OSCR

Shotgun metagenomic analysis reveals taxonomic and functional alterations in the gut microbiome across prodromal and symptomatic Lewy body disease.

Code ↔ Paper

9 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 9 matches
  1. [1] § Results › Microbial features characterizing the disease continuum of Lewy body disease ↔ 2-microbial_taxa_analysis/2-different_prevalence_analysis_final_version.R, lines 192–281 · score 0.98 · Oscillospiraceae bacterium CLA, Actinomyces oris, GGB3730 SGB5060, GGB9627 SGB15081, GGB9719 SGB15272, Akkermansia muciniphila
  2. [2] § Materials and methods › Gut microbiome diversity analysis ↔ 1-alpha_beta_diversity/alpha_beta_diversity_final_version.R, lines 86–124 · score 0.73 · PCoA, species richness, arcsine square root, diversity, household ID, lmer
  3. [3] § Materials and methods › Correlations between clinical characteristics and microbial features ↔ 5-correlation_analysis/partial_correlation_analysis.R, lines 424–489 · score 0.73 · MoCA, CDR SB, partial correlation, clinical measurements, UPDRS, STMS
  4. [4] § Results › Differentially abundant and prevalent gut microbiome features between LBD patients and their cohabitant controls ↔ 2-microbial_taxa_analysis/2-different_prevalence_analysis_final_version.R, lines 192–281 · score 0.71 · Oscillospiraceae bacterium CLA, Longicatena caecimuris, AA H250, prevalent, taxa, species
  5. [5] § Materials and methods › Differential abundance and prevalence analysis ↔ 2-microbial_taxa_analysis/1-differential_abundance_analysis_final_version.R, lines 1–66 · score 0.69 · low abundance, noise, preprocessed, zero, microbial species, downstream
  6. [6] § Materials and methods › Correlations between clinical characteristics and microbial features ↔ 5-correlation_analysis/partial_correlation_analysis.R, lines 81–172 · score 0.65 · pcor.test, partial correlation, clinical measurement, Spearman, age, BMI
  7. [7] § Results › Correlation between clinical measures and microbial features ↔ 5-correlation_analysis/partial_correlation_analysis.R, lines 424–489 · score 0.63 · MoCA, CDR SB, partial correlation, resampling, UPDRS, STMS
  8. [8] § Materials and methods › Correlations between clinical characteristics and microbial features ↔ 5-correlation_analysis/partial_correlation_analysis.R, lines 81–172 · score 0.60 · partial correlation, clinical measurement, resampling, validation, Spearman, 80 %
  9. [9] § Materials and methods › Differential abundance and prevalence analysis ↔ 3-microbial_functional_pathway_analysis/pathway_analysis_final_version.R, lines 539–561 · score 0.59 · arcsine square root, prevalence cutoff, household ID, variables, linear, relative abundance

