OSCR

Habitual coffee intake shapes the gut microbiome and modifies host physiology and cognition.

Code ↔ Paper

10 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 10 matches
  1. [1] § Methods › Self-reported questionnaires › During visits 2, 3 and 4 ↔ scripts/fig_1_behav_cog_health_data.R, lines 62–118 · score 0.86 · Physical Activity, emotional reactivity, BSC, SCL, GI, Trait
  2. [2] § Results › Behavioural and cognitive modulation by coffee consumption and withdrawal in habitual and non-habitual drinkers ↔ scripts/fig_1_behav_cog_health_data.R, lines 62–118 · score 0.82 · STAI Trait, physical activity, emotional reactivity, HSCL, health, sleep
  3. [3] § Results › Coffee consumption shapes fecal metabolite profiles through caffeine and microbial pathways ↔ scripts/fig_2_gi_metabolome_data.R, lines 1–67 · score 0.81 · propionic acid, aminobutyric acid, fumaric acid, hippuric acid, carboxyaldehyde, indoles
  4. [4] § Results › Coffee consumption shapes fecal metabolite profiles through caffeine and microbial pathways ↔ scripts/fig_final_integration.R, lines 108–167 · score 0.80 · propionic acid, aminobutyric acid, fumaric acid, hippuric acid, carboxyaldehyde, indoles
  5. [5] § Results ↔ scripts/fig_1_behav_cog_health_data.R, lines 120–185 · score 0.79 · GIS VAS, physical activity, ModRey, sleep, BDI, PSS
  6. [6] § Results › Coffee intake drives targeted shifts in gut microbial populations without changing overall diversity ↔ scripts/fig_2_gi_microbiome_data.R, lines 1–61 · score 0.72 · Haemophilus parainfluenzae, Eggerthela sp, Cryptobacterium curtum, ACP1, parvula, CAG
  7. [7] § Results › Coffee intake drives targeted shifts in gut microbial populations without changing overall diversity ↔ scripts/fig_final_integration_MB_fecmet_cog.R, lines 1–50 · score 0.71 · Haemophilus parainfluenzae, Eggerthela sp, Cryptobacterium curtum, ACP1, parvula, CAG
  8. [8] § Results › Phenolic acid metabolites in feces and urine reflect coffee-derived dietary signatures independent of caffeine ↔ scripts/fig_final_integration_MB_urmet_cog.R, lines 1–55 · score 0.64 · cinnamic acid, propanoic acid, hydroxybenzoic acid, phenylacetic, NCDs, caffeinated
  9. [9] § Results › Phenolic acid metabolites in feces and urine reflect coffee-derived dietary signatures independent of caffeine ↔ scripts/fig_final_integration_urmet_MB_behav.R, lines 1–55 · score 0.64 · cinnamic acid, propanoic acid, hydroxybenzoic acid, phenylacetic, NCDs, caffeinated
  10. [10] § Methods › Bioinformatics ↔ subscripts/load_and_clean_data.R, lines 1–32 · score 0.61 · clr transformed, Alpha diversity, PERMANOVA

