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Intrathecal (G<sub>4</sub>C<sub>2</sub>)<sub>149</sub> delivery in C9orf72-deficient mice yields mild motor dysfunction and ALS/FTD pathological hallmarks.

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  1. [1] § Results › Mild hyperactivity observed in the open field; anxiety remains unchanged ↔ KAR_Acta_Neuro_Comm_v1.13.Rmd, lines 135–180 · score 0.70 · perimeter zone, center zone, open field, episodes, immobile, speed
  2. [2] § Results › Gait analysis identifies subtle signatures of motor dysfunction ↔ KAR_Acta_Neuro_Comm_v1.13.Rmd, lines 135–180 · score 0.61 · Paw Drag, body mass, Propel, Swing, Stance, Stride

Paper

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The authors' code

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  1. ---
  2. title: "20260620_Plotting_variables_and_groups_v1.12"
  3. author: "Aaron Haeusler"
  4. date: "`r Sys.Date()`"
  5. output: html_document
  6. ---
  8. ```{r setup, include=FALSE}
  10. if (!require("pacman")) install.packages("pacman")
  11. pacman::p_load(
  12. readr,
  13. dplyr,
  14. ggplot2,
  15. forcats,
  16. ggnewscale,
  17. RColorBrewer,
  18. reshape2,
  19. scales
  20. )
  22. wd <- getwd()
  24. data <- read_csv("Combined_Intrathecal_Data.csv")
  26. # Fix column names for merging (remove any stray spaces)
  27. colnames(data) <- gsub(" ", "_", colnames(data))
  29. # Ensure numeric and merge expression columns
  30. data <- data %>%
  31. mutate(
  32. across(c(Expression_12m, Expression_9m, Expression_6m), as.numeric),
  33. Expression_Merged = coalesce(Expression_12m, Expression_9m, Expression_6m, 0),
  34. Expression_Merged = ifelse(Treatment %in% c("None", "Control"), 0, Expression_Merged)
  35. )
  37. # Calculate quartiles (excluding zero)
  38. qtiles <- quantile(data$Expression_Merged[data$Expression_Merged > 0], probs = seq(0, 1, 0.25), na.rm = TRUE)
  40. data <- data %>%
  41. mutate(
  42. Expression_Quartile = case_when(
  43. is.na(Expression_Merged) | Expression_Merged == 0 ~ "0",
  44. Expression_Merged > 0 & Expression_Merged <= qtiles[2] ~ "Q1",
  45. Expression_Merged > qtiles[2] & Expression_Merged <= qtiles[3] ~ "Q2",
  46. Expression_Merged > qtiles[3] & Expression_Merged <= qtiles[4] ~ "Q3",
  47. Expression_Merged > qtiles[4] ~ "Q4"
  48. ),
  49. Expression_Quartile = factor(Expression_Quartile, levels = c("0", "Q1", "Q2", "Q3", "Q4"), ordered = TRUE)
  50. )
  52. # Set up your grayscale palette
  53. grayscale_pal <- c(
  54. "0" = "#FFFFFF", # white
  55. "Q1" = "#CECECE", # very light gray
  56. "Q2" = "#ABABAB", # light gray
  57. "Q3" = "#7D7D7D", # medium gray
  58. "Q4" = "#222222" # near black
  59. )
  61. # Set factors for better color/shape control
  62. data <- data %>%
  63. mutate(
  64. Genotype = factor(Genotype, levels = c("WT", "Het", "Homo")),
  65. Treatment = factor(Treatment, levels = c("Control", "Repeats", "None")),
  66. Sex = factor(Sex, levels = c("Female", "Male")),
  67. Condition = as.factor(Condition)
  68. )
  71. data <- data %>%
  72. mutate(
  73. distribution = case_when(
  74. Unit == "LBF" ~ "gaussian",
  75. Unit == "cm" ~ "gaussian",
  76. Unit == "coefficient" ~ "gaussian",
  77. Unit == "degrees" ~ "gaussian",
  78. Unit == "metric" ~ "gaussian",
  79. Unit == "percentage" ~ "gaussian",
  80. Unit == "cm_squared" ~ "Gamma",
  81. Unit == "cm_squared_per_sec" ~ "Gamma",
  82. Unit == "meters" ~ "Gamma",
  83. Unit == "meters_per_second" ~ "Gamma",
  84. Unit == "steps_per_sec" ~ "Gamma",
  85. Unit == "seconds" ~ "Gamma",
  86. Unit == "ratio" ~ "Gamma",
  87. Unit == "total_degrees" ~ "Gamma",
  88. Unit == "count" ~ "poisson",
  89. Unit == "mV" ~ "gaussian",
  90. Unit == "mV*ms" ~ "Gamma",
  91. Unit == "ms" ~ "gaussian",
  92. Unit == "mm" ~ "gaussian",
  93. StatsVariable == "All latency variables" ~ "Gamma",
  94. all(Value == floor(Value), na.rm = TRUE) ~ "poisson",
  95. TRUE ~ "gaussian"
  96. )
  97. )
  98. library(dplyr)
  100. # 1. Split Digigait from all other experiments
  101. digigait <- data %>% filter(Exp == "Digigait")
  102. data_other <- data %>% filter(Exp != "Digigait")
  104. # 2. Aggregate Digigait Left/Right by Fore/Hind, rename Side as Both
  105. digigait_agg <- digigait %>%
  106. filter(Side %in% c("Left", "Right"), Limb %in% c("Fore", "Hind")) %>%
  107. group_by(across(-c(Value, Stdev, SEM, Side, Condition))) %>%
  108. summarise(
  109. Value = mean(Value, na.rm = TRUE),
  110. Stdev = sd(Value, na.rm = TRUE),
  111. SEM = sd(Value, na.rm = TRUE) / sqrt(sum(!is.na(Value))),
  112. .groups = "drop"
  113. ) %>%
  114. mutate(Side = "Both",
  115. Condition = Limb
  116. )
  118. # 3. Remove original Left/Right Fore/Hind Digigait rows
  119. digigait_rest <- digigait %>%
  120. filter(!(Side %in% c("Left", "Right") & Limb %in% c("Fore", "Hind")))
  122. # 4. Combine everything back together
  123. data_combined <- bind_rows(data_other, digigait_rest, digigait_agg)
  125. # 5. (optional) Arrange for easy viewing
  126. data <- data_combined %>%
  127. arrange(Ear_Tag, Time_month, Exp, Limb, Side)
  129. # Now data_combined is your updated dataset
  130. df <- data
  132. write_csv(data, "combined_intrathecal_data_ave_digigait.csv")
  133. ```
  135. ``` {r publication_boxplots, include=FALSE}
  136. library(dplyr)
  137. library(ggplot2)
  138. library(ggnewscale)
  139. library(purrr)
  140. library(RColorBrewer)
  141. library(scales)
  142. library(tibble)
  144. df <- data # Replace with your actual data object name
  146. # ---- Subset Table ----
  147. subset_table <- tribble(
  148. ~Exp, ~Time_month, ~Condition, ~StatsVariable,
  149. "Grip", 12, NA, "Grip_Strength",
  150. "CMAP", 12, "Ankle", "Amplitude",
  151. "CMAP", 12, "Hip", "Area_Under_Curve",
  152. "OpenField", 10, NA, "Total_distance_travelled",
  153. "OpenField", 10, NA, "Average_speed",
  154. "OpenField", 10, NA, "Total_time_mobile",
  155. "OpenField", 10, NA, "Total_immobile episodes",
  156. "OpenField", 10, NA, "Average_speed_in_the_Perimeter_zone",
  157. "OpenField", 10, NA, "Time_in_the_Center_zone",
  158. "OpenField", 10, NA, "Distance_travelled_in_the_Perimeter_zone",
  159. "Rotarod", 12, NA, "Latency_to_Fall",
