OSCR

Medial entorhinal-hippocampal desynchronization parallels the emergence of memory impairment in a mouse model of Alzheimer's disease pathology.

Code ↔ Paper

14 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 14 matches · 4 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS › METHOD DETAILS › Analysis of local field potential and single-unit data ↔ externalPackages/FMAToolbox/Analyses/FilterLFP.m, the whole file · a weak match · score 0.74 · 10–20 cm, filtered LFPs, local field potential, frequency band, MATLAB, theta
  2. [2] § RESULTS › Reduced hippocampal theta power emerges between 6 and 8 months of age in 3xTg mice ↔ LFP_Analysis/Coherence/coherency_allregions_multi_drift.m, lines 145–222 · score 0.73 · 30–50 Hz, 90–130 Hz, Fast gamma, slow gamma, 90 Hz, theta
  3. [3] § RESULTS › Reduced hippocampal theta power emerges between 6 and 8 months of age in 3xTg mice ↔ LFP_Analysis/Coherence/coherency_allregions_multi_drift_byspeed.m, lines 147–228 · score 0.73 · 30–50 Hz, 90–130 Hz, Fast gamma, slow gamma, 90 Hz, theta
  4. [4] § EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS › METHOD DETAILS › Analysis of local field potential and single-unit data ↔ All Code and Data to Generate Plots and Stats/Vetere_2026_AllPlotsAndStats.Rmd, lines 3629–3711 · score 0.71 · Unit LFP frequency, spike train frequency, theta LFP frequency, precession, speeds, CA1
  5. [5] § EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS › METHOD DETAILS › Analysis of local field potential and single-unit data ↔ LFP_Analysis/Coherence/coherency_test_all_regions_multi_drift_bylyr_noweighting.m, lines 1–37 · score 0.63 · coherency function, Chronux, chosen, concatenated, bins, LFP
  6. [6] § EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS › METHOD DETAILS › Virtual reality training ↔ preprocessing/metadata/BWMetadataSystem/bz_SessionMetadataTextTemplate.m, lines 35–116 · score 0.60 · motion tracker, linear track, surgery, weight, animals
  7. [7] § EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS › METHOD DETAILS › Analysis of local field potential and single-unit data ↔ externalPackages/FMAToolbox/FMAToolbox.m, the whole file · a weak match · score 0.56 · phase precession, place cells, cutting, behavior, neurons, LFP
  8. [8] § EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS › METHOD DETAILS › Analysis of local field potential and single-unit data ↔ externalPackages/CircularStats/circ_plot.m, the whole file · a weak match · score 0.56 · circular statistics toolbox, resultant vector, MATLAB
  9. [9] § EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS › METHOD DETAILS › Headbar surgery ↔ preprocessing/metadata/BWMetadataSystem/bz_AnimalMetadataTextTemplate.m, lines 34–77 · score 0.54 · skin, anesthetized, isoflurane, carprofen, kg, subcutanerously
  10. [10] § RESULTS › Spatial memory impairments emerge between 6 and 8 months of age in 3xTg mice ↔ All Code and Data to Generate Plots and Stats/Vetere_2026_AllPlotsAndStats.Rmd, lines 916–1037 · score 0.54 · amyloid plaques, way ANOVA, subiculum, Sex, Genotype, immunohistochemistry
  11. [11] § EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS › QUANTIFICATION AND STATISTICAL ANALYSIS ↔ All Code and Data to Generate Plots and Stats/Vetere_2026_AllPlotsAndStats.Rmd, lines 3355–3426 · score 0.53 · Watson Williams, equal kappa, Kuiper, mu, circular, Genotypes
  12. [12] § RESULTS › Progressive disruptions in hippocampal phase locking in 3xTg mice ↔ Single_Unit_Analysis/HIPP_SpikeProcessing_LV_all_2023_V3_Aonly.m, lines 84–126 · score 0.52 · DG units, CA1 units, phase locking, broader, waveforms, layer
  13. [13] § EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS › METHOD DETAILS › Analysis of local field potential and single-unit data ↔ Single_Unit_Analysis/SingleUnitProcessing.m, lines 52–74 · score 0.52 · background subtracted, Phy, Kilosort, clusters, waveform, animal
  14. [14] § RESULTS › Progressive disruptions in hippocampal phase locking in 3xTg mice ↔ externalPackages/FMAToolbox/FMAToolbox.m, the whole file · a weak match · score 0.51 · Phase precession, place cells, firing rates, detection, neurons, LFP

Paper

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The authors' code

R Markdown · 4,284 lines · 172 KB · GPL-3.0 · 3 matches

  1. ---
  2. title: "Vetere_2026_AllPlotsAndStats_V2"
  3. output: word_document
  4. date: "2026-05-03"
  5. ---
  7. ```{r setup, echo=FALSE, warning=FALSE}
  8. # load libraries and set up color palettes and general functions
  10. library(readxl)
  11. library(data.table)
  12. library(dplyr)
  13. library(tidyverse)
  14. library(plyr)
  15. library(plotrix)
  16. library(car)
  17. library(broom)
  18. library(reshape2)
  19. library(RColorBrewer)
  20. library(writexl)
  21. library(multcomp)
  22. library(emmeans)
  23. library(ez)
  24. library(rstatix)
  25. library(lme4)
  26. library(lmtest)
  27. library(lmerTest)
  28. library(circular)
  29. library(rlang)
  30. library(twosamples)
  31. library(kuiper.2samp)
  32. library(ggforce)
  33. library(ggbeeswarm)
  34. library(R.matlab)
  35. library(pracma)
  36. library(knitr)
  39. scale_fill_palette = c(
  40. "6wt" = "#509FE9",
  41. "8wt" ="#355CA7",
  42. "63x" ="#de9cbf",
  43. "83x" ="#AE86B6"
  44. )
  46. scale_colour_palette = c(
  47. "6wt" = "#509FE9",
  48. "8wt" ="#355CA7",
  49. "63x" = "#de9cbf",
  50. "83x" ="#AE86B6"
  51. )
  53. scale_fill_palette_6mo = c(
  54. "WT" = "#509FE9",
  55. "3xTg" ="#de9cbf"
  56. )
  58. scale_colour_palette_6mo = c(
  59. "WT" = "#509FE9",
  60. "3xTg" ="#de9cbf"
  61. )
  63. scale_colour_palette_8mo = c(
  64. "WT" ="#355CA7",
  65. "3xTg" ="#AE86B6"
  66. )
  68. scale_fill_palette_8mo = c(
  69. "WT" ="#355CA7",
  70. "3xTg" ="#AE86B6"
  71. )
  73. ################
  75. AnyPlotbygroupandsex_adjaxis_facet <- function(df, y, title, ytitle, ymin, ymax) { # ymin =0, ymax=50){
  76. plot <-ggplot(df ,aes(x=Genotype, y={{y}}))
  77. plot +geom_sina(aes(col= Group_name, shape = Sex), alpha = 0.5, na.rm = TRUE, show.legend=TRUE, jitter_y = FALSE) +
  78. geom_bar(position= "dodge", stat = "summary", width = 0.9, fun.y = "mean", alpha = 0.4, aes(fill=Group_name), color = 'black') +
  79. stat_summary( color = "black", fun.data = mean_se, geom = "errorbar", width = 0.4, alpha = 0.8, show.legend=FALSE) +
  80. ggtitle(title) + ylab(ytitle) +
  81. scale_fill_manual("legend", values = scale_fill_palette, guide = "none")+
  82. scale_color_manual("legend", values = scale_colour_palette, guide = "none")+
  83. scale_y_continuous(expand = c(0,0)) +
  84. coord_cartesian(ylim=c(ymin, ymax))+
  85. scale_alpha(guide = 'none') +
  86. facet_grid(.~Age, switch = "x", labeller = as_labeller(c('6' = "6 mo", '8' = "8 mo"))) +
  87. theme(plot.title = element_text(hjust = 0.5, size = 22, face = "bold"),
  88. axis.text.x = element_text(size = 16, colour = "black"),
  89. axis.text.y = element_text(size = 18, face="bold", colour = "black"),
  90. axis.title.y = element_text(size = 20, face = "bold", colour = "black"),
  91. axis.title.x = element_blank(),
  92. legend.text=element_text(size=16),
  93. legend.title=element_text(size=18),
  94. panel.grid.major = element_blank(),
  95. panel.grid.minor = element_blank(),
  96. panel.background = element_blank(),
  97. axis.line = element_line(colour = "black"),
  98. strip.text.x = element_text(size = 18, face = "bold"),
  99. strip.background = element_rect( fill="white"),
  100. panel.spacing.x = unit(1.4, "lines"),
  101. strip.placement = "outside")
  102. }
  105. General_AOVbyanim_ez <- function(data, y, agevar, subjectvar) {
  107. avar <- substitute(agevar)
  108. svar <-substitute(subjectvar)
  109. gvar <- substitute(Genotype)
  110. yvar <- substitute(y)
  111. df<- substitute(data)
  114. aov <- eval.parent(substitute(ezANOVA(data = df, wid = svar, dv = yvar, between = .(gvar, avar), type = 3, observed = .(gvar, avar), return_aov=TRUE), list(avar = avar, gvar=gvar, svar = svar, yvar=yvar, df=df)))
  116. return(aov)
  117. }
  120. #age x genotype posthocs
  121. General_byanim_holm <- function(data, y, agevar) {
  123. avar <- substitute(agevar)
  124. gvar <- substitute(Genotype)
  125. yvar <- substitute(y)
  126. df<- substitute(data)
  128. formula <-substitute(yvar ~ avar * gvar, list(yvar=yvar, avar=avar, gvar = gvar))
  129. model <-eval.parent(substitute(aov(fm, data=df), list(fm=formula, df=df)))
  130. anova <- eval.parent(substitute(aov(fm, data=df), list(fm=formula, df=df))) %>% summary()
  131. emmtest <- eval.parent(emmeans(model, ~ Genotype * Age_Broad))
  133. #posthoc_genotype <- pairs(emmtest, simple = "Genotype") %>%
  134. #rbind() %>%
  135. #summary(adjust = "holm")
  137. #posthoc_age <- pairs(emmtest, simple = "Age_Broad") %>%
  138. #rbind() %>%
  139. #summary(adjust = "holm")
  141. #posthoc_all <- pairs(emmtest) %>%
  142. #rbind() %>%
  143. #summary(adjust = 'holm')
  145. posthoc_all2 <- rbind(pairs(emmtest, simple = "Age_Broad") , pairs(emmtest, simple = "Genotype")) %>% summary(adjust ="holm")
  148. outlist <-list(posthoc_all2)
  149. return(outlist)
  150. }
  153. General_byanim_holm_sex <- function(data, y, sexvar) {
  155. svar <- substitute(sexvar)
  156. gvar <- substitute(Genotype)
  157. yvar <- substitute(y)
  158. df<- substitute(data)
  160. formula <-substitute(yvar ~ svar * gvar, list(yvar=yvar, svar=svar, gvar = gvar))
  161. model <-eval.parent(substitute(aov(fm, data=df), list(fm=formula, df=df)))
  162. #anova <- eval.parent(substitute(aov(fm, data=df), list(fm=formula, df=df))) %>% summary()
  163. emmtest <- eval.parent(emmeans(model, ~ Genotype * Sex))
  165. posthoc_all <- rbind(pairs(emmtest, simple = "Sex") , pairs(emmtest, simple = "Genotype")) %>% summary(adjust ="holm")
  167. outlist <-list(posthoc_all)
  168. return(outlist)
  169. }
  172. General_byanim_holm_protocol <- function(data, y, protvar) {
  174. pvar <- substitute(protvar)
  175. gvar <- substitute(Genotype)
  176. yvar <- substitute(y)
  177. df<- substitute(data)
  179. formula <-substitute(yvar ~ pvar * gvar, list(yvar=yvar, pvar=pvar, gvar = gvar))
  180. model <-eval.parent(substitute(aov(fm, data=df), list(fm=formula, df=df)))
  181. #anova <- eval.parent(substitute(aov(fm, data=df), list(fm=formula, df=df))) %>% summary()
  182. emmtest <- eval.parent(emmeans(model, ~ Genotype * Protocol))
  184. #posthoc_genotype <- pairs(emmtest, simple = "Genotype") %>%
  185. #rbind() %>%
  186. #summary(adjust = "holm")
  188. #posthoc_age <- pairs(emmtest, simple = "Age_Broad") %>%
  189. #rbind() %>%
  190. #summary(adjust = "holm")
  192. #posthoc_all <- pairs(emmtest) %>%
  193. #rbind() %>%
  194. #summary(adjust = 'holm')
  196. posthoc_all2 <- rbind(pairs(emmtest, simple = "Protocol") , pairs(emmtest, simple = "Genotype")) %>% summary(adjust ="holm")
  199. outlist <-list(posthoc_all2)
  200. return(outlist)
  201. }
  204. ################
  207. PowerCohPhaseStats_3way <- function(data, y, agevar, layervar, subjectvar){
  208. #3 way age x genotype x layer anova
  210. #agevar = Age_Broad or Age, layervar = Layer_name or Layername or similar, subject var = Animals, Animalname or similar, y = Coh, Run_thresh_power etc.
  211. avar <- substitute(agevar)
  212. gvar <- substitute(Genotype)
  213. svar <- substitute(subjectvar)
  214. lvar <-substitute(layervar)
  215. yvar <- substitute(y)
  216. df<- substitute(data)
  218. aov <- eval.parent(substitute(ezANOVA(data = df, wid = svar, dv = yvar, within = lvar, between = .(gvar, avar), type = 3, observed = .(gvar, avar, lvar), return_aov=TRUE), list(avar = avar, gvar=gvar, svar = svar, lvar= lvar, yvar=yvar, df=df)))
  220. return(aov)
  221. }
  223. #2 way genotype by layer, input data already subsetted for age
  224. PowerCohPhaseStats_2waygl <- function(data_age, y, layervar, subjectvar){
  226. gvar <- substitute(Genotype)
  227. svar <- substitute(subjectvar)
  228. lvar <-substitute(layervar)
  229. yvar <- substitute(y)
  230. df<- substitute(data_age)
  232. aov <- eval.parent(substitute(ezANOVA(data = df, wid = svar, dv = yvar, within = lvar, between = gvar, type = 3, observed = .(gvar, lvar), return_aov=TRUE), list(gvar=gvar, svar = svar, lvar= lvar, yvar=yvar, df=df)))
  234. return(aov)
  235. }
  237. #2 way layer x age, input data already subsetted for genotype
  238. PowerCohPhaseStats_2wayal <- function(data_genotype, y, agevar, layervar, subjectvar){
  239. #agevar = Age_Broad or Age, layervar = Layer_name or Layername or similar, subject var = Animals, Animalname or similar, y = Coh, Run_thresh_power etc.
  240. avar <- substitute(agevar)
  241. svar <- substitute(subjectvar)
  242. lvar <-substitute(layervar)
  243. yvar <- substitute(y)
  244. df<- substitute(data_genotype)
  246. aov <- eval.parent(substitute(ezANOVA(data = df, wid = svar, dv = yvar, within = lvar, between = .(avar), type = 3, observed = .(avar, lvar), return_aov=TRUE), list(avar = avar, svar = svar, lvar= lvar, yvar=yvar, df=df)))
  250. return(aov)
  251. }
  254. #3 way layer x genotype x sex, input data already subsetted for age
  255. PowerCohPhaseStats_3waygls <- function(data_age, y, layervar, subjectvar){
  256. #agevar = Age_Broad or Age, layervar = Layer_name or Layername or similar, subject var = Animals, Animalname or similar, y = Coh, Run_thresh_power etc.
  257. gvar <- substitute(Genotype)
  258. svar <- substitute(subjectvar)
  259. sexvar <- substitute(Sex)
  260. lvar <-substitute(layervar)
  261. yvar <- substitute(y)
  262. df<- substitute(data_age)
  264. aov <- eval.parent(substitute(ezANOVA(data = df, wid = svar, dv = yvar, within = lvar, between = .(gvar, sexvar), type = 3, observed = .(gvar, lvar, sexvar), return_aov=TRUE), list(gvar = gvar, svar = svar, lvar= lvar, sexvar=sexvar, yvar=yvar, df=df)))
  268. return(aov)
  269. }
  272. Posthocs_byage <- function(data_age, y, layervar){
  273. #is there a difference between genotypes for each layer at the given age?
  275. #agevar = Age_Broad or Age, layervar = Layer_name or Layername or similar, subject var = Animals, Animalname or similar, y = Coh, Run_thresh_power etc.
  277. gvar <- substitute(Genotype)
  278. lvar <-substitute(layervar)
  279. yvar <- substitute(y)
  280. df<- substitute(data_age)
  282. posthoc <- eval.parent(substitute(df %>% dplyr::group_by(lvar) %>% t_test(yvar ~gvar, p.adjust.method = "none") %>% adjust_pvalue(method = "holm"), list(df =df, lvar=lvar, yvar=yvar, gvar=gvar))) #THIS MATCHES PRISM!!
  285. return(posthoc)
  286. }
  289. #Alternate method that can deal with missing data - matches output from graphpad prism
  290. MECthetastats <- function(MECtheta_age_df) {
  292. df = substitute(MECtheta_age_df)
  293. avar = substitute(Age_Broad)
  294. gvar = substitute(Genotype)
  295. lvar = substitute(Layer_name)
  296. svar = substitute(animals)
  297. yvar = substitute(Run_thresh_power)
  299. #2 way
  300. fm <- substitute(yvar ~ gvar *lvar + (1|svar), list(yvar=yvar, gvar= gvar, svar=svar, lvar=lvar))
  302. model<- eval.parent(substitute(lmer(fm , REML = T, data = df), list(fm = fm, df =df)))
  303. lm <- eval.parent(substitute(lmer(fm , REML = T, data = df), list(fm = fm, df =df))) %>% summary()
  304. lm_aov <- eval.parent(substitute(lmer(fm, REML = T, data=df), list(fm=fm, df=df))) %>% anova()
  306. emmtest <- eval.parent(emmeans(model, ~ Genotype * Layer_name))
  307. posthoc_emmeans <- pairs(emmtest, simple = "Genotype") %>%
  308. rbind() %>%
  309. summary(adjust = "bonferroni")
  311. posthoc_ttest <- eval.parent(substitute(df %>% dplyr::group_by(lvar) %>% t_test(yvar ~gvar, p.adjust.method = "none") %>% adjust_pvalue(method = "bonferroni"), list(df =df, lvar=lvar, yvar=yvar, gvar=gvar)))
  313. outlist <-list(lm, lm_aov, posthoc_emmeans, posthoc_ttest)
  314. return(outlist)
  316. }
  319. #mixed model to account for missing data - ended up using prism for final version?
  320. CSDstats <- function(CSD_age_df) {
  322. df = substitute(CSD_age_df)
  323. avar = substitute(Age_Broad)
  324. gvar = substitute(Genotype)
  325. lvar = substitute(Layer)
  326. svar = substitute(Animal)
  327. yvar = substitute(Value)
  329. #2 way
  330. fm <- substitute(yvar ~ gvar *lvar + (1|svar), list(yvar=yvar, gvar= gvar, svar=svar, lvar=lvar))
  332. model<- eval.parent(substitute(lmer(fm , REML = T, data = df), list(fm = fm, df =df)))
  333. lm <- eval.parent(substitute(lmer(fm , REML = T, data = df), list(fm = fm, df =df))) %>% summary()
  334. lm_aov <- eval.parent(substitute(lmer(fm, REML = T, data=df), list(fm=fm, df=df))) %>% anova()
  336. emmtest <- eval.parent(emmeans(model, ~ Genotype * Layer))
  337. posthoc_emmeans <- pairs(emmtest, simple = "Genotype") %>%
  338. rbind() %>%
  339. summary(adjust = "holm")
  341. posthoc_ttest <- eval.parent(substitute(df %>% dplyr::group_by(lvar) %>% t_test(yvar ~gvar, p.adjust.method = "none") %>% adjust_pvalue(method = "holm"), list(df =df, lvar=lvar, yvar=yvar, gvar=gvar)))
  343. outlist <-list(lm, lm_aov, posthoc_emmeans, posthoc_ttest)
  344. return(outlist)
  346. }
  348. #
  349. CSDstats_3way <- function(CSD_age_df) {
  351. df = substitute(CSD_age_df)
  352. sexvar = substitute(Sex)
  353. gvar = substitute(Genotype)
  354. lvar = substitute(Layer)
  355. svar = substitute(Animal)
  356. yvar = substitute(Value)
  358. #3 way
  359. fm <- substitute(yvar ~ gvar * sexvar *lvar + (1|svar), list(yvar=yvar, gvar= gvar, sexvar = sexvar, svar=svar, lvar = lvar))
  361. model<- eval.parent(substitute(lmer(fm , REML = T, data = df), list(fm = fm, df =df)))
  362. lm <- eval.parent(substitute(lmer(fm , REML = T, data = df), list(fm = fm, df =df))) %>% summary()
  363. lm_aov <- eval.parent(substitute(lmer(fm, REML = T, data=df), list(fm=fm, df=df))) %>% anova()
  365. outlist <-list(lm, lm_aov)
  366. return(outlist)
  368. }
  371. ################
  373. #for r values and firing rates
  374. Singleunit_nonparametric <- function(celltype, y){
  375. #CALCULATE P VALS CORRECTED FOR MULTIPLE COMPARISONS
  377. df <- substitute(celltype)
  379. y <- substitute(y)
  380. yvar <- substitute(df$y, list(y=y, df = df))
  382. gvar <- substitute(Genotype)
  383. grpvar <-substitute(Groupname)
  385. xvar <- substitute(df$x, list(x=grpvar, df = df))
  387. wilcox <- eval.parent(substitute(pairwise_wilcox_test(df, y~grpvar, p.adjust.method="holm", comparisons = list(c("63x", "6wt"), c("83x", "8wt"), c("6wt", "8wt"), c("63x", "83x"))), list(y = y, grpvar = grpvar, df = df)))
  389. return(wilcox)
  390. }
  393. #circular statistics for firing phase/mu values
  394. Circstats_full <- function(celltype_6, celltype_8, y, celltype_6_WT, celltype_6_Tg, celltype_8_WT, celltype_8_Tg){
  395. #CALCULATE P VALS CORRECTED FOR MULTIPLE COMPARISONS
  397. df <- substitute(celltype)
  398. df6 <- substitute(celltype_6)
  399. df8 <- substitute(celltype_8)
  400. WT6data <-substitute(celltype_6_WT)
  401. Tg6data <- substitute(celltype_6_Tg)
  402. WT8data <-substitute(celltype_8_WT)
  403. Tg8data <- substitute(celltype_8_Tg)
  405. y <- substitute(y)
  406. yvar <- substitute(df$y, list(y=y, df = df))
  407. yvar6 <- substitute(df6$y, list(y=y, df6 = df6))
  408. yvar8 <- substitute(df8$y, list(y=y, df8 = df8))
  410. gvar <- substitute(Genotype)
  412. xvar <- substitute(df$x, list(x=gvar, df = df))
  413. xvar6 <- substitute(df6$x, list(x=gvar, df6 = df6))
  414. xvar8 <- substitute(df8$x, list(x=gvar, df8 = df8))
  416. #kuiper test - is anything different about these distributions? (Not sure about this)
  417. kuiper_6 <- eval.parent(substitute(kuiper_test(Tg6data$y, WT6data$y), list(y = y, Tg6data = Tg6data, WT6data = WT6data)))
  419. #equal kappa test - are concentration parameters different?
  420. equalkappa_6 <- eval.parent(substitute(equal.kappa.test(yvar6, xvar6), list(yvar6=yvar6, xvar6=xvar6)))
  422. #watson williams - are means different?
  423. watwill_6 <- eval.parent(substitute(watson.williams.test(yvar6, xvar6), list(yvar6=yvar6, xvar6=xvar6)))
  425. #watson wheeler - are distribution of angles different? unclear if this is the same as watson williams? non-parametric?
  426. watwheel_6 <-eval.parent(substitute(watson.wheeler.test(yvar6, xvar6), list(yvar6=yvar6, xvar6=xvar6)))
  428. aovcirc_6 <- eval.parent(substitute(aov.circular(yvar6, xvar6), list(yvar6=yvar6, xvar6=xvar6)))
  430. #kuiper test - is anything different about these distributions? (Not sure about this)
  431. kuiper_8 <- eval.parent(substitute(kuiper_test(Tg8data$y, WT8data$y), list(y = y, Tg8data = Tg8data, WT8data = WT8data)))
  433. #equal kappa test - are concentration parameters different?
  434. equalkappa_8 <- eval.parent(substitute(equal.kappa.test(yvar8, xvar8), list(yvar8=yvar8, xvar8=xvar8)))
  436. #watson williams - are means different?
  437. watwill_8 <- eval.parent(substitute(watson.williams.test(yvar8, xvar8), list(yvar8=yvar8, xvar8=xvar8)))
  439. #watson wheeler - are distribution of angles different? unclear if this is the same as watson williams? non-parametric?