Paper

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The authors' code

R · 429 lines · 21 KB · GPL-3.0 · 3 matches

  1. # 1=NCD 0=F
  2. # 2=CD 1=M
  4. s1 = readxl::read_xlsx("raw/cognition/raw_xlsx/V2 - SPSS Template.xlsx")
  5. s2 = readxl::read_xlsx("raw/cognition/raw_xlsx/V2 vs V3 - SPSS Template.xlsx")
  6. s3 = readxl::read_xlsx("raw/cognition/raw_xlsx/V2 vs V4 - SPSS Template.xlsx")
  7. s4 = readxl::read_xlsx("raw/cognition/raw_xlsx/V3 vs V4 - SPSS Template.xlsx")
  8. si = readxl::read_xlsx("raw/cognition/raw_xlsx/Intervention timepoints - SPSS template.xlsx")
  9. sw = readxl::read_xlsx("raw/cognition/raw_xlsx/Washout timepoints - SPSS template.xlsx")
  12. colnames(s1) <- str_remove(colnames(s1), pattern = "V2_|v2_|V2-|v2-")
  14. s1 <- s1 %>% mutate(BMI = as.numeric(BMI),
  15. BP_D = as.numeric(BP_D))
  16. s2 <- s2 %>% mutate(BMI = as.numeric(BMI),
  17. BP_D = as.numeric(BP_D))
  18. s3 <- s3 %>% mutate(BMI = as.numeric(BMI),
  19. BP_D = as.numeric(BP_D))
  20. s4 <- s4 %>% mutate(BMI = as.numeric(BMI),
  21. BP_D = as.numeric(BP_D))
  24. do.call(rbind,
  25. list(s1[,1:3],
  26. s2[,1:3],
  27. s3[,c(1,3,2)],
  28. s4[,c(1,3,2)],
  29. sw[,c(1,3,2)],
  30. si[,c(1,3,2)])) %>%
  31. distinct() %>%
  33. left_join(., s1, by = c("ID" = "ID", "visit" = "visit", "Coffee_Type" = "Coffee_Type"), keep = FALSE) %>%
  34. left_join(., s2, by = c("ID" = "ID", "visit" = "visit", "Coffee_Type" = "Coffee_Type"), keep = FALSE) %>%
  35. left_join(., s3, by = c("ID" = "ID", "visit" = "visit", "Coffee_Type" = "Coffee_Type"), keep = FALSE) %>%
  36. left_join(., s4, by = c("ID" = "ID", "visit" = "visit", "Coffee_Type" = "Coffee_Type"), keep = FALSE) %>%
  37. left_join(., sw, by = c("ID" = "ID", "visit" = "visit", "Coffee_Type" = "Coffee_Type"), keep = FALSE) %>%
  38. left_join(., si , by = c("ID" = "ID", "visit" = "visit", "Coffee_Type" = "Coffee_Type"), keep = FALSE) %>%
  42. pivot_longer(!c(ID, Coffee_Type, visit, Gender)) %>%
  43. mutate(name = str_remove(name, "\\.x.*|\\.y.*")) %>%
  44. mutate(name = str_remove(name, "V2_|v2_|V3_|v3_")) %>%
  45. mutate(name = str_replace(name, "IPAQ-", "IPAQ_")) %>%
  47. distinct() %>%
  49. filter(!is.na(value)) %>%
  51. mutate(visit = factor(visit, levels = c("V2", "T2W", "T4W", "V3", "T2I", "T4I", "T14", "V4"))) %>%
  53. mutate(Visit = case_when(visit == "V2" ~ "Baseline",
  54. visit %in% c("T2W", "T4W", "V3") ~ "Post-\nwashout",
  55. visit %in% c("T2I", "T4I", "T14", "V4") ~ "Post-\nreintroduction")) %>%
  56. dplyr::select(!Gender) %>%
  58. mutate(Visit = factor(Visit, levels = c("Baseline","Post-\nwashout", "Post-\nreintroduction")),
  59. Coffee_Type = factor(Coffee_Type, levels = c("NCD", "Coffee", "No Coffee", "DECAF", "CAF"))) %>%
  62. filter(!str_detect(name, "7DFD")) %>%
  63. filter(!str_detect(name, "BLVAS")) %>%
  64. filter(!str_detect(name, "post-pre")) %>%
  65. filter(str_detect(name, "UPPS|ERS|PASAT|MdR|STAI|PSS|BDI|GIS|SCL|PSQI|IPAQ|VAS|QCC|CWSQ")) %>%
  66. mutate(name = str_remove(name, "preSECPT_")) %>%
  69. mutate(meas_group = case_when(str_detect(name, "7DFD_") ~ "Dietary",
  70. str_detect(name, "Blood") ~ "Blood",
  71. str_detect(name, "Plasma") ~ "Blood",
  72. str_detect(name, "Plasma") ~ "Blood",
  73. str_detect(name, "BP_D|BDI") ~ "Mood",
  74. str_detect(name, "UPPS") ~ "Impulsivity",
  75. str_detect(name, "SCL") ~ "Mental Health",
  76. str_detect(name, "PASA") ~ "Stress\n&\nAnxiety",
  77. str_detect(name, "PASAT") ~ "Attention",
  78. str_detect(name, "PSQI") ~ "Sleep",
  79. str_detect(name, "BMI|BSC|GIS_VAS") ~ "GI\nHealth",
  80. str_detect(name, "CAR|STAI|PSS") ~ "Stress\n&\nAnxiety",
  81. str_detect(name, "MdR") ~ "Memory",
  82. str_detect(name, "ERS") ~ "Emotional Reactivity",
  83. str_detect(name, "IPAQ") ~ "Physical Activity",
  84. str_detect(name, "CWSQ_Tot") ~ "Coffee Withdrawal",
  85. str_detect(name, "VASF_Fatigue") ~ "Coffee Withdrawal",
  86. str_detect(name, "VASF_Energy") ~ "Coffee Withdrawal",