  160. "Body_mass", 12, NA, "Weight_grams",
  161. "Digigait", 12, "Hind", "Stride",
  162. "Digigait", 12, "Hind", "Swing",
  163. "Digigait", 12, "Hind", "Stance",
  164. "Digigait", 12, "Hind", "Stance/Swing",
  165. "Digigait", 12, "Hind", "PawDrag",
  166. "Digigait", 12, "Hind", "PercentSharedStance",
  167. "Digigait", 12, "Hind", "AbsolutePawAngle",
  168. "Digigait", 12, "Hind", "Propel",
  169. "GA", 12, NA, "Normalized_Area",
  170. "GP", 12, NA, "Normalized_Area",
  171. "GR", 12, NA, "Normalized_Area",
  172. "GFAP", 12, NA, "Normalized_Area",
  173. "Iba1", 12, NA, "Normalized_Area",
  174. "NeuN", 12, NA, "Normalized_Area",
  175. "pTDP43", 12, NA, "Normalized_Area",
  176. "TDP_CE", 12, NA, "Sort1_CE_normalized",
  177. "NeuN_count", 12, NA, "Mean_Total_Count",
  178. "spleen", 12, NA, "Weight_mg",
  180. )
  182. # ---- Subset and Clean ----
  183. plot_data <- pmap_dfr(subset_table, function(Exp, Time_month, Condition, StatsVariable) {
  184. df %>%
  185. filter(
  186. Exp == !!Exp,
  187. Time_month == !!Time_month,
  188. StatsVariable == !!StatsVariable,
  189. (is.na(Condition) & is.na(.data$Condition)) |
  190. (!is.na(Condition) & Condition == .data$Condition),
  191. !(Exp == "Digigait" & Limb == "Fore")
  192. )
  193. }) %>%
  194. mutate(
  195. Treatment_Combined = ifelse(Treatment %in% c("None", "Control"), "Control", "Repeats"),
  196. Treatment_Shape = factor(Treatment, levels = c("None", "Control", "Repeats")),
  197. Genotype = factor(Genotype, levels = c("WT", "Het", "Homo")),
  198. Sex = factor(Sex, levels = c("Female", "Male")),
  199. Genotype_Tx = factor(
  200. interaction(Genotype, Treatment_Combined, sep = "_"),
  201. levels = c("WT_Control", "Het_Control", "Homo_Control",
  202. "WT_Repeats", "Het_Repeats", "Homo_Repeats")
  203. ),
  204. facet_id = paste(Exp, Condition, StatsVariable, sep = "_")
  205. )
  207. # ---- Split Body_mass by Sex ----
  208. body_split <- plot_data %>%
  209. filter(Exp == "Body_mass") %>%
  210. mutate(facet_id = paste0("Body_mass_", Sex, "_Weight_grams"))
  211. plot_data <- plot_data %>%
  212. filter(!(Exp == "Body_mass" & StatsVariable == "Weight_grams")) %>%
  213. bind_rows(body_split)
  215. # ---- Additional filter ----
  216. imaging_exps <- c("GA", "GP", "GR", "GFAP", "Iba1", "NeuN", "pTDP43", "NeuN_count", "TDP_CE")
  217. plot_data <- plot_data %>%
  218. filter(
  219. !(Exp %in% imaging_exps & Genotype == "Het" & Treatment_Combined == "Repeats") |
  220. (Exp %in% imaging_exps & Genotype == "Het" & Treatment_Combined == "Repeats" &
  221. Expression_Quartile %in% c("Q2", "Q3"))
  222. )
  224. # ---- Facet Fillers ----
  225. facet_combinations <- plot_data %>%
  226. distinct(facet_id, Exp, Condition, StatsVariable)
  228. num_facets <- nrow(facet_combinations)
  229. num_per_page <- 12
  230. num_pages <- ceiling(num_facets / num_per_page)
  232. fillers_needed <- (num_pages * num_per_page) - num_facets
  233. if (fillers_needed > 0) {
  234. filler_data <- tibble(
  235. facet_id = paste0("filler_", seq_len(fillers_needed)),
  236. Exp = "Filler", Condition = NA, StatsVariable = "Empty",
  237. Genotype_Tx = factor(NA, levels = levels(plot_data$Genotype_Tx)),
  238. Value = NA
  239. )
  240. facet_combinations <- bind_rows(facet_combinations, filler_data)
  241. plot_data <- bind_rows(plot_data, filler_data)
  242. }
  244. # ---- Aesthetics ----
  245. sex_colors <- c("Female" = "red", "Male" = "blue")
  246. shape_values <- c("None" = 22, "Control" = 23, "Repeats" = 21)
  247. custom_fill_colors <- c(
  248. "WT_Repeats" = "#f15b60", # red
  249. "Het_Repeats" = "#f79151", # orange
  250. "Homo_Repeats" = "#ffcb38", # yellow
  251. "WT_Control" = "#00b04f", # green
  252. "Het_Control" = "#109ab3", # blue
  253. "Homo_Control" = "#8a77aa" # purple
  254. )
  256. # ---- Plot Loop ----
  257. pdf("Subset_Boxplot_GenoTx_SunsetOceanOrchid_3x4_Fillers.pdf", width = 5.5, height = 7)
  258. for (page in seq_len(num_pages)) {
  259. start_idx <- (page - 1) * num_per_page + 1
  260. end_idx <- min(page * num_per_page, nrow(facet_combinations))
  261. page_facets <- facet_combinations$facet_id[start_idx:end_idx]
  263. pd <- plot_data %>% filter(facet_id %in% page_facets) %>% droplevels()
  265. p <- ggplot(pd, aes(
  266. x = Genotype_Tx,
  267. y = Value,
  268. group = Genotype_Tx,
  269. fill = Genotype_Tx
  270. )) +
  271. geom_boxplot(
  272. position = position_dodge(width = 0.8),
  273. outlier.shape = NA,
  274. alpha = 1,
  275. width = 0.65
  276. ) +
  277. scale_fill_manual(values = custom_fill_colors, name = "Genotype × Treatment") +
  278. ggnewscale::new_scale_fill() +
  279. geom_jitter(
  280. aes(fill = Sex, shape = Treatment_Shape),
  281. color = "black", size = 1, stroke = 0.5, alpha = 0.85,
  282. position = position_jitter(width = 0.18, height = 0)
  283. ) +
  284. scale_fill_manual(values = sex_colors, name = "Sex") +
  285. scale_shape_manual(values = shape_values, name = "Original Treatment") +
  286. scale_x_discrete(drop = FALSE, na.translate = FALSE) +
  287. ylab("Value") +
  288. xlab("Genotype × Treatment Group") +
  289. facet_wrap(~ facet_id, scales = "free_y", ncol = 3, nrow = 4) +
  290. theme_classic(base_size = 8) +
  291. theme(
  292. strip.background = element_rect(fill = "grey90", color = "black"),
  293. strip.text = element_text(face = "bold", size = 4),
  294. axis.text.x = element_text(angle = 45, hjust = 1),
  295. legend.position = "right",
  296. axis.ticks.y = element_line(size = 0.3)
  297. )
  298. print(p)
  299. }
  300. dev.off()
  302. ```
  304. ```{r scatter_expression_correlations_publication, include=FALSE}
  306. ### -----------------------------------------------------------
  307. ### FACETED SCATTER PLOT WITH R², p, p_adj • 4×6 PAGE LAYOUT
  308. ### Genotype × Treatment Coloring (Repeats Only) + Fillers
  309. ### -----------------------------------------------------------
  311. library(dplyr)
  312. library(ggplot2)
  313. library(broom)
  314. library(purrr)
  315. library(tidyr)
  317. # ---- Subset and Clean ----
  318. plot_data <- pmap_dfr(subset_table, function(Exp, Time_month, Condition, StatsVariable) {
  319. df %>%
  320. filter(
  321. Exp == !!Exp,