Paper

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The authors' code

R · 527 lines · 18 KB · no license · 4 matches

  1. library(ggplot2)
  2. library(reshape)
  3. library(tidyr)
  4. library(reshape2)
  5. library(dplyr)
  6. library(tidyverse)
  7. library(ppcor)
  9. ###############################################################################
  10. setwd("../5-correlation_analysis")
  11. ###############################################################################
  13. # 1. Read the species and pathway relative abundance data and modify the sample names.
  14. species_data <- read.table(file = "./metaphlan_results_new.tsv", sep = "\t", header = T, row.names = 1)
  15. species <- species_data[grepl("s__", rownames(species_data)) & !grepl("t__", rownames(species_data)), ]
  16. rownames(species) <- gsub(".*s__", "", rownames(species))
  17. colnames(species) <- gsub("metaphlan_|_S.*", "", names(species))
  19. pathway_data <- read.csv(file = "./pathway_results.tsv", sep = "\t", header = T, row.names = 1)
  20. colnames(pathway_data) <- gsub("_S.*", "", colnames(pathway_data))
  21. pathway <- pathway_data
  23. # 2. Read the metadata
  24. metadata <- read.csv(file = "./0-raw_data/imputed_BMI_metadata.csv", sep = ",", header = T, row.names = 1)
  25. species_clean <- species[, c(rownames(metadata))]
  26. pathway_clean <- pathway[, c(rownames(metadata))]
  28. # 3. Change the taxonomic data from percentage into proportion
  29. colSums(species_clean)
  30. species_prop <- data.frame(apply(species_clean, 2, function(x) x/sum(x)))
  31. colSums(species_prop)
  33. colSums(pathway_clean)
  34. pathway_prop <- data.frame(apply(pathway_clean, 2, function(x) x/sum(x)))
  35. colSums(pathway_prop)
  37. # 4. Use prevalence cut-off (10^-4.5) to remove bacterial species and pathways that are present in very low abundance
  38. cut_off_s <- 10^-4.7
  39. species_cutoff <- species_prop
  40. species_cutoff[species_cutoff <= cut_off_s] <- 0
  42. cut_off_p <- 10^-4.5
  43. pathway_cutoff <- pathway_prop
  44. pathway_cutoff[pathway_cutoff < cut_off_p] <- 0
  46. # 5. Add metadata to the proportion data
  47. species_t <- as.data.frame(t(species_cutoff))
  48. species_all <- merge(species_t, metadata, by = "row.names", all = F)
  49. rownames(species_all) <- species_all$Row.names
  50. species_done <- species_all[, -1]
  52. pathway_t <- as.data.frame(t(pathway_cutoff))
  53. pathway_all <- merge(pathway_t, metadata, by = "row.names", all = F)
  54. rownames(pathway_all) <- pathway_all$Row.names
  55. pathway_done <- pathway_all[, -1]
  57. ###############################################################################
  59. # ── STAGE 1: fast screen via parametric pcor.test ─────────────────────────────
  60. run_partial_cor_screen <- function(df, feature, clinical_measurement) {
  62. df1 <- df[, c(feature, "age", "BMI", clinical_measurement)]
  63. df2 <- na.omit(df1)
  64. if (nrow(df2) < 4) return(c(rho = NA, pval = NA))
  66. Z_full <- tryCatch(
  67. model.matrix(~ age + BMI, data = df2)[, -1, drop = FALSE],
  68. error = function(e) NULL)
  69. if (is.null(Z_full)) return(c(rho = NA, pval = NA))
  71. res <- tryCatch(
  72. pcor.test(x = df2[[1]], y = df2[[clinical_measurement]],
  73. z = Z_full, method = "spearman"),
  74. error = function(e) NULL)
  75. if (is.null(res) || is.na(res$estimate)) return(c(rho = NA, pval = NA))
  77. return(c(rho = res$estimate, pval = res$p.value))
  78. }
  81. # ── STAGE 2: resampling null + LOO stability (only on Stage 1 hits) ────────────
  82. run_partial_cor_validate <- function(df, feature, clinical_measurement, n_perm = 1000) {
  84. df1 <- df[, c(feature, "age", "BMI", clinical_measurement)]
  85. df2 <- na.omit(df1)
  86. if (nrow(df2) < 4) return(c(pval_resample = NA, ci_lower = NA, ci_upper = NA, stability = NA))
  88. n_total <- nrow(df2)
  90. # real rho (recomputed cleanly on complete cases)
  91. Z_full <- tryCatch(
  92. model.matrix(~ age + BMI, data = df2)[, -1, drop = FALSE],