  87. str_detect(name, "QCC_Tot") ~ "Coffee Withdrawal",
  91. .default = NA)
  92. ) %>%
  93. filter(!is.na(meas_group)) %>%
  96. mutate(name = str_replace(name, "UPPS_|UPPS", "(UPPS) ")) %>%
  97. mutate(name = str_replace(name, "ERS_|ERS", "(ERS) ")) %>%
  98. mutate(name = str_replace(name, "MdR_", "(ModRey) ")) %>%
  99. mutate(name = str_replace(name, "SCL_", "(HSCL) ")) %>%
  100. mutate(name = str_replace(name, "GIS_VAS_Total", "(GIS-VAS) Tot ")) %>%
  101. mutate(name = str_replace(name, "PASAT_|PASAT", "(PASAT) ")) %>%
  102. mutate(name = str_replace(name, "STAI_TRAIT", "(STAI-T) ")) %>%
  103. mutate(name = str_replace(name, "PSS", "(PSS)")) %>%
  104. mutate(name = str_replace(name, "BDITot", "(BDI) Tot")) %>%
  105. mutate(name = str_replace(name, "IPAQ_MET", "(IPAQ)\nMET-minutes")) %>%
  106. mutate(name = str_replace(name, "PSQI_GlobalScore", "(PSQI) Tot")) %>%
  107. mutate(name = str_replace(name, "TotalA_F1", "Tot")) %>%
  108. mutate(name = str_replace(name, "Tot ", "Tot")) %>%
  109. mutate(name = str_replace(name, "CWSQ_Tot", "(CWSQ) Tot")) %>%
  110. mutate(name = str_replace(name, "QCC_Tot", "(QCC) Tot")) %>%
  114. filter(!str_detect(name, "\\) [^TM]")) %>%
  115. mutate(name = str_replace(name, "VASF_", "(VASF) ")) %>%
  117. mutate(name = str_replace(name, "IPAQ-|IPAQ_", "(IPAQ) ")) %>%
  118. #mutate(value = case_when(is.na(value)~ 0, .default = value)) %>%
  120. mutate(meas_group = factor(meas_group, levels = c("Impulsivity", "Emotional Reactivity",
  121. "Attention","Memory", "Stress\n&\nAnxiety",
  122. "Mood", "Mental Health", "GI\nHealth",
  123. "Sleep", "Physical Activity", "Coffee Withdrawal"))) %>%
  124. filter(!is.na(meas_group)) %>%
  128. group_by(name) %>%
  129. mutate(avg_cof = case_when(name %in% c("(CWSQ) Tot", "(VASF) Fatigue", "(VASF) Energy", "(QCC) Tot") ~ mean(value[Coffee_Type == "CAF" & visit == "V4"], na.rm = T),
  130. !name %in% c("(CWSQ) Tot", "(VASF) Fatigue", "(VASF) Energy", "(QCC) Tot") ~ mean(value[Coffee_Type == "Coffee"], na.rm = T),
  131. .default = NA)
  132. ) %>%
  133. mutate(sd_tot = sd(value, na.rm = T)) %>%
  134. mutate(value = (value - avg_cof)/sd_tot) %>%
  135. ungroup() %>%
  136. mutate(name = str_replace(name, "\\) ", "\\)\n")) %>%
  137. mutate(name = factor(name, levels = (c("(UPPS)\nTot", "(ERS)\nTot", "(ModRey)\nTot", "(PASAT)\nTot",
  138. "(PSS)", "(STAI-T)\nTot", "(BDI)\nTot", "(GIS-VAS)\nTot",
  139. "(PSQI)\nTot", "(IPAQ)\nMET-minutes",
  140. "(CWSQ)\nTot", "(VASF)\nFatigue", "(VASF)\nEnergy", "(QCC)\nTot")))) %>%
  142. mutate(caffeine = case_when(
  143. ID %in% c("APC115-003", "APC115-005", "APC115-014", "APC115-017", "APC115-037",
  144. "APC115-038", "APC115-039", "APC115-043", "APC115-054", "APC115-069",
  145. "APC115-072", "APC115-087", "APC115-101", "APC115-108", "APC115-109") ~ "DECAF",
  146. ID %in% c("APC115-008", "APC115-011","APC115-016", "APC115-033","APC115-036",
  147. "APC115-042", "APC115-052","APC115-058", "APC115-059", "APC115-060",
  148. "APC115-061","APC115-063","APC115-090","APC115-103","APC115-105","APC115-113") ~ "CAF")) %>%
  149. mutate(caffeine = factor(caffeine, levels = c("CAF", "DECAF"))) %>%
  151. mutate(Visit = factor(Visit, levels = c("Baseline","Post-\nwashout", "Post-\nreintroduction")),
  152. Coffee_Type = factor(Coffee_Type, levels = c("NCD", "Coffee", "No Coffee", "DECAF", "CAF"))) %>%
  154. mutate(mdlab = case_when(Coffee_Type == "NCD" ~ "<img \n src='raw/icons/NCD.png' width='50' />",
  155. Coffee_Type == "Coffee" ~ "<img \n src='raw/icons/CD.png' width='50' />",
  156. Coffee_Type == "No Coffee" ~ "<img \n src='raw/icons/WASHOUT.png' width='50' />",
  157. Coffee_Type == "DECAF" ~ "<img \n src='raw/icons/REINTRO.png' width='50' />",
  158. Coffee_Type == "CAF" ~ "<img \n src='raw/icons/REINTRO.png' width='50' />"),
  159. mdlab = factor(mdlab, levels = c("<img \n src='raw/icons/NCD.png' width='50' />",
  160. "<img \n src='raw/icons/CD.png' width='50' />",