  322. Time_month == !!Time_month,
  323. StatsVariable == !!StatsVariable,
  324. (is.na(Condition) & is.na(.data$Condition)) |
  325. (!is.na(Condition) & Condition == .data$Condition),
  326. !(Exp == "Digigait" & Limb == "Fore")
  327. )
  328. }) %>%
  329. mutate(
  330. Treatment_Combined = ifelse(Treatment %in% c("None", "Control"), "Control", "Repeats"),
  331. Treatment_Shape = factor(Treatment, levels = c("None", "Control", "Repeats")),
  332. Genotype = factor(Genotype, levels = c("WT", "Het", "Homo")),
  333. Sex = factor(Sex, levels = c("Female", "Male")),
  334. Genotype_Tx = factor(
  335. interaction(Genotype, Treatment_Combined, sep = "_"),
  336. levels = c("WT_Control", "Het_Control", "Homo_Control",
  337. "WT_Repeats", "Het_Repeats", "Homo_Repeats")
  338. ),
  339. facet_id = paste(Exp, Condition, StatsVariable, sep = "_")
  340. )
  342. # ---- Split Body_mass by Sex ----
  343. body_split <- plot_data %>%
  344. filter(Exp == "Body_mass") %>%
  345. mutate(facet_id = paste0("Body_mass_", Sex, "_Weight_grams"))
  346. plot_data <- plot_data %>%
  347. filter(!(Exp == "Body_mass" & StatsVariable == "Weight_grams")) %>%
  348. bind_rows(body_split)
  351. ### -----------------------------------------------------------
  352. ### 1. Prepare data with Genotype × Treatment factor
  353. ### -----------------------------------------------------------
  354. scatter_data <- plot_data %>%
  355. mutate(
  356. Treatment_Combined = ifelse(Treatment %in% c("None", "Control"),
  357. "Control", "Repeats"),
  358. Genotype_Tx = factor(
  359. interaction(Genotype, Treatment_Combined, sep = "_"),
  360. levels = c("WT_Control","Het_Control","Homo_Control",
  361. "WT_Repeats","Het_Repeats","Homo_Repeats")
  362. )
  363. ) %>%
  364. filter(
  365. Treatment == "Repeats",
  366. !is.na(Expression_Merged),
  367. Expression_Merged > 0,
  368. !is.na(Value)
  369. ) %>%
  370. mutate(
  371. Expression = as.numeric(Expression_Merged),
  372. Expression_log2 = log2(Expression_Merged)
  373. )
  375. ### -----------------------------------------------------------
  376. ### 2. Compute LM stats per facet (R², p, FDR)
  377. ### -----------------------------------------------------------
  378. facet_stats <- scatter_data %>%
  379. group_by(facet_id) %>%
  380. do({
  381. m <- lm(Value ~ Expression_log2, data = .)
  382. tibble(
  383. r2 = summary(m)$r.squared,
  384. p = glance(m)$p.value
  385. )
  386. }) %>%
  387. ungroup() %>%
  388. mutate(
  389. p_adj = p.adjust(p, method = "BH"),
  390. label = sprintf("R² = %.2f\np = %.3g\nFDR = %.3g",
  391. r2, p, p_adj)
  392. )
  394. scatter_plot_data <- scatter_data %>%
  395. left_join(facet_stats, by = "facet_id")
  397. ### -----------------------------------------------------------
  398. ### 3. Pagination + Fillers (4×6 = 24 plots per page)
  399. ### -----------------------------------------------------------
  400. facets <- sort(unique(scatter_plot_data$facet_id))
  401. n_per_page <- 24 # 4 columns × 6 rows
  402. n_pages <- ceiling(length(facets) / n_per_page)
  404. # Add filler facets to fill last page to 24 slots
  405. fillers_needed <- n_pages * n_per_page - length(facets)
  407. if (fillers_needed > 0) {
  408. filler_ids <- paste0("Filler_", seq_len(fillers_needed))
  410. # Create filler entries with NA values
  411. filler_df <- tibble(
  412. facet_id = rep(filler_ids, each = 1),
  413. Value = NA,
  414. Expression_log2 = NA,
  415. Genotype_Tx = factor(NA, levels = levels(scatter_plot_data$Genotype_Tx)),
  416. label = ""
  417. )
  419. facets <- c(facets, filler_ids)
  420. }
  422. ### -----------------------------------------------------------
  423. ### 4. Genotype × Treatment colors
  424. ### -----------------------------------------------------------
  425. genoTx_colors <- c(
  426. "WT_Repeats" = "#f15b60",
  427. "Het_Repeats" = "#f79151",
  428. "Homo_Repeats" = "#ffcb38",
  429. "WT_Control" = "#00b04f",
  430. "Het_Control" = "#109ab3",
  431. "Homo_Control" = "#8a77aa"
  432. )
  434. ### -----------------------------------------------------------
  435. ### 5. PDF loop (each page is 4×6 facets)
  436. ### -----------------------------------------------------------
  437. pdf("GenotypeTx_vs_Expression_log2_FacetedScatter_Paginated.pdf",
  438. width = 9, height = 10)
  440. for (page in seq_len(n_pages)) {
  442. start <- (page - 1) * n_per_page + 1
  443. end <- page * n_per_page
  445. these_facets <- facets[start:end]
  447. page_data <- scatter_plot_data %>%
  448. filter(facet_id %in% these_facets) %>%
  449. bind_rows(filler_df %>% filter(facet_id %in% these_facets)) %>%
  450. mutate(facet_id = factor(facet_id, levels = these_facets))
  452. p <- ggplot(page_data,
  453. aes(x = Expression_log2, y = Value)) +
  455. geom_point(
  456. aes(color = Genotype_Tx),
  457. size = 1.6, alpha = 0.85, na.rm = TRUE
  458. ) +
  460. geom_smooth(
  461. method = "lm", se = FALSE,
  462. color = "black", linewidth = 0.6, na.rm = TRUE
  463. ) +
  465. geom_text(
  466. aes(label = label),
  467. x = -Inf, y = Inf,
  468. hjust = -0.1, vjust = 1.1,
  469. size = 3, color = "black",
  470. lineheight = 0.9
  471. ) +
  473. facet_wrap(~ facet_id, ncol = 4, nrow = 6, scales = "free_y") +
  475. scale_color_manual(
  476. values = genoTx_colors,
  477. name = "Genotype × Treatment",
  478. na.translate = FALSE
  479. ) +
  481. scale_x_continuous(
  482. breaks = pretty(range(scatter_plot_data$Expression_log2), 5),
  483. labels = function(x) round(2^x, 1),
  484. name = "Expression (raw units, log2 scale)"
  485. ) +
  487. labs(
  488. y = "Phenotype Value",
  489. title = paste0("Genotype × Treatment Effects vs log2(Expression) — Page ", page)
  490. ) +
  492. theme_bw(base_size = 6) +
  493. theme(
  494. plot.title = element_text(face = "bold", size = 14, hjust = 0.5),
  495. strip.background = element_rect(fill = "white"),
  496. strip.text = element_text(face = "bold", size = 5),
  497. legend.position = "bottom",
  498. panel.grid = element_blank(),
  499. panel.spacing = unit(1.1, "lines")
  500. )
  502. print(p)
  503. }
  505. dev.off()
  507. ```
  509. ```{r correlation_matrix_dot_pot_publication, include=FALSE}
  510. library(dplyr)