  440. watwheel_8 <-eval.parent(substitute(watson.wheeler.test(yvar8, xvar8), list(yvar8=yvar8, xvar8=xvar8)))
  442. aovcirc_8 <- eval.parent(substitute(aov.circular(yvar8, xvar8), list(yvar8=yvar8, xvar8=xvar8)))
  444. kuiper_pvals <- c(kuiper_6[[2]], kuiper_8[[2]]) %>% p.adjust(method = 'bonferroni')
  445. equalkappa_pvals <- c(equalkappa_6[[7]], equalkappa_8[[7]]) %>% p.adjust(method = 'bonferroni')
  446. watwill_pvals <- c(watwill_6[[4]], watwill_8[[4]]) %>% p.adjust(method = 'bonferroni')
  447. watwheel_pvals <- c(watwheel_6[[4]], watwheel_8[[4]]) %>% p.adjust(method = 'bonferroni')
  448. aovcirc_pvals <- c(aovcirc_6[[12]], aovcirc_8[[12]]) %>% p.adjust(method = 'bonferroni')
  450. outlist <- list(kuiper_pvals, equalkappa_pvals, watwill_pvals,
  451. watwheel_pvals,aovcirc_pvals)
  452. return(outlist)
  453. }
  456. ################
  458. Bar_graph_t_tests <- function(df, y, agevar) {
  459. df = substitute(df)
  460. yvar = substitute(y)
  461. gvar = substitute(Genotype)
  462. avar = substitute(agevar)
  464. genotype <- eval.parent(substitute(df %>% dplyr::group_by(avar) %>% t_test(yvar ~gvar, p.adjust.method = "none"), list(df =df, yvar=yvar, gvar=gvar, avar=avar)))
  466. age <- eval.parent(substitute(df %>% dplyr::group_by(gvar) %>% t_test(yvar ~avar, p.adjust.method = "none"), list(df =df, yvar=yvar, gvar=gvar, avar=avar)))
  467. #
  468. posthoc_all <- bind_rows(genotype, age) %>% adjust_pvalue(method = "holm")
  469. posthoc_all <- posthoc_all[,-c(2)]
  472. outlist <- list( posthoc_all)
  473. return(outlist)
  475. }
  478. knitr::opts_chunk$set(dpi=300,fig.width=7, warning = FALSE, echo = FALSE)
  479. ```
  481. # Behavior
  483. ## Novel Object Location (Main Figure 1)
  485. ```{r behavior, echo=FALSE, warning = FALSE}
  487. #STILL NEED TO CHECK
  489. #BEHAVIOR
  491. #load data
  492. rawdata <- read_xlsx("NOL_Table_020826_forR_All.xlsx")
  494. #put WT first so it gets plotted on the left
  495. rawdata$Genotype = factor(rawdata$Genotype, levels = c('WT', '3xTg'))
  496. rawdata$Age_Group = factor(rawdata$Age_Group, levels = c('6', '8'))
  498. rawdata <- mutate(rawdata, group = case_when(Genotype == 'WT' & Age_Group == '6' ~ 'WT6',
  499. Genotype == '3xTg' & Age_Group == '6' ~ '3xTg6',
  500. Genotype == 'WT' & Age_Group == '8' ~ 'WT8',
  501. Genotype == '3xTg' & Age_Group == '8' ~ '3xTg8'))
  503. rawdata <- mutate(rawdata, group2 = case_when(Genotype == 'WT'~ 'WT',
  504. Genotype == '3xTg' & Age_Group == '6' ~ '3xTg6',
  505. Genotype == '3xTg' & Age_Group == '8' ~ '3xTg8'))
  507. rawdata <- mutate(rawdata, Protocol = as.factor(Protocol)) #don't treat protocol as numeric
  508. rawdata <-mutate(rawdata, Age_Broad = Age_Group) #create duplicate variable called Age_Broad to get along with all functions
  509. rawdata$Age_Broad= factor(rawdata$Age_Broad, levels = c('6', '8'))
  512. rawdata_P2<-subset(rawdata,Protocol == 2)
  513. rawdata_P2_8mo<-subset(rawdata_P2,Age_Group == 8)
  514. rawdata_P1 <-subset(rawdata, Protocol == 1)
  515. rawdata_P1_6mo <-subset(rawdata_P1, Age_Group == 6)
  517. #Plot 6 month data protocol 1
  518. plot <-ggplot(rawdata_P1_6mo ,aes(x=Genotype, y=NOL_Score)) #Test_Score_Original_20s
  519. behavior_6moP1 <- plot +geom_sina(aes(col= Genotype, shape = Sex), position = position_jitter(width = 0.2), alpha = 0.5, na.rm = TRUE, show.legend=TRUE, jitter_y = FALSE, maxwidth = 0.4, scale = 'width') +
  520. geom_bar(position= "dodge", stat = "summary", width = 0.9, fun = "mean", alpha = 0.4, aes(fill=Genotype), colour = "black") +
  521. stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.4, alpha = 1, show.legend=FALSE, colour = "black") +
  522. geom_hline(yintercept = 0.5, linetype = "dotted", color = "black") +
  523. ggtitle("Novel Object Location \n 6 mo") + ylab('Novel/Total Exploration') +
  524. scale_fill_manual("legend", values = scale_fill_palette_6mo, labels = c("WT", "3xTg"))+
  525. guides(fill=guide_legend("Genotype"), guide = "none") +
  526. scale_color_manual("legend", values = scale_colour_palette_6mo, guide = "none")+
  527. #scale_color_manual(values = c("#509FE9", "#de9cbf"))+
  528. scale_y_continuous(expand = c(0,0), limits = c(0, 1.1)) + #, limits = c(ymin,ymax), breaks = seq(0, 14, by = 2))+
  529. scale_alpha(guide = 'none') +
  530. #facet_grid(.~Age_Group, switch = "x", labeller = as_labeller(c('6' = "6 mo", '8' = "8 mo"))) +
  531. theme(plot.title = element_text(hjust = 0.5, size = 22, face = "bold"),
  532. axis.text.x = element_text(size = 18, colour = "black"),
  533. #axis.text.x = element_blank(),
  534. #axis.ticks.x=element_blank(),
  535. axis.text.y = element_text(size = 18, face="bold", colour = "black"),
  536. axis.title.y = element_text(size = 20, face = "bold", colour = "black"),
  537. axis.title.x = element_blank(),
  538. legend.text=element_text(size=16),
  539. legend.title=element_text(size=18),
  540. panel.grid.major = element_blank(),
  541. panel.grid.minor = element_blank(),
  542. panel.background = element_blank(),
  543. axis.line = element_line(colour = "black"),
  544. strip.text.x = element_text(size = 18, face = "bold"),
  545. strip.background = element_rect( fill="white"),
  546. panel.spacing.x = unit(1.4, "lines"),
  547. strip.placement = "outside")
  549. #Plot 8 month data protocol 2
  550. plot <-ggplot(rawdata_P2_8mo ,aes(x=Genotype, y=NOL_Score)) #Test_Score_Original_20s
  551. behavior_8moP2 <- plot +geom_sina(aes(col= Genotype, shape = Sex), position = position_jitter(width = 0.2), alpha = 0.5, na.rm = TRUE, show.legend=TRUE, jitter_y = FALSE, maxwidth = 0.4, scale = 'width') +
  552. geom_bar(position= "dodge", stat = "summary", width = 0.9, fun = "mean", alpha = 0.4, aes(fill=Genotype), colour = "black") +
  553. stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.4, alpha = 1, show.legend=FALSE, colour = "black") +
  554. geom_hline(yintercept = 0.5, linetype = "dotted", color = "black") +
  555. ggtitle("Novel Object Location \n 8 mo") + ylab('Novel/Total Exploration') +
  556. scale_fill_manual("legend", values = scale_fill_palette_8mo, labels = c("WT", "3xTg"))+
  557. guides(fill=guide_legend("Genotype"), guide = "none") +
  558. scale_color_manual("legend", values = scale_colour_palette_8mo, guide = "none")+
  559. #scale_color_manual(values = c("#509FE9", "#de9cbf"))+
  560. scale_y_continuous(expand = c(0,0), limits = c(0, 1.1)) + #, limits = c(ymin,ymax), breaks = seq(0, 14, by = 2))+
  561. scale_alpha(guide = 'none') +
  562. #facet_grid(.~Age_Group, switch = "x", labeller = as_labeller(c('6' = "6 mo", '8' = "8 mo"))) +
  563. theme(plot.title = element_text(hjust = 0.5, size = 22, face = "bold"),
  564. axis.text.x = element_text(size = 18, colour = "black"),
  565. #axis.text.x = element_blank(),
  566. #axis.ticks.x=element_blank(),
  567. axis.text.y = element_text(size = 18, face="bold", colour = "black"),
  568. axis.title.y = element_text(size = 20, face = "bold", colour = "black"),
  569. axis.title.x = element_blank(),
  570. legend.text=element_text(size=16),
  571. legend.title=element_text(size=18),
  572. panel.grid.major = element_blank(),
  573. panel.grid.minor = element_blank(),
  574. panel.background = element_blank(),
  575. axis.line = element_line(colour = "black"),
  576. strip.text.x = element_text(size = 18, face = "bold"),
  577. strip.background = element_rect( fill="white"),
  578. panel.spacing.x = unit(1.4, "lines"),
  579. strip.placement = "outside")
  582. ggsave(
  583. "NOL_6moP1.svg",
  584. behavior_6moP1,
  585. width = 4,
  586. height = 5,
  587. dpi = 600
  588. )
  591. ggsave(
  592. "NOL_8moP2.svg",
  593. behavior_8moP2,
  594. width = 4,
  595. height = 5,
  596. dpi = 600
  597. )
  600. behavior_6moP1
  601. behavior_8moP2
  604. wt_p1 <-subset(rawdata_P1, Genotype == 'WT')
  605. tg_p1 <-subset(rawdata_P1, Genotype == '3xTg')
  606. wt6_p1 <-subset(rawdata_P1, Age_Group ==6 & Genotype == 'WT')
  607. wt8_p1 <-subset(rawdata_P1, Age_Group ==8 & Genotype == 'WT')
  608. tg6_p1 <-subset(rawdata_P1, Age_Group ==6 & Genotype == '3xTg')
  609. tg8_p1 <-subset(rawdata_P1, Age_Group ==8 & Genotype == '3xTg')
  611. wt_p2 <-subset(rawdata_P2, Genotype == 'WT')
  612. tg_p2 <-subset(rawdata_P2, Genotype == '3xTg')
  613. wt6_p2 <-subset(rawdata_P2, Age_Group ==6 & Genotype == 'WT')
  614. wt8_p2 <-subset(rawdata_P2, Age_Group ==8 & Genotype == 'WT')
  615. tg6_p2 <-subset(rawdata_P2, Age_Group ==6 & Genotype == '3xTg')
  616. tg8_p2 <-subset(rawdata_P2, Age_Group ==8 & Genotype == '3xTg')
  618. WT_P1_P2 <-subset(rawdata, Genotype == "WT")
  619. Tg_P1_P2 <-subset(rawdata, Genotype == "3xTg")
  620. P1_P2_6mo <-subset(rawdata, Age_Group == 6)
  621. P1_P2_8mo <-subset(rawdata, Age_Group == 8)
  623. WT_P1_P2_6mo <- subset(rawdata, Age_Group == 6)
  624. Tg_P1_P2_6mo <- subset(rawdata, Age_Group == 6)
  625. WT_P1_P2_8mo <- subset(rawdata, Age_Group == 8)
  626. Tg_P1_P2_8mo <- subset(rawdata, Age_Group == 8)
  629. print("T test at 6 mo - WT vs 3xTg")
  630. t.test(NOL_Score~Genotype, data = rawdata_P1_6mo)
  632. print("T test at 8 mo - WT vs 3xTg")
  633. t.test(NOL_Score ~Genotype, data = rawdata_P2_8mo)
  635. print("Behavior different from chance? (by group)")
  636. print("For Main Fig1")
  637. print("WT 6mo P1")
  638. t.test(wt6_p1$NOL_Score, mu = 0.5)
  639. print("3xTg 6mo P1")
  640. t.test(tg6_p1$NOL_Score, mu=0.5)
  641. print("WT 8 mo P2")
  642. t.test(wt8_p2$NOL_Score, mu=0.5)
  643. print("3xTg 8 mo P2")
  644. t.test(tg8_p1$NOL_Score, mu=0.5)
  647. #Sex differences in 8 mo animals?
  648. print("Sex Diffs? ")
  649. General_AOVbyanim_ez(rawdata_P2_8mo, NOL_Score, Sex, Mouse)
  653. # Plot WT 6 mo vs 8 mo - facet by protocol
  654. plot <-ggplot(WT_P1_P2 ,aes(x=Age_Group, y=NOL_Score))
  655. behavior3 <- plot +geom_sina(aes(col= Age_Group, shape = Sex), position = position_jitter(width = 0.2), alpha = 0.5, na.rm = TRUE, show.legend=TRUE, jitter_y = FALSE, maxwidth = 0.5, scale = 'width') +
  656. geom_bar(position= "dodge", stat = "summary", width = 0.9, fun = "mean", alpha = 0.4, aes(fill=Age_Group), colour = "black") +
  657. stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.4, alpha = 1, show.legend=FALSE, colour = "black") +
  658. geom_hline(yintercept = 0.5, linetype = "dotted", color = "black") +
  659. ggtitle("Novel Object Location \n by Protocol - WT") + ylab('Novel/Total Exploration') +
  660. scale_fill_manual(values = c("#509FE9", "#355CA7"), labels = c("6 mo", "8 mo"))+
  661. scale_color_manual( values = c("#509FE9", "#355CA7"), labels = c("6 mo", "8 mo"))+
  662. scale_y_continuous(expand = c(0,0), limits = c(0, 1.1)) + #, limits = c(ymin,ymax), breaks = seq(0, 14, by = 2))+
  663. scale_alpha(guide = 'none') +
  664. labs(color = "Age", fill = "Age") +
  665. scale_x_discrete(labels = c("6 mo", "8 mo")) +
  666. facet_grid(.~Protocol, switch = "x", labeller = as_labeller(c('1' = "Protocol 1", '2' = "Protocol 2"))) +
  667. theme(plot.title = element_text(hjust = 0.5, size = 22, face = "bold"),
  668. axis.text.x = element_text(size = 18, colour = "black"),
  669. #axis.text.x = element_blank(),
  670. #axis.ticks.x=element_blank(),
  671. axis.text.y = element_text(size = 18, face="bold", colour = "black"),
  672. axis.title.y = element_text(size = 20, face = "bold", colour = "black"),
  673. axis.title.x = element_blank(),
  674. legend.text=element_text(size=16),
  675. legend.title=element_text(size=18),
  676. panel.grid.major = element_blank(),
  677. panel.grid.minor = element_blank(),
  678. panel.background = element_blank(),
  679. axis.line = element_line(colour = "black"),
  680. strip.text.x = element_text(size = 18, face = "bold"),
  681. strip.background = element_rect( fill="white"),
  682. panel.spacing.x = unit(1.4, "lines"),
  683. strip.placement = "outside")
  686. # Plot Tg Protocol 1 vs. Protocol 2
  687. plot <-ggplot(Tg_P1_P2 ,aes(x=Age_Group, y=NOL_Score))
  688. behavior4 <- plot +geom_sina(aes(col= Age_Group, shape = Sex), position = position_jitter(width = 0.2), alpha = 0.5, na.rm = TRUE, show.legend=TRUE, jitter_y = FALSE, maxwidth = 0.5, scale = 'width') +
  689. geom_bar(position= "dodge", stat = "summary", width = 0.9, fun = "mean", alpha = 0.4, aes(fill=Age_Group), colour = "black") +
  690. stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.4, alpha = 1, show.legend=FALSE, colour = "black") +
  691. geom_hline(yintercept = 0.5, linetype = "dotted", color = "black") +
  692. ggtitle("Novel Object Location \n by Protocol - 3xTg") + ylab('Novel/Total Exploration') +
  693. scale_fill_manual(values = c("#de9cbf", "#AE86B6"), labels = c("6 mo", "8 mo"))+
  694. scale_color_manual( values = c("#de9cbf", "#AE86B6"), labels = c("6 mo", "8 mo"))+
  695. scale_y_continuous(expand = c(0,0), limits = c(0, 1.1)) + #, limits = c(ymin,ymax), breaks = seq(0, 14, by = 2))+
  696. scale_alpha(guide = 'none') +
  697. labs(color = "Age", fill = "Age") +
  698. scale_x_discrete(labels = c("6 mo", "8 mo")) +
  699. facet_grid(.~Protocol, switch = "x", labeller = as_labeller(c('1' = "Protocol 1", '2' = "Protocol 2"))) +
  700. theme(plot.title = element_text(hjust = 0.5, size = 22, face = "bold"),
  701. axis.text.x = element_text(size = 18, colour = "black"),
  702. #axis.text.x = element_blank(),
  703. #axis.ticks.x=element_blank(),
  704. axis.text.y = element_text(size = 18, face="bold", colour = "black"),
  705. axis.title.y = element_text(size = 20, face = "bold", colour = "black"),
  706. axis.title.x = element_blank(),
  707. legend.text=element_text(size=16),
  708. legend.title=element_text(size=18),
  709. panel.grid.major = element_blank(),
  710. panel.grid.minor = element_blank(),
  711. panel.background = element_blank(),
  712. axis.line = element_line(colour = "black"),
  713. strip.text.x = element_text(size = 18, face = "bold"),
  714. strip.background = element_rect( fill="white"),
  715. panel.spacing.x = unit(1.4, "lines"),
  716. strip.placement = "outside")
  719. ggsave(
  720. "NOL_supp1.svg",
  721. behavior3,
  722. width = 6,
  723. height = 5,
  724. dpi = 600
  725. )
  728. ggsave(
  729. "NOL_supp2.svg",
  730. behavior4,
  731. width = 6,
  732. height = 5,
  733. dpi = 600
  734. )
  736. behavior3
  737. behavior4
  741. print("All Protocol 1 - Is performance different from chance?")
  742. print("WT 6mo P1")
  743. t.test(wt6_p1$NOL_Score, mu = 0.5)
  744. print("WT 8mo P1")
  745. t.test(wt8_p1$NOL_Score, mu=0.5)
  746. print("3xTg 6mo P1")
  747. t.test(tg6_p1$NOL_Score, mu=0.5)
  748. print("3xTg 8mo P1")
  749. t.test(tg8_p1$NOL_Score, mu=0.5)
  751. print("All Protocol 2 - Is performance different from chance?")
  752. print("WT 6mo P2")
  753. t.test(wt6_p2$NOL_Score, mu = 0.5)
  754. print("WT 8mo P2")
  755. t.test(wt8_p2$NOL_Score, mu=0.5)
  756. print("3xTg 6mo P2")
  757. t.test(tg6_p2$NOL_Score, mu=0.5)
  758. print("3xTg 8mo P2")
  759. t.test(tg8_p2$NOL_Score, mu=0.5)
  762. #
  763. print("Extra stats for group/protocol comparisons")
  765. print("6 months Protocol x Genotype ANOVA")
  766. General_AOVbyanim_ez(P1_P2_6mo, NOL_Score, Protocol, Mouse)
  767. print("8 months Protocol x Genotype ANOVA + Posthocs")
  768. General_AOVbyanim_ez(P1_P2_8mo, NOL_Score, Protocol, Mouse)
  769. General_byanim_holm_protocol(P1_P2_8mo, NOL_Score, Protocol)
  770. print("Protocol 1 Age x Genotype ANOVA + Posthocs")
  771. General_AOVbyanim_ez(rawdata_P1, NOL_Score, Age_Group, Mouse)
  772. General_byanim_holm(rawdata_P1, NOL_Score, Age_Broad)
  773. print("Protocol 2 Age x Genotype ANOVA + Posthocs")
  774. General_AOVbyanim_ez(rawdata_P2, NOL_Score, Age_Group, Mouse)
  775. General_byanim_holm(rawdata_P2, NOL_Score, Age_Broad)
  776. #print("Protocol 1 Genotype ANOVA")
  777. #General_AOVbyanim_ez(rawdata_P1, NOL_Score, Genotype, Mouse)
  778. #print("Protocol 2 Genotype ANOVA")
  779. #General_AOVbyanim_ez(rawdata_P2, NOL_Score, Genotype, Mouse)
  785. print("Average Age P1")
  786. age_by_group_P1<- rawdata_P1_6mo %>%
  787. dplyr::group_by(Age_Group, Genotype) %>%
  788. dplyr::summarise(mean = mean(Age_Months),
  789. sem = plotrix::std.error(Age_Months),
  790. min = min(Age_Months),
  791. max = max(Age_Months))
  792. age_by_group_P1
  794. print("Average Age P2")
  795. age_by_group_P2<- rawdata_P2_8mo %>%
  796. dplyr::group_by(Age_Group, Genotype) %>%
  797. dplyr::summarise(mean = mean(Age_Months),
  798. sem = plotrix::std.error(Age_Months),
  799. min = min(Age_Months),
  800. max = max(Age_Months))
  801. age_by_group_P2
  806. #Ns for figure 1
  807. print("Ns for Behavior Figure 1")
  808. print("3xTg 6mo (M, F) - P1")
  809. sum(rawdata_P1_6mo$Genotype == "3xTg" )
  810. sum(rawdata_P1_6mo$Genotype == "3xTg" & rawdata_P1_6mo$Sex == 'M')
  811. sum(rawdata_P1_6mo$Genotype == "3xTg" & rawdata_P1_6mo$Sex == 'F')
  812. print("WT 6mo (M, F) - P1")
  813. sum(rawdata_P1_6mo$Genotype == "WT" )
  814. sum(rawdata_P1_6mo$Genotype == "WT" & rawdata_P1_6mo$Sex == 'M')
  815. sum(rawdata_P1_6mo$Genotype == "WT" & rawdata_P1_6mo$Sex == 'F')
  816. print("3xTg 8mo (M, F) - P1")
  817. sum(rawdata_P2_8mo$Genotype == "3xTg" )
  818. sum(rawdata_P2_8mo$Genotype == "3xTg" & rawdata_P2_8mo$Sex == 'M')
  819. sum(rawdata_P2_8mo$Genotype == "3xTg" & rawdata_P2_8mo$Sex == 'F')
  820. print("WT 8mo (M, F) - P1")
  821. sum(rawdata_P2_8mo$Genotype == "WT")
  822. sum(rawdata_P2_8mo$Genotype == "WT" & rawdata_P2_8mo$Sex == 'M')
  823. sum(rawdata_P2_8mo$Genotype == "WT" & rawdata_P2_8mo$Sex == 'F')
  826. #Ns from 6 mo P1 and 8 month P2 (supplemental only)
  827. print("Ns for additional groups in figure S1")
  828. print("3xTg 6mo (M, F) - P2")
  829. sum(tg6_p2$Genotype == "3xTg" )
  830. sum(tg6_p2$Sex == 'M')
  831. sum(tg6_p2$Sex == 'F')
  832. print("WT 6mo (M, F) - P2")
  833. sum(wt6_p2$Genotype == "WT")
  834. sum(wt6_p2$Sex == 'M')
  835. sum(wt6_p2$Sex == 'F')
  836. print("3xTg 8mo (M, F) - P1")
  837. sum(tg8_p1$Genotype == "3xTg")
  838. sum(tg8_p1$Sex == 'M')
  839. sum(tg8_p1$Sex == 'F')
  840. print("WT 8mo (M, F) - P1")
  841. sum(wt8_p1$Genotype == "WT")
  842. sum(wt8_p1$Sex == 'M')
  843. sum(wt8_p1$Sex == 'F')
  845. ```
  847. ## Locomotion During Habituation (Figure S1)
  849. ```{r behavior2, echo=FALSE, warning = FALSE}
  851. #load data
  852. rawdata <- read_xlsx("NOL_Revision_2025_habituation.xlsx")
  854. #set genotype and age variables
  855. rawdata <- mutate(rawdata, Group_name = case_when(Genotype == 'WT' & Age_Group == '6' ~ '6wt',
  856. Genotype == '3xTg' & Age_Group == '6' ~ '63x',
  857. Genotype == 'WT' & Age_Group == '8' ~ '8wt',
  858. Genotype == '3xTg' & Age_Group == '8' ~ '83x'))
  860. rawdata <- mutate(rawdata, group2 = case_when(Genotype == 'WT'~ 'WT',
  861. Genotype == '3xTg' & Age_Group == '6' ~ '3xTg6',
  862. Genotype == '3xTg' & Age_Group == '8' ~ '3xTg8'))
  864. rawdata <- mutate(rawdata, Age = Age_Group) #rename to work with plotting functions expected input
  865. rawdata <- mutate(rawdata, Age_Broad = Age_Group) #rename to work with stats functions expected input
  867. rawdata$Genotype = factor(rawdata$Genotype, levels = c('WT', '3xTg'))
  868. rawdata$Age = factor(rawdata$Age, levels = c('6', '8'))
  869. rawdata$Age_Broad = factor(rawdata$Age_Broad, levels = c('6', '8'))
  870. rawdata$Group_name = factor(rawdata$Group_name, levels = c('6wt', '8wt', '63x', '83x'))
  872. rawdata$Distance_traveled_hab2 = as.numeric(rawdata$Distance_traveled_hab2)
  873. AnyPlotbygroupandsex_adjaxis_facet (rawdata , Distance_traveled_hab2, 'Distance Traveled', 'Distance (cm)', 0, 4000)
  874. General_AOVbyanim_ez(rawdata, Distance_traveled_hab2, Age_Broad, Animal)
  875. General_byanim_holm(rawdata, Distance_traveled_hab2, Age_Broad)
  877. rawdata$Avg_Velocity_Moving_Hab2 = as.numeric(rawdata$Avg_Velocity_Moving_Hab2)
  878. AnyPlotbygroupandsex_adjaxis_facet (rawdata, Avg_Velocity_Moving_Hab2, 'Movement Velocity', 'Velocity (m/s)', 0, 0.25)
  879. General_AOVbyanim_ez(rawdata, Avg_Velocity_Moving_Hab2, Age_Broad, Animal)
  880. General_byanim_holm(rawdata, Avg_Velocity_Moving_Hab2, Age_Broad)
  882. rawdata$Ratio_Mobility_Hab2 = as.numeric(rawdata$Ratio_Mobility_Hab2)
  883. AnyPlotbygroupandsex_adjaxis_facet (rawdata , Ratio_Mobility_Hab2, 'Proportion of Time \n Mobile', 'Time Moving/Total Time', 0, 0.31)
  884. General_AOVbyanim_ez(rawdata, Ratio_Mobility_Hab2, Age_Broad, Animal)
  885. General_byanim_holm(rawdata, Ratio_Mobility_Hab2, Age_Broad)
  887. #save plots
  888. ggsave(
  889. "Distance_Traveled_Hab_2.svg",
  890. AnyPlotbygroupandsex_adjaxis_facet (rawdata , Distance_traveled_hab2, 'Distance Traveled', 'Distance (cm)', 0, 4000),
  891. width = 4,
  892. height = 5,
  893. dpi = 600
  894. )
  896. rawdata <- mutate(rawdata, Avg_Velocity_Moving_Hab2 = as.numeric(Avg_Velocity_Moving_Hab2))
  898. ggsave(
  899. "Velocity_Hab2.svg",
  900. AnyPlotbygroupandsex_adjaxis_facet (rawdata, Avg_Velocity_Moving_Hab2, 'Movement Velocity', 'Velocity (m/s)', 0, 0.25),
  901. width = 4,
  902. height = 5,
  903. dpi = 600
  904. )
  906. ggsave(
  907. "Ratio_Mobility_Hab2.svg",
  908. AnyPlotbygroupandsex_adjaxis_facet (rawdata , Ratio_Mobility_Hab2, 'Proportion of Time \n Mobile', 'Time Moving/Total Time', 0, 0.31),
  909. width = 4,
  910. height = 5,
  911. dpi = 600
  912. )
  914. ```
  916. # Amyloid-Beta Immunohistochemistry (Figure 1)
  918. ```{r amyloid, echo=FALSE, warning = FALSE}
  921. PlaqueCounts<- read_xlsx("amyloid_quantification.xlsx")
  923. PlaqueCounts$Genotype = factor(PlaqueCounts$Genotype, levels = c('WT', '3xTg'))
  925. PlaqueCounts$Age_Broad = factor(PlaqueCounts$Age_Broad, levels = c('6', '8', '15'))
  927. PlaqueCounts <- mutate(PlaqueCounts, Groupname = case_when(Genotype == 'WT' & Age_Broad == '6' ~ 'WT6',
  928. Genotype == '3xTg' & Age_Broad == '6' ~ '3xTg6',
  929. Genotype == 'WT' & Age_Broad == '8' ~ 'WT8',
  930. Genotype == '3xTg' & Age_Broad == '8' ~ '3xTg8',
  931. Genotype == 'WT' & Age_Broad == '15' ~ 'WT15',
  932. Genotype == '3xTg' & Age_Broad == '15' ~ '3xTg15'
  933. ))
  935. PlaqueCounts$Groupname = factor(PlaqueCounts$Groupname, levels = c('WT6', 'WT8', 'WT15', '3xTg6', '3xTg8','3xTg15'))
  938. #reformat data to get dataframes with values by animal instead of by slice
  940. PlaqueCounts <- mutate(PlaqueCounts, Slice= as.factor(Slice))
  941. PlaqueCounts_byanimal <- PlaqueCounts %>%
  942. dplyr::select(Animal, Genotype, Sex, Age_Broad, Age_months, Groupname, Wave, Slice, Plaquespermmsq)
  943. PlaqueCounts_byanimal<- dcast(PlaqueCounts_byanimal, Animal + Genotype + Sex + Age_Broad + Age_months + Groupname + Wave ~ Slice, na.rm=TRUE)
  944. PlaqueCounts_byanimal <- mutate(PlaqueCounts_byanimal, Avg = rowMeans(dplyr::select(PlaqueCounts_byanimal,'1','2','3','4','5', '6', '7', '8', '9'), na.rm =TRUE))
  946. PlotPlaques_byageandsex <- function(df, y, title, ymin = -0.25, ymax = 20) {
  947. plot <-ggplot(df ,aes(x=Genotype, y={{y}}))
  948. plot+ geom_jitter(aes(col= Genotype, shape = Sex), alpha = 0.6, width = 0.1, show.legend = TRUE) +
  949. geom_bar(position="dodge", stat = "summary", width = 1, fun.y = "mean", alpha = 0.4, aes(fill=Genotype), colour = 'black') +
  950. stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.25, colour= 'black', show.legend = FALSE) +
  951. ggtitle(title) +labs(y= expression(bold("Plaques per" ~mm^2))) +
  952. facet_grid(.~Age_Broad, switch = "x", labeller = as_labeller(c('6' = "6 mo", '8' = "8 mo", '15' = "15 mo"))) +
  953. theme(plot.title = element_text(hjust = 0.5, size = 24, face = "bold"),
  954. axis.text.x = element_text(size = 19, colour = "black"),
  955. axis.text.y = element_text(size = 19, colour = "black", face = "bold"),
  956. axis.title.y = element_text(size = 21, colour = "black", face = "bold"),