  161. "<img \n src='raw/icons/WASHOUT.png' width='50' />",
  162. "<img \n src='raw/icons/REINTRO.png' width='50' />"))
  163. ) %>%
  165. group_by(name, visit, Coffee_Type) %>%
  166. mutate(avg_by_caf_timepoint = mean(value)) %>%
  167. ungroup() %>% #filter(name == "Cryptobacterium curtum") %>% View
  169. group_by(name, visit, mdlab) %>%
  170. mutate(avg_delta_caf_decaf = case_when(visit == "V2" ~ 0,
  171. visit != "V2" ~ abs(
  172. mean(avg_by_caf_timepoint[caffeine == "CAF"]) - mean(avg_by_caf_timepoint[caffeine == "DECAF"])
  173. ),
  174. .default = NA)
  175. ) %>%
  177. mutate(plot_label = case_when((max(abs(avg_by_caf_timepoint)) > 0.5 | max(abs(avg_delta_caf_decaf)) > 0.5 ) ~ 'moderate',
  178. .default = "less")) %>%
  179. ungroup() %>%
  181. group_by(name, mdlab) %>%
  182. mutate(max_per_group = max(abs(avg_by_caf_timepoint))) %>%
  183. ungroup() %>%
  184. group_by(name) %>%
  185. mutate(abs_delta_NCD = mean(abs(avg_by_caf_timepoint[Coffee_Type == "NCD"]))) %>%
  187. #Currently no filters for cognition
  188. # filter(any(plot_label == "moderate")) %>%
  189. # filter(any(max_per_group > 0.5) ) %>%
  190. #
  191. # filter(abs_delta_NCD > 0.5) %>%
  192. ungroup() -> df_long_cog
  195. CAF_DECAF_order <- c(df_long_cog %>%
  196. filter(Visit == "Post-\nreintroduction") %>%
  197. filter(Coffee_Type == "CAF") %>% .$ID %>% sort %>% unique,
  198. df_long_cog %>%
  199. filter(Visit == "Post-\nreintroduction") %>%
  200. filter(Coffee_Type == "DECAF") %>% .$ID %>% sort %>% unique)
  203. plot_cog_NCD <-
  204. df_long_cog %>%
  205. filter(Coffee_Type =="NCD") %>%
  206. mutate(mdlab = factor("<img \n src='raw/icons/NCD.png' width='50' /> \nNon-Coffee", levels = c("<img \n src='raw/icons/NCD.png' width='50' /> \nNon-Coffee"))) %>%
  207. mutate(visit = factor("V2", levels = c("V2"))) %>%
  208. mutate(lab = "NCD") %>%
  210. group_by(Coffee_Type, name) %>%
  211. mutate(avg_by_group = round(mean(value, na.rm = T), digits = 2)) %>%
  212. ungroup() %>%
  213. mutate( facet_name = factor(
  214. x = paste0(
  215. str_replace_all(meas_group, "\n", " "),
  216. "\n",
  217. str_replace(str_remove(name, "\\)"),"\n", " "),
  218. ")"),
  219. levels = paste0(
  220. str_replace_all(levels(meas_group)[c(1:2, 4, 5, 5, 5, 6, 8, 9, 10)], "\n", " "),
  221. "\n",
  222. str_replace(levels(name)[c(1:10)], "\\)\n", " "),
  223. ")") %>% str_replace(., "\\)\\)", ")"))
  224. ) %>%
  225. # rbind(., c(ID = "APC115-001", visit = "V2", Coffee_Type = "NCD", name = "(CWSQ) Tot", value = 0, Visit = "Baseline", meas_group = "Coffee Withdrawal", mdlab = "<img \n src='raw/icons/NCD.png' width='50' />", lab = "NCD")) %>%
  226. # rbind(., c(ID = "APC115-001", visit = "V2", Coffee_Type = "NCD", name = "(VASF) Energy", value = 0, Visit = "Baseline", meas_group = "Coffee Withdrawal", mdlab = "<img \n src='raw/icons/NCD.png' width='50' />", lab = "NCD")) %>%
  227. # rbind(., c(ID = "APC115-001", visit = "V2", Coffee_Type = "NCD", name = "(VASF) Fatigue", value = 0, Visit = "Baseline", meas_group = "Coffee Withdrawal", mdlab = "<img \n src='raw/icons/NCD.png' width='50' />", lab = "NCD")) %>%
  228. # rbind(., c(ID = "APC115-001", visit = "V2", Coffee_Type = "NCD", name = "(QCC) Tot", value = 0, Visit = "Baseline", meas_group = "Coffee Withdrawal", mdlab = "<img \n src='raw/icons/NCD.png' width='50' />", lab = "NCD")) %>%
  229. #
  230. # mutate(value = as.numeric(value)) %>%
  232. ggplot() +
  233. aes(y = ID, x = visit, fill = value, label = avg_by_group) +
  234. geom_tile() +
  235. geom_label(data = . %>% group_by(visit, Coffee_Type, name, mdlab, meas_group, facet_name) %>%
  236. summarise(n = length(unique(ID)),
  237. avg_by_group = mean(avg_by_group)) %>% ungroup(),
  238. # x = 1,
  239. aes(x = visit, y = n/2, fill = avg_by_group, label = avg_by_group)) +
  240. #geom_point(shape = 24) +
  242. scale_fill_gradientn(colours = c(
  243. "#053061","#053061",
  244. "#2166ac","#2166ac",
  245. "#4393c3","#4393c3",