  93. error = function(e) NULL)
  94. if (is.null(Z_full)) return(c(pval_resample = NA, ci_lower = NA, ci_upper = NA, stability = NA))
  96. real_res <- tryCatch(
  97. pcor.test(x = df2[[1]], y = df2[[clinical_measurement]],
  98. z = Z_full, method = "spearman"),
  99. error = function(e) NULL)
  100. if (is.null(real_res) || is.na(real_res$estimate)) {
  101. return(c(pval_resample = NA, ci_lower = NA, ci_upper = NA, stability = NA))
  102. }
  103. real_rho <- real_res$estimate
  105. # resampling null distribution
  106. set.seed(2026)
  107. null_rho <- c()
  108. for (i in seq_len(n_perm)) {
  109. size <- sample(ceiling(0.8 * n_total):(n_total - 1), 1)
  110. idx <- sample(seq_len(n_total), size, replace = FALSE)
  111. df_sub <- df2[idx, ]
  113. if (var(df_sub[[1]], na.rm = TRUE) == 0) next
  115. df_sub[[clinical_measurement]] <- sample(df_sub[[clinical_measurement]])
  117. Z_sub <- tryCatch(
  118. model.matrix(~ age + BMI, data = df_sub)[, -1, drop = FALSE],
  119. error = function(e) NULL)
  120. if (is.null(Z_sub)) next
  122. res_null <- tryCatch(
  123. pcor.test(x = df_sub[[1]], y = df_sub[[clinical_measurement]],
  124. z = Z_sub, method = "spearman"),
  125. error = function(e) NULL)
  126. if (is.null(res_null) || is.na(res_null$estimate)) next
  128. null_rho <- c(null_rho, res_null$estimate)
  129. }
  131. pval_resample <- if (length(null_rho) > 0) mean(abs(null_rho) >= abs(real_rho)) else NA
  133. # LOO stability
  134. sig_count <- 0
  135. total_count <- 0
  136. all_rho <- c()
  138. for (iter in seq_len(n_total)) {
  139. df_sub <- df2[-iter, ]
  141. if (var(df_sub[[1]], na.rm = TRUE) == 0) next
  143. Z_sub <- tryCatch(
  144. model.matrix(~ age + BMI, data = df_sub)[, -1, drop = FALSE],
  145. error = function(e) NULL)
  146. if (is.null(Z_sub)) next
  148. res_loo <- tryCatch(
  149. pcor.test(x = df_sub[[1]], y = df_sub[[clinical_measurement]],
  150. z = Z_sub, method = "spearman"),
  151. error = function(e) NULL)
  152. if (is.null(res_loo) || is.na(res_loo$estimate)) next
  154. total_count <- total_count + 1
  155. all_rho <- c(all_rho, res_loo$estimate)
  157. if (sign(res_loo$estimate) == sign(real_rho) && abs(res_loo$estimate) >= 0.4) {
  158. sig_count <- sig_count + 1
  159. }
  160. cat(sprintf(" Feature: %-40s | LOO iter: %d/%d | rho: %.3f\n",
  161. feature, iter, n_total, res_loo$estimate))
  162. }
  164. stability <- if (total_count > 0) sig_count / total_count else NA
  165. ci_lower <- unname(quantile(all_rho, 0.025, na.rm = TRUE))
  166. ci_upper <- unname(quantile(all_rho, 0.975, na.rm = TRUE))
  168. return(c(pval_resample = pval_resample,
  169. ci_lower = ci_lower,
  170. ci_upper = ci_upper,
  171. stability = stability))
  172. }
  175. # ── WRAPPER: two-stage pipeline ────────────────────────────────────────────────
  176. partial_cor_subsample <- function(data, data_type, clinical_measurement, group, threshold, n_perm = 1000) {
  178. df <- data %>% filter(condition == group)
  180. filtered_df <- df[, c(1:(ncol(df) - 32))][, colSums(df[, c(1:(ncol(df) - 32))] > 0) >= threshold * nrow(df)]
  182. features <- colnames(filtered_df)
  183. n_feat <- length(features)
  185. # ── open log file ────────────────────────────────────────────────────────────
  186. log_file <- paste0("partial_cor_log_", clinical_measurement, "_", group, "_", data_type, ".txt")
  187. log_con <- file(log_file, open = "wt")
  189. log <- function(...) {
  190. msg <- paste0(...)
  191. cat(msg, "\n")
  192. cat(msg, "\n", file = log_con)
  193. }
  195. on.exit(close(log_con), add = TRUE)
  197. log("=== STAGE 1: parametric screen across ", n_feat, " features ===")
  198. log("clinical_measurement : ", clinical_measurement)
  199. log("group : ", group)
  200. log("data_type : ", data_type)
  201. log("prevalence threshold : ", threshold)
  202. log("n_perm (Stage 2) : ", n_perm)
  203. log("timestamp : ", format(Sys.time(), "%Y-%m-%d %H:%M:%S"))
  204. log(strrep("-", 60))