  957. axis.title.x = element_blank(),
  958. legend.text=element_text(size=16),
  959. legend.title=element_text(size=18),
  960. strip.background = element_rect(colour="white", fill="white"),
  961. panel.grid.major = element_blank(),
  962. panel.grid.minor = element_blank(),
  963. panel.background = element_blank(),
  964. strip.text.x = element_text(size = 19, face = "bold"),
  965. panel.spacing.x = unit(1.4, "lines"),
  966. strip.placement = "outside",
  967. axis.line = element_line(colour = "black")) +
  968. scale_y_continuous(limits = c(ymin, ymax), expand = c(0, 0)) + #+
  969. #scale_fill_manual("legend", values = scale_fill_palette_8mo, labels = c("WT", "3xTg"))+
  970. scale_fill_manual("legend", values = scale_fill_palette_8mo, guide = "none")+
  971. guides(fill=guide_legend("Genotype")) +
  972. scale_color_manual("legend", values = scale_colour_palette_8mo, guide = "none")
  973. }
  975. PlotPlaques_byageandsex(PlaqueCounts_byanimal, Avg, "Amyloid Plaque Counts \n Hippocampus + Subiculum", ymin= -0.25, ymax = 15)
  976. General_AOVbyanim_ez(PlaqueCounts_byanimal, Avg, Age_Broad, Animal)
  979. CellCounts_nonparametric <- function(df, y){
  980. #CALCULATE P VALS CORRECTED FOR MULTIPLE COMPARISONS
  982. df <- substitute(df)
  984. y <- substitute(y)
  985. yvar <- substitute(df$y, list(y=y, df = df))
  987. gvar <- substitute(Genotype)
  988. grpvar <-substitute(Groupname)
  990. xvar <- substitute(df$x, list(x=grpvar, df = df))
  992. wilcox <- eval.parent(substitute(pairwise_wilcox_test(df, y~grpvar, p.adjust.method="holm", comparisons = list(c("WT6", "3xTg6"), c("WT8", "3xTg8"), c("WT15", "3xTg15"))), list(y = y, grpvar = grpvar, df = df)))
  994. #wilcox_6 <- eval.parent(substitute(wilcox.test(celltype_6, y~gvar, p.adjust.method="none"), list(y = y, gvar = gvar, celltype_6 = celltype_6)))
  996. return(wilcox)
  997. }
  1000. Immuno_AOV_3way <- function(data, y, agevar, subjectvar){
  1001. #3 way age x genotype x sex anova
  1003. avar <- substitute(agevar)
  1004. gvar <- substitute(Genotype)
  1005. svar <- substitute(subjectvar)
  1006. sexvar <-substitute(Sex)
  1007. yvar <- substitute(y)
  1008. df<- substitute(data)
  1010. aov <- eval.parent(substitute(ezANOVA(data = df, wid = svar, dv = yvar, between = .(gvar, avar, sexvar), type = 3, observed = .(gvar, avar, sexvar), return_aov=TRUE), list(avar = avar, gvar=gvar, svar = svar, sexvar= sexvar, yvar=yvar, df=df)))
  1012. return(aov)
  1013. }
  1016. #3 way age x genotype x sex anova
  1017. print("3 way ANOVA Age x Genotype x Sex - Amyloid Plaque Counts")
  1018. Immuno_AOV_3way(PlaqueCounts_byanimal, Avg, Age_Broad, Animal)
  1019. CellCounts_nonparametric(PlaqueCounts_byanimal, Avg)
  1021. PlaqueCounts_byanimal %>%
  1022. dplyr::group_by(Groupname) %>%
  1023. dplyr::summarise(mean = mean(Age_months),
  1024. sem = plotrix::std.error(Age_months),
  1025. min = min(Age_months),
  1026. max = max(Age_months))
  1029. ggsave(
  1030. "PlaqueCounts.svg",
  1031. PlotPlaques_byageandsex(PlaqueCounts_byanimal, Avg, "Amyloid Plaque Counts \n Hippocampus + Subiculum", ymin= -0.25, ymax = 15),
  1032. width = 7,
  1033. height = 5,
  1034. dpi = 600
  1035. )
  1037. ```
  1039. # Ephys Results
  1041. ## Running in VR (Figure S3)
  1042. ```{r running-speed, echo=FALSE, warning = FALSE}
  1045. theta_data <- read.csv("Running_Power_Theta_HIPP.csv")
  1047. theta_data$Group_name <- factor(theta_data$Group_name , # Reordering factor levels
  1048. levels = c("6wt", "8wt", "63x", "83x"))
  1049. theta_data<-mutate(theta_data, Genotype = ifelse(Group_name =='6wt'|Group_name =='8wt', "WT", "3xTg"))
  1051. theta_data<-mutate(theta_data, Age = ifelse(Group_name =='6wt'|Group_name =='63x', 6, 8))
  1052. theta_data$Age <- factor(theta_data$Age, # Reordering factor levels
  1053. levels = c("6", "8"))
  1054. theta_data<-mutate(theta_data, Age_Broad = ifelse(Group_name =='6wt'|Group_name =='63x', 6, 8)) #duplicate variables to get along with all functions that expect either Age or Age_Broad
  1055. theta_data$Age_Broad <- factor(theta_data$Age_Broad, # Reordering factor levels
  1056. levels = c("6", "8"))
  1058. theta_data$Layer_name <- factor(theta_data$Layer_name, # Reordering region factor levels
  1059. levels = c("Hil", "GC", "Mol", "LM", "Rad", "Pyr", "Or"))
  1060. theta_data$Genotype <- factor(theta_data$Genotype, # Reordering genotype factor levels
  1061. levels = c("WT", "3xTg"))
  1062. theta_data$Sex <- factor(theta_data$Sex, levels = c("F", "M"))
  1064. theta_data <-subset(theta_data, Track =='all')
  1065. theta_data_6mo <-subset(theta_data, Age =='6')
  1066. theta_data_8mo <-subset(theta_data, Age =='8')
  1068. age_data <- subset(theta_data, Layer_name == 'Or')
  1069. age_by_group_df <- age_data %>%
  1070. dplyr::group_by(Group_name) %>%
  1071. dplyr::summarise(mean = mean(Age_months),
  1072. sem = plotrix::std.error(Age_months),
  1073. min = min(Age_months),
  1074. max = max(Age_months))
  1076. runspeed_data <-subset(theta_data, Layer_name == 'LM') #only one value per animal for speed
  1078. runspeed_data <- mutate(runspeed_data, relativespeed_all = RunSpeed-Run_baseline ) #re-calculate speed by subtracting ball tracker baseline
  1079. runspeed_data <- mutate(runspeed_data, relativespeed_thresh = RunSpeedinThresh-Run_baseline )
  1080. runspeed_data <- mutate(runspeed_data, percent_run = as.numeric(LengthRun/(LengthRun + LengthNonRun)))
  1083. #all subjects sex = shape
  1084. Run1 <- AnyPlotbygroupandsex_adjaxis_facet(runspeed_data, relativespeed_all, 'Run Speed' , 'Speed (m/s)', 0, 0.6)
  1085. Run2 <- AnyPlotbygroupandsex_adjaxis_facet(runspeed_data, relativespeed_thresh, 'Run Speed \nAfter Subsampling' , 'Speed (m/s)', 0, 0.31)
  1086. Run3 <- AnyPlotbygroupandsex_adjaxis_facet(runspeed_data, percent_run, 'Time Spent \n Running' , 'Ratio', 0, 1)
  1088. Run1
  1089. General_AOVbyanim_ez(runspeed_data, relativespeed_all, Age_Broad, animals)
  1091. Run2
  1092. General_AOVbyanim_ez(runspeed_data, relativespeed_thresh, Age_Broad, animals)
  1094. Run3
  1095. General_AOVbyanim_ez(runspeed_data, percent_run, Age_Broad, animals)
  1098. plot_list = list(Run1, Run2, Run3)
  1099. ID = 1
  1101. for (p in plot_list) {
  1102. ggsave(
  1103. p,
  1104. filename=paste("running",ID,".svg",sep=""),
  1105. width = 4,
  1106. height = 5,
  1107. dpi = 600)
  1108. ID = ID + 1
  1109. }
  1111. print("Ns for Running Speed/LFP Analysis")
  1112. print("3xTg 6mo (M, F)")
  1113. sum(runspeed_data$Genotype == "3xTg" & runspeed_data$Age_Broad == '6')
  1114. sum(runspeed_data$Genotype == "3xTg" & runspeed_data$Age_Broad == '6' & runspeed_data$Sex == 'M')
  1115. sum(runspeed_data$Genotype == "3xTg" & runspeed_data$Age_Broad == '6' & runspeed_data$Sex == 'F')
  1116. print("3xTg 8mo (M, F)")
  1117. sum(runspeed_data$Genotype == "3xTg" & runspeed_data$Age_Broad == '8')
  1118. sum(runspeed_data$Genotype == "3xTg" & runspeed_data$Age_Broad == '8' & runspeed_data$Sex == 'M')
  1119. sum(runspeed_data$Genotype == "3xTg" & runspeed_data$Age_Broad == '8' & runspeed_data$Sex == 'F')
  1120. print("WT 6mo (M, F)")
  1121. sum(runspeed_data$Genotype == "WT" & runspeed_data$Age_Broad == '6')
  1122. sum(runspeed_data$Genotype == "WT" & runspeed_data$Age_Broad == '6' & runspeed_data$Sex == 'M')
  1123. sum(runspeed_data$Genotype == "WT" & runspeed_data$Age_Broad == '6' & runspeed_data$Sex == 'F')
  1124. print("WT 8mo (M, F)")
  1125. sum(runspeed_data$Genotype == "WT" & runspeed_data$Age_Broad == '8')
  1126. sum(runspeed_data$Genotype == "WT" & runspeed_data$Age_Broad == '8' & runspeed_data$Sex == 'M')
  1127. sum(runspeed_data$Genotype == "WT" & runspeed_data$Age_Broad == '8' & runspeed_data$Sex == 'F')
  1129. print("Age Ranges")
  1130. age_data
  1133. ```
  1136. ```{r lfp-functions, echo=FALSE, warning = FALSE}
  1138. #LOAD FUNCTIONS FOR LFP PLOTS AND ADDED STATS
  1139. Powerbygroup_6mo <- function(freq_df_6mo, state_power, title, ytitle, ymin, ymax) {
  1141. plot <-ggplot(freq_df_6mo, aes(x=Layer_name, y={{state_power}}))
  1142. plot + geom_line(aes(x = Layer_name, y = {{state_power}}, group = Genotype, colour=Genotype), stat="summary", fun.y = "mean", size = 1.2, alpha =1) +
  1143. geom_point(aes(x = Layer_name, y = {{state_power}}, group = Genotype, colour=Genotype), stat="summary", fun.y = "mean", alpha = 0.7, size = 0.7) +
  1144. stat_summary(fun.data = mean_se, geom = "pointrange", size = 0.7, alpha = 0.7, linewidth = 1.2, aes(x=Layer_name, y={{state_power}}, group = Genotype, colour=Genotype), show.legend = FALSE) +
  1145. theme(panel.grid.major = element_blank(),
  1146. panel.grid.minor = element_blank(),
  1147. panel.background = element_blank(),
  1148. axis.line = element_line(colour = "black"),
  1149. plot.title = element_text(size = 22, face ="bold", hjust = 0.5),
  1150. axis.text=element_text(size=20, face="bold", colour = "black"),
  1151. axis.title=element_text(size=22, face="bold", colour = "black"),
  1152. legend.text=element_text(size=16),
  1153. legend.title=element_text(size=18),
  1154. strip.text.x = element_text(size = 18, face = "bold"),
  1155. strip.background = element_rect( fill="white"),
  1156. panel.spacing.x = unit(1.4, "lines"),
  1157. legend.key=element_rect(fill="white", colour =NA),
  1158. strip.placement = "outside") +
  1159. ggtitle(title) + xlab("Layer") + ylab(ytitle)+
  1160. scale_color_manual("legend", values = scale_colour_palette_6mo, labels = c("WT 6 mo", "3xTg 6 mo"))+
  1161. guides(colour=guide_legend("Group", override.aes = list(size = 4))) +
  1162. coord_flip(ylim=c(ymin, ymax)) +
  1163. labs(colour= "Group") +
  1164. scale_alpha(guide = 'none')
  1166. }
  1168. Powerbygroup_8mo <- function(freq_df_8mo, state_power, title, ytitle, ymin, ymax) {
  1170. plot <-ggplot(freq_df_8mo, aes(x=Layer_name, y={{state_power}}))
  1171. plot + geom_line(aes(x = Layer_name, y = {{state_power}}, group = Genotype, colour=Genotype), stat="summary", fun.y = "mean", size = 1.2, alpha =1) +
  1172. geom_point(aes(x = Layer_name, y = {{state_power}}, group = Genotype, colour=Genotype), stat="summary", fun.y = "mean", alpha = 0.7, size = 0.7) +
  1173. stat_summary(fun.data = mean_se, geom = "pointrange", size = 0.7, alpha = 0.7, linewidth = 1.2,
  1174. aes(x=Layer_name, y={{state_power}}, group = Genotype, colour=Genotype), show.legend = FALSE) +
  1175. theme(panel.grid.major = element_blank(),
  1176. panel.grid.minor = element_blank(),
  1177. panel.background = element_blank(),
  1178. axis.line = element_line(colour = "black"),
  1179. plot.title = element_text(size = 22, face ="bold", hjust = 0.5),
  1180. axis.text=element_text(size=20, face="bold", colour = "black"),
  1181. axis.title=element_text(size=22, face="bold", colour = "black"),
  1182. legend.text=element_text(size=16),
  1183. legend.title=element_text(size=18),
  1184. strip.text.x = element_text(size = 18, face = "bold"),
  1185. strip.background = element_rect( fill="white"),
  1186. panel.spacing.x = unit(1.4, "lines"),
  1187. legend.key=element_rect(fill="white", colour =NA),
  1188. strip.placement = "outside") +
  1189. ggtitle(title) + xlab("Layer") + ylab(ytitle)+
  1190. scale_color_manual("legend", values = scale_colour_palette_8mo, labels = c("WT 8 mo", "3xTg 8 mo"))+
  1191. guides(colour=guide_legend("Group",override.aes = list(size = 4))) +
  1192. coord_flip(ylim=c(ymin, ymax)) +
  1193. labs(colour= "Group") +
  1194. scale_alpha(guide = 'none')
  1196. }
  1202. Posthocs_byage <- function(data_age, y, layervar){
  1203. #is there a difference between genotypes for each layer at the given age?
  1205. #agevar = Age_Broad or Age, layervar = Layer_name or Layername or similar, subject var = Animals, Animalname or similar, y = Coh, Run_thresh_power etc.
  1207. gvar <- substitute(Genotype)
  1208. lvar <-substitute(layervar)
  1209. yvar <- substitute(y)
  1210. df<- substitute(data_age)
  1212. posthoc <- eval.parent(substitute(df %>% dplyr::group_by(lvar) %>% t_test(yvar ~gvar, p.adjust.method = "none") %>% adjust_pvalue(method = "holm"), list(df =df, lvar=lvar, yvar=yvar, gvar=gvar))) #THIS MATCHES PRISM!!
  1215. return(posthoc)
  1216. }
  1220. ```
  1222. ### Theta Power - Hippocampus (Figure 3)
  1223. ```{r theta-power-h, echo=FALSE, warning = FALSE}
  1225. Power1 <- Powerbygroup_6mo(theta_data_6mo, Run_thresh_power, 'Hippocampus \n Theta Power (5-12 Hz)', "Power", 0, 350)
  1226. Power2 <- Powerbygroup_8mo(theta_data_8mo, Run_thresh_power, 'Hippocampus \n Theta Power (5-12 Hz)', "Power", 0, 350)
  1229. Power1
  1230. print("2 way Genotype x Layer ANOVA - 6mo")
  1231. PowerCohPhaseStats_2waygl(theta_data_6mo, Run_thresh_power, Layer_name, animals)
  1232. print("6 mo posthocs")
  1233. Posthocs_byage(theta_data_6mo, Run_thresh_power, Layer_name)
  1236. Power2
  1237. print("2 way Genotype x Layer ANOVA - 8mo")
  1238. PowerCohPhaseStats_2waygl(theta_data_8mo, Run_thresh_power, Layer_name, animals)
  1239. print("8 mo posthocs")
  1240. Posthocs_byage(theta_data_8mo, Run_thresh_power, Layer_name)
  1243. print("8 mo Genotype x Layer x Sex")
  1244. PowerCohPhaseStats_3waygls(theta_data_8mo, Run_thresh_power, Layer_name, animals)
  1247. ```
  1249. ### Gamma Power - Hippocampus (Figure S5)
  1250. ```{r gamma-power-h, echo=FALSE, warning = FALSE}
  1253. slow_gamma_data <- read.csv("Running_Power_slow_gamma_HIPP.csv")
  1255. slow_gamma_data$Group_name <- factor(slow_gamma_data$Group_name , # Reordering factor levels
  1256. levels = c("6wt", "8wt", "63x", "83x"))
  1258. slow_gamma_data<-mutate(slow_gamma_data, Genotype = ifelse(Group_name =='6wt'|Group_name =='8wt', "WT", "3xTg"))
  1259. slow_gamma_data<-mutate(slow_gamma_data, Age = ifelse(Group_name =='6wt'|Group_name =='63x', 6, 8))
  1261. slow_gamma_data$Layer_name <- factor(slow_gamma_data$Layer_name, # Reordering region factor levels
  1262. levels = c("Hil", "GC", "Mol", "LM", "Rad", "Pyr", "Or"))
  1263. slow_gamma_data$Genotype <- factor(slow_gamma_data$Genotype, # Reordering genotype factor levels
  1264. levels = c("WT", "3xTg"))
  1266. slow_gamma_data$Age <- factor(slow_gamma_data$Age, # Reordering factor levels
  1267. levels = c("6", "8"))
  1269. slow_gamma_data <-subset(slow_gamma_data, Track =='all')
  1271. slow_gamma_data_6mo <-subset(slow_gamma_data, Age =='6')
  1272. slow_gamma_data_8mo <-subset(slow_gamma_data, Age =='8')
  1275. Power3 <- Powerbygroup_6mo(slow_gamma_data_6mo, Run_thresh_power, 'Hippocampus \n Slow Gamma Power (30-50 Hz)', "Power", 0, 100)
  1276. Power4 <- Powerbygroup_8mo(slow_gamma_data_8mo, Run_thresh_power, 'Hippocampus \n Slow Gamma Power (30-50 Hz)', "Power", 0, 100)
  1279. Power3
  1280. print("2 way Genotype x Layer ANOVA - 6mo")
  1281. PowerCohPhaseStats_2waygl(slow_gamma_data_6mo, Run_thresh_power, Layer_name, animals)
  1282. print("6 mo posthocs")
  1283. Posthocs_byage(slow_gamma_data_6mo, Run_thresh_power, Layer_name)
  1286. Power4
  1287. print("2 way Genotype x Layer ANOVA - 8mo")
  1288. PowerCohPhaseStats_2waygl(slow_gamma_data_8mo, Run_thresh_power, Layer_name, animals)
  1289. print("8 mo posthocs")
  1290. Posthocs_byage(slow_gamma_data_8mo, Run_thresh_power, Layer_name)
  1293. ##############
  1294. fast_gamma_data <- read.csv("Running_Power_fast_gamma_HIPP.csv")
  1296. fast_gamma_data$Group_name <- factor(fast_gamma_data$Group_name , # Reordering factor levels
  1297. levels = c("6wt", "8wt", "63x", "83x"))
  1299. fast_gamma_data<-mutate(fast_gamma_data, Genotype = ifelse(Group_name =='6wt'|Group_name =='8wt', "WT", "3xTg"))
  1300. fast_gamma_data<-mutate(fast_gamma_data, Age = ifelse(Group_name =='6wt'|Group_name =='63x', 6, 8))
  1302. fast_gamma_data$Layer_name <- factor(fast_gamma_data$Layer_name, # Reordering region factor levels
  1303. levels = c("Hil", "GC", "Mol", "LM", "Rad", "Pyr", "Or"))
  1304. fast_gamma_data$Genotype <- factor(fast_gamma_data$Genotype, # Reordering genotype factor levels
  1305. levels = c("WT", "3xTg"))
  1307. fast_gamma_data$Age <- factor(fast_gamma_data$Age, # Reordering factor levels
  1308. levels = c("6", "8"))
  1310. fast_gamma_data <-subset(fast_gamma_data, Track =='all')
  1312. fast_gamma_data_6mo <-subset(fast_gamma_data, Age =='6')
  1313. fast_gamma_data_8mo <-subset(fast_gamma_data, Age =='8')
  1316. Power5 <- Powerbygroup_6mo(fast_gamma_data_6mo, Run_thresh_power, 'Hippocampus \n Fast Gamma Power (90-130 Hz)', "Power",0, 30)
  1317. Power6 <- Powerbygroup_8mo(fast_gamma_data_8mo, Run_thresh_power, 'Hippocampus \n Fast Gamma Power (90-130 Hz)', "Power",0, 30)
  1320. Power5
  1321. print("2 way Genotype x Layer ANOVA - 6mo")
  1322. PowerCohPhaseStats_2waygl(fast_gamma_data_6mo, Run_thresh_power, Layer_name, animals)
  1323. print("6 mo posthocs")
  1324. Posthocs_byage(fast_gamma_data_6mo, Run_thresh_power, Layer_name)
  1326. Power6
  1327. print("2 way Genotype x Layer ANOVA - 8mo")
  1328. PowerCohPhaseStats_2waygl(fast_gamma_data_8mo, Run_thresh_power, Layer_name, animals)
  1329. print("8 mo posthocs")
  1330. Posthocs_byage(fast_gamma_data_8mo, Run_thresh_power, Layer_name)
  1332. ```
  1334. ### Theta Power - MEC (Figure S4)
  1335. ```{r theta-power-m, echo=FALSE, warning = FALSE}
  1338. theta_data <- read.csv("Running_Power_theta_MEC.csv")
  1340. theta_data$Group_name <- factor(theta_data$Group_name , # Reordering factor levels
  1341. levels = c("6wt", "8wt", "63x", "83x"))
  1343. theta_data<-mutate(theta_data, Genotype = ifelse(Group_name =='6wt'|Group_name =='8wt', "WT", "3xTg"))
  1344. theta_data<-mutate(theta_data, Age = ifelse(Group_name =='6wt'|Group_name =='63x', 6, 8))
  1346. theta_data$Age <- factor(theta_data$Age, # Reordering factor levels
  1347. levels = c("6", "8"))
  1349. theta_data$Layer_name <- factor(theta_data$Layer_name, # Reordering region factor levels
  1350. levels = c("MEC2", "MEC3"))
  1351. theta_data$Genotype <- factor(theta_data$Genotype, # Reordering genotype factor levels
  1352. levels = c("WT", "3xTg"))
  1354. theta_data <-subset(theta_data, Track =='all')
  1356. theta_data_6mo <-subset(theta_data, Age =='6')
  1357. theta_data_8mo <-subset(theta_data, Age =='8')
  1360. Power7 <- Powerbygroup_6mo(theta_data_6mo, Run_thresh_power, 'MEC \n Theta Power (5-12 Hz)', "Power", 0, 80)
  1361. Power8 <- Powerbygroup_8mo(theta_data_8mo, Run_thresh_power, 'MEC \n Theta Power (5-12 Hz)', "Power", 0, 80)
  1362. Power7
  1363. Power8
  1365. MECthetastats <- function(MECtheta_age_df) {
  1367. df = substitute(MECtheta_age_df)
  1368. avar = substitute(Age_Broad)
  1369. gvar = substitute(Genotype)
  1370. lvar = substitute(Layer_name)
  1371. svar = substitute(animals)
  1372. yvar = substitute(Run_thresh_power)
  1374. #2 way
  1375. fm <- substitute(yvar ~ gvar *lvar + (1|svar), list(yvar=yvar, gvar= gvar, svar=svar, lvar=lvar))
  1377. model<- eval.parent(substitute(lmer(fm , REML = T, data = df), list(fm = fm, df =df)))
  1378. lm <- eval.parent(substitute(lmer(fm , REML = T, data = df), list(fm = fm, df =df))) %>% summary()
  1379. lm_aov <- eval.parent(substitute(lmer(fm, REML = T, data=df), list(fm=fm, df=df))) %>% anova()
  1381. emmtest <- eval.parent(emmeans(model, ~ Genotype * Layer_name))
  1382. posthoc_emmeans <- pairs(emmtest, simple = "Genotype") %>%
  1383. rbind() %>%
  1384. summary(adjust = "bonferroni")
  1386. posthoc_ttest <- eval.parent(substitute(df %>% dplyr::group_by(lvar) %>% t_test(yvar ~gvar, p.adjust.method = "none") %>% adjust_pvalue(method = "bonferroni"), list(df =df, lvar=lvar, yvar=yvar, gvar=gvar))) #THIS MATCHES PRISM!!
  1388. outlist <-list(lm, lm_aov, posthoc_emmeans, posthoc_ttest)
  1389. return(outlist)
  1391. }
  1393. MECthetastats(theta_data_6mo)
  1394. MECthetastats(theta_data_8mo)
  1397. ```
  1400. ```{r theta-gamma-power-export, echo=FALSE, warning = FALSE}
  1401. #export all power plots
  1402. plot_list = list(Power1, Power2, Power3, Power4, Power5, Power6, Power7, Power8)
  1403. ID = 1
  1405. for (p in plot_list) {
  1406. ggsave(
  1407. p,
  1408. filename=paste("Power",ID,".svg",sep=""),
  1409. width = 6,
  1410. height = 7,
  1411. dpi = 600)
  1412. ID = ID + 1
  1413. }
  1416. ```
  1418. ### Theta CSD - Hippocampus (Figure 3)
  1420. note:heatmaps were made in matlab
  1421. ```{r theta-csd-h, echo=FALSE, warning = FALSE}
  1423. CSDbygroup_6mo <- function(df_6mo, y, title, ytitle, ymin, ymax) {
  1425. plot <-ggplot(df_6mo, aes(x=Layer, y={{y}}))
  1426. plot + geom_line(aes(x = Layer, y = {{y}}, group = Genotype, colour=Genotype), stat="summary", fun.y = "mean", size = 1.2, alpha =1) +
  1427. geom_point(aes(x = Layer, y = {{y}}, group = Genotype, colour=Genotype), stat="summary", fun.y = "mean", alpha = 0.7, size = 0.7) +
  1428. stat_summary(fun.data = mean_se, geom = "pointrange", size = 0.7, alpha = 0.7, linewidth = 1.2,
  1429. aes(x=Layer, y={{y}}, group = Genotype, colour=Genotype), show.legend = FALSE) +
  1430. theme(panel.grid.major = element_blank(),
  1431. panel.grid.minor = element_blank(),
  1432. panel.background = element_blank(),
  1433. axis.line = element_line(colour = "black"),
  1434. plot.title = element_text(size = 22, face ="bold", hjust = 0.5),
  1435. axis.text=element_text(size=20, face="bold", colour = "black"),
  1436. axis.title=element_text(size=22, face="bold", colour = "black"),
  1437. legend.text=element_text(size=16),
  1438. legend.title=element_text(size=18),
  1439. strip.text.x = element_text(size = 18, face = "bold"),
  1440. strip.background = element_rect( fill="white"),
  1441. panel.spacing.x = unit(1.4, "lines"),
  1442. legend.key=element_rect(fill="white"),
  1443. strip.placement = "outside") +
  1444. ggtitle(title) + xlab("Layer") + ylab(ytitle)+
  1445. scale_color_manual("legend", values = scale_colour_palette_6mo, labels = c("WT 6 mo", "3xTg 6 mo"))+
  1446. coord_flip(ylim=c(ymin, ymax)) +
  1447. guides(colour=guide_legend("Group",override.aes = list(size = 4))) +
  1448. scale_alpha(guide = 'none')
  1450. }
  1452. CSDbygroup_8mo <- function(df_8mo, y, title, ytitle, ymin, ymax) {
  1454. plot <-ggplot(df_8mo, aes(x=Layer, y={{y}}))
  1455. plot + geom_line(aes(x = Layer, y = {{y}}, group = Genotype, colour=Genotype), stat="summary", fun.y = "mean", size = 1.2, alpha =1) +
  1456. geom_point(aes(x = Layer, y = {{y}}, group = Genotype, colour=Genotype), stat="summary", fun.y = "mean", alpha = 0.7, size = 0.7) +
  1457. stat_summary(fun.data = mean_se, geom = "pointrange", size = 0.7, alpha = 0.7, linewidth = 1.2,
  1458. aes(x=Layer, y={{y}}, group = Genotype, colour=Genotype), show.legend = FALSE) +
  1459. theme(panel.grid.major = element_blank(),
  1460. panel.grid.minor = element_blank(),
  1461. panel.background = element_blank(),
  1462. axis.line = element_line(colour = "black"),
  1463. plot.title = element_text(size = 22, face ="bold", hjust = 0.5),
  1464. axis.text=element_text(size=20, face="bold", colour = "black"),
  1465. axis.title=element_text(size=22, face="bold", colour = "black"),
  1466. legend.text=element_text(size=16),
  1467. legend.title=element_text(size=18),
  1468. strip.text.x = element_text(size = 18, face = "bold"),
  1469. strip.background = element_rect( fill="white"),
  1470. panel.spacing.x = unit(1.4, "lines"),
  1471. legend.key=element_rect(fill="white"),
  1472. strip.placement = "outside") +
  1473. ggtitle(title) + xlab("Layer") + ylab(ytitle)+
  1474. scale_color_manual("legend", values = scale_colour_palette_8mo, labels = c( "WT 8 mo", "3xTg 8 mo"))+
  1475. coord_flip(ylim=c(ymin, ymax)) +
  1476. guides(colour=guide_legend("Group",override.aes = list(size = 4))) +
  1477. scale_alpha(guide = 'none')
  1479. }
  1482. #mixed model to account for missing data - ultimately used prism for this (?)