  511. library(tidyr)
  512. library(ggplot2)
  513. library(reshape2)
  514. library(purrr)
  516. df <- data # Replace with your actual data object name
  518. # ---- Subset Table ----
  519. subset_table <- tribble(
  520. ~Exp, ~Time_month, ~Condition, ~StatsVariable,
  521. "Grip", 12, NA, "Grip_Strength",
  522. "CMAP", 12, "Ankle", "Amplitude",
  523. #"CMAP", 12, "Hip", "Area_Under_Curve",
  524. #"OpenField", 10, NA, "Total_distance_travelled",
  525. "OpenField", 10, NA, "Average_speed",
  526. #"OpenField", 10, NA, "Total_time_mobile",
  527. #"OpenField", 10, NA, "Total_immobile episodes",
  528. #"OpenField", 10, NA, "Average_speed_in_the_Perimeter_zone",
  529. "OpenField", 10, NA, "Time_in_the_Center_zone",
  530. #"OpenField", 10, NA, "Distance_travelled_in_the_Perimeter_zone",
  531. "Rotarod", 12, NA, "Latency_to_Fall",
  532. "Body_mass", 12, NA, "Weight_grams",
  533. "Digigait", 12, "Hind", "Stride",
  534. #"Digigait", 12, "Hind", "Swing",
  535. "Digigait", 12, "Hind", "Stance",
  536. #"Digigait", 12, "Hind", "StanceOverSwing",
  537. #"Digigait", 12, "Hind", "PawDrag",
  538. #"Digigait", 12, "Hind", "PercentSharedStance",
  539. #"Digigait", 12, "Hind", "AbsolutePawAngle",
  540. "GA", 12, NA, "Normalized_Area",
  541. "GP", 12, NA, "Normalized_Area",
  542. "GR", 12, NA, "Normalized_Area",
  543. "GFAP", 12, NA, "Normalized_Area",
  544. "Iba1", 12, NA, "Normalized_Area",
  545. "NeuN", 12, NA, "Normalized_Area",
  546. #"pTDP43", 12, NA, "Normalized_Area",
  547. "TDP_CE", 12, NA, "Sort1_CE_normalized",
  548. "NeuN_count", 12, NA, "Mean_Total_Count",
  549. #"NeuN_count", 12, NA, "Mean_Area_um2",
  550. )
  552. # ---- Subset and Clean ----
  553. plot_data <- pmap_dfr(subset_table, function(Exp, Time_month, Condition, StatsVariable) {
  554. df %>%
  555. filter(
  556. Exp == !!Exp,
  557. Time_month == !!Time_month,
  558. StatsVariable == !!StatsVariable,
  559. (is.na(Condition) & is.na(.data$Condition)) |
  560. (!is.na(Condition) & Condition == .data$Condition),
  561. !(Exp == "Digigait" & Limb == "Fore")
  562. )
  563. }) %>%
  564. mutate(
  565. Treatment_Combined = ifelse(Treatment %in% c("None", "Control"), "Control", "Repeats"),
  566. Treatment_Shape = factor(Treatment, levels = c("None", "Control", "Repeats")),
  567. Genotype = factor(Genotype, levels = c("WT", "Het", "Homo")),
  568. Sex = factor(Sex, levels = c("Female", "Male")),
  569. Genotype_Tx = factor(
  570. interaction(Genotype, Treatment_Combined, sep = "_"),
  571. levels = c("WT_Control", "Het_Control", "Homo_Control",
  572. "WT_Repeats", "Het_Repeats", "Homo_Repeats")
  573. ),
  574. facet_id = paste(Exp, Condition, StatsVariable, sep = "_")
  575. )
  577. #---------------------------------------------------------------#
  578. # 1. Summarize each Exp + StatsVariable to one value per mouse
  579. #---------------------------------------------------------------#
  580. exp_summary <- plot_data %>%
  581. mutate(Expression_Merged = ifelse(Treatment != "Repeats", NA, Expression_Merged)) %>%
  582. group_by(Ear_Tag, Exp, StatsVariable) %>%
  583. summarise(Exp_Value = mean(Value, na.rm = TRUE), .groups = "drop") %>%
  584. mutate(ExpVar = paste(Exp, StatsVariable, sep = "_")) %>%
  585. select(Ear_Tag, ExpVar, Exp_Value) %>%
  586. pivot_wider(
  587. names_from = ExpVar,
  588. values_from = Exp_Value
  589. )
  591. #---------------------------------------------------------------#
  592. # 2. Attach Expression_Merged directly (not summarized)
  593. #---------------------------------------------------------------#
  594. expr_values <- plot_data %>%
  595. distinct(Ear_Tag, Expression_Merged)
  597. combined_df <- exp_summary %>%
  598. left_join(expr_values, by = "Ear_Tag")
  600. #---------------------------------------------------------------#
  601. # 3. Prepare numeric vars (drop ID, keep sufficient non-NA vars)
  602. #---------------------------------------------------------------#
  603. numeric_vars <- combined_df %>%
  604. select(where(is.numeric)) %>%
  605. select(-Ear_Tag) %>% # ⭐ REMOVE EAR_TAG ⭐
  606. select(where(~ sum(!is.na(.)) >= 3))
  608. #---------------------------------------------------------------#
  609. # 4. Correlation + P-values
  610. #---------------------------------------------------------------#
  611. corr_mat <- cor(numeric_vars, use = "pairwise.complete.obs", method = "pearson")
  613. p_mat <- matrix(NA, ncol = ncol(numeric_vars), nrow = ncol(numeric_vars))
  614. colnames(p_mat) <- rownames(p_mat) <- colnames(numeric_vars)
  616. for (i in seq_len(ncol(numeric_vars))) {
  617. for (j in seq_len(ncol(numeric_vars))) {
  618. x <- numeric_vars[[i]]
  619. y <- numeric_vars[[j]]
  620. if (sum(complete.cases(x, y)) >= 3) {
  621. p_mat[i, j] <- cor.test(x, y)$p.value
  622. }
  623. }
  624. }
  626. # Adjusted p-values
  627. padj_mat <- matrix(
  628. p.adjust(as.vector(p_mat), method = "BH"),
  629. nrow = ncol(numeric_vars),
  630. dimnames = list(colnames(numeric_vars), colnames(numeric_vars))
  631. )
  633. #---------------------------------------------------------------#
  634. # 5. Melt matrices + hierarchical order
  635. #---------------------------------------------------------------#
  636. hc <- hclust(dist(corr_mat))
  637. ord <- hc$order
  638. corr_ord <- corr_mat[ord, ord]
  639. padj_ord <- padj_mat[ord, ord]
  641. df_plot <- reshape2::melt(corr_ord) %>%
  642. rename(Var1 = Var1, Var2 = Var2, cor = value) %>%
  643. mutate(
  644. padj = as.vector(padj_ord),
  645. size_value = -log10(padj), # ⭐ new transform
  646. size_value = pmin(size_value, 3) # ⭐ optional: cap extremes
  647. ) %>%
  648. filter(as.numeric(Var1) < as.numeric(Var2)) %>%
  649. filter(!is.na(cor))
  651. #---------------------------------------------------------------#
  652. # 6. Dot plot
  653. #---------------------------------------------------------------#
  654. p <- ggplot(df_plot, aes(Var1, Var2, color = cor, size = size_value)) +
  655. geom_point(alpha = 0.9) +
  656. scale_color_gradient2(
  657. low = "#b2182b",
  658. mid = "#f7f7f7",
  659. high = "#1f78b4",
  660. midpoint = 0,
  661. limits = c(-1, 1),
  662. name = "Correlation"
  663. ) +
  664. scale_size(