  206. # ── Stage 1: parametric screen ──────────────────────────────────────────────
  207. screen_rho <- numeric(n_feat)
  208. screen_pval <- numeric(n_feat)
  210. for (i in seq_len(n_feat)) {
  211. log(sprintf("[%d/%d] %s", i, n_feat, features[i]))
  212. res <- run_partial_cor_screen(df, features[i], clinical_measurement)
  213. screen_rho[i] <- res[["rho"]]
  214. screen_pval[i] <- res[["pval"]]
  215. }
  217. screen_result <- data.frame(
  218. feature = features,
  219. rho = screen_rho,
  220. pval = screen_pval,
  221. qval = p.adjust(screen_pval, method = "BH")
  222. )
  224. # save ALL screen results
  225. screen_result_order <- screen_result[order(screen_result$pval), ]
  226. write.csv(screen_result_order,
  227. file = paste0("partial_cor_screen_", clinical_measurement, "_", group, "_", data_type, ".csv"),
  228. row.names = FALSE)
  230. hits <- screen_result[!is.na(screen_result$pval) & screen_result$pval < 0.05, ]
  232. log(strrep("-", 60))
  233. log(sprintf("Stage 1 complete: %d / %d features pass (raw p < 0.05)", nrow(hits), n_feat))
  234. log(sprintf("Full screen results saved to: partial_cor_screen_%s_%s_%s.csv", clinical_measurement, group, data_type))
  235. log(strrep("-", 60))
  237. if (nrow(hits) == 0) {
  238. log("No features passed Stage 1. Returning screen results only.")
  239. return(screen_result_order)
  240. }
  242. # ── Stage 2: resampling + LOO on hits only ───────────────────────────────────
  243. log(sprintf("\n=== STAGE 2: resampling + LOO on %d hits ===", nrow(hits)))
  245. val_pval <- numeric(nrow(hits))
  246. val_ci_lower <- numeric(nrow(hits))
  247. val_ci_upper <- numeric(nrow(hits))
  248. val_stability <- numeric(nrow(hits))
  250. for (i in seq_len(nrow(hits))) {
  251. feat <- hits$feature[i]
  252. log(sprintf("\n[%d/%d] %s", i, nrow(hits), feat))
  253. res <- run_partial_cor_validate(df, feat, clinical_measurement, n_perm = n_perm)
  254. val_pval[i] <- res[["pval_resample"]]
  255. val_ci_lower[i] <- res[["ci_lower"]]
  256. val_ci_upper[i] <- res[["ci_upper"]]
  257. val_stability[i] <- res[["stability"]]
  258. }
  260. result <- data.frame(
  261. feature = hits$feature,
  262. rho = hits$rho,
  263. pval_parametric = hits$pval,
  264. qval_parametric = hits$qval,
  265. pval_resample = val_pval,
  266. qval_resample = p.adjust(val_pval, method = "BH"),
  267. ci_lower = val_ci_lower,
  268. ci_upper = val_ci_upper,
  269. stability = val_stability
  270. )
  272. result_order <- result[order(result$pval_resample), ]
  274. log(strrep("-", 60))
  275. log("Stage 2 complete. Results saved to:")
  276. log(sprintf(" Screen (all) : partial_cor_screen_%s_%s_%s.csv", clinical_measurement, group, data_type))
  277. log(sprintf(" Validated (hits) : partial_cor_res_%s_%s_%s.csv", clinical_measurement, group, data_type))
  278. log(sprintf(" Log : %s", log_file))
  279. log(sprintf(" Timestamp : %s", format(Sys.time(), "%Y-%m-%d %H:%M:%S")))
  281. write.csv(result_order,
  282. file = paste0("partial_cor_res_", clinical_measurement, "_", group, "_", data_type, ".csv"),
  283. row.names = FALSE)
  285. return(result_order)
  286. }
  289. ## LBD: species: CDR-SB
  290. res_PC_cdr_species_lbd_new <- partial_cor_subsample(
  291. data = species_done,
  292. data_type = "species",
  293. clinical_measurement = "CDR_SB",
  294. group = "lbd",
  295. threshold = 0.5,
  296. n_perm = 1000)
  298. ## LBD: species: MOCA
  299. res_PC_moca_species_lbd_new <- partial_cor_subsample(
  300. data = species_done,
  301. data_type = "species",
  302. clinical_measurement = "MOCA",
  303. group = "lbd",
  304. threshold = 0.5,
  305. n_perm = 1000)
  307. ## LBD: species: STMS
  308. res_PC_stms_species_lbd_new <- partial_cor_subsample(
  309. data = species_done,
  310. data_type = "species",
  311. clinical_measurement = "STMS",
  312. group = "lbd",
  313. threshold = 0.5,
  314. n_perm = 1000)
  316. ## LBD: species: UPDRS3
  317. res_PC_updrs_species_lbd_new <- partial_cor_subsample(