  1483. CSDstats <- function(CSD_age_df) {
  1485. df = substitute(CSD_age_df)
  1486. avar = substitute(Age_Broad)
  1487. gvar = substitute(Genotype)
  1488. lvar = substitute(Layer)
  1489. svar = substitute(Animal)
  1490. yvar = substitute(Value)
  1492. #2 way
  1493. fm <- substitute(yvar ~ gvar *lvar + (1|svar), list(yvar=yvar, gvar= gvar, svar=svar, lvar=lvar))
  1495. model<- eval.parent(substitute(lmer(fm , REML = T, data = df), list(fm = fm, df =df)))
  1496. lm <- eval.parent(substitute(lmer(fm , REML = T, data = df), list(fm = fm, df =df))) %>% summary()
  1497. lm_aov <- eval.parent(substitute(lmer(fm, REML = T, data=df), list(fm=fm, df=df))) %>% anova()
  1499. emmtest <- eval.parent(emmeans(model, ~ Genotype * Layer))
  1500. posthoc_emmeans <- pairs(emmtest, simple = "Genotype") %>%
  1501. rbind() %>%
  1502. summary(adjust = "holm")
  1504. posthoc_ttest <- eval.parent(substitute(df %>% dplyr::group_by(lvar) %>% t_test(yvar ~gvar, p.adjust.method = "none") %>% adjust_pvalue(method = "holm"), list(df =df, lvar=lvar, yvar=yvar, gvar=gvar)))
  1506. outlist <-list(lm, lm_aov, posthoc_emmeans, posthoc_ttest)
  1507. return(outlist)
  1509. }
  1511. #
  1512. CSDstats_3way <- function(CSD_age_df) {
  1514. df = substitute(CSD_age_df)
  1515. sexvar = substitute(Sex)
  1516. gvar = substitute(Genotype)
  1517. lvar = substitute(Layer)
  1518. svar = substitute(Animal)
  1519. yvar = substitute(Value)
  1521. #3 way
  1522. fm <- substitute(yvar ~ gvar * sexvar *lvar + (1|svar), list(yvar=yvar, gvar= gvar, sexvar = sexvar, svar=svar, lvar = lvar))
  1524. model<- eval.parent(substitute(lmer(fm , REML = T, data = df), list(fm = fm, df =df)))
  1525. lm <- eval.parent(substitute(lmer(fm , REML = T, data = df), list(fm = fm, df =df))) %>% summary()
  1526. lm_aov <- eval.parent(substitute(lmer(fm, REML = T, data=df), list(fm=fm, df=df))) %>% anova()
  1528. outlist <-list(lm, lm_aov)
  1529. return(outlist)
  1531. }
  1533. CSD_data <- read.csv("CSDrunthreshoutput_HIPP_theta.csv")
  1535. CSD_data$Animal <- factor(CSD_data$Animal)
  1537. CSD_data <- gather(CSD_data, Layer, Value, Hil:Or, factor_key=TRUE) #convert data wide to long
  1539. CSD_data$Group <- factor(CSD_data$Group , # Reordering factor levels
  1540. # levels = c("6wt", "8wt", "63x", "83x"))
  1541. levels = c("6wt", "8wt", "63x", "83x"))
  1542. CSD_data<-mutate(CSD_data, Genotype = ifelse(Group =='6wt'|Group =='8wt', "WT", "3xTg"))
  1543. CSD_data<-mutate(CSD_data, Age_Broad = ifelse(Group =='6wt'|Group =='63x', 6, 8))
  1545. CSD_data$Age <- factor(CSD_data$Age_Broad, # Reordering factor levels
  1546. levels = c("6", "8"))
  1548. CSD_data$Layer <- factor(CSD_data$Layer, # Reordering region factor levels
  1549. levels = c("Hil", "GC", "Mol", "LM", "Rad", "Pyr", "Or"))
  1550. CSD_data$Genotype <- factor(CSD_data$Genotype, # Reordering genotype factor levels
  1551. levels = c("WT", "3xTg"))
  1553. CSD_data_maxbylyr_cycle = subset(CSD_data, DataType == 'maxbylyr_cycle')
  1555. CSD_data_maxbylyr_cycle_6mo = subset(CSD_data_maxbylyr_cycle, Age_Broad == '6')
  1556. CSD_data_maxbylyr_cycle_8mo = subset(CSD_data_maxbylyr_cycle, Age_Broad == '8')
  1559. ggsave(
  1560. "CSDtheta_6mo.svg",
  1561. CSDbygroup_6mo(CSD_data_maxbylyr_cycle_6mo, Value, 'CSD Magnitude', 'CSD Magnitude', 0, 100),
  1562. width = 6,
  1563. height = 7,
  1564. dpi = 600)
  1566. ggsave(
  1567. "CSDtheta_8mo.svg",
  1568. CSDbygroup_8mo(CSD_data_maxbylyr_cycle_8mo, Value, 'CSD Magnitude', 'CSD Magnitude', 0, 100),
  1569. width = 6,
  1570. height = 7,
  1571. dpi = 600)
  1573. CSDbygroup_6mo(CSD_data_maxbylyr_cycle_6mo, Value, 'CSD Magnitude', 'CSD Magnitude', 0, 100)
  1574. CSDbygroup_8mo(CSD_data_maxbylyr_cycle_8mo, Value, 'CSD Magnitude', 'CSD Magnitude', 0, 100)
  1576. print("used prism for CSD stats due to missing data")
  1578. print("3 way Sex x Genotype x Layer for CSD data from 8 mo animals")
  1579. CSDstats_3way(CSD_data_maxbylyr_cycle_8mo)
  1581. ```
  1584. ### Theta Coherence Within and Between MEC and Hippocampus (Figure 3, S4)
  1586. ```{r theta-coh, echo=FALSE, warning = FALSE}
  1588. Cohbygroup_6mo <- function(region_freq_statedf_6mo, y, title, ytitle, ymin, ymax) {
  1590. plot <-ggplot(region_freq_statedf_6mo, aes(x=Region2, y={{y}}))
  1591. plot + geom_line(aes(x = Region2, y = {{y}}, group = Genotype, colour=Genotype), stat="summary", fun.y = "mean", size = 1.2, alpha =1) +
  1592. geom_point(aes(x = Region2, y = {{y}}, group = Genotype, colour=Genotype), stat="summary", fun.y = "mean", alpha = 0.7, size = 0.7) +
  1593. stat_summary(fun.data = mean_se, geom = "pointrange", size = 0.7, alpha = 0.7, linewidth = 1.2,
  1594. aes(x=Region2, y={{y}}, group = Genotype, colour=Genotype), show.legend = FALSE) +
  1595. theme(panel.grid.major = element_blank(),
  1596. panel.grid.minor = element_blank(),
  1597. panel.background = element_blank(),
  1598. axis.line = element_line(colour = "black"),
  1599. plot.title = element_text(size = 22, face ="bold", hjust = 0.5),
  1600. axis.text=element_text(size=20, face="bold", colour = "black"),
  1601. axis.title=element_text(size=22, face="bold", colour = "black"),
  1602. legend.text=element_text(size=16),
  1603. legend.title=element_text(size=18),
  1604. strip.text.x = element_text(size = 18, face = "bold"),
  1605. strip.background = element_rect( fill="white"),
  1606. panel.spacing.x = unit(1.4, "lines"),
  1607. legend.key=element_rect(fill="white", colour = NA),
  1608. strip.placement = "outside") +
  1609. ggtitle(title) + xlab("Layer") + ylab(ytitle)+
  1610. scale_color_manual("legend", values = scale_colour_palette_6mo, labels = c("WT 6 mo", "3xTg 6 mo"))+
  1611. coord_flip(ylim=c(ymin, ymax)) +
  1612. guides(colour=guide_legend("Group",override.aes = list(size = 4))) +
  1613. labs(colour= "Group") +
  1614. scale_alpha(guide = 'none')
  1616. }
  1618. Cohbygroup_8mo <- function(region_freq_statedf_8mo, y, title, ytitle, ymin, ymax) {
  1620. plot <-ggplot(region_freq_statedf_8mo, aes(x=Region2, y={{y}}))
  1621. plot + geom_line(aes(x = Region2, y = {{y}}, group = Genotype, colour=Genotype), stat="summary", fun.y = "mean", size = 1.2, alpha =1) +
  1622. geom_point(aes(x = Region2, y = {{y}}, group = Genotype, colour=Genotype), stat="summary", fun.y = "mean", alpha = 0.7, size = 0.7) +
  1623. stat_summary(fun.data = mean_se, geom = "pointrange", size = 0.7, alpha = 0.7, linewidth = 1.2,
  1624. aes(x=Region2, y={{y}}, group = Genotype, colour=Genotype), show.legend = FALSE) +
  1625. theme(panel.grid.major = element_blank(),
  1626. panel.grid.minor = element_blank(),
  1627. panel.background = element_blank(),
  1628. axis.line = element_line(colour = "black"),
  1629. plot.title = element_text(size = 22, face ="bold", hjust = 0.5),
  1630. axis.text=element_text(size=20, face="bold", colour = "black"),
  1631. axis.title=element_text(size=22, face="bold", colour = "black"),
  1632. legend.text=element_text(size=16),
  1633. legend.title=element_text(size=18),
  1634. strip.text.x = element_text(size = 18, face = "bold"),
  1635. strip.background = element_rect( fill="white"),
  1636. panel.spacing.x = unit(1.4, "lines"),
  1637. legend.key=element_rect(fill="white", colour = NA),
  1638. strip.placement = "outside") +
  1639. ggtitle(title) + xlab("Layer") + ylab(ytitle)+
  1640. scale_color_manual("legend", values = scale_colour_palette_8mo, labels = c( "WT 8 mo", "3xTg 8 mo"))+
  1641. coord_flip(ylim=c(ymin, ymax)) +
  1642. guides(colour=guide_legend("Group",override.aes = list(size = 4))) +
  1643. labs(colour= "Group") +
  1644. scale_alpha(guide = 'none')
  1646. }
  1650. theta_data <- read.csv('Coherence_average_by_layer.csv')
  1652. theta_data$Group <- factor(theta_data$Group , # Reordering factor levels
  1653. levels = c("6wt", "8wt", "63x", "83x"))
  1654. theta_data$Region2 <- factor(theta_data$Region2 , # Reordering factor levels
  1655. levels = c("Hil", "GC", "Mol", "LM", "Rad" , "Pyr", "Or", "MEC2", "MEC3"))
  1657. theta_data$Region1 <- factor(theta_data$Region1 , # Reordering factor levels
  1658. levels = c("Hil", "GC", "Mol", "LM", "Rad" , "Pyr", "Or", "MEC2", "MEC3"))
  1660. theta_data <- mutate(theta_data, Genotype = ifelse(Group =='6wt'|Group =='8wt', "WT", "3xTg"))
  1661. theta_data<-mutate(theta_data, Age_Broad = ifelse(Group =='6wt'|Group =='63x', 6, 8))
  1663. theta_data$Age_Broad <- factor(theta_data$Age_Broad, # Reordering factor levels
  1664. levels = c("6", "8"))
  1666. theta_data$Genotype <- factor(theta_data$Genotype, # Reordering factor levels
  1667. levels = c("WT", "3xTg"))
  1669. theta_data <-mutate(theta_data, Speed_over_baseline = Avg_speed-Run_bl)
  1672. theta_data_runthresh <-subset(theta_data, Run_state == 'runthresh')
  1674. hil_theta_runthresh <- subset(theta_data_runthresh, Region1 == 'Hil')
  1675. gc_theta_runthresh <- subset(theta_data_runthresh, Region1 == 'GC')
  1676. mol_theta_runthresh <- subset(theta_data_runthresh, Region1 == 'Mol')
  1677. lm_theta_runthresh <- subset(theta_data_runthresh, Region1 == 'LM')
  1678. rad_theta_runthresh <- subset(theta_data_runthresh, Region1 == 'Rad')
  1679. pyr_theta_runthresh <- subset(theta_data_runthresh, Region1 == 'Pyr')
  1680. or_theta_runthresh <- subset(theta_data_runthresh, Region1 == 'Or')
  1681. mec2_theta_runthresh <- subset(theta_data_runthresh, Region1 == 'MEC2')
  1682. mec3_theta_runthresh <- subset(theta_data_runthresh, Region1 == 'MEC3')
  1684. mec2_theta_runthresh_HIPP = subset(mec2_theta_runthresh, Region2 != 'MEC3' & Region2 != 'MEC2') # get all MEC vs. HIPP rows
  1685. mec2_theta_runthresh_HIPP = subset(mec2_theta_runthresh_HIPP, Animal != 'AD-WT-44-1' & Animal != '3xTg132') #Exclude animals with missing MEC data
  1686. pyr_theta_runthresh_HIPP = subset(pyr_theta_runthresh, Region2 != 'MEC3' & Region2 != 'MEC2') #get within hippocampus coherence
  1688. #split by age group
  1689. mec2_theta_runthresh_HIPP_6mo = subset(mec2_theta_runthresh_HIPP, Age_Broad == '6')
  1690. mec2_theta_runthresh_HIPP_8mo = subset(mec2_theta_runthresh_HIPP, Age_Broad == '8')
  1691. pyr_theta_runthresh_HIPP_6mo = subset(pyr_theta_runthresh_HIPP, Age_Broad == '6')
  1692. pyr_theta_runthresh_HIPP_8mo = subset(pyr_theta_runthresh_HIPP, Age_Broad == '8')
  1694. Coh1 <-Cohbygroup_6mo(mec2_theta_runthresh_HIPP_6mo, Coh, 'Hippocampus \n Theta Coherence vs. MEC', 'Coherence', 0.5, 1)
  1695. Coh2 <-Cohbygroup_8mo(mec2_theta_runthresh_HIPP_8mo, Coh, 'Hippocampus \n Theta Coherence vs. MEC', 'Coherence', 0.5, 1)
  1697. Coh3 <-Cohbygroup_6mo(pyr_theta_runthresh_HIPP_6mo, Coh, 'Hippocampus \n Theta Coherence vs. Pyr', 'Coherence', 0.5, 1)
  1698. Coh4 <-Cohbygroup_8mo(pyr_theta_runthresh_HIPP_8mo, Coh, 'Hippocampus \n Theta Coherence vs. Pyr', 'Coherence', 0.5, 1)
  1701. plot_list = list(Coh1, Coh2, Coh3, Coh4)
  1702. ID = 1
  1704. for (p in plot_list) {
  1705. ggsave(
  1706. p,
  1707. filename=paste("Coh",ID,".svg",sep=""),
  1708. width = 6,
  1709. height = 7,
  1710. dpi = 600)
  1711. ID = ID + 1
  1712. }
  1714. Coh1
  1715. Coh2
  1718. print("Theta Coh vs MEC")
  1719. print("3 way Age x Genotype x Layer ANOVA")
  1720. PowerCohPhaseStats_3way(mec2_theta_runthresh_HIPP, Coh, Age_Broad, Region2, Animal)
  1722. mec2_theta_runthresh_HIPP_WT = subset(mec2_theta_runthresh_HIPP, Genotype == 'WT')
  1723. mec2_theta_runthresh_HIPP_Tg = subset(mec2_theta_runthresh_HIPP, Genotype == '3xTg')
  1724. print("2 way Genotype x Layer ANOVA - 6mo")
  1725. PowerCohPhaseStats_2waygl(mec2_theta_runthresh_HIPP_6mo, Coh, Region2, Animal)
  1726. print("2 way Genotype x Layer ANOVA - 8mo")
  1727. PowerCohPhaseStats_2waygl(mec2_theta_runthresh_HIPP_8mo, Coh, Region2, Animal)
  1728. #print("2 way Age x Layer ANOVA - WT")
  1729. #PowerCohPhaseStats_2wayal(mec2_theta_runthresh_HIPP_WT, Coh, Age_Broad, Region2, Animal)
  1730. #print("2 way Age x Layer ANOVA - 3xTg")
  1731. #PowerCohPhaseStats_2wayal(mec2_theta_runthresh_HIPP_Tg, Coh, Age_Broad, Region2, Animal)
  1732. print("6 mo posthocs")
  1733. Posthocs_byage(mec2_theta_runthresh_HIPP_6mo, Coh, Region2)
  1734. print("8 mo posthocs")
  1735. Posthocs_byage(mec2_theta_runthresh_HIPP_8mo, Coh, Region2)
  1738. print("8 mo Sex + Geontype x Layer ANOVA")
  1739. PowerCohPhaseStats_3waygls(mec2_theta_runthresh_HIPP_8mo, Coh, Region2, Animal)
  1743. Coh3
  1744. Coh4
  1747. print("Theta Coh vs Pyr")
  1748. print("3 way Age x Genotype x Layer ANOVA")
  1749. PowerCohPhaseStats_3way(pyr_theta_runthresh_HIPP, Coh, Age_Broad, Region2, Animal)
  1751. pyr_theta_runthresh_HIPP_WT = subset(pyr_theta_runthresh_HIPP, Genotype == 'WT')
  1752. pyr_theta_runthresh_HIPP_Tg = subset(pyr_theta_runthresh_HIPP, Genotype == '3xTg')
  1753. print("2 way Genotype x Layer ANOVA - 6mo")
  1754. PowerCohPhaseStats_2waygl(pyr_theta_runthresh_HIPP_6mo, Coh, Region2, Animal)
  1755. print("2 way Genotype x Layer ANOVA - 8mo")
  1756. PowerCohPhaseStats_2waygl(pyr_theta_runthresh_HIPP_8mo, Coh, Region2, Animal)
  1757. #print("2 way Age x Layer ANOVA - WT")
  1758. #PowerCohPhaseStats_2wayal(pyr_theta_runthresh_HIPP_WT, Coh, Age_Broad, Region2, Animal)
  1759. #print("2 way Age x Layer ANOVA - 3xTg")
  1760. #PowerCohPhaseStats_2wayal(pyr_theta_runthresh_HIPP_Tg, Coh, Age_Broad, Region2, Animal)
  1761. print("6 mo posthocs")
  1762. Posthocs_byage(pyr_theta_runthresh_HIPP_6mo, Coh, Region2)
  1763. print("8 mo posthocs")
  1764. Posthocs_byage(pyr_theta_runthresh_HIPP_8mo, Coh, Region2)
  1770. # Within MEC Coherence (Fig S4)
  1771. mec2_mec3_theta_runthresh <-subset(theta_data_runthresh, Region1 == 'MEC2' & Region2 == 'MEC3')
  1772. mec2_mec3_theta_runthresh = subset(mec2_mec3_theta_runthresh, Animal != 'AD-WT-44-1' & Animal != '3xTg132' & Animal != 'WT45-1')
  1773. #rename group variable so plotting function can find it
  1774. mec2_mec3_theta_runthresh <- mutate(mec2_mec3_theta_runthresh, Group_name = Group)
  1775. mec2_mec3_theta_runthresh <- mutate(mec2_mec3_theta_runthresh, Age = Age_Broad)
  1776. mec2_mec3_theta_runthresh$Group_name <- factor(mec2_mec3_theta_runthresh$Group_name,
  1777. levels =c("6wt", "8wt", "63x", "83x"))
  1778. mec2_mec3_theta_runthresh$Age <- factor(mec2_mec3_theta_runthresh$Age,
  1779. levels =c("6", "8"))
  1781. mec2_mec3_theta_runthresh$Sex <- factor(mec2_mec3_theta_runthresh$Sex, levels = c("F", "M"))
  1783. ggsave(
  1784. "MEC2vMEC3Coh.svg",
  1785. AnyPlotbygroupandsex_adjaxis_facet(mec2_mec3_theta_runthresh, Coh, 'Theta Coherence \n MEC2 vs MEC3', 'Coherence', 0.5, 1),
  1786. width = 4,
  1787. height = 5,
  1788. dpi = 600)
  1790. AnyPlotbygroupandsex_adjaxis_facet(mec2_mec3_theta_runthresh, Coh, 'Theta Coherence \n MEC2 vs MEC3', 'Coherence', 0.5, 1)
  1792. print("Theta Coh within MEC")
  1793. mec2_mec3_theta_runthresh <-subset(mec2_mec3_theta_runthresh, !(is.na(Coh)))
  1795. General_AOVbyanim_ez(mec2_mec3_theta_runthresh, Coh, Age_Broad, Animal)
  1798. ```
  1800. ### Main LFP findings plotted by running speed (Fig S3)
  1802. ```{r lfp-by-speed, echo=FALSE, warning = FALSE}
  1805. Plotbygroup_setaxislabels <- function(df, x, y, title, xtitle, ytitle, ymin, ymax) { #ymin, ymax
  1807. plot <-ggplot(df, aes(x={{x}}, y={{y}}))
  1808. plot + geom_line(aes(x = {{x}}, y = {{y}}, group = Group, colour=Group), stat="summary", fun.y = "mean", linewidth = 1.2, alpha =1) +
  1809. geom_point(aes(x={{x}}, y={{y}}, group = Group, colour=Group), stat="summary", fun.y = "mean", alpha = 0.7, size = 0.7) +
  1810. stat_summary(fun.data = mean_se, geom = "pointrange", size = 0.7, alpha = 0.7, linewidth = 1.2, aes(x={{x}}, y={{y}}, group = Group, colour=Group), show.legend = FALSE) +
  1811. theme(panel.grid.major = element_blank(),
  1812. panel.grid.minor = element_blank(),
  1813. panel.background = element_blank(),
  1814. axis.line = element_line(colour = "black"),
  1815. plot.title = element_text(size = 22, face ="bold", hjust = 0.5),
  1816. axis.text.x =element_text(size=16, face="bold", colour = "black"), #angle = 45, hjust = 1
  1817. axis.text.y=element_text(size=18, face="bold", colour = "black"),
  1818. axis.title=element_text(size=20, face="bold", colour = "black"),
  1819. legend.text=element_text(size=16),
  1820. legend.title=element_text(size=18),
  1821. legend.key = element_rect(fill = "white", color ="white"),
  1822. strip.text.x = element_text(size = 18, face = "bold"),
  1823. strip.background = element_rect( fill="white"),
  1824. strip.placement = "outside") +
  1825. ggtitle(title) + xlab(xtitle) + ylab(ytitle)+
  1826. scale_color_manual("legend", values = scale_colour_palette, labels = c("WT 6 mo", "WT 8 mo", "3xTg 6 mo","3xTg 8 mo"))+
  1827. guides(colour=guide_legend("Group", override.aes = list(size = 4))) +
  1828. #scale_color_manual(values = c("#509FE9","#1721A6","#E8B7EB", "#8817BD"), labels = c("WT 6 mo", "WT 8 mo", "3xTg 6 mo","3xTg 8 mo"))+
  1829. coord_cartesian(ylim=c(ymin, ymax)) +
  1830. #scale_x_continuous(breaks = numbreaks, labels=xlabels)
  1831. #coord_flip() +
  1832. labs(colour= "Group") +
  1833. scale_alpha(guide = 'none')
  1835. }
  1837. # theta power in LM by speed
  1838. data <-read.csv("Power_by_speed_theta_HIPP_LM.csv")
  1840. data <- mutate(data, Genotype = ifelse(Group =='6wt'|Group =='8wt', "WT", "3xTg"))
  1841. data <- mutate(data, Age_broad = ifelse(Group =='6wt'|Group =='63x', 6, 8))
  1843. data$Genotype <- factor(data$Genotype , # Reordering group factor levels
  1844. levels = c("WT", "3xTg"))
  1846. data$Age_broad <- factor(data$Age_broad, # Reordering group factor levels
  1847. levels = c("6", "8"))
  1849. data$Sex <- factor(data$Sex, # Reordering group factor levels
  1850. levels = c("F", "M"))
  1852. data$Group <- factor(data$Group, # Reordering group factor levels
  1853. levels = c("6wt", "8wt", "63x", "83x"))
  1856. data_wideLM<-data
  1857. data_wideLM <- data_wideLM %>%
  1858. dplyr::select(Animal, Genotype, Group, Sex, Age_broad, Power_in_range, Speed_bin_min)
  1859. data_wideLM <- dcast(data_wideLM , Animal + Genotype +Group +Sex + Age_broad ~ Speed_bin_min , value.var = "Power_in_range", na.rm=TRUE)
  1860. data_wideLM[data_wideLM == "NaN"] <- NA
  1862. data_longLM <- melt(data_wideLM, id = c("Animal", "Genotype", "Group", "Sex", "Age_broad" ))
  1863. colnames(data_longLM) <- c("Animal", "Genotype", "Group", "Sex", "Age_broad", "Speed_bin", "Power")
  1865. data_long_numLM <- mutate(data_longLM, Speed_bin = as.numeric(as.character(Speed_bin)))
  1867. data_long_subLM_0.7 <-subset(data_long_numLM, Speed_bin < 0.7)
  1868. LMPower_0.7 <- Plotbygroup_setaxislabels(data_long_subLM_0.7, Speed_bin, Power, "LM Power vs. Speed", "Speed (m/s)", "Theta Power", 0, 400)
  1869. LMPower_0.7 <- LMPower_0.7 + scale_x_continuous(breaks = seq(0, 0.6, 0.1), labels=c("0", "0.1", "0.2", "0.3", "0.4", "0.5", "0.6"))
  1870. LMPower_0.7
  1872. ggsave(
  1873. "LMPower_0.7.svg",
  1874. LMPower_0.7,
  1875. width = 7,
  1876. height = 5,
  1877. dpi = 600
  1878. )
  1880. data_long_subLM_0.7_fac <- data_long_subLM_0.7
  1881. #make speed bin a factor and set contrasts for type 3 anova
  1882. data_long_subLM_0.7_fac$Speed_bin <- factor(data_long_subLM_0.7_fac$Speed_bin)
  1883. contrasts(data_long_subLM_0.7_fac$Speed_bin) <- contr.sum
  1884. contrasts(data_long_subLM_0.7_fac$Group) <- contr.sum
  1886. #generate linear model
  1887. lmpowertest <- lm(Power ~ Group + Speed_bin + Group:Speed_bin, data = data_long_subLM_0.7_fac)
  1888. #get anova table
  1889. anova_test(lmpowertest, type = 3)
  1892. # plot time in each speed bin
  1893. data_wide_length <- data%>%
  1894. dplyr::select(Animal, Genotype, Group, Sex, Age_broad, Speed_bin_min, Length_time_s)
  1895. data_wide_length <- dcast(data_wide_length , Animal + Genotype +Group +Sex + Age_broad ~ Speed_bin_min , value.var = "Length_time_s", na.rm=TRUE)
  1896. data_wide_length [data_wide_length == "NaN"] <- NA
  1898. data_long_length <- melt(data_wide_length, id = c("Animal", "Genotype", "Group", "Sex", "Age_broad" ))
  1899. colnames(data_long_length) <- c("Animal", "Genotype", "Group", "Sex", "Age_broad", "Speed_bin", "Length_s")
  1901. data_long_length <- mutate(data_long_length , Speed_bin = as.numeric(as.character(Speed_bin)))
  1902. data_long_length_sub <- subset(data_long_length , Speed_bin < 1)
  1903. data_long_length_sub <- subset(data_long_length_sub , Speed_bin > 0)
  1904. LengthbySpeed <- Plotbygroup_setaxislabels(data_long_length_sub, Speed_bin, Length_s, "Time in Speed Bin", "Speed (m/s)", "Time (s)", 0, 500)
  1905. LengthbySpeed <- LengthbySpeed + scale_x_continuous(breaks = seq(0.1, 0.9, 0.1), labels=c("0.1", "0.2", "0.3", "0.4", "0.5", "0.6", "0.7", "0.8", "0.9"))
  1906. LengthbySpeed
  1908. ggsave(
  1909. "LengthbySpeed.svg",
  1910. LengthbySpeed ,
  1911. width = 7,
  1912. height = 5,
  1913. dpi = 600
  1914. )
  1916. data_long_length_sub_fac <- data_long_length_sub
  1917. #make speed bin a factor and set contrasts for type 3 anova
  1918. data_long_length_sub_fac$Speed_bin <- factor(data_long_length_sub_fac$Speed_bin)
  1919. contrasts(data_long_length_sub_fac$Speed_bin) <- contr.sum
  1920. contrasts(data_long_length_sub_fac$Group) <- contr.sum
  1922. #generate linear model
  1923. lengthtest <- lm(Length_s ~ Group + Speed_bin + Group:Speed_bin, data = data_long_length_sub_fac)
  1925. #get anova table
  1926. anova_test(lengthtest, type = 3) #Anova gives same p values
  1929. # plot LM CSD magnitude by speed bin
  1931. data <-read.csv("CSDrunoutput_byspeed_HIPP.csv")
  1933. data <- mutate(data, Genotype = ifelse(Group =='6wt'|Group =='8wt', "WT", "3xTg"))
  1934. data <- mutate(data, Age_broad = ifelse(Group =='6wt'|Group =='63x', 6, 8))
  1936. data$Genotype <- factor(data$Genotype , # Reordering group factor levels
  1937. levels = c("WT", "3xTg"))
  1939. data$Age_broad <- factor(data$Age_broad, # Reordering group factor levels
  1940. levels = c("6", "8"))
  1942. data$Sex <- factor(data$Sex, # Reordering group factor levels
  1943. levels = c("F", "M"))
  1945. data$Group <- factor(data$Group, # Reordering group factor levels
  1946. levels = c("6wt", "8wt", "63x", "83x"))
  1949. data <- subset(data, DataType == "maxbylyr_cycle")
  1951. data_wide<- mutate(data, Binmin= as.numeric(Binmin))
  1953. data_wide_LM <- data_wide %>%
  1954. dplyr::select(Animal, Genotype, Group, Sex, Age_broad, Binmin, LM)
  1955. data_wide_LM <- dcast(data_wide_LM , Animal + Genotype +Group +Sex + Age_broad ~ Binmin , value.var = "LM", na.rm=TRUE)
  1956. data_wide_LM [data_wide_LM == "NaN"] <- NA
  1958. data_long_LM <- melt(data_wide_LM , id = c("Animal", "Genotype", "Group", "Sex", "Age_broad" ))
  1959. colnames(data_long_LM ) <- c("Animal", "Genotype", "Group", "Sex", "Age_broad", "Speed_bin", "CSD_Mag_LM")
  1961. data_long_LM_num <- mutate(data_long_LM, Speed_bin = as.numeric(as.character(Speed_bin)))
  1962. data_long_LM_sub <-subset(data_long_LM_num, Speed_bin < 0.7)
  1963. LMCSD <- Plotbygroup_setaxislabels(data_long_LM_sub, Speed_bin, CSD_Mag_LM, "LM CSD Magnitude vs. Speed", "Speed (m/s)", "Theta CSD Mag", 0, 100)