  665. range = c(2, 10),
  666. name = expression("-log"[10]*"(adj p)"),
  667. limits = c(0, 5) # explicitly bound scale
  668. ) +
  669. coord_fixed() +
  670. labs(title = "Correlation Matrix Across Experiment × Variable + Expression_Merged") +
  671. theme_minimal(base_size = 16) +
  672. theme(
  673. axis.title = element_blank(),
  674. axis.text.x = element_text(angle = 45, hjust = 1),
  675. axis.text.y = element_text(size = 12),
  676. panel.grid.major = element_line(color = "gray85", linewidth = 0.3),
  677. plot.title = element_text(size = 20, face = "bold", hjust = 0.5)
  678. )
  680. #---------------------------------------------------------------#
  681. # 7. Export as PDF
  682. #---------------------------------------------------------------#
  683. ggsave(
  684. filename = "correlation_matrix_experiment_variable_pairs.pdf",
  685. plot = p,
  686. width = 9,
  687. height = 9,
  688. units = "in",
  689. dpi = 300
  690. )
  692. ```
  694. ```{r plot_signifcant_correlations_publication,include=FALSE}
  696. ### -----------------------------------------------------------
  697. ### SIGNIFICANT PHENOTYPE–PHENOTYPE SCATTER PLOTS (R²-based)
  698. ### Paginated 4×6 Faceting + Fillers • Colored by Genotype × Treatment
  699. ### -----------------------------------------------------------
  701. library(dplyr)
  702. library(ggplot2)
  703. library(broom)
  704. library(purrr)
  705. library(tidyr)
  707. alpha <- 0.05 # FDR significance cutoff
  709. ### -----------------------------------------------------------
  710. ### 1. Identify significant NON-expression pairs
  711. ### -----------------------------------------------------------
  713. sig_pairs <- df_plot %>%
  714. filter(padj < alpha) %>%
  715. filter(!grepl("Expression", Var1),
  716. !grepl("Expression", Var2)) %>%
  717. select(Var1, Var2, cor, padj)
  719. if (nrow(sig_pairs) == 0) stop("No significant phenotype–phenotype correlations found.")
  722. ### -----------------------------------------------------------
  723. ### 2. Build phenotype matrix with Genotype × Treatment mapping
  724. ### -----------------------------------------------------------
  726. geno_tx_map <- plot_data %>%
  727. mutate(
  728. Treatment_Combined = ifelse(Treatment %in% c("None", "Control"), "Control", "Repeats"),
  729. Genotype_Tx = factor(
  730. interaction(Genotype, Treatment_Combined, sep = "_"),
  731. levels = c("WT_Control", "Het_Control", "Homo_Control",
  732. "WT_Repeats", "Het_Repeats", "Homo_Repeats")
  733. )
  734. ) %>%
  735. distinct(Ear_Tag, Genotype_Tx)
  737. scatter_wide <- exp_summary %>%
  738. left_join(geno_tx_map, by = "Ear_Tag")
  741. ### -----------------------------------------------------------
  742. ### 3. Build long format for each significant pair (with SAFETY CHECKS)
  743. ### -----------------------------------------------------------
  745. scatter_list <- list()
  747. for (i in seq_len(nrow(sig_pairs))) {
  749. v1 <- sig_pairs$Var1[i]
  750. v2 <- sig_pairs$Var2[i]
  752. # safety: skip comparisons whose variables are missing
  753. if (!(v1 %in% colnames(scatter_wide)) | !(v2 %in% colnames(scatter_wide))) {
  754. message("Skipping pair (missing column): ", v1, " -- ", v2)
  755. next
  756. }
  758. tmp <- scatter_wide %>%
  759. select(Ear_Tag, Genotype_Tx, all_of(v1), all_of(v2)) %>%
  760. rename(Value_x = all_of(v1),
  761. Value_y = all_of(v2)) %>%
  762. filter(!is.na(Value_x) & !is.na(Value_y)) %>%
  763. mutate(
  764. Var1 = v1,
  765. Var2 = v2,
  766. cor_original = sig_pairs$cor[i], padj_original = sig_pairs$padj[i], facet_id = paste(v1, "vs", v2)
  767. )
  769. scatter_list[[length(scatter_list) + 1]] <- tmp
  770. }
  772. scatter_df <- bind_rows(scatter_list)
  776. ### -----------------------------------------------------------
  777. ### 4. Compute R² per facet, but use original FDR from df_plot
  778. ### -----------------------------------------------------------
  780. pair_stats <- scatter_df %>%
  781. group_by(facet_id) %>%
  782. do({
  783. m <- lm(Value_y ~ Value_x, data = .)
  784. tibble(
  785. r2 = summary(m)$r.squared,
  786. p = glance(m)$p.value,
  787. cor_original = unique(.$cor_original),
  788. padj_original = unique(.$padj_original)
  789. )
  790. }) %>%
  791. ungroup() %>%
  792. mutate(
  793. label = sprintf(
  794. "R² = %.2f\np = %.3g\nFDR = %.3g",
  795. r2, p, padj_original
  796. )
  797. )
  799. scatter_df <- left_join(scatter_df, pair_stats, by = "facet_id")
  802. ### -----------------------------------------------------------
  803. ### 5. Genotype × Treatment Colors
  804. ### -----------------------------------------------------------
  806. genoTx_colors <- c(
  807. "WT_Repeats" = "#f15b60",
  808. "Het_Repeats" = "#f79151",
  809. "Homo_Repeats" = "#ffcb38",
  810. "WT_Control" = "#00b04f",
  811. "Het_Control" = "#109ab3",
  812. "Homo_Control" = "#8a77aa"
  813. )
  816. ### -----------------------------------------------------------
  817. ### 6. PAGINATION + FILLERS (4×6 layout)
  818. ### -----------------------------------------------------------
  820. facet_ids <- unique(scatter_df$facet_id)
  821. n_per_page <- 24 # 4 columns × 6 rows
  822. n_pages <- ceiling(length(facet_ids) / n_per_page)
  824. # filler facets to complete the last page
  825. fillers_needed <- n_pages * n_per_page - length(facet_ids)
  827. filler_df <- tibble()
  828. if (fillers_needed > 0) {
  829. filler_ids <- paste0("Filler_", seq_len(fillers_needed))
  831. filler_df <- tibble(
  832. facet_id = filler_ids,
  833. Value_x = NA,
  834. Value_y = NA,
  835. Genotype_Tx = factor(NA, levels = levels(scatter_df$Genotype_Tx)),
  836. label = ""
  837. )
  839. facet_ids <- c(facet_ids, filler_ids)
  840. }
  843. ### -----------------------------------------------------------
  844. ### 7. PDF output (each page is 4×6)
  845. ### -----------------------------------------------------------
  847. pdf("Significant_PhenotypePhenotype_ScatterPlots_R2_GenoTx_Paginated.pdf",
  848. width = 9, height = 10.5)
  850. for (page in seq_len(n_pages)) {
  852. start <- (page - 1) * n_per_page + 1
  853. end <- page * n_per_page
  855. these_facets <- facet_ids[start:end]
  857. page_data <- scatter_df %>%