  318. data = species_done,
  319. data_type = "species",
  320. clinical_measurement = "UPDRS3",
  321. group = "lbd",
  322. threshold = 0.5,
  323. n_perm = 1000)
  325. ## LBD: pathway: CDR-SB
  326. res_PC_cdr_pathway_lbd_new <- partial_cor_subsample(
  327. data = pathway_done,
  328. data_type = "pathway",
  329. clinical_measurement = "CDR_SB",
  330. group = "lbd",
  331. threshold = 0.5,
  332. n_perm = 1000)
  334. ## LBD: pathway: MOCA
  335. res_PC_moca_pathway_lbd_new <- partial_cor_subsample(
  336. data = pathway_done,
  337. data_type = "pathway",
  338. clinical_measurement = "MOCA",
  339. group = "lbd",
  340. threshold = 0.5,
  341. n_perm = 1000)
  343. ## LBD: pathway: STMS
  344. res_PC_stms_pathway_lbd_new <- partial_cor_subsample(
  345. data = pathway_done,
  346. data_type = "pathway",
  347. clinical_measurement = "STMS",
  348. group = "lbd",
  349. threshold = 0.5,
  350. n_perm = 1000)
  352. ## LBD: pathway: UPDRS
  353. res_PC_updrs_pathway_lbd_new <- partial_cor_subsample(
  354. data = pathway_done,
  355. data_type = "pathway",
  356. clinical_measurement = "UPDRS3",
  357. group = "lbd",
  358. threshold = 0.5,
  359. n_perm = 1000)
  361. ## iRBD: species: CDR-SB
  362. res_PC_cdr_species_irbd_new <- partial_cor_subsample(
  363. data = species_done,
  364. data_type = "species",
  365. clinical_measurement = "CDR_SB",
  366. group = "irbd",
  367. threshold = 0.5,
  368. n_perm = 1000)
  370. ## iRBD: species: MOCA
  371. res_PC_moca_species_irbd_new <- partial_cor_subsample(
  372. data = species_done,
  373. data_type = "species",
  374. clinical_measurement = "MOCA",
  375. group = "irbd",
  376. threshold = 0.5,
  377. n_perm = 1000)
  379. ## iRBD: species: STMS
  380. res_PC_stms_species_irbd_new <- partial_cor_subsample(
  381. data = species_done,
  382. data_type = "species",
  383. clinical_measurement = "STMS",
  384. group = "irbd",
  385. threshold = 0.5,
  386. n_perm = 1000)
  388. ## iRBD: species: UPDRS3
  389. res_PC_updrs_species_irbd_new <- partial_cor_subsample(
  390. data = species_done,
  391. data_type = "species",
  392. clinical_measurement = "UPDRS3",
  393. group = "irbd",
  394. threshold = 0.5,
  395. n_perm = 1000)
  397. ## iRBD: pathway: CDR-SB
  398. res_PC_cdr_pathway_irbd_new <- partial_cor_subsample(
  399. data = pathway_done,
  400. data_type = "pathway",
  401. clinical_measurement = "CDR_SB",
  402. group = "irbd",
  403. threshold = 0.5,
  404. n_perm = 1000)
  406. ## iRBD: pathway: MOCA
  407. res_PC_moca_pathway_irbd_new <- partial_cor_subsample(
  408. data = pathway_done,
  409. data_type = "pathway",
  410. clinical_measurement = "MOCA",
  411. group = "irbd",
  412. threshold = 0.5,
  413. n_perm = 1000)
  415. ## iRBD: pathway: STMS
  416. res_PC_stms_pathway_irbd_new <- partial_cor_subsample(
  417. data = pathway_done,
  418. data_type = "pathway",
  419. clinical_measurement = "STMS",
  420. group = "irbd",
  421. threshold = 0.5,
  422. n_perm = 1000)
  424. ## iRBD: pathway: UPDRS3
  425. res_PC_updrs_pathway_irbd_new <- partial_cor_subsample(
  426. data = pathway_done,
  427. data_type = "pathway",
  428. clinical_measurement = "UPDRS3",
  429. group = "irbd",
  430. threshold = 0.5,
  431. n_perm = 1000)
  433. ## LBD group:
  435. sig_cdr_sp_lbd <- res_PC_cdr_species_lbd_new %>% filter(pval_resample < 0.05) %>% mutate(group = "CDR-SB")
  436. sig_moca_sp_lbd <- res_PC_moca_species_lbd_new %>% filter(pval_resample < 0.05) %>% mutate(group = "MoCA")
  437. sig_stms_sp_lbd <- res_PC_stms_species_lbd_new %>% filter(pval_resample < 0.05) %>% mutate(group = "STMS")
  439. lbd_species_all <- rbind(sig_cdr_sp_lbd, sig_moca_sp_lbd, sig_stms_sp_lbd)
  441. lbd_species_all$feature <- sub("_", " ", lbd_species_all$feature)
  442. lbd_species_all_ordered <- lbd_species_all[order(lbd_species_all$feature), ]
  444. sig_cdr_pwy_lbd <- res_PC_cdr_pathway_lbd_new %>% filter(pval_resample < 0.05) %>% mutate(group = "CDR-SB")