  1964. LMCSD <- LMCSD + scale_x_continuous(breaks = seq(0, 0.6, 0.1), labels=c("0", "0.1", "0.2", "0.3", "0.4", "0.5", "0.6"))
  1965. LMCSD
  1967. ggsave(
  1968. "LMCSD.svg",
  1969. LMCSD,
  1970. width = 7,
  1971. height = 5,
  1972. dpi = 600
  1973. )
  1976. data_long_LM_sub_fac <- data_long_LM_sub
  1977. #make speed bin a factor and set contrasts for type 3 anova
  1978. data_long_LM_sub_fac$Speed_bin <- factor(data_long_LM_sub_fac$Speed_bin)
  1979. contrasts(data_long_LM_sub_fac$Speed_bin) <- contr.sum
  1980. contrasts(data_long_LM_sub_fac$Group) <- contr.sum
  1982. #generate linear model
  1983. lmcsd<- lm(CSD_Mag_LM ~ Group + Speed_bin + Group:Speed_bin, data = data_long_LM_sub_fac)
  1985. #get anova table
  1986. anova_test(lmcsd, type = 3) #Anova gives same p values
  1990. # MEC vs CA1 coherence by speed
  1991. #load data
  1992. coh_data <- read.csv("Coherence_byspeed_PyrvMEC2.csv")
  1994. #set up grouping variables
  1995. coh_data <- mutate(coh_data, Genotype = ifelse(Group =='6wt'|Group =='8wt', "WT", "3xTg"))
  1996. coh_data <- mutate(coh_data, Age_broad = ifelse(Group =='6wt'|Group =='63x', 6, 8))
  1998. coh_data$Genotype <- factor(coh_data$Genotype , # Reordering group factor levels
  1999. levels = c("WT", "3xTg"))
  2001. coh_data$Age_broad <- factor(coh_data$Age_broad, # Reordering group factor levels
  2002. levels = c("6", "8"))
  2004. coh_data$Sex <- factor(coh_data$Sex, # Reordering group factor levels
  2005. levels = c("F", "M"))
  2007. coh_data$Group <- factor(coh_data$Group, # Reordering group factor levels
  2008. levels = c("6wt", "8wt", "63x", "83x"))
  2011. coh_data<- mutate(coh_data, RunThresh_Low= as.numeric(RunThresh_Low))
  2013. #make data frame with lengths of windows in each speed threshold
  2014. coh_data_wide_length <- coh_data %>%
  2015. dplyr::select(Animal, Genotype, Group, Sex, Age_broad, RunThresh_Low, Length_Run_Thresh)
  2016. coh_data_wide_length <- dcast(coh_data_wide_length , Animal + Genotype +Group +Sex + Age_broad ~ RunThresh_Low , value.var = "Length_Run_Thresh", na.rm=TRUE)
  2017. coh_data_wide_length [coh_data_wide_length == "NaN"] <- NA
  2019. coh_data_long_length <- melt(coh_data_wide_length, id = c("Animal", "Genotype", "Group", "Sex", "Age_broad" ))
  2020. colnames(coh_data_long_length) <- c("Animal", "Genotype", "Group", "Sex", "Age_broad", "Speed_bin", "Length_s")
  2023. #make data frame with coherence at each speed threshold
  2024. coh_data_wide<- coh_data %>%
  2025. dplyr::select(Animal, Genotype, Group, Sex, Age_broad, RunThresh_Low, Coh_RunThresh)
  2026. coh_data_wide <- dcast(coh_data_wide , Animal + Genotype +Group +Sex + Age_broad ~ RunThresh_Low , value.var = "Coh_RunThresh", na.rm=TRUE)
  2027. coh_data_wide [coh_data_wide == "NaN"] <- NA
  2029. coh_data_long <- melt(coh_data_wide, id = c("Animal", "Genotype", "Group", "Sex", "Age_broad" ))
  2030. colnames(coh_data_long) <- c("Animal", "Genotype", "Group", "Sex", "Age_broad", "Speed_bin", "Coh")
  2033. coh_data_long <- mutate(coh_data_long , Speed_bin = as.numeric(as.character(Speed_bin)))
  2034. coh_data_long_sub <- subset(coh_data_long , Speed_bin < 0.7)
  2035. CohbySpeed <- Plotbygroup_setaxislabels(coh_data_long_sub, Speed_bin, Coh, "MEC-CA1 \n Coherence vs. Speed ", "Speed (m/s)", "MEC-CA1 Theta Coh ", 0.5, 1)
  2036. CohbySpeed<- CohbySpeed + scale_x_continuous(breaks = seq(0, 0.6, 0.1), labels=c("0", "0.1", "0.2", "0.3", "0.4", "0.5", "0.6"))
  2037. CohbySpeed
  2039. ggsave(
  2040. "MEC-CA1CohbySpeed.svg",
  2041. CohbySpeed ,
  2042. width = 7,
  2043. height = 5,
  2044. dpi = 600
  2045. )
  2047. coh_data_long_sub_fac <- coh_data_long_sub
  2048. #make speed bin a factor and set contrasts for type 3 anova
  2049. coh_data_long_sub_fac$Speed_bin <- factor(coh_data_long_sub_fac$Speed_bin)
  2050. contrasts(coh_data_long_sub_fac$Speed_bin) <- contr.sum
  2051. contrasts(coh_data_long_sub_fac$Group) <- contr.sum
  2053. #generate linear model
  2054. mecca1cohtest <- lm(Coh~ Group + Speed_bin + Group:Speed_bin, data = coh_data_long_sub_fac)
  2056. #get anova table
  2057. anova_test(mecca1cohtest, type = 3) #Anova gives same p values
  2059. ```
  2062. ### Theta Frequency (PSD) (Figure S6)
  2064. ```{r theta-psd, echo=FALSE, warning = FALSE}
  2066. PeakFreq_bygroupandsex_final <- function(df, y, title, ytitle, ymin, ymax) {
  2067. plot <-ggplot(df ,aes(x=Groupname, y={{y}}))
  2068. plot <-ggplot(df ,aes(x=Genotype, y={{y}}))
  2069. plot +geom_sina(aes(col= Groupname, shape = Sex), alpha = 0.5, na.rm = TRUE, show.legend=TRUE, jitter_y = FALSE) +
  2070. geom_bar(position= "dodge", stat = "summary", width = 0.9, fun.y = "mean", alpha = 0.4, aes(fill=Groupname), color = "black") +
  2071. stat_summary( color = "black", fun.data = mean_se, geom = "errorbar", width = 0.4, alpha = 0.8, show.legend=FALSE) +
  2072. ggtitle(title) + ylab(ytitle) + # + xlab("Age”)+
  2073. scale_fill_manual("legend", values = scale_fill_palette, labels = c("WT 6 mo", "WT 8 mo", "3xTg 6 mo", "3xTg 8 mo"))+
  2074. guides(fill=guide_legend("Group")) +
  2075. scale_color_manual("legend", values = scale_colour_palette, guide = "none")+
  2076. scale_y_continuous(expand = c(0,0)) + #, limits = c(ymin,ymax), breaks = seq(0, 14, by = 2))
  2077. coord_cartesian(ylim=c(ymin, ymax))+
  2078. scale_alpha(guide = 'none') +
  2079. facet_grid(.~Age_Broad, switch = "x", labeller = as_labeller(c('6' = "6 mo", '8' = "8 mo"))) +
  2080. theme(plot.title = element_text(hjust = 0.5, size = 22, face = "bold"),
  2081. axis.text.x = element_text(size = 16, colour = "black"),
  2082. axis.text.y = element_text(size = 18, face="bold", colour = "black"),
  2083. axis.title.y = element_text(size = 20, face = "bold", colour = "black"),
  2084. axis.title.x = element_blank(),
  2085. legend.text=element_text(size=12),
  2086. legend.title=element_text(size=14),
  2087. panel.grid.major = element_blank(),
  2088. panel.grid.minor = element_blank(),
  2089. panel.background = element_blank(),
  2090. axis.line = element_line(colour = "black"),
  2091. strip.text.x = element_text(size = 18, face = "bold"),
  2092. strip.background = element_rect( fill="white"),
  2093. panel.spacing.x = unit(1.4, "lines"),
  2094. strip.placement = "outside")
  2095. }
  2098. #Oriens
  2100. data <- readMat("WaveletPSD_trackAlyrOr_theta.mat")
  2102. data <- data[[1]]
  2104. animals = data[[1]]
  2105. animals<- matrix(unlist(animals),byrow=TRUE)
  2107. group = data[[2]]
  2108. group<- matrix(unlist(group),byrow=TRUE)
  2110. sex = data[[3]]
  2111. sex<- matrix(unlist(sex),byrow=TRUE)
  2113. age = data[[4]]
  2114. age<- matrix(unlist(age),byrow=TRUE)
  2116. ###
  2118. Peakfreq_runthresh = data[[22]]
  2119. Peakfreq_runthresh<- unlist(Peakfreq_runthresh,recursive=FALSE)
  2120. Peakfreq_runthresh <- t(as.data.frame(do.call(cbind, Peakfreq_runthresh)))
  2122. Peakfreq_runthresh_df<- cbind(animals, group, sex, age, Peakfreq_runthresh)
  2124. colnames(Peakfreq_runthresh_df) <- c("Animal" ,"Groupname", "Sex", "Age", "PeakThetaFreq")
  2126. Peakfreq_runthresh_df <- as.data.frame(Peakfreq_runthresh_df)
  2129. Peakfreq_runthresh_df$Groupname<- factor(Peakfreq_runthresh_df$Groupname, # Reordering group factor levels
  2130. levels = c("6wt", "8wt", "63x", "83x"))
  2133. Peakfreq_runthresh_df$PeakThetaFreq<- as.numeric(Peakfreq_runthresh_df$PeakThetaFreq)
  2136. #add for stats
  2137. Peakfreq_runthresh_df <- mutate(Peakfreq_runthresh_df, Genotype = ifelse(Groupname =='6wt'|Groupname =='8wt', "WT", "3xTg"))
  2138. Peakfreq_runthresh_df<-mutate(Peakfreq_runthresh_df, Age_Broad = ifelse(Groupname =='6wt'|Groupname =='63x', 6, 8))
  2140. Peakfreq_runthresh_df$Genotype <- factor(Peakfreq_runthresh_df$Genotype ,
  2141. levels = c("WT", "3xTg"))
  2143. Peakfreq_runthresh_df$Age_Broad <- factor(Peakfreq_runthresh_df$Age_Broad,
  2144. levels = c("6", "8"))
  2149. PeakFreq_bygroupandsex_final(Peakfreq_runthresh_df, PeakThetaFreq, 'Peak Theta (5-12 Hz) \n Frequency Oriens', 'Frequency (Hz)', 0, 8.5)
  2151. General_AOVbyanim_ez(Peakfreq_runthresh_df, PeakThetaFreq, Age_Broad, Animal)
  2152. General_byanim_holm(Peakfreq_runthresh_df, PeakThetaFreq, Age_Broad)
  2155. ggsave(
  2156. "PSDfreqbygroupOr_sex.svg",
  2157. PeakFreq_bygroupandsex_final(Peakfreq_runthresh_df, PeakThetaFreq, 'Peak Theta (5-12 Hz) \n Frequency Oriens', 'Frequency (Hz)', 0, 8.5),
  2158. width = 5,
  2159. height = 5,
  2160. dpi = 600
  2161. )
  2165. #PYRAMIDAL LAYER
  2167. data <- readMat("WaveletPSD_trackAlyrPyr_theta.mat")
  2169. data <- data[[1]]
  2171. animals = data[[1]]
  2172. animals<- matrix(unlist(animals),byrow=TRUE)
  2174. group = data[[2]]
  2175. group<- matrix(unlist(group),byrow=TRUE)
  2177. sex = data[[3]]
  2178. sex<- matrix(unlist(sex),byrow=TRUE)
  2180. age = data[[4]]
  2181. age<- matrix(unlist(age),byrow=TRUE)
  2183. ###
  2185. Peakfreq_runthresh = data[[22]]
  2186. Peakfreq_runthresh<- unlist(Peakfreq_runthresh,recursive=FALSE)
  2187. Peakfreq_runthresh <- t(as.data.frame(do.call(cbind, Peakfreq_runthresh)))
  2189. Peakfreq_runthresh_df<- cbind(animals, group, sex, age, Peakfreq_runthresh)
  2191. colnames(Peakfreq_runthresh_df) <- c("Animal" ,"Groupname", "Sex", "Age", "PeakThetaFreq")
  2193. Peakfreq_runthresh_df <- as.data.frame(Peakfreq_runthresh_df)
  2196. Peakfreq_runthresh_df$Groupname<- factor(Peakfreq_runthresh_df$Groupname, # Reordering group factor levels
  2197. levels = c("6wt", "8wt", "63x", "83x"))
  2200. Peakfreq_runthresh_df$PeakThetaFreq<- as.numeric(Peakfreq_runthresh_df$PeakThetaFreq)
  2203. #add for stats
  2204. Peakfreq_runthresh_df <- mutate(Peakfreq_runthresh_df, Genotype = ifelse(Groupname =='6wt'|Groupname =='8wt', "WT", "3xTg"))
  2205. Peakfreq_runthresh_df<-mutate(Peakfreq_runthresh_df, Age_Broad = ifelse(Groupname =='6wt'|Groupname =='63x', 6, 8))
  2207. Peakfreq_runthresh_df$Genotype <- factor(Peakfreq_runthresh_df$Genotype ,
  2208. levels = c("WT", "3xTg"))
  2210. Peakfreq_runthresh_df$Age_Broad <- factor(Peakfreq_runthresh_df$Age_Broad,
  2211. levels = c("6", "8"))
  2216. PeakFreq_bygroupandsex_final(Peakfreq_runthresh_df, PeakThetaFreq, 'Peak Theta (5-12 Hz) \n Frequency Pyr', 'Frequency (Hz)', 0, 8.5)
  2218. General_AOVbyanim_ez(Peakfreq_runthresh_df, PeakThetaFreq, Age_Broad, Animal)
  2219. General_byanim_holm(Peakfreq_runthresh_df, PeakThetaFreq, Age_Broad)
  2222. ggsave(
  2223. "PSDfreqbygroupPyr_sex.svg",
  2224. PeakFreq_bygroupandsex_final(Peakfreq_runthresh_df, PeakThetaFreq, 'Peak Theta (5-12 Hz) \n Frequency Pyr', 'Frequency (Hz)', 0, 8.5),
  2225. width = 5,
  2226. height = 5,
  2227. dpi = 600
  2228. )
  2233. #RADIATUM
  2234. data <- readMat("WaveletPSD_trackAlyrRad_theta.mat")
  2236. data <- data[[1]]
  2238. animals = data[[1]]
  2239. animals<- matrix(unlist(animals),byrow=TRUE)
  2241. group = data[[2]]
  2242. group<- matrix(unlist(group),byrow=TRUE)
  2244. sex = data[[3]]
  2245. sex<- matrix(unlist(sex),byrow=TRUE)
  2247. age = data[[4]]
  2248. age<- matrix(unlist(age),byrow=TRUE)
  2250. ###
  2252. Peakfreq_runthresh = data[[22]]
  2253. Peakfreq_runthresh<- unlist(Peakfreq_runthresh,recursive=FALSE)
  2254. Peakfreq_runthresh <- t(as.data.frame(do.call(cbind, Peakfreq_runthresh)))
  2256. Peakfreq_runthresh_df<- cbind(animals, group, sex, age, Peakfreq_runthresh)
  2258. colnames(Peakfreq_runthresh_df) <- c("Animal" ,"Groupname", "Sex", "Age", "PeakThetaFreq")
  2260. Peakfreq_runthresh_df <- as.data.frame(Peakfreq_runthresh_df)
  2263. Peakfreq_runthresh_df$Groupname<- factor(Peakfreq_runthresh_df$Groupname, # Reordering group factor levels
  2264. levels = c("6wt", "8wt", "63x", "83x"))
  2267. Peakfreq_runthresh_df$PeakThetaFreq<- as.numeric(Peakfreq_runthresh_df$PeakThetaFreq)
  2270. #add for stats
  2271. Peakfreq_runthresh_df <- mutate(Peakfreq_runthresh_df, Genotype = ifelse(Groupname =='6wt'|Groupname =='8wt', "WT", "3xTg"))
  2272. Peakfreq_runthresh_df<-mutate(Peakfreq_runthresh_df, Age_Broad = ifelse(Groupname =='6wt'|Groupname =='63x', 6, 8))
  2274. Peakfreq_runthresh_df$Genotype <- factor(Peakfreq_runthresh_df$Genotype ,
  2275. levels = c("WT", "3xTg"))
  2277. Peakfreq_runthresh_df$Age_Broad <- factor(Peakfreq_runthresh_df$Age_Broad,
  2278. levels = c("6", "8"))
  2283. PeakFreq_bygroupandsex_final(Peakfreq_runthresh_df, PeakThetaFreq, 'Peak Theta (5-12 Hz) \n Frequency Rad', 'Frequency (Hz)', 0, 8.5)
  2285. General_AOVbyanim_ez(Peakfreq_runthresh_df, PeakThetaFreq, Age_Broad, Animal)
  2286. General_byanim_holm(Peakfreq_runthresh_df, PeakThetaFreq, Age_Broad)
  2289. ggsave(
  2290. "PSDfreqbygroupRad_sex.svg",
  2291. PeakFreq_bygroupandsex_final(Peakfreq_runthresh_df, PeakThetaFreq, 'Peak Theta (5-12 Hz) \n Frequency Rad', 'Frequency (Hz)', 0, 8.5),
  2292. width = 5,
  2293. height = 5,
  2294. dpi = 600
  2295. )
  2301. ## LACUNOSUM MOLECULARE
  2302. data <- readMat("WaveletPSD_trackAlyrLM_theta.mat")
  2304. data <- data[[1]]
  2306. animals = data[[1]]
  2307. animals<- matrix(unlist(animals),byrow=TRUE)
  2309. group = data[[2]]
  2310. group<- matrix(unlist(group),byrow=TRUE)
  2312. sex = data[[3]]
  2313. sex<- matrix(unlist(sex),byrow=TRUE)
  2315. age = data[[4]]
  2316. age<- matrix(unlist(age),byrow=TRUE)
  2318. ###
  2320. Peakfreq_runthresh = data[[22]]
  2321. Peakfreq_runthresh<- unlist(Peakfreq_runthresh,recursive=FALSE)
  2322. Peakfreq_runthresh <- t(as.data.frame(do.call(cbind, Peakfreq_runthresh)))
  2324. Peakfreq_runthresh_df<- cbind(animals, group, sex, age, Peakfreq_runthresh)
  2326. colnames(Peakfreq_runthresh_df) <- c("Animal" ,"Groupname", "Sex", "Age", "PeakThetaFreq")
  2328. Peakfreq_runthresh_df <- as.data.frame(Peakfreq_runthresh_df)
  2331. Peakfreq_runthresh_df$Groupname<- factor(Peakfreq_runthresh_df$Groupname, # Reordering group factor levels
  2332. levels = c("6wt", "8wt", "63x", "83x"))
  2335. Peakfreq_runthresh_df$PeakThetaFreq<- as.numeric(Peakfreq_runthresh_df$PeakThetaFreq)
  2338. #add for stats
  2339. Peakfreq_runthresh_df <- mutate(Peakfreq_runthresh_df, Genotype = ifelse(Groupname =='6wt'|Groupname =='8wt', "WT", "3xTg"))
  2340. Peakfreq_runthresh_df<-mutate(Peakfreq_runthresh_df, Age_Broad = ifelse(Groupname =='6wt'|Groupname =='63x', 6, 8))
  2342. Peakfreq_runthresh_df$Genotype <- factor(Peakfreq_runthresh_df$Genotype ,
  2343. levels = c("WT", "3xTg"))
  2345. Peakfreq_runthresh_df$Age_Broad <- factor(Peakfreq_runthresh_df$Age_Broad,
  2346. levels = c("6", "8"))
  2351. PeakFreq_bygroupandsex_final(Peakfreq_runthresh_df, PeakThetaFreq, 'Peak Theta (5-12 Hz) \n Frequency LM', 'Frequency (Hz)', 0, 8.5)
  2353. General_AOVbyanim_ez(Peakfreq_runthresh_df, PeakThetaFreq, Age_Broad, Animal)
  2354. General_byanim_holm(Peakfreq_runthresh_df, PeakThetaFreq, Age_Broad)
  2357. ggsave(
  2358. "PSDfreqbygroupLM_sex.svg",
  2359. PeakFreq_bygroupandsex_final(Peakfreq_runthresh_df, PeakThetaFreq, 'Peak Theta (5-12 Hz) \n Frequency LM', 'Frequency (Hz)', 0, 8.5),
  2360. width = 5,
  2361. height = 5,
  2362. dpi = 600
  2363. )
  2368. #MOLECULAR LAYER
  2369. data <- readMat("WaveletPSD_trackAlyrMol_theta.mat")
  2371. data <- data[[1]]
  2373. animals = data[[1]]
  2374. animals<- matrix(unlist(animals),byrow=TRUE)
  2376. group = data[[2]]
  2377. group<- matrix(unlist(group),byrow=TRUE)
  2379. sex = data[[3]]
  2380. sex<- matrix(unlist(sex),byrow=TRUE)
  2382. age = data[[4]]
  2383. age<- matrix(unlist(age),byrow=TRUE)
  2385. ###
  2387. Peakfreq_runthresh = data[[22]]
  2388. Peakfreq_runthresh<- unlist(Peakfreq_runthresh,recursive=FALSE)
  2389. Peakfreq_runthresh <- t(as.data.frame(do.call(cbind, Peakfreq_runthresh)))
  2391. Peakfreq_runthresh_df<- cbind(animals, group, sex, age, Peakfreq_runthresh)
  2393. colnames(Peakfreq_runthresh_df) <- c("Animal" ,"Groupname", "Sex", "Age", "PeakThetaFreq")
  2395. Peakfreq_runthresh_df <- as.data.frame(Peakfreq_runthresh_df)
  2398. Peakfreq_runthresh_df$Groupname<- factor(Peakfreq_runthresh_df$Groupname, # Reordering group factor levels
  2399. levels = c("6wt", "8wt", "63x", "83x"))
  2402. Peakfreq_runthresh_df$PeakThetaFreq<- as.numeric(Peakfreq_runthresh_df$PeakThetaFreq)
  2405. #add for stats
  2406. Peakfreq_runthresh_df <- mutate(Peakfreq_runthresh_df, Genotype = ifelse(Groupname =='6wt'|Groupname =='8wt', "WT", "3xTg"))
  2407. Peakfreq_runthresh_df<-mutate(Peakfreq_runthresh_df, Age_Broad = ifelse(Groupname =='6wt'|Groupname =='63x', 6, 8))
  2409. Peakfreq_runthresh_df$Genotype <- factor(Peakfreq_runthresh_df$Genotype ,
  2410. levels = c("WT", "3xTg"))
  2412. Peakfreq_runthresh_df$Age_Broad <- factor(Peakfreq_runthresh_df$Age_Broad,
  2413. levels = c("6", "8"))
  2418. PeakFreq_bygroupandsex_final(Peakfreq_runthresh_df, PeakThetaFreq, 'Peak Theta (5-12 Hz) \n Frequency Mol', 'Frequency (Hz)', 0, 8.5)
  2420. General_AOVbyanim_ez(Peakfreq_runthresh_df, PeakThetaFreq, Age_Broad, Animal)
  2421. General_byanim_holm(Peakfreq_runthresh_df, PeakThetaFreq, Age_Broad)
  2424. ggsave(
  2425. "PSDfreqbygroupMol_sex.svg",
  2426. PeakFreq_bygroupandsex_final(Peakfreq_runthresh_df, PeakThetaFreq, 'Peak Theta (5-12 Hz) \n Frequency Mol', 'Frequency (Hz)', 0, 8.5),
  2427. width = 5,
  2428. height = 5,
  2429. dpi = 600
  2430. )
  2435. #HILUS
  2436. data <- readMat("WaveletPSD_trackAlyrHil_theta.mat")
  2438. data <- data[[1]]
  2440. animals = data[[1]]
  2441. animals<- matrix(unlist(animals),byrow=TRUE)
  2443. group = data[[2]]
  2444. group<- matrix(unlist(group),byrow=TRUE)
  2446. sex = data[[3]]
  2447. sex<- matrix(unlist(sex),byrow=TRUE)
  2449. age = data[[4]]
  2450. age<- matrix(unlist(age),byrow=TRUE)
  2452. ###
  2454. Peakfreq_runthresh = data[[22]]
  2455. Peakfreq_runthresh<- unlist(Peakfreq_runthresh,recursive=FALSE)
  2456. Peakfreq_runthresh <- t(as.data.frame(do.call(cbind, Peakfreq_runthresh)))
  2458. Peakfreq_runthresh_df<- cbind(animals, group, sex, age, Peakfreq_runthresh)
  2460. colnames(Peakfreq_runthresh_df) <- c("Animal" ,"Groupname", "Sex", "Age", "PeakThetaFreq")
  2462. Peakfreq_runthresh_df <- as.data.frame(Peakfreq_runthresh_df)
  2465. Peakfreq_runthresh_df$Groupname<- factor(Peakfreq_runthresh_df$Groupname, # Reordering group factor levels
  2466. levels = c("6wt", "8wt", "63x", "83x"))
  2469. Peakfreq_runthresh_df$PeakThetaFreq<- as.numeric(Peakfreq_runthresh_df$PeakThetaFreq)
  2472. #add for stats
  2473. Peakfreq_runthresh_df <- mutate(Peakfreq_runthresh_df, Genotype = ifelse(Groupname =='6wt'|Groupname =='8wt', "WT", "3xTg"))
  2474. Peakfreq_runthresh_df<-mutate(Peakfreq_runthresh_df, Age_Broad = ifelse(Groupname =='6wt'|Groupname =='63x', 6, 8))
  2476. Peakfreq_runthresh_df$Genotype <- factor(Peakfreq_runthresh_df$Genotype ,
  2477. levels = c("WT", "3xTg"))
  2479. Peakfreq_runthresh_df$Age_Broad <- factor(Peakfreq_runthresh_df$Age_Broad,
  2480. levels = c("6", "8"))
  2484. PeakFreq_bygroupandsex_final(Peakfreq_runthresh_df, PeakThetaFreq, 'Peak Theta (5-12 Hz) \n Frequency Hil', 'Frequency (Hz)', 0, 8.5)
  2486. General_AOVbyanim_ez(Peakfreq_runthresh_df, PeakThetaFreq, Age_Broad, Animal)
  2487. General_byanim_holm(Peakfreq_runthresh_df, PeakThetaFreq, Age_Broad)
  2490. ggsave(
  2491. "PSDfreqbygroupHil_sex.svg",
  2492. PeakFreq_bygroupandsex_final(Peakfreq_runthresh_df, PeakThetaFreq, 'Peak Theta (5-12 Hz) \n Frequency Hil', 'Frequency (Hz)', 0, 8.5),
  2493. width = 5,
  2494. height = 5,
  2495. dpi = 600
  2496. )
  2501. #MEC2
  2502. data <- readMat("WaveletPSD_trackAlyrMEC2_theta.mat")
  2504. data <- data[[1]]
  2506. animals = data[[1]]
  2507. animals<- matrix(unlist(animals),byrow=TRUE)
  2509. group = data[[2]]
  2510. group<- matrix(unlist(group),byrow=TRUE)
  2512. sex = data[[3]]
  2513. sex<- matrix(unlist(sex),byrow=TRUE)
  2515. age = data[[4]]
  2516. age<- matrix(unlist(age),byrow=TRUE)
  2518. ###
  2520. Peakfreq_runthresh = data[[22]]
  2521. Peakfreq_runthresh<- unlist(Peakfreq_runthresh,recursive=FALSE)
  2522. Peakfreq_runthresh <- t(as.data.frame(do.call(cbind, Peakfreq_runthresh)))
  2524. Peakfreq_runthresh_df<- cbind(animals, group, sex, age, Peakfreq_runthresh)
  2526. colnames(Peakfreq_runthresh_df) <- c("Animal" ,"Groupname", "Sex", "Age", "PeakThetaFreq")
  2528. Peakfreq_runthresh_df <- as.data.frame(Peakfreq_runthresh_df)
  2531. Peakfreq_runthresh_df$Groupname<- factor(Peakfreq_runthresh_df$Groupname, # Reordering group factor levels
  2532. levels = c("6wt", "8wt", "63x", "83x"))
  2535. Peakfreq_runthresh_df$PeakThetaFreq<- as.numeric(Peakfreq_runthresh_df$PeakThetaFreq)
  2538. #add for stats
  2539. Peakfreq_runthresh_df <- mutate(Peakfreq_runthresh_df, Genotype = ifelse(Groupname =='6wt'|Groupname =='8wt', "WT", "3xTg"))
  2540. Peakfreq_runthresh_df<-mutate(Peakfreq_runthresh_df, Age_Broad = ifelse(Groupname =='6wt'|Groupname =='63x', 6, 8))
  2542. Peakfreq_runthresh_df$Genotype <- factor(Peakfreq_runthresh_df$Genotype ,
  2543. levels = c("WT", "3xTg"))
  2545. Peakfreq_runthresh_df$Age_Broad <- factor(Peakfreq_runthresh_df$Age_Broad,
  2546. levels = c("6", "8"))
  2550. PeakFreq_bygroupandsex_final(Peakfreq_runthresh_df, PeakThetaFreq, 'Peak Theta (5-12 Hz) \n Frequency MEC', 'Frequency (Hz)', 0, 8.5)
  2552. General_AOVbyanim_ez(Peakfreq_runthresh_df, PeakThetaFreq, Age_Broad, Animal)
  2553. General_byanim_holm(Peakfreq_runthresh_df, PeakThetaFreq, Age_Broad)
  2556. ggsave(
  2557. "PSDfreqbygroupMEC2_sex.svg",
  2558. PeakFreq_bygroupandsex_final(Peakfreq_runthresh_df, PeakThetaFreq, 'Peak Theta (5-12 Hz) \n Frequency MEC', 'Frequency (Hz)', 0, 8.5),
  2559. width = 5,
  2560. height = 5,
  2561. dpi = 600
  2562. )
  2566. ```
  2570. ## Single Unit Results (Figure 4, 5, 6, S8D-G)
  2571. note: fig S7 and parts of fig S8 generated in matlab
  2574. ```{r single-unit, echo=FALSE, warning = FALSE}
  2576. #firing rates, phase locking, precession
  2578. Singleunit_nonparametric <- function(celltype, y){
  2579. #CALCULATE P VALS CORRECTED FOR MULTIPLE COMPARISONS
  2581. df <- substitute(celltype)
  2583. y <- substitute(y)
  2584. yvar <- substitute(df$y, list(y=y, df = df))
  2586. gvar <- substitute(Genotype)
  2587. grpvar <-substitute(Groupname)
  2589. xvar <- substitute(df$x, list(x=grpvar, df = df))
  2591. wilcox <- eval.parent(substitute(pairwise_wilcox_test(df, y~grpvar, p.adjust.method="holm", comparisons = list(c("63x", "6wt"), c("83x", "8wt"), c("6wt", "8wt"), c("63x", "83x"))), list(y = y, grpvar = grpvar, df = df)))
  2594. return(wilcox)
  2595. }
  2598. #get non adjusted p values
  2599. Circstats_age <- function(celltype_age, y, celltype_age_WT, celltype_age_Tg){
  2600. #using aov.circular
  2601. df <- substitute(celltype_age)
  2602. WTdata <-substitute(celltype_age_WT)
  2603. Tgdata <- substitute(celltype_age_Tg)
  2605. y = substitute(y)
  2606. yvar <- substitute(df$y, list(y=y, df = df))
  2608. gvar = substitute(Genotype)
  2609. xvar <- substitute(df$x, list(x=gvar, df = df))
  2611. #kuiper test - is anything different about these distributions? (Not sure about this)
  2612. kuiper <- eval.parent(substitute(kuiper_test(Tgdata$y, WTdata$y), list(y = y, Tgdata = Tgdata, WTdata = WTdata)))
  2614. #equal kappa test - are concentration parameters different?
