 score):
  325. #
  326. # Figure 4E — split by deathplace_comparison
  327. # (which side of the H/E comparison the tissue fell on)
  328. # Supplemental Fig 8 — split by Proliferative_Group
  329. # (above vs below median proliferation_score)
  330. #
  331. # Two CSVs (Prism wide format, no tissue labels) and two PNGs are written
  332. # to Results/Figures/Figure_4D_4E/. Figure_4E.png is also copied to the
  333. # shared Results/Figures/ folder; Supplemental_Fig8.png is not.
  334. # ============================================================================
  335. message("\n=== Generating Figure 4E + Supplemental Figure 8 ===")
  337. # --- Helper: build a Prism-style wide CSV (one column per group, no labels) ---
  338. prism_wide <- function(df, group_col, value_col, group_levels, col_names) {
  339. out <- lapply(group_levels, function(g) df[[value_col]][df[[group_col]] == g])
  340. n <- max(lengths(out))
  341. out <- lapply(out, function(x) c(x, rep(NA_real_, n - length(x))))
  342. result <- as.data.frame(out, stringsAsFactors = FALSE)
  343. colnames(result) <- col_names
  344. result
  345. }
  347. # --- Helper: two-group dot plot in the reference-image style ---
  348. two_group_dot_plot <- function(df, group_col, value_col,
  349. group_levels, group_labels, group_colors,
  350. title_text, y_lab) {
  351. df_local <- df %>%
  352. filter(.data[[group_col]] %in% group_levels) %>%
  353. mutate(.grp = factor(.data[[group_col]],
  354. levels = group_levels,
  355. labels = group_labels))
  357. # Wilcoxon p + Hedges g
  358. # Sign convention: cohen.d returns positive when level-1's mean exceeds
  359. # level-2's. We display |g| on the plot (direction is visually obvious),
  360. # and print the full-precision signed value to the console so the
  361. # caller can sanity-check the magnitude.
  362. vals <- split(df_local[[value_col]], df_local[[group_col]])
  363. v1 <- vals[[group_levels[1]]]
  364. v2 <- vals[[group_levels[2]]]
  366. wilcox_p <- tryCatch(
  367. wilcox.test(v1, v2)$p.value,
  368. error = function(e) NA_real_
  369. )
  370. hedges <- tryCatch(
  371. effsize::cohen.d(df_local[[value_col]],
  372. factor(df_local[[group_col]], levels = group_levels),
  373. hedges.correction = TRUE)$estimate,
  374. error = function(e) NA_real_
  375. )
  376. hedges_g <- unname(hedges)
  378. # ---- Console diagnostics: full-precision summary so 1.00 displayed on
  379. # ---- the plot can be checked against the underlying value.
  380. message(" -- ", group_col, " --")
  381. message(" n(", group_levels[1], ") = ", length(v1),
  382. ", mean = ", signif(mean(v1, na.rm = TRUE), 5),
  383. ", median = ", signif(median(v1, na.rm = TRUE), 5))
  384. message(" n(", group_levels[2], ") = ", length(v2),
  385. ", mean = ", signif(mean(v2, na.rm = TRUE), 5),
  386. ", median = ", signif(median(v2, na.rm = TRUE), 5))
  387. message(" Wilcoxon p (raw) = ", signif(wilcox_p, 6))
  388. message(" Hedges g (signed, level1 - level2) = ",
  389. signif(hedges_g, 6))
  390. message(" |Hedges g| (plot label) = ", signif(abs(hedges_g), 6))
  392. p_label <- ifelse(is.na(wilcox_p), "ns",
  393. ifelse(wilcox_p < 0.0001, "****",
  394. ifelse(wilcox_p < 0.001, "***",
  395. ifelse(wilcox_p < 0.01, "**",
  396. ifelse(wilcox_p < 0.05, "*", "ns")))))
  397. # Display with 3 decimals so a true 0.987 / 1.013 isn't masked as "1.00".
  398. g_label <- if (!is.na(hedges_g)) {
  399. paste0("g = ", format(round(abs(hedges_g), 3), nsmall = 3))
  400. } else "g = NA"
  402. y_max <- max(df_local[[value_col]], na.rm = TRUE)
  403. y_min <- min(df_local[[value_col]], na.rm = TRUE)
  404. y_pad <- (y_max - y_min) * 0.18
  406. ggplot(df_local, aes(x = .grp, y = .data[[value_col]], color = .grp)) +
  407. geom_jitter(width = 0.15, height = 0, size = 4, alpha = 0.85) +
  408. stat_summary(fun = median, geom = "crossbar",
  409. width = 0.55, linewidth = 0.6, fatten = 1.5) +
  410. # Significance bracket + stars + g annotation, stacked above data
  411. annotate("segment",
  412. x = 1, xend = 2,
  413. y = y_max + y_pad * 0.7, yend = y_max + y_pad * 0.7,
  414. color = "black", linewidth = 0.5) +
  415. annotate("text",
  416. x = 1.5, y = y_max + y_pad * 1.1,
  417. label = p_label,
  418. size = 8, fontface = "bold") +
  419. annotate("text",
  420. x = 1.5, y = y_max + y_pad * 0.4,