  445. sig_moca_pwy_lbd <- res_PC_moca_pathway_lbd_new %>% filter(pval_resample < 0.05) %>% mutate(group = "MoCA")
  446. sig_stms_pwy_lbd <- res_PC_stms_pathway_lbd_new %>% filter(pval_resample < 0.05) %>% mutate(group = "STMS")
  448. lbd_pathway_all <- rbind(sig_cdr_pwy_lbd, sig_moca_pwy_lbd, sig_stms_pwy_lbd)
  450. lbd_pathway_all$feature <- sub(".*: ", "", lbd_pathway_all$feature)
  451. lbd_pathway_all_ordered <- lbd_pathway_all[order(lbd_pathway_all$feature), ]
  453. lbd_all <- rbind(lbd_species_all_ordered, lbd_pathway_all_ordered)
  455. lbd_all$log10p <- -log10(lbd_all$pval_parametric)
  456. lbd_all$group <- factor(lbd_all$group, levels = c("CDR-SB", "MoCA", "STMS"))
  457. lbd_all$feature <- factor(lbd_all$feature, levels = rev(unique(lbd_all$feature)))
  459. lbd_all$overall <- "lbd"
  461. sig_cdr_sp_irbd <- res_PC_cdr_species_irbd_new %>% filter(pval_resample < 0.05) %>% mutate(group = "CDR-SB")
  462. sig_moca_sp_irbd <- res_PC_moca_species_irbd_new %>% filter(pval_resample < 0.05) %>% mutate(group = "MoCA")
  463. sig_stms_sp_irbd <- res_PC_stms_species_irbd_new %>% filter(pval_resample < 0.05) %>% mutate(group = "STMS")
  465. irbd_species_all <- rbind(sig_cdr_sp_irbd, sig_moca_sp_irbd, sig_stms_sp_irbd)
  467. irbd_species_all$feature <- sub("_", " ", irbd_species_all$feature)
  468. irbd_species_all_ordered <- irbd_species_all[order(irbd_species_all$feature), ]
  470. sig_cdr_pwy_irbd <- res_PC_cdr_pathway_irbd_new %>% filter(pval_resample < 0.05) %>% mutate(group = "CDR-SB")
  471. sig_moca_pwy_irbd <- res_PC_moca_pathway_irbd_new %>% filter(pval_resample < 0.05) %>% mutate(group = "MoCA")
  472. sig_stms_pwy_irbd <- res_PC_stms_pathway_irbd_new %>% filter(pval_resample < 0.05) %>% mutate(group = "STMS")
  474. irbd_pathway_all <- rbind(sig_cdr_pwy_irbd, sig_moca_pwy_irbd, sig_stms_pwy_irbd)
  476. irbd_pathway_all$feature <- sub(".*: ", "", irbd_pathway_all$feature)
  477. irbd_pathway_all_ordered <- irbd_pathway_all[order(irbd_pathway_all$feature), ]
  479. irbd_all <- rbind(irbd_species_all_ordered, irbd_pathway_all_ordered)
  481. irbd_all$log10p <- -log10(irbd_all$pval_parametric)
  482. irbd_all$group <- factor(irbd_all$group, levels = c("CDR-SB", "MoCA", "STMS"))
  483. irbd_all$feature <- factor(irbd_all$feature, levels = rev(unique(irbd_all$feature)))
  485. irbd_all$overall <- "irbd"
  488. lbd_irbd_all <- rbind(lbd_all, irbd_all)
  489. lbd_irbd_all$overall <- factor(lbd_irbd_all$overall, levels = c("lbd", "irbd"))
  491. #lbd_irbd_all$overall_label <- factor(lbd_irbd_all$overall_label, levels = unique(lbd_irbd_all$overall_label))
  493. summary(lbd_irbd_all$pval_parametric)
  495. pdf(file = "lbd_irbd_correlation_v13.pdf", height = 5, width = 11)
  496. ggplot(lbd_irbd_all, aes(x = group, y = feature)) +
  497. geom_point(aes(size = log10p, fill = rho),
  498. shape = 21, color = "black", stroke = 1) + # stroke controls border thickness
  499. scale_fill_gradient2(
  500. low = "#55b7e6", mid = "white", high = "#ff9274", midpoint = 0,
  501. name = "Spearman Rho",
  502. limits = c(-1, 1)
  503. ) +
  504. scale_size_continuous(
  505. name = "P-value",
  506. breaks = c(-log10(0.0348), -log10(0.01), -log10(0.005), -log10(0.00058)), # Define specific sizes to show in legend
  507. labels = c("P = 0.0348", "P = 0.01", "P = 0.005", "P = 0.00058")) +
  508. facet_wrap(~ overall, scales = "free_y") +
  509. theme_bw() +
  510. labs(x = NULL, y = NULL) +
  511. theme(
  512. axis.text.x = element_text(size = 10, angle = 45, hjust = 1),
  513. axis.text.y = element_text(size = 10),
  514. panel.grid.major = element_line(color = "gray90"),
  515. panel.grid.minor = element_blank(),
  516. legend.key.height = unit(0.6, "cm"),
  517. legend.key.width = unit(0.6, "cm")
  518. ) +
  519. scale_y_discrete(position = "right")
  520. dev.off()
  522. # Save current RNG state
  523. rng_state <- .Random.seed
  524. saveRDS(rng_state, "rng_state.rds")
  526. # Later, to restore it before re-running:
  527. .Random.seed <- readRDS("rng_state.rds")