  858. filter(facet_id %in% these_facets) %>%
  859. bind_rows(filler_df %>% filter(facet_id %in% these_facets)) %>%
  860. mutate(facet_id = factor(facet_id, levels = these_facets))
  862. p_page <- ggplot(page_data, aes(x = Value_x, y = Value_y)) +
  864. geom_point(aes(color = Genotype_Tx),
  865. size = 1.8, alpha = 0.85, na.rm = TRUE) +
  867. geom_smooth(method = "lm",
  868. se = FALSE,
  869. color = "black",
  870. linewidth = 0.7,
  871. na.rm = TRUE) +
  873. geom_text(
  874. aes(label = label),
  875. x = -Inf, y = Inf,
  876. hjust = -0.1, vjust = 1.2,
  877. size = 3,
  878. lineheight = 0.9,
  879. color = "black"
  880. ) +
  882. facet_wrap(~ facet_id, ncol = 4, nrow = 6, scales = "free") +
  884. scale_color_manual(
  885. name = "Genotype × Treatment",
  886. values = genoTx_colors,
  887. na.translate = FALSE
  888. ) +
  890. labs(
  891. title = paste("Significant Phenotype–Phenotype Scatter Plots (Page", page, ")"),
  892. x = "Phenotype A",
  893. y = "Phenotype B"
  894. ) +
  896. theme_bw(base_size = 6) +
  897. theme(
  898. plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
  899. strip.background = element_rect(fill = "white", color = "black"),
  900. strip.text = element_text(size = 5, face = "bold"),
  901. legend.position = "bottom",
  902. panel.grid = element_blank()
  903. )
  905. print(p_page)
  906. }
  908. dev.off()
  910. ```
  912. ``` {r select_publication_boxplots_over_time}
  913. library(dplyr)
  914. library(ggplot2)
  915. library(ggnewscale)
  916. library(tibble)
  918. # --- Set parameters
  919. target_exps <- c("Iba1", "GR", "GP", "GFAP", "NeuN", "GA", "TDP_CE")
  920. target_stats <- c("Normalized_Area", "Sort1_CE_normalized")
  921. facet_ids_expected <- expand.grid(Exp = target_exps, StatsVariable = target_stats) %>%
  922. mutate(facet_id = paste(Exp, StatsVariable, sep = "_")) %>%
  923. pull(facet_id)
  925. # --- Filter + format base data
  926. plot_data <- data %>%
  927. filter(
  928. Exp %in% target_exps,
  929. Genotype == "Het",
  930. StatsVariable %in% target_stats
  931. ) %>%
  932. mutate(
  933. Treatment_Combined = ifelse(Treatment %in% c("None", "Control"), "Control", "Repeats"),
  934. Genotype_Tx = factor(interaction(Genotype, Treatment_Combined, sep = "_"),
  935. levels = c("Het_Control", "Het_Repeats")),
  936. Treatment_Shape = factor(Treatment, levels = c("None", "Control", "Repeats")),
  937. Sex = factor(Sex, levels = c("Female", "Male")),
  938. Time_month = factor(Time_month, levels = sort(unique(Time_month))),
  939. facet_id = paste(Exp, StatsVariable, sep = "_")
  940. ) %>%
  942. # ---- NEW FILTER: Keep only Het + Repeats imaging with Expression_Merged between 30–630 ----
  943. filter(
  944. # Keep ALL non-imaging OR non-Repeats rows
  945. !(Exp %in% imaging_exps & Treatment_Combined == "Repeats") |
  947. # Apply the 30–630 filter *only* for imaging × Repeats × Het rows
  948. (Exp %in% imaging_exps &
  949. Treatment_Combined == "Repeats" &
  950. Expression_Merged >= 30 &
  951. Expression_Merged <= 630)
  952. )
  954. # --- Fill in missing facets to ensure 3x4 layout
  955. existing_facets <- unique(plot_data$facet_id)
  956. missing_facets <- setdiff(facet_ids_expected, existing_facets)
  958. if (length(missing_facets) > 0) {
  959. filler_data <- tibble(
  960. Exp = gsub("_.*", "", missing_facets),
  961. StatsVariable = gsub(".*_", "", missing_facets),
  962. facet_id = missing_facets,
  963. Time_month = factor(NA, levels = levels(plot_data$Time_month)),
  964. Value = NA,
  965. Genotype_Tx = factor(NA, levels = c("Het_Control", "Het_Repeats")),
  966. Treatment_Shape = NA,
  967. Sex = NA
  968. )
  969. plot_data <- bind_rows(plot_data, filler_data)
  970. }
  972. # --- Add additional fillers to reach 12 panels (3x4)
  973. total_panels <- 12
  974. current_panels <- length(unique(plot_data$facet_id))
  975. if (current_panels < total_panels) {
  976. extra_fillers <- tibble(
  977. Exp = "Filler",
  978. StatsVariable = "Empty",
  979. facet_id = paste0("filler_", seq_len(total_panels - current_panels)),
  980. Time_month = factor(NA, levels = levels(plot_data$Time_month)),
  981. Value = NA,
  982. Genotype_Tx = factor(NA, levels = c("Het_Control", "Het_Repeats")),
  983. Treatment_Shape = NA,
  984. Sex = NA
  985. )
  986. plot_data <- bind_rows(plot_data, extra_fillers)
  987. }
  989. # --- Aesthetics
  990. custom_fill_colors <- c("Het_Control" = "#109ab3", "Het_Repeats" = "#f79151")
  991. sex_colors <- c("Female" = "red", "Male" = "blue")
  992. shape_values <- c("None" = 22, "Control" = 23, "Repeats" = 21)
  994. plot_data <- plot_data %>%
  995. mutate(
  996. facet_id = case_when(
  997. facet_id == "Iba1_Normalized_Area" ~ "Iba1\nNormalized\nArea",
  998. facet_id == "Iba1_Normalized_Count" ~ "Iba1\nNormalized\nCount",
  999. facet_id == "GR_Normalized_Area" ~ "GR\nNormalized\nArea",
  1000. facet_id == "GR_Normalized_Count" ~ "GR\nNormalized\nCount",
  1001. facet_id == "GFAP_Normalized_Area" ~ "GFAP\nNormalized\nArea",
  1002. facet_id == "GFAP_Normalized_Count" ~ "GFAP\nNormalized\nCount",
  1004. TRUE ~ facet_id
  1005. )
  1006. )
  1007. # --- Plot
  1008. pdf("Iba1_GR_GFAP_Het_AcrossTime_FIXED_3x4.pdf", width = 7, height = 9)
  1010. p <- ggplot(plot_data, aes(
  1011. x = Time_month,
  1012. y = Value,
  1013. fill = Genotype_Tx
  1014. )) +
  1015. geom_boxplot(
  1016. aes(group = interaction(Time_month, Genotype_Tx)),
  1017. position = position_dodge2(width = 0.75, preserve = "single"),
  1018. outlier.shape = NA,
  1019. width = 1,
  1020. alpha = 0.9
  1021. ) +
  1022. scale_fill_manual(values = custom_fill_colors, name = "Genotype × Treatment") +
  1023. ggnewscale::new_scale_fill() +
  1024. geom_jitter(
  1025. aes(
  1026. fill = Sex,
  1027. shape = Treatment_Shape,
  1028. group = interaction(Time_month, Genotype_Tx)
  1029. ),
  1030. color = "black",
  1031. size = 1.5,
  1032. stroke = 0.5,
  1033. alpha = 0.8,
  1034. position = position_jitterdodge(dodge.width = 0.75, jitter.width = 0.2)
  1035. ) +
  1036. scale_fill_manual(values = sex_colors, name = "Sex") +
  1037. scale_shape_manual(values = shape_values, name = "Original Treatment") +