  246. "#f7f7f7","#f7f7f7",
  247. "#d6604d","#d6604d",
  248. "#d73027","#d73027",
  249. "#a50026", "#a50026"
  252. ),
  253. limits = c(-4.5, 4.5), "Effect size (d)"
  254. ) +
  255. scale_y_discrete(position = "right") +
  256. scale_x_discrete(expand = expansion(mult = c(0))) +
  257. facet_grid(facet_name~mdlab, scales = "free", switch = "y") +
  259. # geom_image(x = 1, y = 3, image = "/home/thomaz/Downloads/Cryan, John 2015.png", size = 3)+
  260. xlab(NULL) + ylab(NULL) +
  261. theme_test() +
  262. theme(
  263. strip.text.x = element_markdown(angle = 0, vjust = 0),
  264. strip.text.y.left = element_text(angle = 0),
  265. axis.text.y = element_blank(), axis.ticks.y = element_blank(),
  266. # axis.text.x = element_text(angle = 330, hjust = 0),
  267. axis.text.x = element_blank(), axis.ticks.x = element_blank(),
  268. panel.spacing.x = unit(0, "lines")
  269. )
  271. plot_cog_CD <- df_long_cog %>%
  272. filter(Coffee_Type != "NCD") %>%
  273. filter(visit %in% c("V2", "V3","V4")) %>%
  274. filter(meas_group != "Coffee Withdrawal") %>%
  275. mutate(mdlab = paste0(mdlab, " \n", Visit) %>%
  276. str_remove(., "Post-\n") %>%
  277. str_replace(., "Baseline", "Coffee") %>%
  278. str_replace(., "reintroduction$", "Reintroduction") %>%
  279. str_replace(., "washout$", "Washout")
  280. ) %>%
  281. mutate(mdlab = factor(mdlab, levels = unique(mdlab))) %>%
  282. mutate(caffeine = case_when(
  283. ID %in% c("APC115-003", "APC115-005", "APC115-014", "APC115-017", "APC115-037",
  284. "APC115-038", "APC115-039", "APC115-043", "APC115-054", "APC115-069",
  285. "APC115-072", "APC115-087", "APC115-101", "APC115-108", "APC115-109") ~ "DECAF",
  286. ID %in% c("APC115-008", "APC115-011","APC115-016", "APC115-033","APC115-036",
  287. "APC115-042", "APC115-052","APC115-058", "APC115-059", "APC115-060",
  288. "APC115-061","APC115-063","APC115-090","APC115-103","APC115-105","APC115-113") ~ "CAF")) %>%
  289. mutate(caffeine = factor(caffeine, levels = c("CAF", "DECAF"))) %>%
  290. mutate(lab = case_when(Visit == "Baseline" ~ "Coffee\n(baseline)", .default = Visit),
  291. lab = factor(lab, levels = c("Coffee\n(baseline)", "Post-\nwashout", "Post-\nreintroduction"))) %>%
  293. mutate(facet_lab = case_when(Visit == "Baseline" ~ "CD",
  294. Visit == "Post-\nwashout" ~ "wash\nout",
  295. Visit == "Post-\nreintroduction" ~ "inter\nvention"),
  296. facet_lab = factor(facet_lab, levels = c("CD","wash\nout", "inter\nvention"))) %>%
  298. group_by(Coffee_Type, caffeine, name) %>%
  299. mutate(avg_by_group = round(mean(value, na.rm = T), digits = 2)) %>%
  301. ungroup() %>%
  305. ggplot() +
  306. aes(y = ID, x = visit, fill = value, label = avg_by_group)+
  307. geom_tile() +
  309. geom_label(data = . %>% group_by(visit, Coffee_Type, caffeine, name, mdlab, plot_label) %>%
  310. summarise(n = length(unique(ID)),
  311. avg_by_group = mean(avg_by_group)) %>%
  312. ungroup() %>% group_by(name, mdlab)
  313. %>% filter(any(plot_label == "moderate")) %>% ungroup(),
  315. aes(x = visit, y = n/2, fill = avg_by_group, label = avg_by_group)) +
  317. scale_fill_gradientn(colours = c(
  318. "#053061","#053061",
  319. "#2166ac","#2166ac",
  320. "#4393c3","#4393c3",
  321. "#f7f7f7","#f7f7f7",
  322. "#d6604d","#d6604d",
  323. "#d73027","#d73027",
  324. "#a50026", "#a50026"
  327. ),
  328. limits = c(-4.5, 4.5), "Effect size (d)"
  329. ) +
  330. scale_y_discrete(position = "right" ) +
  332. scale_x_discrete(expand = expansion(mult = c(0))) +
  333. ggh4x::facet_nested(name * caffeine ~ mdlab, scales = "free", space = "free_x",
  334. strip = ggh4x::strip_nested(size = "variable",
  335. background_y = list(element_blank(), element_rect(), element_rect()),
  336. text_y = list(element_blank(), element_text(), element_text()),
  337. by_layer_y = TRUE )) +
  338. xlab(NULL) + ylab(NULL) +
  339. theme_test() +
  340. theme(#strip.text.y = element_blank(),
  341. strip.text.x = element_markdown(vjust = 0), #,