  421. label = g_label,
  422. size = 5) +
  423. scale_color_manual(values = setNames(group_colors, group_labels),
  424. guide = "none") +
  425. scale_y_continuous(expand = expansion(mult = c(0.05, 0.18))) +
  426. labs(title = title_text, x = NULL, y = y_lab) +
  427. theme_classic(base_size = 14) +
  428. theme(
  429. plot.title = element_text(face = "bold", hjust = 0.5,
  430. margin = margin(b = 10)),
  431. axis.text = element_text(color = "black"),
  432. axis.title = element_text(color = "black")
  433. )
  434. }
  436. # ---------------------------------------------------------------------------
  437. # Figure 4E: proliferation_score split by deathplace_comparison
  438. # (Matches the Prism reference image with g = 1.17.)
  439. # ---------------------------------------------------------------------------
  440. fig4e_levels <- c("ER>HI", "HI>ER")
  441. fig4e_labels <- c("ER > HI", "HI > ER")
  442. fig4e_colors <- c("#A04C7A", "#E89B3C") # magenta-pink, orange
  444. # CSV (Prism wide)
  445. fig4e_csv <- prism_wide(
  446. df = data_sig %>% filter(deathplace_comparison %in% fig4e_levels),
  447. group_col = "deathplace_comparison",
  448. value_col = "proliferation_score",
  449. group_levels = fig4e_levels,
  450. col_names = c("ER_greater_than_HI", "HI_greater_than_ER")
  451. )
  452. fig4e_csv_path <- file.path(out_dir, "4E_for_prism.csv")
  453. write.csv(fig4e_csv, fig4e_csv_path, row.names = FALSE, na = "")
  454. message(" Saved: ", fig4e_csv_path)
  456. # Plot
  457. fig4e_plot <- two_group_dot_plot(
  458. df = data_sig,
  459. group_col = "deathplace_comparison",
  460. value_col = "proliferation_score",
  461. group_levels = fig4e_levels,
  462. group_labels = fig4e_labels,
  463. group_colors = fig4e_colors,
  464. title_text = "Proliferation scores by place of death",
  465. y_lab = "Proliferation score (log2)"
  466. )
  467. fig4e_png <- file.path(shared_out_dir, "Figure_4E.png")
  468. ggsave(fig4e_png, plot = fig4e_plot, width = 5, height = 6, dpi = 300)
  469. message(" Saved: ", fig4e_png)
  472. # ---------------------------------------------------------------------------
  473. # Supplemental Figure 8: Avg_Factor1 split by Proliferative_Group
  474. # ---------------------------------------------------------------------------
  475. suppfig8_levels <- c("Proliferative", "Non-Proliferative")
  476. suppfig8_labels <- c("Proliferative", "Non-\nproliferative")
  477. suppfig8_colors <- c("#A878B7", "#4D7BB7") # purple, blue
  479. suppfig8_csv <- prism_wide(
  480. df = data_sig %>% filter(Proliferative_Group %in% suppfig8_levels),
  481. group_col = "Proliferative_Group",
  482. value_col = "Avg_Factor1",
  483. group_levels = suppfig8_levels,
  484. col_names = c("Proliferative", "Non_proliferative")
  485. )
  486. suppfig8_csv_path <- file.path(out_dir, "Supplemental_Fig8.csv")
  487. write.csv(suppfig8_csv, suppfig8_csv_path, row.names = FALSE, na = "")
  488. message(" Saved: ", suppfig8_csv_path)
  490. suppfig8_plot <- two_group_dot_plot(
  491. df = data_sig,
  492. group_col = "Proliferative_Group",
  493. value_col = "Avg_Factor1",
  494. group_levels = suppfig8_levels,
  495. group_labels = suppfig8_labels,
  496. group_colors = suppfig8_colors,
  497. title_text = expression(bold("ISR"^"GDF15" ~
  498. "scores based on proliferation score")),
  499. y_lab = expression("ISR"^"GDF15" ~ "Score")
  500. )
  501. suppfig8_png <- file.path(out_dir, "Supplemental_Fig8.png")
  502. ggsave(suppfig8_png, plot = suppfig8_plot, width = 5, height = 6, dpi = 300)
  503. message(" Saved: ", suppfig8_png)
  504. # (Supplemental_Fig8.png is intentionally NOT copied to Results/Figures/.)
  506. # ============================================================================
  507. # SUMMARY
  508. # ============================================================================
  509. message(paste0("\n", strrep("=", 65)))
  510. message("FIGURES 4D & 4E — JOURNAL-COMPLIANT DOT PLOTS COMPLETE")
  511. message(strrep("=", 65))
  512. message("\nOutput folder: ", out_dir)
  513. message(" ", per_donor_prolif_file)
  514. message("\nFigure 4E + Supplemental Figure 8 outputs:")
  515. message(" ", fig4e_csv_path)
  516. message(" ", fig4e_png)
  517. message(" ", suppfig8_csv_path)
  518. message(" ", suppfig8_png)
  519. message(strrep("=", 65))