partial_correlation_analysis.R at commit f63e83c, no license · at the source

Overview

Authors: Xiaowei Zhao1,2, Stuart J. McCarter3,4, Vinod K. Gupta2,5, Kiera M. Grant6, Erik K. St. Louis3,4, Kejal Kantarci7, Rodolfo Savica3, Max Hill1, Helen E. Vuong8, Christopher Staley9, Bradley F. Boeve3,4, Owen A. Ross10, Levi M. Teigen11, Jaeyun Sung2,5
ORCID iDs: Jaeyun Sung
  1. Bioinformatics and Computational Biology Program, University of Minnesota, Rochester, MN, United States
  2. Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
  3. Department of Neurology, Mayo Clinic, Rochester, MN, United States
  4. Center for Sleep Medicine, Mayo Clinic, Rochester, MN, United States
  5. Microbiomics Program, Center of Individualized Medicine, Mayo Clinic, Rochester, MN, United States
  6. Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
  7. Department of Radiology, Mayo Clinic, Rochester, MN, United States
  8. Division of Neonatology, Department of Pediatrics, University of Minnesota, Minneapolis, MN, United States
  9. Department of Surgery, School of Medicine, University of Minnesota, Minneapolis, MN, United States
  10. Department of Neuroscience, Mayo Clinic, Jacksonville, FL, United States
  11. Department of Food Science and Nutrition, University of Minnesota, St. Paul, MN, United States
Institutions: University of Minnesota (United States); Mayo Clinic (United States)
Journal: Frontiers in microbiomes, volume 5, article 1834726
Dates: received 19 March 2026; accepted 16 June 2026; published online 15 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/frmbi.2026.1834726 · PMID 42529077 · PMCID PMC13416100 · OpenAlex W7168408676
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population), Parkinson's (population), sleep disorders (population), cellular / molecular (subfield)
Methods: Connectivity, Statistics
Keywords: dementia, gut microbiome, gut-brain axis, isolated REM sleep behavior disorder (iRBD), Lewy body disease (LBD), mild cognitive impairment (MCI), shotgun metagenomic sequencing, α-synucleinopathy
Topic: Gut microbiota and health (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIA NIH HHS (R34 AG056639, U19 AG071754, P30 AG062677); NINDS NIH HHS (U01 NS100620)
Citations: not cited yet (Europe PMC); 92 references in the paper

Abstract

Background: Lewy body disease (LBD) is a progressive neurodegenerative a-synucleinopathy, whereas isolated REM sleep behavior disorder (iRBD) is recognized as a prodromal stage of LBD. Although growing evidence implicates the gut–brain axis in neurodegeneration, the taxonomic and functional roles of the gut microbiome across the prodromal-to-symptomatic LBD continuum remain poorly defined.

Methods: Here, we performed shotgun metagenomic sequencing on stool samples from 25 patients with LBD (10 mild cognitive impairment due to LBD [MCI-LB] and 15 dementia with Lewy bodies [DLB]), 10 individuals with iRBD, and their household matched cohabitant controls to characterize disease-associated microbial alterations while minimizing environmental confounding.

Results: Despite no significant differences in global microbial diversity, we identified convergent shifts in microbial taxa, metabolic pathways, and gene families across disease stages. Both LBD and iRBD showed increased abundance of microbial taxa potentially associated with gut barrier disruption, as well as higher abundance of functional pathways related to lipopolysaccharide biosynthesis. LBD showed lower abundance of pathways related to complex carbohydrate fermentation, and both groups showed lower abundance of pathways associated with neurotransmitter-related metabolism. In particular, pathways and gene families associated with starch degradation were reduced in LBD, and those associated with histidine-to-glutamate/ GABA metabolism were reduced in both groups.