  342. # strip.text.x = element_blank(), strip.background.x = element_blank()
  343. strip.text.y = element_text(angle =0),
  344. axis.text.y = element_blank(), axis.ticks.y = element_blank(),
  345. #axis.text.x = element_text(angle = 330, hjust = 0)#, panel.spacing.x =unit(0, "lines")
  346. axis.text.x = element_blank(),
  347. axis.ticks.x = element_blank(),
  348. panel.spacing.x = unit(0, "lines")
  349. )
  352. plot_cog_craving <- df_long_cog %>%
  353. filter(Coffee_Type != "NCD") %>%
  354. filter(name %in% c("(CWSQ)\nTot", "(VASF)\nFatigue", "(VASF)\nEnergy", "(QCC)\nTot")) %>%
  355. mutate(mdlab = paste0(mdlab, " \n", Visit) %>%
  356. str_remove(., "Post-\n") %>%
  357. str_replace(., "Baseline", "Coffee") %>%
  358. str_replace(., "reintroduction$", "Reintroduction") %>%
  359. str_replace(., "washout$", "Washout")) %>%
  360. mutate(mdlab = factor(mdlab, levels = unique(mdlab))) %>%
  362. mutate(caffeine = case_when(
  363. ID %in% c("APC115-003", "APC115-005", "APC115-014", "APC115-017", "APC115-037",
  364. "APC115-038", "APC115-039", "APC115-043", "APC115-054", "APC115-069",
  365. "APC115-072", "APC115-087", "APC115-101", "APC115-108", "APC115-109") ~ "DECAF",
  366. ID %in% c("APC115-008", "APC115-011","APC115-016", "APC115-033","APC115-036",
  367. "APC115-042", "APC115-052","APC115-058", "APC115-059", "APC115-060",
  368. "APC115-061","APC115-063","APC115-090","APC115-103","APC115-105","APC115-113") ~ "CAF")) %>%
  369. mutate(caffeine = factor(caffeine, levels = c("CAF", "DECAF"))) %>%
  370. mutate(lab = case_when(Visit == "Baseline" ~ "Coffee\n(baseline)", .default = Visit),
  371. lab = factor(lab, levels = c("Coffee\n(baseline)", "Post-\nwashout", "Post-\nreintroduction"))) %>%
  373. mutate(facet_lab = case_when(Visit == "Baseline" ~ "CD",
  374. Visit == "Post-\nwashout" ~ "wash\nout",
  375. Visit == "Post-\nreintroduction" ~ "inter\nvention"),
  376. facet_lab = factor(facet_lab, levels = c("CD","wash\nout", "inter\nvention"))) %>%
  378. group_by(Coffee_Type, caffeine, name, visit) %>%
  379. mutate(avg_by_group = round(mean(value, na.rm = T), digits = 2)) %>%
  381. ungroup() %>%
  385. ggplot() +
  386. aes(y = ID, x = visit, fill = value, label = avg_by_group)+
  387. geom_tile() +
  389. geom_label(data = . %>% group_by(visit, Coffee_Type, caffeine, name, mdlab, plot_label) %>%
  390. summarise(n = length(unique(ID)),
  391. avg_by_group = mean(avg_by_group)) %>%
  392. ungroup() %>% group_by(name, mdlab)
  393. %>% filter(any(plot_label == "moderate")) %>% ungroup(),
  395. aes(x = visit, y = n/2, fill = avg_by_group, label = avg_by_group)) +
  397. scale_fill_gradientn(colours = c(
  398. "#053061","#053061",
  399. "#2166ac","#2166ac",
  400. "#4393c3","#4393c3",
  401. "#f7f7f7","#f7f7f7",
  402. "#d6604d","#d6604d",
  403. "#d73027","#d73027",
  404. "#a50026", "#a50026"
  407. ),
  408. limits = c(-4.5, 4.5), "Effect size (d)"
  409. ) +
  410. scale_y_discrete(position = "right" ) +
  412. scale_x_discrete(expand = expansion(mult = c(0))) +
  413. ggh4x::facet_nested(name * caffeine ~ mdlab, scales = "free", space = "free_x", switch = "y",
  414. strip = ggh4x::strip_nested(size = "variable",
  415. background_y = list(element_rect(), element_rect(), element_rect()),
  416. text_y = list(element_text(), element_text(), element_text()),
  417. by_layer_y = TRUE )) +
  418. xlab(NULL) + ylab(NULL) +
  419. theme_test() +
  420. theme(
  421. strip.text.y.left = element_text(angle =0),
  422. strip.text.x = element_markdown(angle = 0, vjust = 0), #,
  423. # strip.text.x = element_blank(), strip.background.x = element_blank()
  424. #strip.text.y = element_text(angle =0),
  425. axis.text.y = element_blank(), axis.ticks.y = element_blank(),
  426. #axis.text.x = element_text(angle = 330, hjust = 0)#, panel.spacing.x =unit(0, "lines")
  427. axis.text.x = element_blank(), axis.ticks.x = element_blank(),
  428. panel.spacing.x = unit(0, "lines")
  429. )