Figure_4D_4E.R at commit 0cb4c55, no license · at the source

Overview

Authors: Janell L. M. Smith1, Kamaryn T. Tanner2, Jack Devine1, Anna S. Monzel1, Taivan Batjargal3, Maxwell Z. Wilson3, Alan A. Cohen2,4, Martin Picard1,2,5,6
ORCID iDs: Janell L. M. Smith, Kamaryn T. Tanner, Anna S. Monzel, Maxwell Z. Wilson, Alan A. Cohen, Martin Picard
  1. Department of Psychiatry, Division of Behavioral Medicine, Columbia University Irving Medical Center,New York, NY USA
  2. Robert N Butler Columbia Aging Center, Mailman School of Public Health,New York, NY USA
  3. Biomolecular Science and Engineering Program, University of California Santa Barbara,Santa Barbara, CA USA
  4. Department of Environmental Health Sciences, Mailman School of Public Health,New York, NY USA
  5. Department of Neurology, H. Houston Merritt Center for Neurological and Mitochondrial Disorders, Columbia University Irving Medical Center,New York, NY USA
  6. New York State Psychiatric Institute,New York, NY USA
Institutions: Columbia University Irving Medical Center (United States); Columbia University (United States); University of California, Santa Barbara (United States); New York State Psychiatric Institute
Journal: Communications biology, volume 9, issue 1, article 1126
Dates: received 21 July 2025; accepted 11 May 2026; published online 27 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-10312-x · PMID 42203840 · PMCID PMC13493765 · OpenAlex W4407057049
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.01.31.635929
Status: code verified
Categories: genetics / omics (modality), human (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity
Keywords: Stress signalling, Gene expression profiling, Bioinformatics, Mitochondria
MeSH: Growth Differentiation Factor 15*, Integrated Stress Response*, Fibroblasts, Humans, Mitochondria (* major topic)
Topic: GDF15 and Related Biomarkers (Rheumatology, Medicine), according to OpenAlex
Funding: Nathaniel Wharton Fund (Wharton Fund); NIH (R01AG066828, R01AG086764); NIGMS (R35GM119793); Baszucki Group
Citations: cited by 4 papers (Europe PMC); 37 references in the paper