  1038. facet_wrap(~ facet_id, scales = "free_y", ncol = 3, nrow = 4) +
  1039. labs(x = "Time (months)", y = "Value") +
  1040. theme_classic(base_size = 10) +
  1041. theme(
  1042. strip.background = element_rect(fill = "grey90", color = "black"),
  1043. strip.text = element_text(face = "bold", size = 9),
  1044. axis.text.x = element_text(angle = 45, hjust = 1),
  1045. legend.position = "right"
  1046. )
  1048. print(p)
  1049. dev.off()
  1051. ```
  1053. ```{r Expression_distribution_plot_publication, include=FALSE}
  1054. library(dplyr)
  1055. library(tidyr)
  1056. library(ggplot2)
  1058. # ---------------------------------------------------------------
  1059. # 1. PREPARE DATA (unique mice, log2-transform, compute stats)
  1060. # ---------------------------------------------------------------
  1062. repeats_data <- data %>%
  1063. filter(
  1064. Treatment == "Repeats",
  1065. Expression_Merged > 0
  1066. )
  1068. # Extract Expression_* columns
  1069. expression_cols <- grep("^Expression_", colnames(repeats_data), value = TRUE)
  1071. repeats_data <- repeats_data %>%
  1072. mutate(across(all_of(expression_cols), as.numeric))
  1074. # Pivot Expression_* → one column, then keep UNIQUE mice
  1075. unique_expression <- repeats_data %>%
  1076. pivot_longer(
  1077. cols = all_of(expression_cols),
  1078. names_to = "Timepoint",
  1079. values_to = "Expression"
  1080. ) %>%
  1081. filter(!is.na(Expression), Expression > 0) %>%
  1082. distinct(Ear_Tag, .keep_all = TRUE) %>%
  1083. select(Ear_Tag, Expression)
  1085. # Raw-scale summary stats
  1086. min_exp <- min(unique_expression$Expression)
  1087. max_exp <- max(unique_expression$Expression)
  1088. quart_raw <- quantile(unique_expression$Expression, probs = c(.25, .50, .75))
  1090. mean_expression <- mean(unique_expression$Expression)
  1091. median_expression <- median(unique_expression$Expression)
  1093. # Convert everything to log2 space for plotting
  1094. unique_expression <- unique_expression %>%
  1095. mutate(Expression_log2 = log2(Expression))
  1097. min_log <- log2(min_exp)
  1098. max_log <- log2(max_exp)
  1099. quart_log <- log2(quart_raw)
  1100. mean_log <- log2(mean_expression)
  1101. median_log <- log2(median_expression)
  1103. # Dynamic bin width for histogram
  1104. log_range <- max_log - min_log
  1105. n_bins <- 20
  1106. bin_width <- log_range / n_bins
  1109. # ---------------------------------------------------------------
  1110. # 2. HISTOGRAM PDF OUTPUT
  1111. # ---------------------------------------------------------------
  1113. pdf("Expression_Histogram_UniqueMice_log2.pdf", width = 3, height = 2)
  1115. hist_plot <- ggplot(unique_expression, aes(x = Expression_log2)) +
  1116. geom_rect(aes(xmin = min_log, xmax = quart_log[1], ymin = 0, ymax = Inf),
  1117. fill = "lightblue", alpha = 0.4, inherit.aes = FALSE) +
  1118. geom_rect(aes(xmin = quart_log[1], xmax = quart_log[2], ymin = 0, ymax = Inf),
  1119. fill = "lightgreen", alpha = 0.4, inherit.aes = FALSE) +
  1120. geom_rect(aes(xmin = quart_log[2], xmax = quart_log[3], ymin = 0, ymax = Inf),
  1121. fill = "lightyellow", alpha = 0.4, inherit.aes = FALSE) +
  1122. geom_rect(aes(xmin = quart_log[3], xmax = max_log, ymin = 0, ymax = Inf),
  1123. fill = "lightpink", alpha = 0.4, inherit.aes = FALSE) +
  1125. geom_histogram(binwidth = bin_width,
  1126. color = "black", fill = "steelblue", alpha = 0.75) +
  1128. geom_vline(xintercept = mean_log, color = "red", linetype = "dashed", linewidth = 1) +
  1129. geom_vline(xintercept = median_log, color = "darkgreen", linetype = "dashed", linewidth = 1) +
  1131. annotate("text", x = mean_log, y = 0,
  1132. label = paste0("Mean: ", round(mean_expression,1)),
  1133. hjust = -0.1, vjust = -1.5, color = "red", size = 3) +
  1134. annotate("text", x = median_log, y = 0,
  1135. label = paste0("Median: ", round(median_expression,1)),
  1136. hjust = -0.1, vjust = -3, color = "darkgreen", size = 3) +
  1138. scale_x_continuous(
  1139. breaks = pretty(c(min_log, max_log), n = 5),
  1140. labels = function(x) round(2^x, 1)
  1141. ) +
  1142. theme_classic() +
  1143. labs(
  1144. title = "Expression Histogram (Unique Mice, Repeats Only)",
  1145. x = "Expression (raw units, log2 axis)",
  1146. y = "Count"
  1147. )
  1149. print(hist_plot)
  1150. dev.off()
  1153. # ---------------------------------------------------------------
  1154. # 3. DENSITY PDF OUTPUT
  1155. # ---------------------------------------------------------------
  1157. pdf("Expression_Density_UniqueMice_log2.pdf", width = 3, height = 2)
  1159. density_plot <- ggplot(unique_expression, aes(x = Expression_log2)) +
  1160. geom_rect(aes(xmin = min_log, xmax = quart_log[1], ymin = 0, ymax = Inf),
  1161. fill = "lightblue", alpha = 0.4, inherit.aes = FALSE) +
  1162. geom_rect(aes(xmin = quart_log[1], xmax = quart_log[2], ymin = 0, ymax = Inf),
  1163. fill = "lightgreen", alpha = 0.4, inherit.aes = FALSE) +
  1164. geom_rect(aes(xmin = quart_log[2], xmax = quart_log[3], ymin = 0, ymax = Inf),
  1165. fill = "lightyellow", alpha = 0.4, inherit.aes = FALSE) +
  1166. geom_rect(aes(xmin = quart_log[3], xmax = max_log, ymin = 0, ymax = Inf),
  1167. fill = "lightpink", alpha = 0.4, inherit.aes = FALSE) +
  1169. geom_density(fill = "steelblue", alpha = 0.6) +
  1171. geom_vline(xintercept = mean_log, color = "red", linetype = "dashed", linewidth = 1) +
  1172. geom_vline(xintercept = median_log, color = "darkgreen", linetype = "dashed", linewidth = 1) +
  1174. annotate("text", x = mean_log, y = 0.01,
  1175. label = paste0("Mean: ", round(mean_expression, 1)),
  1176. hjust = -0.1, color = "red", size = 4) +
  1177. annotate("text", x = median_log, y = 0.015,
  1178. label = paste0("Median: ", round(median_expression, 1)),
  1179. hjust = -0.1, color = "darkgreen", size = 4) +
  1181. scale_x_continuous(
  1182. breaks = pretty(c(min_log, max_log), n = 5),
  1183. labels = function(x) round(2^x, 1)
  1184. ) +
  1185. theme_classic() +
  1186. labs(
  1187. title = "Expression Density (Unique Mice, Repeats Only)",
  1188. x = "Expression (raw units, log2 axis)",
  1189. y = "Density"
  1190. )
  1192. print(density_plot)
  1193. dev.off()
  1195. ```