fig_1_behav_cog_health_data.R at commit 6c80dad, under GPL-3.0 · at the source

Overview

Authors: Serena Boscaini1,2, Thomaz F S Bastiaanssen1,2, Gerard M Moloney1,2, Federica Bergamo3, Laila Zeraik3, Caroline O’Leary1, Aimone Ferri1,2, Maha Irfan1, Maaike van der Rhee1,2, Thaïs I F Lindemann1,2, Elizabeth Schneider1,4, Arthi Chinna Meyyappan1, Kirsten Berding Harold1, Caitríona M Long-Smith1, Carina Carbia1, Kenneth J O’Riordan1, José Fernando Rinaldi de Alvarenga3, Nicole Tosi3, Daniele Del Rio3,5, Alice Rosi3, Letizia Bresciani3, Pedro Mena3,5, Gerard Clarke1,6, John F Cryan1,2
ORCID iDs: Gerard M Moloney, Federica Bergamo, Aimone Ferri, Elizabeth Schneider, Arthi Chinna Meyyappan, Carina Carbia, Nicole Tosi, Daniele Del Rio, Alice Rosi, Pedro Mena, Gerard Clarke, John F Cryan
  1. APC Microbiome Ireland, University College Cork, Cork, Ireland
  2. Department of Anatomy and Neuroscience, University College Cork, Cork, Ireland
  3. Department of Food and Drug, University of Parma, Parma, Italy
  4. School of Applied Psychology, University College Cork, Cork, Ireland
  5. Microbiome Research Hub, University of Parma, Parma, Italy
  6. Department of Psychiatry and Neurobehavioural Science, University College Cork, Cork, Ireland
Institutions: University College Cork (Ireland); APC Microbiome Institute (Ireland); University of Parma (Italy)
Journal: Nature communications, volume 17, issue 1, article 3439
Dates: received 21 October 2024; accepted 17 March 2026; published online 21 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-71264-8 · PMID 42014402 · PMCID PMC13100100 · OpenAlex W7155099732
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), cognitive (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Preprocessing, Connectivity, fMRI & imaging
Keywords: Predictive markers, Metabolomics
MeSH: Coffee*, Cognition*, Gastrointestinal Microbiome*, Adult, Brain, Caffeine, Feces, Female, Humans, Male, Metabolome (* major topic)
Topic: Coffee research and impacts (Pharmacology, Medicine), according to OpenAlex
Funding: EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020) (950050); ISIC Institute for Scientific Information on Coffee
Citations: cited by 4 papers (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.