Abstract

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 23 matches between paragraphs and lines of code.

mitopsychobio/2025_ISR_GDF15_JS_v2

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 0cb4c5556dca7ae365f8608dcec76d7fa9df50e7, 6 May 2026
Languages: R (34)
Size: 55 files, 34 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (30 files), ggplot2 (21 files), ggpubr (16 files), circlize (13 files), edgeR (13 files), ComplexHeatmap (11 files), Plotly (2 files), psych (2 files), broom (1 file), data.table (1 file), limma (1 file), pheatmap (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
35 files
At the source: github.com/mitopsychobio/2025_ISR_GDF15_JS_v2

Zenodo 20057240

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (30 files), ggplot2 (21 files), ggpubr (16 files), circlize (13 files), edgeR (13 files), ComplexHeatmap (11 files), Plotly (2 files), psych (2 files), broom (1 file), data.table (1 file), limma (1 file), pheatmap (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
35 files
At the source:

Code availability statement

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

Read it in the paper: doi.org/10.1038/s42003-026-10312-x.

Tracing map

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What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 68 scripts, each with its path and the digest of its content;
  • 23 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

No dataset and no data link were found in the paper.

Data availability statement

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  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s42003-026-10312-x.

Versions

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

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 4 keywords, 5 MeSH terms, 4 funders, 28 references.

Cite

This paper

Smith, J. L. M., Tanner, K. T., Devine, J., Monzel, A. S., Batjargal, T., Wilson, M. Z., Cohen, A. A., & Picard, M. (2026). Mapping the GDF15 arm of the integrated stress response in human cells and tissues. Communications biology, 9(1), 1126. https://doi.org/10.1038/s42003-026-10312-x

BibTeX

@article{smith2026mapping,
author = {Smith, Janell L. M. and Tanner, Kamaryn T. and Devine, Jack and Monzel, Anna S. and Batjargal, Taivan and Wilson, Maxwell Z. and Cohen, Alan A. and Picard, Martin},
title = {{Mapping the GDF15 arm of the integrated stress response in human cells and tissues}},
journal = {Communications biology},
year = {2026},
month = may,
volume = {9},
number = {1},
pages = {1126},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10312-x},
url = {https://doi.org/10.1038/s42003-026-10312-x},
pmid = {42203840},
pmcid = {PMC13493765}
}

RIS

TY - JOUR
AU - Smith, Janell L. M.
AU - Tanner, Kamaryn T.
AU - Devine, Jack
AU - Monzel, Anna S.
AU - Batjargal, Taivan
AU - Wilson, Maxwell Z.
AU - Cohen, Alan A.
AU - Picard, Martin
TI - Mapping the GDF15 arm of the integrated stress response in human cells and tissues
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/05/27
VL - 9
IS - 1
SP - 1126
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10312-x
UR - https://doi.org/10.1038/s42003-026-10312-x
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s42003-026-10312-x",
"type": "article-journal",
"title": "Mapping the GDF15 arm of the integrated stress response in human cells and tissues",
"container-title": "Communications biology",
"author": [
{
"family": "Smith",
"given": "Janell L. M."
},
{
"family": "Tanner",
"given": "Kamaryn T."
},
{
"family": "Devine",
"given": "Jack"
},
{
"family": "Monzel",
"given": "Anna S."
},
{
"family": "Batjargal",
"given": "Taivan"
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{
"family": "Wilson",
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},
{
"family": "Cohen",
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},
{
"family": "Picard",
"given": "Martin"
}
],
"container-title-short": "Commun Biol",
"volume": "9",
"issue": "1",
"page": "1126",
"DOI": "10.1038/s42003-026-10312-x",
"PMID": "42203840",
"PMCID": "PMC13493765",
"ISSN": "2399-3642",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s42003-026-10312-x",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
27
]
]
}
}

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

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