  2615. equalkappa <- eval.parent(substitute(equal.kappa.test(yvar, xvar), list(yvar=yvar, xvar=xvar)))
  2617. #watson williams - are means different?
  2618. watwill <- eval.parent(substitute(watson.williams.test(yvar, xvar), list(yvar=yvar, xvar=xvar)))
  2620. #watson wheeler - are distribution of angles different? unclear if this is the same as watson williams? non-parametric?
  2621. watwheel <-eval.parent(substitute(watson.wheeler.test(yvar, xvar), list(yvar=yvar, xvar=xvar)))
  2623. aovcirc <- eval.parent(substitute(aov.circular(yvar, xvar), list(yvar=yvar, xvar=xvar)))
  2626. outlist <- list(kuiper, equalkappa, watwill, watwheel, aovcirc)
  2627. return(outlist)
  2628. }
  2630. #get final adjusted p values
  2631. Circstats_full <- function(celltype_6, celltype_8, y, celltype_6_WT, celltype_6_Tg, celltype_8_WT, celltype_8_Tg){
  2632. #CALCULATE P VALS CORRECTED FOR MULTIPLE COMPARISONS - For Mu values
  2634. df <- substitute(celltype)
  2635. df6 <- substitute(celltype_6)
  2636. df8 <- substitute(celltype_8)
  2637. WT6data <-substitute(celltype_6_WT)
  2638. Tg6data <- substitute(celltype_6_Tg)
  2639. WT8data <-substitute(celltype_8_WT)
  2640. Tg8data <- substitute(celltype_8_Tg)
  2642. y <- substitute(y)
  2643. yvar <- substitute(df$y, list(y=y, df = df))
  2644. yvar6 <- substitute(df6$y, list(y=y, df6 = df6))
  2645. yvar8 <- substitute(df8$y, list(y=y, df8 = df8))
  2647. gvar <- substitute(Genotype)
  2649. xvar <- substitute(df$x, list(x=gvar, df = df))
  2650. xvar6 <- substitute(df6$x, list(x=gvar, df6 = df6))
  2651. xvar8 <- substitute(df8$x, list(x=gvar, df8 = df8))
  2653. #kuiper test - is anything different about these distributions? (Not sure about this)
  2654. kuiper_6 <- eval.parent(substitute(kuiper_test(Tg6data$y, WT6data$y), list(y = y, Tg6data = Tg6data, WT6data = WT6data)))
  2656. #equal kappa test - are concentration parameters different?
  2657. equalkappa_6 <- eval.parent(substitute(equal.kappa.test(yvar6, xvar6), list(yvar6=yvar6, xvar6=xvar6)))
  2659. #watson williams - are means different?
  2660. watwill_6 <- eval.parent(substitute(watson.williams.test(yvar6, xvar6), list(yvar6=yvar6, xvar6=xvar6)))
  2662. #watson wheeler - are distribution of angles different? unclear if this is the same as watson williams? non-parametric?
  2663. watwheel_6 <-eval.parent(substitute(watson.wheeler.test(yvar6, xvar6), list(yvar6=yvar6, xvar6=xvar6)))
  2665. aovcirc_6 <- eval.parent(substitute(aov.circular(yvar6, xvar6), list(yvar6=yvar6, xvar6=xvar6)))
  2667. #kuiper test - is anything different about these distributions? (Not sure about this)
  2668. kuiper_8 <- eval.parent(substitute(kuiper_test(Tg8data$y, WT8data$y), list(y = y, Tg8data = Tg8data, WT8data = WT8data)))
  2670. #equal kappa test - are concentration parameters different?
  2671. equalkappa_8 <- eval.parent(substitute(equal.kappa.test(yvar8, xvar8), list(yvar8=yvar8, xvar8=xvar8)))
  2673. #watson williams - are means different?
  2674. watwill_8 <- eval.parent(substitute(watson.williams.test(yvar8, xvar8), list(yvar8=yvar8, xvar8=xvar8)))
  2676. #watson wheeler - are distribution of angles different? unclear if this is the same as watson williams? non-parametric?
  2677. watwheel_8 <-eval.parent(substitute(watson.wheeler.test(yvar8, xvar8), list(yvar8=yvar8, xvar8=xvar8)))
  2679. aovcirc_8 <- eval.parent(substitute(aov.circular(yvar8, xvar8), list(yvar8=yvar8, xvar8=xvar8)))
  2681. kuiper_pvals <- c(kuiper_6[[2]], kuiper_8[[2]]) %>% p.adjust(method = 'bonferroni')
  2682. equalkappa_pvals <- c(equalkappa_6[[7]], equalkappa_8[[7]]) %>% p.adjust(method = 'bonferroni')
  2683. watwill_pvals <- c(watwill_6[[4]], watwill_8[[4]]) %>% p.adjust(method = 'bonferroni')
  2684. watwheel_pvals <- c(watwheel_6[[4]], watwheel_8[[4]]) %>% p.adjust(method = 'bonferroni')
  2685. aovcirc_pvals <- c(aovcirc_6[[12]], aovcirc_8[[12]]) %>% p.adjust(method = 'bonferroni')
  2687. outlist <- list(kuiper_pvals, equalkappa_pvals, watwill_pvals,
  2688. watwheel_pvals,aovcirc_pvals)
  2689. return(outlist)
  2690. }
  2693. hipp_data <- read.csv('HIPP Single Unit Data.csv')
  2696. mec_data <-read.csv('MEC Single Unit Data.csv')
  2699. hipp_data$Animalname <-factor(hipp_data$Animalname)
  2700. mec_data$Animalname <-factor(mec_data$Animalname)
  2703. hipp_data$Groupname <- factor(hipp_data$Groupname , # Reordering group factor levels
  2704. levels = c("6wt", "8wt", "63x", "83x"))
  2706. mec_data$Groupname <- factor(mec_data$Groupname , # Reordering group factor levels
  2707. levels = c("6wt", "8wt", "63x", "83x"))
  2710. hipp_data <- mutate(hipp_data, Genotype = ifelse(Groupname =='6wt'|Groupname =='8wt', "WT", "3xTg"))
  2711. hipp_data<-mutate(hipp_data, Age_Broad = ifelse(Groupname =='6wt'|Groupname =='63x', 6, 8))
  2712. mec_data <- mutate(mec_data, Genotype = ifelse(Groupname =='6wt'|Groupname =='8wt', "WT", "3xTg"))
  2713. mec_data<-mutate(mec_data, Age_Broad = ifelse(Groupname =='6wt'|Groupname =='63x', 6, 8))
  2715. hipp_data$Genotype <- factor(hipp_data$Genotype, # Reordering group factor levels
  2716. levels = c("WT", "3xTg"))
  2718. mec_data$Genotype <- factor(mec_data$Genotype , # Reordering group factor levels
  2719. levels = c("WT", "3xTg"))
  2721. hipp_data$Age_Broad <- factor(hipp_data$Age_Broad, # Reordering group factor levels
  2722. levels = c("6", "8"))
  2724. mec_data$Age_Broad <- factor(mec_data$Age_Broad , # Reordering group factor levels
  2725. levels = c("6", "8"))
  2728. hipp_data <- mutate(hipp_data, mu2MECtheta_run_thresh = as.circular(mu2MECtheta_run_thresh + pi, units = 'radians'))
  2730. hipp_data <- mutate(hipp_data, mu2CA1theta_run_thresh = as.circular(mu2CA1theta_run_thresh + pi, units = 'radians'))
  2732. mec_data <- mutate(mec_data, mu2MECtheta_run_thresh = as.circular(mu2MECtheta_run_thresh + pi, units = 'radians'))
  2734. mec_data <- mutate(mec_data, mu2CA1theta_run_thresh = as.circular(mu2CA1theta_run_thresh + pi, units = 'radians'))
  2736. mec_data <- mutate(mec_data, mu2LMtheta_run_thresh = as.circular(mu2LMtheta_run_thresh + pi, units = 'radians'))
  2739. ##
  2741. iDG = subset(hipp_data, Region == 'DG' & CellType == 'i')
  2742. eDG= subset(hipp_data, Region == 'DG' & CellType == 'e')
  2744. iCA1 = subset(hipp_data, Region == 'CA1' & CellType == 'i')
  2745. eCA1 = subset(hipp_data, Region == 'CA1' & CellType == 'e')
  2747. iMEC2 = subset(mec_data, Region == 'MEC2' & CellType == 'i')
  2748. eMEC2 = subset(mec_data, Region == 'MEC2' & CellType == 'e')
  2750. iMEC3 = subset(mec_data, Region == 'MEC3' & CellType == 'i')
  2751. eMEC3 = subset(mec_data, Region == 'MEC3' & CellType == 'e')
  2753. eMEC3 <- subset(eMEC3, mFR_all_run < 25) #remove cells with overly high firing rates that were incorrectly clustered as excitatory based on waveform shape
  2758. hipp_8mo = subset(hipp_data, Groupname =='83x' | Groupname == '8wt')
  2759. hipp_6mo = subset(hipp_data, Groupname =='63x' | Groupname == '6wt')
  2760. mec_8mo = subset(mec_data, Groupname =='83x' | Groupname == '8wt')
  2761. mec_6mo = subset(mec_data, Groupname =='63x' | Groupname == '6wt')
  2763. hipp_tg = subset(hipp_data, Groupname == '83x' | Groupname == '63x')
  2764. hipp_wt = subset(hipp_data, Groupname == '8wt' | Groupname == '6wt')
  2765. mec_tg = subset(mec_data, Groupname == '83x' | Groupname == '63x')
  2766. mec_wt = subset(mec_data, Groupname == '8wt' | Groupname == '6wt')
  2769. ##subset by genotype
  2771. iDG_Tg = subset(iDG, Genotype == '3xTg')
  2772. iDG_WT = subset(iDG, Genotype == 'WT')
  2774. eDG_Tg = subset(eDG, Genotype == '3xTg')
  2775. eDG_WT = subset(eDG, Genotype == 'WT')
  2777. iCA1_Tg = subset(iCA1, Genotype == '3xTg')
  2778. iCA1_WT = subset(iCA1, Genotype == 'WT')
  2780. eCA1_Tg = subset(eCA1, Genotype == '3xTg')
  2781. eCA1_WT = subset(eCA1, Genotype == 'WT')
  2783. iMEC2_Tg = subset(iMEC2, Genotype == '3xTg')
  2784. iMEC2_WT = subset(iMEC2, Genotype == 'WT')
  2786. eMEC2_Tg = subset(eMEC2, Genotype == '3xTg')
  2787. eMEC2_WT = subset(eMEC2, Genotype == 'WT')
  2789. iMEC3_Tg = subset(iMEC3, Genotype == '3xTg')
  2790. iMEC3_WT = subset(iMEC2, Genotype == 'WT')
  2792. eMEC3_Tg = subset(eMEC3, Genotype == '3xTg')
  2793. eMEC3_WT = subset(eMEC3, Genotype == 'WT')
  2797. ##subset by age
  2799. iDG_8mo = subset(iDG, Age_Broad == '8')
  2800. iDG_6mo = subset(iDG, Age_Broad == '6')
  2802. eDG_8mo = subset(eDG, Age_Broad == '8')
  2803. eDG_6mo = subset(eDG, Age_Broad == '6')
  2805. iCA1_8mo = subset(iCA1, Age_Broad == '8')
  2806. iCA1_6mo = subset(iCA1, Age_Broad == '6')
  2808. eCA1_8mo = subset(eCA1, Age_Broad == '8')
  2809. eCA1_6mo = subset(eCA1, Age_Broad == '6')
  2811. iMEC2_8mo = subset(iMEC2, Age_Broad == '8')
  2812. iMEC2_6mo = subset(iMEC2, Age_Broad == '6')
  2814. eMEC2_8mo = subset(eMEC2, Age_Broad == '8')
  2815. eMEC2_6mo = subset(eMEC2, Age_Broad == '6')
  2817. iMEC3_8mo = subset(iMEC3, Age_Broad == '8')
  2818. iMEC3_6mo = subset(iMEC3, Age_Broad == '6')
  2820. eMEC3_8mo = subset(eMEC3, Age_Broad == '8')
  2821. eMEC3_6mo = subset(eMEC3, Age_Broad == '6')
  2825. #subset by genotype and age
  2827. iDG_8mo_Tg = subset(iDG, Age_Broad == '8' & Genotype == '3xTg')
  2828. iDG_6mo_Tg = subset(iDG, Age_Broad == '6' & Genotype == '3xTg')
  2830. eDG_8mo_Tg = subset(eDG, Age_Broad == '8' & Genotype == '3xTg')
  2831. eDG_6mo_Tg = subset(eDG, Age_Broad == '6' & Genotype == '3xTg')
  2833. iCA1_8mo_Tg = subset(iCA1, Age_Broad == '8' & Genotype == '3xTg')
  2834. iCA1_6mo_Tg = subset(iCA1, Age_Broad == '6' & Genotype == '3xTg')
  2836. eCA1_8mo_Tg = subset(eCA1, Age_Broad == '8' & Genotype == '3xTg')
  2837. eCA1_6mo_Tg = subset(eCA1, Age_Broad == '6' & Genotype == '3xTg')
  2839. iMEC2_8mo_Tg = subset(iMEC2, Age_Broad == '8' & Genotype == '3xTg')
  2840. iMEC2_6mo_Tg = subset(iMEC2, Age_Broad == '6' & Genotype == '3xTg')
  2842. eMEC2_8mo_Tg = subset(eMEC2, Age_Broad == '8' & Genotype == '3xTg')
  2843. eMEC2_6mo_Tg = subset(eMEC2, Age_Broad == '6' & Genotype == '3xTg')
  2845. iMEC3_8mo_Tg = subset(iMEC3, Age_Broad == '8' & Genotype == '3xTg')
  2846. iMEC3_6mo_Tg = subset(iMEC3, Age_Broad == '6' & Genotype == '3xTg')
  2848. eMEC3_8mo_Tg = subset(eMEC3, Age_Broad == '8' & Genotype == '3xTg')
  2849. eMEC3_6mo_Tg = subset(eMEC3, Age_Broad == '6' & Genotype == '3xTg')
  2853. iDG_8mo_WT = subset(iDG, Age_Broad == '8' & Genotype == 'WT')
  2854. iDG_6mo_WT = subset(iDG, Age_Broad == '6' & Genotype == 'WT')
  2856. eDG_8mo_WT = subset(eDG, Age_Broad == '8' & Genotype == 'WT')
  2857. eDG_6mo_WT = subset(eDG, Age_Broad == '6' & Genotype == 'WT')
  2859. iCA1_8mo_WT = subset(iCA1, Age_Broad == '8' & Genotype == 'WT')
  2860. iCA1_6mo_WT = subset(iCA1, Age_Broad == '6' & Genotype == 'WT')
  2862. eCA1_8mo_WT = subset(eCA1, Age_Broad == '8' & Genotype == 'WT')
  2863. eCA1_6mo_WT = subset(eCA1, Age_Broad == '6' & Genotype == 'WT')
  2865. iMEC2_8mo_WT = subset(iMEC2, Age_Broad == '8' & Genotype == 'WT')
  2866. iMEC2_6mo_WT = subset(iMEC2, Age_Broad == '6' & Genotype == 'WT')
  2868. eMEC2_8mo_WT = subset(eMEC2, Age_Broad == '8' & Genotype == 'WT')
  2869. eMEC2_6mo_WT = subset(eMEC2, Age_Broad == '6' & Genotype == 'WT')
  2871. iMEC3_8mo_WT = subset(iMEC3, Age_Broad == '8' & Genotype == 'WT')
  2872. iMEC3_6mo_WT = subset(iMEC3, Age_Broad == '6' & Genotype == 'WT')
  2874. eMEC3_8mo_WT = subset(eMEC3, Age_Broad == '8' & Genotype == 'WT')
  2875. eMEC3_6mo_WT = subset(eMEC3, Age_Broad == '6' & Genotype == 'WT')
  2878. lvls <- levels(mec_data$Groupname)
  2879. lvlsA <- levels(mec_data$Age_Broad)
  2880. lvlsG <- levels(mec_data$Genotype)
  2883. AnyPlotbygroup_adjaxis_facet <- function(df, y, title, ytitle, ymin, ymax) { # ymin =0, ymax=50){
  2884. plot <-ggplot(df ,aes(x=Genotype, y={{y}}))
  2885. plot +geom_sina(aes(col= Groupname), alpha = 0.5, na.rm = TRUE, show.legend=FALSE, jitter_y = FALSE) +
  2886. geom_bar(position= "dodge", stat = "summary", width = 0.9, fun.y = "mean", alpha = 0.4, aes(fill=Groupname), colour = "black") +
  2887. stat_summary(color = 'black', fun.data = mean_se, geom = "errorbar", width = 0.4, alpha = 1, show.legend=FALSE) +
  2888. ggtitle(title) + ylab(ytitle) +
  2889. scale_fill_manual("legend", values = scale_fill_palette, labels = c("WT 6 mo", "WT 8 mo", "3xTg 6 mo", "3xTg 8 mo"))+
  2890. guides(fill=guide_legend("Group")) +
  2891. scale_color_manual("legend", values = scale_colour_palette, guide = "none")+
  2892. scale_y_continuous(expand = c(0,0)) +
  2893. coord_cartesian(ylim=c(ymin, ymax))+
  2894. scale_alpha(guide = 'none') +
  2895. facet_grid(.~Age_Broad, switch = "x", labeller = as_labeller(c('6' = "6 mo", '8' = "8 mo"))) +
  2896. theme(plot.title = element_text(hjust = 0.5, size = 22, face = "bold"),
  2897. axis.text.x = element_text(size = 15, colour = "black"),
  2898. axis.text.y = element_text(size = 18, face="bold", colour = "black"),
  2899. axis.title.y = element_text(size = 20, face = "bold", colour = "black"),
  2900. axis.title.x = element_blank(),
  2901. legend.text=element_text(size=16),
  2902. legend.title=element_text(size=18),
  2903. panel.grid.major = element_blank(),
  2904. panel.grid.minor = element_blank(),
  2905. panel.background = element_blank(),
  2906. axis.line = element_line(colour = "black"),
  2907. strip.text.x = element_text(size = 18, face = "bold"),
  2908. strip.background = element_rect( fill="white"),
  2909. panel.spacing.x = unit(1.4, "lines"),
  2910. strip.placement = "outside")
  2912. }
  2916. print("eMEC2 Ns")
  2917. print("3xTg 6mo (M, F)")
  2918. sum(eMEC2$Genotype == "3xTg" & eMEC2$Age_Broad == '6')
  2919. sum(eMEC2$Genotype == "3xTg" & eMEC2$Age_Broad == '6' & eMEC2$Sex == 'M')
  2920. sum(eMEC2$Genotype == "3xTg" & eMEC2$Age_Broad == '6' & eMEC2$Sex == 'F')
  2921. print("3xTg 8mo (M, F)")
  2922. sum(eMEC2$Genotype == "3xTg" & eMEC2$Age_Broad == '8')
  2923. sum(eMEC2$Genotype == "3xTg" & eMEC2$Age_Broad == '8' & eMEC2$Sex == 'M')
  2924. sum(eMEC2$Genotype == "3xTg" & eMEC2$Age_Broad == '8' & eMEC2$Sex == 'F')
  2925. print("WT 6mo (M, F)")
  2926. sum(eMEC2$Genotype == "WT" & eMEC2$Age_Broad == '6')
  2927. sum(eMEC2$Genotype == "WT" & eMEC2$Age_Broad == '6' & eMEC2$Sex == 'M')
  2928. sum(eMEC2$Genotype == "WT" & eMEC2$Age_Broad == '6' & eMEC2$Sex == 'F')
  2929. print("WT 8mo (M, F)")
  2930. sum(eMEC2$Genotype == "WT" & eMEC2$Age_Broad == '8')
  2931. sum(eMEC2$Genotype == "WT" & eMEC2$Age_Broad == '8' & eMEC2$Sex == 'M')
  2932. sum(eMEC2$Genotype == "WT" & eMEC2$Age_Broad == '8' & eMEC2$Sex == 'F')
  2934. print("eMEC3 Ns")
  2935. print("3xTg 6mo (M, F)")
  2936. sum(eMEC3$Genotype == "3xTg" & eMEC3$Age_Broad == '6')
  2937. sum(eMEC3$Genotype == "3xTg" & eMEC3$Age_Broad == '6' & eMEC3$Sex == 'M')
  2938. sum(eMEC3$Genotype == "3xTg" & eMEC3$Age_Broad == '6' & eMEC3$Sex == 'F')
  2939. print("3xTg 8mo (M, F)")
  2940. sum(eMEC3$Genotype == "3xTg" & eMEC3$Age_Broad == '8')
  2941. sum(eMEC3$Genotype == "3xTg" & eMEC3$Age_Broad == '8' & eMEC3$Sex == 'M')
  2942. sum(eMEC3$Genotype == "3xTg" & eMEC3$Age_Broad == '8' & eMEC3$Sex == 'F')
  2943. print("WT 6mo (M, F)")
  2944. sum(eMEC3$Genotype == "WT" & eMEC3$Age_Broad == '6')
  2945. sum(eMEC3$Genotype == "WT" & eMEC3$Age_Broad == '6' & eMEC3$Sex == 'M')
  2946. sum(eMEC3$Genotype == "WT" & eMEC3$Age_Broad == '6' & eMEC3$Sex == 'F')
  2947. print("WT 8mo (M, F)")
  2948. sum(eMEC3$Genotype == "WT" & eMEC3$Age_Broad == '8')
  2949. sum(eMEC3$Genotype == "WT" & eMEC3$Age_Broad == '8' & eMEC3$Sex == 'M')
  2950. sum(eMEC3$Genotype == "WT" & eMEC3$Age_Broad == '8' & eMEC3$Sex == 'F')
  2952. print("iMEC2 Ns")
  2953. print("3xTg 6mo (M, F)")
  2954. sum(iMEC2$Genotype == "3xTg" & iMEC2$Age_Broad == '6')
  2955. sum(iMEC2$Genotype == "3xTg" & iMEC2$Age_Broad == '6' & iMEC2$Sex == 'M')
  2956. sum(iMEC2$Genotype == "3xTg" & iMEC2$Age_Broad == '6' & iMEC2$Sex == 'F')
  2957. print("3xTg 8mo (M, F)")
  2958. sum(iMEC2$Genotype == "3xTg" & iMEC2$Age_Broad == '8')
  2959. sum(iMEC2$Genotype == "3xTg" & iMEC2$Age_Broad == '8' & iMEC2$Sex == 'M')
  2960. sum(iMEC2$Genotype == "3xTg" & iMEC2$Age_Broad == '8' & iMEC2$Sex == 'F')
  2961. print("WT 6mo (M, F)")
  2962. sum(iMEC2$Genotype == "WT" & iMEC2$Age_Broad == '6')
  2963. sum(iMEC2$Genotype == "WT" & iMEC2$Age_Broad == '6' & iMEC2$Sex == 'M')
  2964. sum(iMEC2$Genotype == "WT" & iMEC2$Age_Broad == '6' & iMEC2$Sex == 'F')
  2965. print("WT 8mo (M, F)")
  2966. sum(iMEC2$Genotype == "WT" & iMEC2$Age_Broad == '8')
  2967. sum(iMEC2$Genotype == "WT" & iMEC2$Age_Broad == '8' & iMEC2$Sex == 'M')
  2968. sum(iMEC2$Genotype == "WT" & iMEC2$Age_Broad == '8' & iMEC2$Sex == 'F')
  2971. print("iMEC3 Ns")
  2972. print("3xTg 6mo (M, F)")
  2973. sum(iMEC3$Genotype == "3xTg" & iMEC3$Age_Broad == '6')
  2974. sum(iMEC3$Genotype == "3xTg" & iMEC3$Age_Broad == '6' & iMEC3$Sex == 'M')
  2975. sum(iMEC3$Genotype == "3xTg" & iMEC3$Age_Broad == '6' & iMEC3$Sex == 'F')
  2976. print("3xTg 8mo (M, F)")
  2977. sum(iMEC3$Genotype == "3xTg" & iMEC3$Age_Broad == '8')
  2978. sum(iMEC3$Genotype == "3xTg" & iMEC3$Age_Broad == '8' & iMEC3$Sex == 'M')
  2979. sum(iMEC3$Genotype == "3xTg" & iMEC3$Age_Broad == '8' & iMEC3$Sex == 'F')
  2980. print("WT 6mo (M, F)")
  2981. sum(iMEC3$Genotype == "WT" & iMEC3$Age_Broad == '6')
  2982. sum(iMEC3$Genotype == "WT" & iMEC3$Age_Broad == '6' & iMEC3$Sex == 'M')
  2983. sum(iMEC3$Genotype == "WT" & iMEC3$Age_Broad == '6' & iMEC3$Sex == 'F')
  2984. print("WT 8mo (M, F)")