Repositories

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qiyunzhu/woltka

License: BSD-3-Clause
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 42df8869da98af3721b4bd29c40ef9b28d925118, 8 September 2025
Languages: Python (32), Shell (1)
Size: 194 files, 33 scripts
Software Heritage: archived
Found in: the text, “Microbiome: taxonomic and functional analysis”
Holds: README, license file, environment (environment.yml, pyproject.toml), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: NumPy (5 files), pandas (3 files), Numba (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
35 files
At the source: github.com/qiyunzhu/woltka/blob/master/doc/wolsop.sh

thomazbastiaanssen/coffee

License: GPL-3.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 6c80dad8d30f79c2d8fb1e17692a7c4d6a79c56e, 25 September 2025
Languages: R (20)
Size: 119 files, 20 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 1 notebook
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (18 files), patchwork (3 files), broom (1 file), ggplot2 (1 file), lme4 (1 file), lmerTest (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
22 files
At the source: github.com/thomazbastiaanssen/coffee/

Code availability statement

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Read it in the paper: doi.org/10.1038/s41467-026-71264-8.

Tracing map

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  • 10 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Code and data availability statement

The paper has a code and data 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/s41467-026-71264-8.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 24 authors, 2 keywords, 11 MeSH terms, 2 funders, 88 references.

Cite

This paper

Boscaini, S., Bastiaanssen, T. F. S., Moloney, G. M., Bergamo, F., Zeraik, L., O’Leary, C., Ferri, A., Irfan, M., van der Rhee, M., Lindemann, T. I. F., Schneider, E., Meyyappan, A. C., Harold, K. B., Long-Smith, C. M., Carbia, C., O’Riordan, K. J., de Alvarenga, J. F. R., Tosi, N., Del Rio, D., . . . Cryan, J. F. (2026). Habitual coffee intake shapes the gut microbiome and modifies host physiology and cognition. Nature communications, 17(1), 3439. https://doi.org/10.1038/s41467-026-71264-8

BibTeX

@article{boscaini2026habitual,
author = {Boscaini, Serena and Bastiaanssen, Thomaz F S and Moloney, Gerard M and Bergamo, Federica and Zeraik, Laila and O’Leary, Caroline and Ferri, Aimone and Irfan, Maha and van der Rhee, Maaike and Lindemann, Thaïs I F and Schneider, Elizabeth and Meyyappan, Arthi Chinna and Harold, Kirsten Berding and Long-Smith, Caitríona M and Carbia, Carina and O’Riordan, Kenneth J and de Alvarenga, José Fernando Rinaldi and Tosi, Nicole and Del Rio, Daniele and Rosi, Alice and Bresciani, Letizia and Mena, Pedro and Clarke, Gerard and Cryan, John F},
title = {{Habitual coffee intake shapes the gut microbiome and modifies host physiology and cognition}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {3439},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71264-8},
url = {https://doi.org/10.1038/s41467-026-71264-8},
pmid = {42014402},
pmcid = {PMC13100100}
}

RIS

TY - JOUR
AU - Boscaini, Serena
AU - Bastiaanssen, Thomaz F S
AU - Moloney, Gerard M
AU - Bergamo, Federica
AU - Zeraik, Laila
AU - O’Leary, Caroline
AU - Ferri, Aimone
AU - Irfan, Maha
AU - van der Rhee, Maaike
AU - Lindemann, Thaïs I F
AU - Schneider, Elizabeth
AU - Meyyappan, Arthi Chinna
AU - Harold, Kirsten Berding
AU - Long-Smith, Caitríona M
AU - Carbia, Carina
AU - O’Riordan, Kenneth J
AU - de Alvarenga, José Fernando Rinaldi
AU - Tosi, Nicole
AU - Del Rio, Daniele
AU - Rosi, Alice
AU - Bresciani, Letizia
AU - Mena, Pedro
AU - Clarke, Gerard
AU - Cryan, John F
TI - Habitual coffee intake shapes the gut microbiome and modifies host physiology and cognition
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/21
VL - 17
IS - 1
SP - 3439
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71264-8
UR - https://doi.org/10.1038/s41467-026-71264-8
LA - en
ER -

CSL-JSON

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"title": "Habitual coffee intake shapes the gut microbiome and modifies host physiology and cognition",
"container-title": "Nature communications",
"author": [
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"family": "Boscaini",
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"given": "Carina"
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"given": "Kenneth J"
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