KAR_Acta_Neuro_Comm_v1.13.Rmd, under CC-BY-4.0 · at the source

Overview

Authors: Katelyn A Russell1,2, Amelia A Shahrabi1,2, Suleyman C Akerman3,4, Matthew D Byrne1, Jeffrey D Rothstein3,4, Davide Trotti1,2, Brigid K Jensen1,2, Aaron R Haeusler1,2
ORCID iDs: Suleyman C Akerman
  1. Department of Neuroscience, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107 USA
  2. Jefferson Weinberg ALS Center, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107 USA
  3. Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
  4. Brain Science Institute, Johns Hopkins University School of Medicine, Baltimore, MD 21205 USA
Institutions: Thomas Jefferson University (United States); Johns Hopkins University (United States); Johns Hopkins Medicine (United States)
Journal: Acta neuropathologica communications, volume 14, issue 1, article 167
Dates: received 11 February 2026; accepted 1 June 2026; published online 18 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s40478-026-02341-8 · PMID 42316301 · PMCID PMC13476920 · OpenAlex W7165190099
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), mouse (organism), other condition (population), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Preprocessing, Statistics, Machine learning, Evoked potentials, Connectivity, fMRI & imaging
Keywords: ALS, FTD, ALS/FTD, C9orf72, Mouse models, C9orf72 repeat expansions, Neurodegenerative disease, Motor neuron disease
MeSH: Amyotrophic Lateral Sclerosis*, C9orf72 Protein*, Frontotemporal Dementia*, Animals, Dependovirus, Disease Models, Animal, DNA Repeat Expansion, DNA-Binding Proteins, Humans, Injections, Spinal, Male, Mice, Mice, Inbred C57BL, Mice, Transgenic, Motor Neurons, Spinal Cord (* major topic)
Topic: Amyotrophic Lateral Sclerosis Research (Neurology, Medicine), according to OpenAlex
Funding: NCI NIH HHS (P30 CA056036); NINDS NIH HHS (R01 NS114128, R01 NS109150, R01NS109150, RF1 NS114128, RF1NS114128); National Institute of Neurological Disorders and Stroke (RF1NS114128, R01NS109150); Farber Family Foundation; Aldrich Foundation
Citations: not cited yet (Europe PMC); 97 references in the paper

Abstract

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

Repository

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figshare 32825642

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (1)
Size: 2 files, 1 script
Software Heritage: not checked
Found in: “Code availability”
Holds: 1 notebook
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: broom (1 file), ggplot2 (1 file), reshape2 (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
1 file
At the source: doi.org/10.6084/m9.figshare.32825642

Code availability statement

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Data

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Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 8 keywords, 16 MeSH terms, 5 funders, 94 references.

Cite

This paper

Russell, K. A., Shahrabi, A. A., Akerman, S. C., Byrne, M. D., Rothstein, J. D., Trotti, D., Jensen, B. K., & Haeusler, A. R. (2026). Intrathecal (G&lt;sub&gt;4&lt;/sub&gt;C&lt;sub&gt;2&lt;/sub&gt;)&lt;sub&gt;149&lt;/sub&gt; delivery in C9orf72-deficient mice yields mild motor dysfunction and ALS/FTD pathological hallmarks. Acta neuropathologica communications, 14(1), 167. https://doi.org/10.1186/s40478-026-02341-8

BibTeX

@article{russell2026intrathecal,
author = {Russell, Katelyn A and Shahrabi, Amelia A and Akerman, Suleyman C and Byrne, Matthew D and Rothstein, Jeffrey D and Trotti, Davide and Jensen, Brigid K and Haeusler, Aaron R},
title = {{Intrathecal (G\&lt;sub\&gt;4\&lt;/sub\&gt;C\&lt;sub\&gt;2\&lt;/sub\&gt;)\&lt;sub\&gt;149\&lt;/sub\&gt; delivery in C9orf72-deficient mice yields mild motor dysfunction and ALS/FTD pathological hallmarks}},
journal = {Acta neuropathologica communications},
year = {2026},
month = jun,
volume = {14},
number = {1},
pages = {167},
publisher = {BMC},
issn = {2051-5960},
doi = {10.1186/s40478-026-02341-8},
url = {https://doi.org/10.1186/s40478-026-02341-8},
pmid = {42316301},
pmcid = {PMC13476920}
}

RIS

TY - JOUR
AU - Russell, Katelyn A
AU - Shahrabi, Amelia A
AU - Akerman, Suleyman C
AU - Byrne, Matthew D
AU - Rothstein, Jeffrey D
AU - Trotti, Davide
AU - Jensen, Brigid K
AU - Haeusler, Aaron R
TI - Intrathecal (G&lt;sub&gt;4&lt;/sub&gt;C&lt;sub&gt;2&lt;/sub&gt;)&lt;sub&gt;149&lt;/sub&gt; delivery in C9orf72-deficient mice yields mild motor dysfunction and ALS/FTD pathological hallmarks
T2 - Acta neuropathologica communications
J2 - Acta Neuropathol Commun
PY - 2026
DA - 2026/06/18
VL - 14
IS - 1
SP - 167
SN - 2051-5960
PB - BMC
DO - 10.1186/s40478-026-02341-8
UR - https://doi.org/10.1186/s40478-026-02341-8
LA - en
ER -

CSL-JSON

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"id": "10.1186/s40478-026-02341-8",
"type": "article-journal",
"title": "Intrathecal (G&lt;sub&gt;4&lt;/sub&gt;C&lt;sub&gt;2&lt;/sub&gt;)&lt;sub&gt;149&lt;/sub&gt; delivery in C9orf72-deficient mice yields mild motor dysfunction and ALS/FTD pathological hallmarks",
"container-title": "Acta neuropathologica communications",
"author": [
{
"family": "Russell",
"given": "Katelyn A"
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{
"family": "Shahrabi",
"given": "Amelia A"
},
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{
"family": "Byrne",
"given": "Matthew D"
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{
"family": "Rothstein",
"given": "Jeffrey D"
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"container-title-short": "Acta Neuropathol Commun",
"volume": "14",
"issue": "1",
"page": "167",
"DOI": "10.1186/s40478-026-02341-8",
"PMID": "42316301",
"PMCID": "PMC13476920",
"ISSN": "2051-5960",
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"language": "en",
"issued": {
"date-parts": [
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}
}

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