Discussion: These exploratory findings represent the first high-resolution, shotgun metagenomic characterization of gut microbiome alterations across the LBD continuum, highlighting functional patterns that may serve as candidate markers of disease progression in future longitudinal and mechanistic studies.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 9 matches between paragraphs and lines of code.

xiaowei-zhao-1111/LBD_Gut_Microbiome_2026

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f63e83c4ee0af68f46830b2546beec93dcff8182, 16 July 2026
Languages: R (8)
Size: 47 files, 8 scripts
Software Heritage: not archived
Found in: “Data availability statement”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (8 files), ggplot2 (7 files), reshape2 (6 files), ggpubr (4 files), lmerTest (4 files), easystats (1 file), pheatmap (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
9 files
At the source: github.com/xiaowei-zhao-1111/LBD_Gut_Microbiome_2026

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

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 8 scripts, each with its path and the digest of its content;
  • 9 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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

Data

Datasets cited

Data availability statement

Sequencing data for stool metagenomes used in this study have been deposited at NCBI’s SRA data repository (PRJNA1393457) and can be downloaded without any restrictions at https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1393457/. The deposited sequences include .fastq files for 70 stool metagenomes collected from 25 LBD patients, 10 iRBD patients, as well as their 35 cohabitant controls. Human reads were identified and removed prior to data upload. Code used for data analysis is available in the following GitHub repository: https://github.com/xiaowei-zhao-1111/LBD_Gut_Microbiome_2026.

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

Versions

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

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 14 authors, 8 keywords, 2 funders, 89 references.

Cite

This paper

Zhao, X., McCarter, S. J., Gupta, V. K., Grant, K. M., St. Louis, E. K., Kantarci, K., Savica, R., Hill, M., Vuong, H. E., Staley, C., Boeve, B. F., Ross, O. A., Teigen, L. M., & Sung, J. (2026). Shotgun metagenomic analysis reveals taxonomic and functional alterations in the gut microbiome across prodromal and symptomatic Lewy body disease. Frontiers in microbiomes, 5, 1834726. https://doi.org/10.3389/frmbi.2026.1834726

BibTeX

@article{zhao2026shotgun,
author = {Zhao, Xiaowei and McCarter, Stuart J. and Gupta, Vinod K. and Grant, Kiera M. and St. Louis, Erik K. and Kantarci, Kejal and Savica, Rodolfo and Hill, Max and Vuong, Helen E. and Staley, Christopher and Boeve, Bradley F. and Ross, Owen A. and Teigen, Levi M. and Sung, Jaeyun},
title = {{Shotgun metagenomic analysis reveals taxonomic and functional alterations in the gut microbiome across prodromal and symptomatic Lewy body disease}},
journal = {Frontiers in microbiomes},
year = {2026},
month = jul,
volume = {5},
pages = {1834726},
publisher = {Frontiers Media SA},
issn = {2813-4338},
doi = {10.3389/frmbi.2026.1834726},
url = {https://doi.org/10.3389/frmbi.2026.1834726},
pmid = {42529077},
pmcid = {PMC13416100}
}

RIS

TY - JOUR
AU - Zhao, Xiaowei
AU - McCarter, Stuart J.
AU - Gupta, Vinod K.
AU - Grant, Kiera M.
AU - St. Louis, Erik K.
AU - Kantarci, Kejal
AU - Savica, Rodolfo
AU - Hill, Max
AU - Vuong, Helen E.
AU - Staley, Christopher
AU - Boeve, Bradley F.
AU - Ross, Owen A.
AU - Teigen, Levi M.
AU - Sung, Jaeyun
TI - Shotgun metagenomic analysis reveals taxonomic and functional alterations in the gut microbiome across prodromal and symptomatic Lewy body disease
T2 - Frontiers in microbiomes
J2 - Front Microbiomes
PY - 2026
DA - 2026/07/15
VL - 5
SP - 1834726
SN - 2813-4338
PB - Frontiers Media SA
DO - 10.3389/frmbi.2026.1834726
UR - https://doi.org/10.3389/frmbi.2026.1834726
LA - en
ER -

CSL-JSON

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"id": "10.3389/frmbi.2026.1834726",
"type": "article-journal",
"title": "Shotgun metagenomic analysis reveals taxonomic and functional alterations in the gut microbiome across prodromal and symptomatic Lewy body disease",
"container-title": "Frontiers in microbiomes",
"author": [
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"family": "Zhao",
"given": "Xiaowei"
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{
"family": "McCarter",
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{
"family": "Gupta",
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},
{
"family": "Grant",
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},
{
"family": "St. Louis",
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},
{
"family": "Kantarci",
"given": "Kejal"
},
{
"family": "Savica",
"given": "Rodolfo"
},
{
"family": "Hill",
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},
{
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"language": "en",
"issued": {
"date-parts": [
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2026,
7,
15
]
]
}
}

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