  2985. sum(iMEC3$Genotype == "WT" & iMEC3$Age_Broad == '8')
  2986. sum(iMEC3$Genotype == "WT" & iMEC3$Age_Broad == '8' & iMEC3$Sex == 'M')
  2987. sum(iMEC3$Genotype == "WT" & iMEC3$Age_Broad == '8' & iMEC3$Sex == 'F')
  2989. print("eDG Ns")
  2990. print("3xTg 6mo (M, F)")
  2991. sum(eDG$Genotype == "3xTg" & eDG$Age_Broad == '6')
  2992. sum(eDG$Genotype == "3xTg" & eDG$Age_Broad == '6' & eDG$Sex == 'M')
  2993. sum(eDG$Genotype == "3xTg" & eDG$Age_Broad == '6' & eDG$Sex == 'F')
  2994. print("3xTg 8mo (M, F)")
  2995. sum(eDG$Genotype == "3xTg" & eDG$Age_Broad == '8')
  2996. sum(eDG$Genotype == "3xTg" & eDG$Age_Broad == '8' & eDG$Sex == 'M')
  2997. sum(eDG$Genotype == "3xTg" & eDG$Age_Broad == '8' & eDG$Sex == 'F')
  2998. print("WT 6mo (M, F)")
  2999. sum(eDG$Genotype == "WT" & eDG$Age_Broad == '6')
  3000. sum(eDG$Genotype == "WT" & eDG$Age_Broad == '6' & eDG$Sex == 'M')
  3001. sum(eDG$Genotype == "WT" & eDG$Age_Broad == '6' & eDG$Sex == 'F')
  3002. print("WT 8mo (M, F)")
  3003. sum(eDG$Genotype == "WT" & eDG$Age_Broad == '8')
  3004. sum(eDG$Genotype == "WT" & eDG$Age_Broad == '8' & eDG$Sex == 'M')
  3005. sum(eDG$Genotype == "WT" & eDG$Age_Broad == '8' & eDG$Sex == 'F')
  3007. print("eCA1 Ns")
  3008. print("3xTg 6mo (M, F)")
  3009. sum(eCA1$Genotype == "3xTg" & eCA1$Age_Broad == '6')
  3010. sum(eCA1$Genotype == "3xTg" & eCA1$Age_Broad == '6' & eCA1$Sex == 'M')
  3011. sum(eCA1$Genotype == "3xTg" & eCA1$Age_Broad == '6' & eCA1$Sex == 'F')
  3012. print("3xTg 8mo (M, F)")
  3013. sum(eCA1$Genotype == "3xTg" & eCA1$Age_Broad == '8')
  3014. sum(eCA1$Genotype == "3xTg" & eCA1$Age_Broad == '8' & eCA1$Sex == 'M')
  3015. sum(eCA1$Genotype == "3xTg" & eCA1$Age_Broad == '8' & eCA1$Sex == 'F')
  3016. print("WT 6mo (M, F)")
  3017. sum(eCA1$Genotype == "WT" & eCA1$Age_Broad == '6')
  3018. sum(eCA1$Genotype == "WT" & eCA1$Age_Broad == '6' & eCA1$Sex == 'M')
  3019. sum(eCA1$Genotype == "WT" & eCA1$Age_Broad == '6' & eCA1$Sex == 'F')
  3020. print("WT 8mo (M, F)")
  3021. sum(eCA1$Genotype == "WT" & eCA1$Age_Broad == '8')
  3022. sum(eCA1$Genotype == "WT" & eCA1$Age_Broad == '8' & eCA1$Sex == 'M')
  3023. sum(eCA1$Genotype == "WT" & eCA1$Age_Broad == '8' & eCA1$Sex == 'F')
  3026. print("iDG Ns")
  3027. print("3xTg 6mo (M, F)")
  3028. sum(iDG$Genotype == "3xTg" & iDG$Age_Broad == '6')
  3029. sum(iDG$Genotype == "3xTg" & iDG$Age_Broad == '6' & iDG$Sex == 'M')
  3030. sum(iDG$Genotype == "3xTg" & iDG$Age_Broad == '6' & iDG$Sex == 'F')
  3031. print("3xTg 8mo (M, F)")
  3032. sum(iDG$Genotype == "3xTg" & iDG$Age_Broad == '8')
  3033. sum(iDG$Genotype == "3xTg" & iDG$Age_Broad == '8' & iDG$Sex == 'M')
  3034. sum(iDG$Genotype == "3xTg" & iDG$Age_Broad == '8' & iDG$Sex == 'F')
  3035. print("WT 6mo (M, F)")
  3036. sum(iDG$Genotype == "WT" & iDG$Age_Broad == '6')
  3037. sum(iDG$Genotype == "WT" & iDG$Age_Broad == '6' & iDG$Sex == 'M')
  3038. sum(iDG$Genotype == "WT" & iDG$Age_Broad == '6' & iDG$Sex == 'F')
  3039. print("WT 8mo (M, F)")
  3040. sum(iDG$Genotype == "WT" & iDG$Age_Broad == '8')
  3041. sum(iDG$Genotype == "WT" & iDG$Age_Broad == '8' & iDG$Sex == 'M')
  3042. sum(iDG$Genotype == "WT" & iDG$Age_Broad == '8' & iDG$Sex == 'F')
  3046. print("iCA1 Ns")
  3047. print("3xTg 6mo (M, F)")
  3048. sum(iCA1$Genotype == "3xTg" & iCA1$Age_Broad == '6')
  3049. sum(iCA1$Genotype == "3xTg" & iCA1$Age_Broad == '6' & iCA1$Sex == 'M')
  3050. sum(iCA1$Genotype == "3xTg" & iCA1$Age_Broad == '6' & iCA1$Sex == 'F')
  3051. print("3xTg 8mo (M, F)")
  3052. sum(iCA1$Genotype == "3xTg" & iCA1$Age_Broad == '8')
  3053. sum(iCA1$Genotype == "3xTg" & iCA1$Age_Broad == '8' & iCA1$Sex == 'M')
  3054. sum(iCA1$Genotype == "3xTg" & iCA1$Age_Broad == '8' & iCA1$Sex == 'F')
  3055. print("WT 6mo (M, F)")
  3056. sum(iCA1$Genotype == "WT" & iCA1$Age_Broad == '6')
  3057. sum(iCA1$Genotype == "WT" & iCA1$Age_Broad == '6' & iCA1$Sex == 'M')
  3058. sum(iCA1$Genotype == "WT" & iCA1$Age_Broad == '6' & iCA1$Sex == 'F')
  3059. print("WT 8mo (M, F)")
  3060. sum(iCA1$Genotype == "WT" & iCA1$Age_Broad == '8')
  3061. sum(iCA1$Genotype == "WT" & iCA1$Age_Broad == '8' & iCA1$Sex == 'M')
  3062. sum(iCA1$Genotype == "WT" & iCA1$Age_Broad == '8' & iCA1$Sex == 'F')
  3064. print("eMEC2 cells by animal")
  3065. kable(table(unlist(eMEC2$Animalname)))
  3066. print("iMEC2 cells by animal")
  3067. kable(table(unlist(iMEC2$Animalname)))
  3069. print("eMEC3 cells by animal")
  3070. kable(table(unlist(eMEC3$Animalname)))
  3071. print("iMEC3 cells by animal")
  3072. kable(table(unlist(iMEC3$Animalname)))
  3074. print("eCA1 cells by animal")
  3075. kable(table(unlist(eCA1$Animalname)))
  3076. print("iCA1 cells by animal")
  3077. kable(table(unlist(iCA1$Animalname)))
  3079. print("eDG cells by animal")
  3080. kable(table(unlist(eDG$Animalname)))
  3081. print("iDG cells by animal")
  3082. kable(table(unlist(iDG$Animalname)))
  3085. ##### MEC R values and Firing Rates Figure 4 + 5 and S8
  3086. fr_mec_p1 <- AnyPlotbygroup_adjaxis_facet(eMEC3, mFR_run_thresh, 'Excitatory MEC3 \n Firing Rates',"Firing Rate (Hz)", 0, 12)
  3087. fr_mec_p1
  3088. print('eMEC3 FR')
  3089. print('Check Normality')
  3090. shapiro.test(eMEC3_6mo_WT$mFR_run_thresh)
  3091. shapiro.test(eMEC3_6mo_Tg$mFR_run_thresh)
  3092. shapiro.test(eMEC3_8mo_WT$mFR_run_thresh)
  3093. shapiro.test(eMEC3_8mo_Tg$mFR_run_thresh)
  3094. print('nonparametric')
  3095. Singleunit_nonparametric(eMEC3, mFR_run_thresh)
  3097. fr_mec_p2 <- AnyPlotbygroup_adjaxis_facet(iMEC3, mFR_run_thresh, 'Inhibitory MEC3 \nFiring Rates', "Firing Rate (Hz)", 0, 125)
  3098. fr_mec_p2
  3099. print("iMEC3 FR")
  3100. ('check normality')
  3101. shapiro.test(iMEC3_6mo_WT$mFR_run_thresh)
  3102. shapiro.test(iMEC3_6mo_Tg$mFR_run_thresh)
  3103. shapiro.test(iMEC3_8mo_WT$mFR_run_thresh)
  3104. shapiro.test(iMEC3_8mo_Tg$mFR_run_thresh)
  3105. print('nonparametric')
  3106. Singleunit_nonparametric(iMEC3, mFR_run_thresh)
  3108. r_mec_p1 <- AnyPlotbygroup_adjaxis_facet(eMEC3, r2MECtheta_run_thresh, 'Excitatory MEC3 R-vals \n(ref:MEC Theta)' ,'R', 0, 1)
  3109. r_mec_p1
  3110. print('eMEC3 R to MEC theta')
  3111. print('nonparametric')
  3112. Singleunit_nonparametric(eMEC3, r2MECtheta_run_thresh)
  3115. r_mec_p2 <- AnyPlotbygroup_adjaxis_facet(eMEC3, r2CA1theta_run_thresh, 'Excitatory MEC3 R-vals \n(ref:CA1 Pyr Theta)' ,'R', 0, 1)
  3116. r_mec_p2
  3117. print('eMEC3 R to CA1 theta')
  3118. print('nonparametric')
  3119. Singleunit_nonparametric(eMEC3, r2CA1theta_run_thresh)
  3122. r_mec_p3 <- AnyPlotbygroup_adjaxis_facet(iMEC3, r2MECtheta_run_thresh, 'Inhibitory MEC3 R-values \n(ref:MEC Theta)' ,'R', 0, 1)
  3123. r_mec_p3
  3124. print("iMEC3 R to MEC theta")
  3125. print('nonparametric')
  3126. Singleunit_nonparametric(iMEC3, r2MECtheta_run_thresh)
  3129. r_mec_p4 <- AnyPlotbygroup_adjaxis_facet(iMEC3, r2CA1theta_run_thresh, 'Inhibitory MEC3 R-values \n(ref:CA1 Pyr Theta)' ,'R', 0, 1)
  3130. r_mec_p4
  3131. print("iMEC3 R to CA1 theta")
  3132. print('nonparametric')
  3133. Singleunit_nonparametric(iMEC3, r2CA1theta_run_thresh)
  3136. fr_mec_p3 <- AnyPlotbygroup_adjaxis_facet(eMEC2, mFR_run_thresh, 'Excitatory MEC2 \n Firing Rates', "Firing Rate (Hz)", 0, 12)
  3137. fr_mec_p3
  3138. print('eMEC2 FR')
  3139. print('Check Normality')
  3140. shapiro.test(eMEC2_6mo_WT$mFR_run_thresh)
  3141. shapiro.test(eMEC2_6mo_Tg$mFR_run_thresh)
  3142. shapiro.test(eMEC2_8mo_WT$mFR_run_thresh)
  3143. shapiro.test(eMEC2_8mo_Tg$mFR_run_thresh)
  3144. print('nonparametric')
  3145. Singleunit_nonparametric(eMEC2, mFR_run_thresh)
  3148. fr_mec_p4 <- AnyPlotbygroup_adjaxis_facet(iMEC2, mFR_run_thresh, 'Inhibitory MEC2 \nFiring Rates', "Firing Rate (Hz)", 0, 125)
  3149. fr_mec_p4
  3150. print("iMEC2 FR")
  3151. print('Check normality')
  3152. shapiro.test(iMEC2_6mo_WT$mFR_run_thresh)
  3153. shapiro.test(iMEC2_6mo_Tg$mFR_run_thresh)
  3154. shapiro.test(iMEC2_8mo_WT$mFR_run_thresh)
  3155. shapiro.test(iMEC2_8mo_Tg$mFR_run_thresh)
  3156. print('nonparametric')
  3157. Singleunit_nonparametric(iMEC2, mFR_run_thresh)
  3160. r_mec_p5 <- AnyPlotbygroup_adjaxis_facet(eMEC2, r2MECtheta_run_thresh, 'Excitatory MEC2 R-vals \n(ref: MEC Theta)' , 'R', 0, 1)
  3161. r_mec_p5
  3162. print('eMEC2 R to MEC theta')
  3163. print('nonparametric')
  3164. Singleunit_nonparametric(eMEC2, r2MECtheta_run_thresh)
  3166. r_mec_p6 <- AnyPlotbygroup_adjaxis_facet(eMEC2, r2CA1theta_run_thresh, 'Excitatory MEC2 R-vals \n(ref: CA1 Pyr Theta)' ,'R', 0, 1)
  3167. r_mec_p6
  3168. print('eMEC2 R to CA1 theta')
  3169. print('Check normality')
  3170. shapiro.test(eMEC2_6mo_WT$r2CA1theta_run_thresh)
  3171. shapiro.test(eMEC2_6mo_Tg$r2CA1theta_run_thresh)
  3172. shapiro.test(eMEC2_8mo_WT$r2CA1theta_run_thresh)
  3173. shapiro.test(eMEC2_8mo_Tg$r2CA1theta_run_thresh)
  3174. print('nonparametric')
  3175. Singleunit_nonparametric(eMEC2, r2CA1theta_run_thresh)
  3178. r_mec_p7 <- AnyPlotbygroup_adjaxis_facet(iMEC2, r2MECtheta_run_thresh, 'Inhibitory MEC2 R-values \n(ref: MEC Theta)' ,'R', 0, 1)
  3179. r_mec_p7
  3180. print("iMEC2 R to MEC theta")
  3181. ('check normality')
  3182. shapiro.test(iMEC2_6mo_WT$r2MECtheta_run_thresh)
  3183. shapiro.test(iMEC2_6mo_Tg$r2MECtheta_run_thresh)
  3184. shapiro.test(iMEC2_8mo_WT$r2MECtheta_run_thresh)
  3185. shapiro.test(iMEC2_8mo_Tg$r2MECtheta_run_thresh)
  3186. print('nonparametric')
  3187. Singleunit_nonparametric(iMEC2, r2MECtheta_run_thresh)
  3190. r_mec_p8 <- AnyPlotbygroup_adjaxis_facet(iMEC2, r2CA1theta_run_thresh, 'Inhibitory MEC2 R-values \n(ref: CA1 Pyr Theta)' ,'R', 0, 1)
  3191. r_mec_p8
  3192. print("iMEC2 R to CA1 theta")
  3193. ('check normality')
  3194. shapiro.test(iMEC2_6mo_WT$r2CA1theta_run_thresh)
  3195. shapiro.test(iMEC2_6mo_Tg$r2CA1theta_run_thresh)
  3196. shapiro.test(iMEC2_8mo_WT$r2CA1theta_run_thresh)
  3197. shapiro.test(iMEC2_8mo_Tg$r2CA1theta_run_thresh)
  3198. print('nonparametric')
  3199. Singleunit_nonparametric(iMEC2, r2CA1theta_run_thresh)
  3203. #### Hippocampus R values and Firing Rates - Figure 6 and S10
  3205. fr_hipp_p1 <- AnyPlotbygroup_adjaxis_facet(eDG, mFR_run_thresh, 'Excitatory DG \n Firing Rates', "Firing Rate (Hz)", 0, 12)
  3206. fr_hipp_p1
  3207. print("eDG FR")
  3208. print('check normality')
  3209. shapiro.test(eDG_6mo_WT$mFR_run_thresh)
  3210. shapiro.test(eDG_6mo_Tg$mFR_run_thresh)
  3211. shapiro.test(eDG_8mo_WT$mFR_run_thresh)
  3212. shapiro.test(eDG_8mo_Tg$mFR_run_thresh)
  3213. print('nonparametric')
  3214. Singleunit_nonparametric(eDG, mFR_run_thresh)
  3216. fr_hipp_p2 <- AnyPlotbygroup_adjaxis_facet(iDG, mFR_run_thresh, 'Inhibitory DG \n Firing Rates', "Firing Rate (Hz)", 0, 100)
  3217. fr_hipp_p2
  3218. print("iDG FR")
  3219. ('check normality')
  3220. shapiro.test(iDG_6mo_WT$mFR_run_thresh)
  3221. shapiro.test(iDG_6mo_Tg$mFR_run_thresh)
  3222. shapiro.test(iDG_8mo_WT$mFR_run_thresh)
  3223. shapiro.test(iDG_8mo_Tg$mFR_run_thresh)
  3224. print('nonparametric')
  3225. Singleunit_nonparametric(iDG, mFR_run_thresh)
  3227. fr_hipp_p3 <- AnyPlotbygroup_adjaxis_facet(eCA1, mFR_run_thresh, 'Excitatory CA1 \n Firing Rates', "Firing Rate (Hz)", 0, 12)
  3228. fr_hipp_p3
  3229. print("eCA1 FR")
  3230. print('check normality')
  3231. shapiro.test(eCA1_6mo_WT$mFR_run_thresh)
  3232. shapiro.test(eCA1_6mo_Tg$mFR_run_thresh)
  3233. shapiro.test(eCA1_8mo_WT$mFR_run_thresh)
  3234. shapiro.test(eCA1_8mo_Tg$mFR_run_thresh)
  3235. print('nonparametric')
  3236. Singleunit_nonparametric(eCA1, mFR_run_thresh)
  3239. fr_hipp_p4 <- AnyPlotbygroup_adjaxis_facet(iCA1, mFR_run_thresh, 'Inhibitory CA1 \n Firing Rates', "Firing Rate (Hz)", 0, 100)
  3240. fr_hipp_p4
  3241. print("iCA1 FR")
  3242. ('check normality')
  3243. shapiro.test(iCA1_6mo_WT$mFR_run_thresh)
  3244. shapiro.test(iCA1_6mo_Tg$mFR_run_thresh)
  3245. shapiro.test(iCA1_8mo_WT$mFR_run_thresh)
  3246. shapiro.test(iCA1_8mo_Tg$mFR_run_thresh)
  3247. print('nonparametric')
  3248. Singleunit_nonparametric(iCA1, mFR_run_thresh)
  3251. r_hipp_p1 <- AnyPlotbygroup_adjaxis_facet(iCA1, r2CA1theta_run_thresh, 'Inhibitory CA1 R-values \n(ref:CA1 Pyr Theta)' ,'R', 0, 1)
  3252. r_hipp_p1
  3253. print("iCA1 R to CA1 theta")
  3254. print('nonparametric')
  3255. Singleunit_nonparametric(iCA1, r2CA1theta_run_thresh)
  3258. r_hipp_p2 <-AnyPlotbygroup_adjaxis_facet(iDG, r2CA1theta_run_thresh, 'Inhibitory DG R-values \n(ref:CA1 Pyr Theta)' ,'R', 0, 1)
  3259. r_hipp_p2
  3260. print("iDG R to CA1 theta")
  3261. print('check normality')
  3262. shapiro.test(iDG_6mo_WT$r2CA1theta_run_thresh)
  3263. shapiro.test(iDG_6mo_Tg$r2CA1theta_run_thresh)
  3264. shapiro.test(iDG_8mo_WT$r2CA1theta_run_thresh)
  3265. shapiro.test(iDG_8mo_Tg$r2CA1theta_run_thresh)
  3266. print('nonparametric')
  3267. Singleunit_nonparametric(iDG, r2CA1theta_run_thresh)
  3270. r_hipp_p3 <- AnyPlotbygroup_adjaxis_facet(eCA1, r2CA1theta_run_thresh, 'Excitatory CA1 R-values \n(ref:CA1 Pyr Theta)' ,'R', 0, 1)
  3271. r_hipp_p3
  3272. print("eCA1 R to CA1 theta")
  3273. print('check normality')
  3274. shapiro.test(eCA1_6mo_WT$r2CA1theta_run_thresh)
  3275. shapiro.test(eCA1_6mo_Tg$r2CA1theta_run_thresh)
  3276. shapiro.test(eCA1_8mo_WT$r2CA1theta_run_thresh)
  3277. shapiro.test(eCA1_8mo_Tg$r2CA1theta_run_thresh)
  3278. print('nonparametric')
  3279. Singleunit_nonparametric(eCA1, r2CA1theta_run_thresh)
  3282. r_hipp_p4 <- AnyPlotbygroup_adjaxis_facet(eDG, r2CA1theta_run_thresh, 'Excitatory DG R-values \n(ref:CA1 Pyr Theta)' ,'R', 0, 1)
  3283. r_hipp_p4
  3284. print("eDG R to CA1 theta")
  3285. print('check normality')
  3286. shapiro.test(eDG_6mo_WT$r2CA1theta_run_thresh)
  3287. shapiro.test(eDG_6mo_Tg$r2CA1theta_run_thresh)
  3288. shapiro.test(eDG_8mo_WT$r2CA1theta_run_thresh)
  3289. shapiro.test(eDG_8mo_Tg$r2CA1theta_run_thresh)
  3290. print('nonparametric')
  3291. Singleunit_nonparametric(eDG, r2CA1theta_run_thresh)
  3294. Mubygroup_2 <- function(df, y, title, showmean, means, var=circmean){
  3296. if (showmean ==1 ){
  3297. plot <-ggplot(df ,aes(x=Genotype, y={{y}}))
  3298. plot+ geom_sina(aes(col = Groupname, y = {{y}}), alpha = 0.3, size =2, na.rm=TRUE, jitter_y = FALSE, show.legend=FALSE) +
  3299. ggtitle(title) + ylab("Phase of Theta (Degrees)") +
  3300. facet_grid(rows = vars(Age_Broad), labeller = as_labeller(c('6' = "6 mo", '8' = "8 mo"))) +
  3301. theme(panel.grid.major = element_blank(),
  3302. panel.grid.minor = element_blank(),
  3303. panel.background = element_blank(),
  3304. axis.line = element_line(colour = "black"),
  3305. plot.title = element_text(size = 22, face ="bold", hjust = 0.5),
  3306. axis.text=element_text(size=20, face="bold", colour = 'black'),
  3307. axis.title.x=element_text(size=20, colour = 'black', face = "bold"),
  3308. axis.title.y=element_blank(),
  3309. legend.text=element_text(size=18),
  3310. legend.title=element_text(size=20),
  3311. strip.text = element_text(size = 18, face = "bold"),
  3312. strip.background = element_rect( fill="white"),
  3313. panel.spacing = unit(2.5, "lines"),
  3314. strip.placement = "outside") +
  3315. scale_fill_manual("legend", values = scale_fill_palette, labels = c("WT 6 mo", "WT 8 mo", "3xTg 6 mo", "3xTg 8 mo"))+
  3316. guides(fill=guide_legend("Group")) +
  3317. scale_color_manual("legend", values = scale_colour_palette, guide = "none")+
  3318. scale_y_continuous(limits=c(0,2*pi), breaks=c(0, pi, 2*pi), labels = c(0,180,360))+
  3319. coord_flip() +
  3320. labs(colour= "Genotype") +
  3321. geom_hline(yintercept=pi, alpha = 0.3, linetype = 'dashed')+
  3322. geom_tile(data = means, mapping = aes(x=Genotype, y={{var}}, col=Groupname), alpha = 1, width = 0.5, linetype = 1, linewidth = 1, height = 0, show.legend = FALSE)
  3323. }
  3324. else if (showmean == 0) {
  3325. plot <-ggplot(df ,aes(x=Genotype, y={{y}}))
  3326. plot+ geom_sina(aes(col = Groupname, y = {{y}}), alpha = 0.3, size =2, na.rm=TRUE, jitter_y = FALSE, show.legend=FALSE) +
  3327. ggtitle(title) + ylab("Phase of Theta (Degrees)") +
  3328. facet_grid(rows = vars(Age_Broad), labeller = as_labeller(c('6' = "6 mo", '8' = "8 mo"))) +
  3329. theme(panel.grid.major = element_blank(),
  3330. panel.grid.minor = element_blank(),
  3331. panel.background = element_blank(),
  3332. axis.line = element_line(colour = "black"),
  3333. plot.title = element_text(size = 22, face ="bold", hjust = 0.5),
  3334. axis.text=element_text(size=20, face="bold", colour = 'black'),
  3335. axis.title.x=element_text(size=20, colour = 'black', face = "bold"),
  3336. axis.title.y=element_blank(),
  3337. legend.text=element_text(size=18),
  3338. legend.title=element_text(size=20),
  3339. strip.text = element_text(size = 18, face = "bold"),
  3340. strip.background = element_rect( fill="white"),
  3341. panel.spacing = unit(2.5, "lines"),
  3342. strip.placement = "outside") +
  3343. scale_fill_manual("legend", values = scale_fill_palette, labels = c("6 mo WT", "8 mo WT", "6 mo 3xTg", "8 mo 3xTg"))+
  3344. guides(fill=guide_legend("Group")) +
  3345. scale_color_manual("legend", values = scale_colour_palette, guide = "none")+
  3346. scale_y_continuous(limits=c(0,2*pi), breaks=c(0, pi, 2*pi), labels = c(0,180,360))+
  3347. coord_flip() +
  3348. labs(colour= "Genotype") +
  3349. geom_hline(yintercept=pi, alpha = 0.3, linetype = 'dashed')
  3350. }
  3351. }
  3355. #flip levels to get WT to plot on top in facet_grid layout - no longer needed?
  3356. eMEC2$Genotype <- factor(eMEC2$Genotype, levels = c("3xTg", "WT"))
  3357. iMEC2$Genotype <- factor(iMEC2$Genotype, levels = c("3xTg", "WT"))
  3358. eMEC3$Genotype <- factor(eMEC3$Genotype, levels = c("3xTg", "WT"))
  3359. iMEC3$Genotype <- factor(iMEC3$Genotype, levels = c("3xTg", "WT"))
  3360. eDG$Genotype <- factor(eDG$Genotype, levels = c("3xTg", "WT"))
  3361. iDG$Genotype <- factor(iDG$Genotype, levels = c("3xTg", "WT"))
  3362. eCA1$Genotype <- factor(eCA1$Genotype, levels = c("3xTg", "WT"))
  3363. iCA1$Genotype <- factor(iCA1$Genotype, levels = c("3xTg", "WT"))
  3366. ##### MEC Mu Values Figure S8
  3368. circ_means_eMEC2vCA1 <- eMEC2 %>%
  3369. dplyr::group_by(Groupname, Genotype, Age_Broad) %>%
  3370. dplyr::summarise(circmean = mean.circular(mu2CA1theta_run_thresh, na.rm=TRUE))
  3371. circ_means_eMEC2vCA1 <-mutate(circ_means_eMEC2vCA1 , circmean = ifelse(circmean <'0', circmean + 2*pi, circmean))
  3372. mu_mec_p1 <- Mubygroup_2(eMEC2, mu2CA1theta_run_thresh, 'Excitatory MEC2 Mu \n(ref:CA1 theta)',1, circ_means_eMEC2vCA1)
  3373. mu_mec_p1
  3375. print('Circ Stats eMEC2 Mu vs CA1 theta (kuiper, equal-kappa, watson-williams, watson-wheeler, aov-circular)')
  3376. print('eMEC2 6mo')
  3377. Circstats_age(eMEC2_6mo, mu2CA1theta_run_thresh, eMEC2_6mo_WT, eMEC2_6mo_Tg)
  3378. print('eMEC2 8mo')
  3379. Circstats_age(eMEC2_8mo, mu2CA1theta_run_thresh, eMEC2_8mo_WT, eMEC2_8mo_Tg)
  3380. print('Corrected P-Vals')
  3381. Circstats_full(eMEC2_6mo, eMEC2_8mo, mu2CA1theta_run_thresh, eMEC2_6mo_WT, eMEC2_6mo_Tg, eMEC2_8mo_WT, eMEC2_8mo_Tg)
  3383. circ_means_eMEC3vCA1 <- eMEC3 %>%
  3384. dplyr::group_by(Groupname, Genotype, Age_Broad) %>%
  3385. dplyr::summarise(circmean = mean.circular(mu2CA1theta_run_thresh, na.rm=TRUE))
  3386. circ_means_eMEC3vCA1 <-mutate(circ_means_eMEC3vCA1 , circmean = ifelse(circmean <'0', circmean + 2*pi, circmean))
  3387. mu_mec_p2 <- Mubygroup_2(eMEC3, mu2CA1theta_run_thresh, 'Excitatory MEC3 Mu \n(ref:CA1 theta)',0, circ_means_eMEC3vCA1)
  3388. mu_mec_p2
  3390. print('Circ Stats eMEC3 Mu vs CA1 theta (kuiper, equal-kappa, watson-williams, watson-wheeler, aov-circular)')
  3391. print('eMEC3 6mo')
  3392. Circstats_age(eMEC3_6mo, mu2CA1theta_run_thresh, eMEC3_6mo_WT, eMEC3_6mo_Tg)
  3393. print('eMEC3 8mo')
  3394. Circstats_age(eMEC3_8mo, mu2CA1theta_run_thresh, eMEC3_8mo_WT, eMEC3_8mo_Tg)
  3395. print('Corrected P-Vals')
  3396. Circstats_full(eMEC3_6mo, eMEC3_8mo, mu2CA1theta_run_thresh, eMEC3_6mo_WT, eMEC3_6mo_Tg, eMEC3_8mo_WT, eMEC3_8mo_Tg)
  3398. circ_means_iMEC2vCA1 <- iMEC2 %>%
  3399. dplyr::group_by(Groupname, Genotype, Age_Broad) %>%
  3400. dplyr::summarise(circmean = mean.circular(mu2CA1theta_run_thresh, na.rm=TRUE))
  3401. circ_means_iMEC2vCA1 <-mutate(circ_means_iMEC2vCA1 , circmean = ifelse(circmean <'0', circmean + 2*pi, circmean))
  3402. mu_mec_p3 <- Mubygroup_2(iMEC2, mu2CA1theta_run_thresh, 'Inhibitory MEC2 Mu \n(ref:CA1 theta)',1, circ_means_iMEC2vCA1)
  3403. mu_mec_p3
  3405. print('Circ Stats iMEC2 Mu vs CA1 theta (kuiper, equal-kappa, watson-williams, watson-wheeler, aov-circular)')
  3406. print('iMEC2 6mo')
  3407. Circstats_age(iMEC2_6mo, mu2CA1theta_run_thresh, iMEC2_6mo_WT, iMEC2_6mo_Tg)
  3408. print('iMEC2 8mo')
  3409. Circstats_age(iMEC2_8mo, mu2CA1theta_run_thresh, iMEC2_8mo_WT, iMEC2_8mo_Tg)
  3410. print('Corrected P-Vals')
  3411. Circstats_full(iMEC2_6mo, iMEC2_8mo, mu2CA1theta_run_thresh, iMEC2_6mo_WT, iMEC2_6mo_Tg, iMEC2_8mo_WT, iMEC2_8mo_Tg)
  3413. circ_means_iMEC3vCA1 <- iMEC3 %>%
  3414. dplyr::group_by(Groupname, Genotype, Age_Broad) %>%
  3415. dplyr::summarise(circmean = mean.circular(mu2CA1theta_run_thresh, na.rm=TRUE))
  3416. circ_means_iMEC3vCA1 <-mutate(circ_means_iMEC3vCA1 , circmean = ifelse(circmean <'0', circmean + 2*pi, circmean))
  3417. mu_mec_p4 <- Mubygroup_2(iMEC3, mu2CA1theta_run_thresh, 'Inhibitory MEC3 Mu \n(ref:CA1 theta)', 1, circ_means_iMEC3vCA1)
  3418. mu_mec_p4
  3420. print('Circ Stats iMEC3 Mu vs CA1 theta (kuiper, equal-kappa, watson-williams, watson-wheeler, aov-circular)')
  3421. print('iMEC3 6mo')
  3422. Circstats_age(iMEC3_6mo, mu2CA1theta_run_thresh, iMEC3_6mo_WT, iMEC3_6mo_Tg)
  3423. print('iMEC3 8mo')
  3424. Circstats_age(iMEC3_8mo, mu2CA1theta_run_thresh, iMEC3_8mo_WT, iMEC3_8mo_Tg)
  3425. print('Corrected P-Vals')
  3426. Circstats_full(iMEC3_6mo, iMEC3_8mo, mu2CA1theta_run_thresh, iMEC3_6mo_WT, iMEC3_6mo_Tg, iMEC3_8mo_WT, iMEC3_8mo_Tg)
  3429. ##### Hippocampus Mu Values Figure 6
  3431. circ_means_eDGvCA1 <- eDG %>%
  3432. dplyr::group_by(Groupname, Genotype, Age_Broad) %>%
  3433. dplyr::summarise(circmean = mean.circular(mu2CA1theta_run_thresh, na.rm=TRUE))
  3434. circ_means_eDGvCA1 <-mutate(circ_means_eDGvCA1, circmean = ifelse(circmean <'0', circmean + 2*pi, circmean))
  3435. mu_hipp_p1 <- Mubygroup_2(eDG, mu2CA1theta_run_thresh, 'Excitatory DG Mu \n(ref:CA1 theta)', 1, circ_means_eDGvCA1)
  3436. mu_hipp_p1
  3438. print('Circ Stats eDG Mu vs CA1 theta (kuiper, equal-kappa, watson-williams, watson-wheeler, aov-circular)')
  3439. print('eDG 6mo')
  3440. Circstats_age(eDG_6mo, mu2CA1theta_run_thresh, eDG_6mo_WT, eDG_6mo_Tg)
  3441. print('eDG 8mo')
  3442. Circstats_age(eDG_8mo, mu2CA1theta_run_thresh, eDG_8mo_WT, eDG_8mo_Tg)
  3443. print('Corrected P-Vals')
  3444. Circstats_full(eDG_6mo, eDG_8mo, mu2CA1theta_run_thresh, eDG_6mo_WT, eDG_6mo_Tg, eDG_8mo_WT, eDG_8mo_Tg)
  3446. circ_means_eCA1vCA1 <- eCA1 %>%
  3447. dplyr::group_by(Groupname, Genotype, Age_Broad) %>%
  3448. dplyr::summarise(circmean = mean.circular(mu2CA1theta_run_thresh, na.rm=TRUE))
  3449. circ_means_eCA1vCA1 <-mutate(circ_means_eCA1vCA1, circmean = ifelse(circmean <'0', circmean + 2*pi, circmean))
  3450. mu_hipp_p2 <- Mubygroup_2(eCA1, mu2CA1theta_run_thresh, 'Excitatory CA1 Mu \n (ref:CA1 theta)', 1, circ_means_eCA1vCA1)
  3451. mu_hipp_p2
  3453. print('Circ Stats eCA1 Mu vs CA1 theta (kuiper, equal-kappa, watson-williams, watson-wheeler, aov-circular)')
  3454. print('eCA1 6mo')
  3455. Circstats_age(eCA1_6mo, mu2CA1theta_run_thresh, eCA1_6mo_WT, eCA1_6mo_Tg)
  3456. print('eCA1 8mo')
  3457. Circstats_age(eCA1_8mo, mu2CA1theta_run_thresh, eCA1_8mo_WT, eCA1_8mo_Tg)
  3458. print('Corrected P-Vals')
  3459. Circstats_full(eCA1_6mo, eCA1_8mo, mu2CA1theta_run_thresh, eCA1_6mo_WT, eCA1_6mo_Tg, eCA1_8mo_WT, eCA1_8mo_Tg)
  3461. circ_means_iDGvCA1 <- iDG %>%
  3462. dplyr::group_by(Groupname, Genotype, Age_Broad) %>%
  3463. dplyr::summarise(circmean = mean.circular(mu2CA1theta_run_thresh, na.rm=TRUE))
  3464. circ_means_iDGvCA1 <-mutate(circ_means_iDGvCA1, ifelse(circmean <'0', circmean + 2*pi, circmean))
  3465. mu_hipp_p3 <- Mubygroup_2(iDG, mu2CA1theta_run_thresh, 'Inhibitory DG Mu \n (ref:CA1 theta)',1, circ_means_iDGvCA1)
  3466. mu_hipp_p3
  3468. print('Circ Stats iDG Mu vs CA1 theta (kuiper, equal-kappa, watson-williams, watson-wheeler, aov-circular)')
  3469. print('iDG 6mo')
  3470. Circstats_age(iDG_6mo, mu2CA1theta_run_thresh, iDG_6mo_WT, iDG_6mo_Tg)
  3471. print('iDG 8mo')
  3472. Circstats_age(iDG_8mo, mu2CA1theta_run_thresh, iDG_8mo_WT, iDG_8mo_Tg)
  3473. print('Corrected P-Vals')
  3474. Circstats_full(iDG_6mo, iDG_8mo, mu2CA1theta_run_thresh, iDG_6mo_WT, iDG_6mo_Tg, iDG_8mo_WT, iDG_8mo_Tg)
  3476. circ_means_iCA1vCA1 <- iCA1 %>%
  3477. dplyr::group_by(Groupname, Genotype, Age_Broad) %>%
  3478. dplyr::summarise(circmean = mean.circular(mu2CA1theta_run_thresh, na.rm=TRUE))
  3479. circ_means_iCA1vCA1 <-mutate(circ_means_iCA1vCA1, circmean = ifelse(circmean <'0', circmean + 2*pi, circmean))
  3480. mu_hipp_p4 <- Mubygroup_2(iCA1, mu2CA1theta_run_thresh, 'Inhibitory CA1 Mu \n(ref:CA1 theta)',1,circ_means_iCA1vCA1)
  3481. mu_hipp_p4
  3483. print('Circ Stats iCA1 Mu vs CA1 theta (kuiper, equal-kappa, watson-williams, watson-wheeler, aov-circular)')
  3484. print('iCA1 6mo')
  3485. Circstats_age(iCA1_6mo, mu2CA1theta_run_thresh, iCA1_6mo_WT, iCA1_6mo_Tg)
  3486. print('iCA1 8mo')
  3487. Circstats_age(iCA1_8mo, mu2CA1theta_run_thresh, iCA1_8mo_WT, iCA1_8mo_Tg)
  3488. print('Corrected P-Vals')
  3489. Circstats_full(iCA1_6mo, iCA1_8mo, mu2CA1theta_run_thresh, iCA1_6mo_WT, iCA1_6mo_Tg, iCA1_8mo_WT, iCA1_8mo_Tg)
  3491. # circ_means_eMEC2vMEC <- eMEC2 %>%
  3492. # dplyr::group_by(Groupname, Genotype, Age_Broad) %>%
  3493. # dplyr::summarise(circmean = mean.circular(mu2MECtheta_run_thresh, na.rm=TRUE))
  3494. # circ_means_eMEC2vMEC <-mutate(circ_means_eMEC2vMEC , circmean = ifelse(circmean <'0', circmean + 2*pi, circmean))
  3495. # Mubygroup_2(eMEC2, mu2MECtheta_run_thresh, 'Excitatory MEC2 Mu \n(ref:MEC theta)',1, circ_means_eMEC2vMEC)
  3496. #
  3497. # print('Circ Stats eMEC2 Mu vs MEC theta (kuiper, equal-kappa, watson-williams, watson-wheeler, aov-circular)')
  3498. # print('eMEC2 6mo')
  3499. # Circstats_age(eMEC2_6mo, mu2MECtheta_run_thresh, eMEC2_6mo_WT, eMEC2_6mo_Tg)
  3500. # print('eMEC2 8mo')
  3501. # Circstats_age(eMEC2_8mo, mu2MECtheta_run_thresh, eMEC2_8mo_WT, eMEC2_8mo_Tg)
  3502. # print('Corrected P-Vals')
  3503. # Circstats_full(eMEC2_6mo, eMEC2_8mo, mu2MECtheta_run_thresh, eMEC2_6mo_WT, eMEC2_6mo_Tg, eMEC2_8mo_WT, eMEC2_8mo_Tg)
  3504. #
  3505. # circ_means_eMEC3vMEC <- eMEC3 %>%
  3506. # dplyr::group_by(Groupname, Genotype, Age_Broad) %>%
  3507. # dplyr::summarise(circmean = mean.circular(mu2MECtheta_run_thresh, na.rm=TRUE))
  3508. # circ_means_eMEC3vMEC <-mutate(circ_means_eMEC3vMEC , circmean = ifelse(circmean <'0', circmean + 2*pi, circmean))
  3509. # Mubygroup_2(eMEC3, mu2MECtheta_run_thresh, 'Excitatory MEC3 Mu \n(ref:MEC theta)',0, circ_means_eMEC3vMEC)
  3510. #
  3511. # print('Circ Stats eMEC3 Mu vs MEC theta (kuiper, equal-kappa, watson-williams, watson-wheeler, aov-circular)')
  3512. # print('eMEC3 6mo')
  3513. # Circstats_age(eMEC3_6mo, mu2MECtheta_run_thresh, eMEC3_6mo_WT, eMEC3_6mo_Tg)
  3514. # print('eMEC3 8mo')
  3515. # Circstats_age(eMEC3_8mo, mu2MECtheta_run_thresh, eMEC3_8mo_WT, eMEC3_8mo_Tg)
  3516. # print('Corrected P-Vals')
  3517. # Circstats_full(eMEC3_6mo, eMEC3_8mo, mu2MECtheta_run_thresh, eMEC3_6mo_WT, eMEC3_6mo_Tg, eMEC3_8mo_WT, eMEC3_8mo_Tg)
  3518. #
  3519. # circ_means_iMEC2vMEC <- iMEC2 %>%
  3520. # dplyr::group_by(Groupname, Genotype, Age_Broad) %>%
  3521. # dplyr::summarise(circmean = mean.circular(mu2MECtheta_run_thresh, na.rm=TRUE))
  3522. # circ_means_iMEC2vMEC <-mutate(circ_means_iMEC2vMEC , circmean = ifelse(circmean <'0', circmean + 2*pi, circmean))
  3523. # Mubygroup_2(iMEC2, mu2MECtheta_run_thresh, 'Inhibitory MEC2 Mu \n(ref:MEC theta)',1, circ_means_iMEC2vMEC)
  3524. #
  3525. # print('Circ Stats iMEC2 Mu vs MEC theta (kuiper, equal-kappa, watson-williams, watson-wheeler, aov-circular)')
  3526. # print('iMEC2 6mo')
  3527. # Circstats_age(iMEC2_6mo, mu2MECtheta_run_thresh, iMEC2_6mo_WT, iMEC2_6mo_Tg)
  3528. # print('iMEC2 8mo')
  3529. # Circstats_age(iMEC2_8mo, mu2MECtheta_run_thresh, iMEC2_8mo_WT, iMEC2_8mo_Tg)
  3530. # print('Corrected P-Vals')
  3531. # Circstats_full(iMEC2_6mo, iMEC2_8mo, mu2MECtheta_run_thresh, iMEC2_6mo_WT, iMEC2_6mo_Tg, iMEC2_8mo_WT, iMEC2_8mo_Tg)
  3532. #
  3533. #
  3534. # circ_means_iMEC3vMEC <- iMEC3 %>%
  3535. # dplyr::group_by(Groupname, Genotype, Age_Broad) %>%
  3536. # dplyr::summarise(circmean = mean.circular(mu2MECtheta_run_thresh, na.rm=TRUE))
  3537. # circ_means_iMEC3vMEC <-mutate(circ_means_iMEC3vMEC , circmean = ifelse(circmean <'0', circmean + 2*pi, circmean))
  3538. # Mubygroup_2(iMEC3, mu2MECtheta_run_thresh, 'Inhibitory MEC3 Mu \n(ref:MEC theta)', 1, circ_means_iMEC3vMEC)
  3539. #
  3540. # print('Circ Stats iMEC3 Mu vs MEC theta (kuiper, equal-kappa, watson-williams, watson-wheeler, aov-circular)')
  3541. # print('iMEC3 6mo')
  3542. # Circstats_age(iMEC3_6mo, mu2MECtheta_run_thresh, iMEC3_6mo_WT, iMEC3_6mo_Tg)
  3543. # print('iMEC3 8mo')
  3544. # Circstats_age(iMEC3_8mo, mu2MECtheta_run_thresh, iMEC3_8mo_WT, iMEC3_8mo_Tg)
  3545. # print('Corrected P-Vals')
  3546. # Circstats_full(iMEC3_6mo, iMEC3_8mo, mu2MECtheta_run_thresh, iMEC3_6mo_WT, iMEC3_6mo_Tg, iMEC3_8mo_WT, iMEC3_8mo_Tg)
  3547. #
  3549. plot_list_fr_mec= list(fr_mec_p1, fr_mec_p2)
  3550. plot_list_fr_hipp= list(fr_hipp_p1, fr_hipp_p2, fr_hipp_p3, fr_hipp_p4)
  3551. plot_list_r_mec= list(r_mec_p1, r_mec_p2, r_mec_p3, r_mec_p4, r_mec_p5, r_mec_p6, r_mec_p7, r_mec_p8)
  3552. plot_list_r_hipp= list(r_hipp_p1, r_hipp_p2, r_hipp_p3, r_hipp_p4)
  3553. plot_list_mu_mec= list(mu_mec_p1, mu_mec_p2, mu_mec_p3, mu_mec_p4 )
  3554. plot_list_mu_hipp= list(mu_hipp_p1, mu_hipp_p2, mu_hipp_p3, mu_hipp_p4)
  3556. ID = 1
  3558. for (p in plot_list_fr_mec) {
  3559. ggsave(
  3560. p,
  3561. filename=paste("FR_MEC_Plot",ID,".svg",sep=""),
  3562. width = 5,
  3563. height = 5,
  3564. dpi = 600)
  3565. ID = ID + 1
  3566. }
  3568. ID = 1
  3570. for (p in plot_list_fr_hipp) {
  3571. ggsave(
  3572. p,
  3573. filename=paste("FR_HIPP_Plot",ID,".svg",sep=""),
  3574. width = 5,
  3575. height = 5,
  3576. dpi = 600)
  3577. ID = ID + 1
  3578. }
  3580. ID = 1
  3582. for (p in plot_list_r_mec) {
  3583. ggsave(
  3584. p,
  3585. filename=paste("R_MEC_Plot",ID,".svg",sep=""),
  3586. width = 5,
  3587. height = 5,
  3588. dpi = 600)
  3589. ID = ID + 1
  3590. }
  3592. ID = 1
  3594. for (p in plot_list_r_hipp) {
  3595. ggsave(
  3596. p,
  3597. filename=paste("R_HIPP_Plot",ID,".svg",sep=""),
  3598. width = 5,
  3599. height = 5,
  3600. dpi = 600)
  3601. ID = ID + 1
  3602. }
  3605. ID = 1
  3607. for (p in plot_list_mu_mec) {
  3608. ggsave(
  3609. p,
  3610. filename=paste("Mu_MEC_Plot",ID,".svg",sep=""),
  3611. width = 5,
  3612. height = 5,
  3613. dpi = 600)
  3614. ID = ID + 1
  3615. }
  3617. ID = 1
  3619. for (p in plot_list_mu_hipp) {
  3620. ggsave(
  3621. p,
  3622. filename=paste("Mu_HIPP_Plot",ID,".svg",sep=""),
  3623. width = 5,
  3624. height = 5,
  3625. dpi = 600)
  3626. ID = ID + 1
  3627. }
  3629. #Precession - Figure S10
  3631. dataHIPP <-read.csv("precession_HIPP.csv")
  3632. dataHIPP <- subset(dataHIPP, Celltype != 'unknown')
  3634. dataHIPP<- mutate(dataHIPP, Genotype = case_when(Group == '83x' | Group == '63x' ~ '3xTg',
  3635. Group == '6wt' | Group == '8wt' ~ 'WT'))
  3637. dataHIPP<- mutate(dataHIPP, Age_Broad = case_when(Group == '83x' | Group == '8wt' ~ '8',
  3638. Group == '63x' | Group == '6wt' ~ '6'))
  3640. dataHIPP$Groupname = factor(dataHIPP$Group, levels = c('6wt', '8wt', '63x', '83x'))
  3641. dataHIPP$Genotype = factor(dataHIPP$Genotype, levels = c('WT', '3xTg'))
  3642. dataHIPP$Age_Broad = factor(dataHIPP$Age_Broad, levels = c('6', '8'))
  3644. #get frequency values from my wavelet calculation - 1 channel in mid HIPP
  3645. WaveletdataHIPP <-read.csv("Wavelet_Freq_by_Speed_Theta_midPyr_HIPP.csv") #this sheet has a bunch of other excessive info - maybe edit - just pulling out column for all running time for each animal here
  3646. WaveletdataHIPP_runall <- WaveletdataHIPP %>% distinct(Animal, .keep_all=TRUE)
  3647. WaveletdataHIPP_runall = subset(WaveletdataHIPP_runall, select = c(Animal, Group, Sex, Subregion, Length_run, PFreq5to12_run))
  3649. data2HIPP <- mutate(dataHIPP, wavelet_freq = 1) #create new column
  3651. for (row in 1:nrow(data2HIPP)) { #add theta lfp frequency info to dataframe
  3652. animal <- data2HIPP[row, "Animal"]
  3653. anim_data <- subset(WaveletdataHIPP_runall, Animal == animal)
  3654. waveletfreq = anim_data$PFreq5to12_run
  3655. data2HIPP[row, 19] <- waveletfreq
  3657. }
  3659. data2HIPP$wavelet_freq = as.numeric(data2HIPP$wavelet_freq)
  3661. data2HIPP <- mutate(data2HIPP, unitvswaveletfreq = Unitfreq - wavelet_freq) #get difference between unit and wavelet freq
  3663. data2HIPP<- mutate(data2HIPP, unit_waveletfreq_ratio = Unitfreq/wavelet_freq) #ratio
  3666. #subset
  3667. CA1 <- subset(data2HIPP, Region == 'CA1')
  3668. DG <-subset(data2HIPP, Region == 'DG')
  3669. eHIPP <-subset(data2HIPP, Celltype == 'exc')
  3670. iHIPP <-subset(data2HIPP, Celltype == 'inh')
  3671. eCA1 <-subset(CA1, Celltype == 'exc')
  3672. eDG <-subset(DG, Celltype == 'exc')
  3673. iCA1 <-subset(CA1, Celltype == 'inh')
  3674. iDG <-subset(DG, Celltype == 'inh')
  3677. #plot
  3679. #UNIT VS WAVELET FREQ
  3681. AnyPlotbygroup_adjaxis_facet(eCA1,unitvswaveletfreq, "Unit vs LFP frequency - All CA1 Exc Cells", "Unit - LFP freq", -20, 20)
  3683. Singleunit_nonparametric(eCA1, unitvswaveletfreq)
  3685. ggsave(
  3686. "UnitvLFPfreq_eCA1.svg",
  3687. AnyPlotbygroup_adjaxis_facet(eCA1,unitvswaveletfreq, "Excitatory CA1 \n Unit vs LFP frequency", "Unit - LFP freq (Hz)", -2, 15),
  3688. width = 5,
  3689. height = 5,
  3690. dpi = 600
  3691. )
  3694. #UNIT FREQ
  3696. AnyPlotbygroup_adjaxis_facet(eCA1, Unitfreq, "Unit frequency exc CA1", "Unit freq", 0, 20)
  3698. ggsave(
  3699. "Unitfreq_eCA1.svg",
  3700. AnyPlotbygroup_adjaxis_facet(eCA1,Unitfreq, "Excitatory CA1 \n Spike Train Frequency", "Spike Train Frequency (Hz)", 0, 20),
  3701. width = 5,
  3702. height = 5,
  3703. dpi = 600
  3704. )
  3706. Singleunit_nonparametric(eCA1, Unitfreq)
  3711. ```
  3715. # PV + NeuN Immunohistochemistry
  3716. ```{r pv-neun, echo=FALSE, warning = FALSE}
  3718. #TO DO: Remove wave 0 hippocampus
  3719. #remove 15 mo animals
  3721. #Figure S9
  3723. CellCountsbygroup_adjaxis<- function(df, y, title, ytitle, ymin, ymax) { # ymin =0, ymax=50){
  3724. plot <-ggplot(df ,aes(x=Genotype, y={{y}}))
  3725. plot +geom_sina(aes(col= Groupname, shape = Sex), alpha = 0.5, na.rm = TRUE, jitter_y = FALSE) +
  3726. geom_bar(position= "dodge", stat = "summary", width = 0.9, fun.y = "mean", alpha = 0.4, aes(fill=Groupname), color = "black") +
  3727. stat_summary( color = 'black', fun.data = mean_se, geom = "errorbar", width = 0.4, alpha = 0.8, show.legend=FALSE) +
  3728. ggtitle(title) + ylab(ytitle) +
  3729. scale_fill_manual("legend", values = scale_fill_palette, labels = c("WT 6 mo", "WT 8 mo", "3xTg 6 mo", "3xTg 8 mo"))+
  3730. guides(fill=guide_legend("Group")) +
  3731. #scale_fill_manual("legend", values = scale_fill_palette, guide = "none")+
  3732. scale_color_manual("legend", values = scale_colour_palette, guide = "none")+
  3733. scale_y_continuous(expand = c(0,0)) + #, limits = c(ymin,ymax), breaks = seq(0, 14, by = 2))
  3734. coord_cartesian(ylim=c(ymin, ymax))+
  3735. scale_alpha(guide = 'none') +
  3736. facet_grid(.~Age_Broad, switch = "x", labeller = as_labeller(c('6' = "6 mo", '8' = "8 mo"))) +
  3737. theme(plot.title = element_text(hjust = 0.5, size = 22, face = "bold"),
  3738. axis.text.x = element_text(size = 15, colour = "black"),
  3739. #axis.text.x = element_blank(),
  3740. #axis.ticks.x=element_blank(),
  3741. axis.text.y = element_text(size = 18, face="bold", colour = "black"),
  3742. axis.title.y = element_text(size = 20, face = "bold", colour = "black"),
  3743. axis.title.x = element_blank(),
  3744. legend.text=element_text(size=16),
  3745. legend.title=element_text(size=18),
  3746. panel.grid.major = element_blank(),
  3747. panel.grid.minor = element_blank(),
  3748. panel.background = element_blank(),
  3749. axis.line = element_line(colour = "black"),
  3750. strip.text.x = element_text(size = 18, face = "bold"),
  3751. strip.background = element_rect( fill="white"),
  3752. panel.spacing.x = unit(1.4, "lines"),
  3753. strip.placement = "outside")
  3755. }
  3757. #PV
  3759. CellCounts <- read_xlsx("PV_IHC_MEC_HIPP_final.xlsx")
  3762. CellCounts$Genotype = factor(CellCounts$Genotype, levels = c('WT', '3xTg'))
  3764. CellCounts <- mutate(CellCounts, Age_Broad = ifelse(Age < 7.5, "6",
  3765. ifelse(Age > 7.5 & Age < 10, "8", "NA")))
  3768. CellCounts <- mutate(CellCounts, Groupname = case_when(Genotype == 'WT' & Age_Broad == '6' ~ '6wt',
  3769. Genotype == '3xTg' & Age_Broad == '6' ~ '63x',
  3770. Genotype == 'WT' & Age_Broad == '8' ~ '8wt',
  3771. Genotype == '3xTg' & Age_Broad == '8' ~ '83x',
  3773. ))
  3776. MEC2_Counts <- subset(CellCounts, Subregion == 'MEC2')
  3777. MEC3_Counts <- subset(CellCounts, Subregion == 'MEC3')
  3779. DG_Counts <- subset(CellCounts, Subregion == 'DG')
  3780. CA1_Counts <- subset(CellCounts, Subregion == 'CA1')
  3781. CA3_Counts <- subset(CellCounts, Subregion == 'CA3')
  3784. #reformat data to get dataframes with values by animal instead of by slice
  3786. MEC2_Counts <- mutate(MEC2_Counts, Slice= as.factor(Slice))
  3787. MEC2_byanimal <- MEC2_Counts %>%
  3788. dplyr::select(Mouse, Genotype, Groupname, Sex, Age_Broad, Age, Wave, Cage, Slice, CellsPerSqmm)
  3789. MEC2_byanimal<- dcast(MEC2_byanimal, Mouse + Genotype + Groupname + Sex + Age_Broad + Age + Wave + Cage ~ Slice, na.rm=TRUE)
  3790. MEC2_byanimal <- mutate(MEC2_byanimal, Avg = rowMeans(dplyr::select(MEC2_byanimal,'1','2','3','4','5'), na.rm =TRUE))
  3792. MEC3_Counts <- mutate(MEC3_Counts, Slice= as.factor(Slice))
  3793. MEC3_byanimal <- MEC3_Counts %>%
  3794. dplyr::select(Mouse, Genotype, Groupname, Sex, Age_Broad, Age, Wave, Cage, Slice, CellsPerSqmm)
  3795. MEC3_byanimal<- dcast(MEC3_byanimal, Mouse + Genotype + Groupname + Sex + Age_Broad + Age + Wave + Cage ~ Slice, na.rm=TRUE)
  3796. MEC3_byanimal <- mutate(MEC3_byanimal, Avg = rowMeans(dplyr::select(MEC3_byanimal,'1','2','3','4','5'), na.rm =TRUE))
  3800. DG_Counts <- mutate(DG_Counts, Slice= as.factor(Slice))
  3801. DG_byanimal <- DG_Counts %>%
  3802. dplyr::select(Mouse, Genotype, Groupname, Sex, Age_Broad, Age, Wave, Cage, Slice, CellsPerSqmm)
  3803. DG_byanimal<- dcast(DG_byanimal, Mouse + Genotype + Groupname + Sex + Age_Broad + Age + Wave + Cage ~ Slice, na.rm=TRUE)
  3804. DG_byanimal <- mutate(DG_byanimal, Avg = rowMeans(dplyr::select(DG_byanimal,'1','2','3','4'), na.rm =TRUE))
  3806. CA1_Counts <- mutate(CA1_Counts, Slice= as.factor(Slice))
  3807. CA1_byanimal <- CA1_Counts %>%
  3808. dplyr::select(Mouse, Genotype, Groupname, Sex, Age_Broad, Age, Wave, Cage, Slice, CellsPerSqmm)
  3809. CA1_byanimal<- dcast(CA1_byanimal, Mouse + Genotype + Groupname + Sex + Age_Broad + Age + Wave + Cage ~ Slice, na.rm=TRUE)
  3810. CA1_byanimal <- mutate(CA1_byanimal, Avg = rowMeans(dplyr::select(CA1_byanimal,'1','2','3','4'), na.rm =TRUE))
  3812. CA3_Counts <- mutate(CA3_Counts, Slice= as.factor(Slice))
  3813. CA3_byanimal <- CA3_Counts %>%
  3814. dplyr::select(Mouse, Genotype, Groupname, Sex, Age_Broad, Age, Wave, Cage, Slice, CellsPerSqmm)
  3815. CA3_byanimal<- dcast(CA3_byanimal, Mouse + Genotype + Groupname + Sex + Age_Broad + Age + Wave + Cage ~ Slice, na.rm=TRUE)
  3816. CA3_byanimal <- mutate(CA3_byanimal, Avg = rowMeans(dplyr::select(CA3_byanimal,'1','2','3','4'), na.rm =TRUE))
  3819. MEC2_byanimal$Groupname= factor(MEC2_byanimal$Groupname, levels = c('6wt', '8wt', '63x', '83x'))
  3820. MEC3_byanimal$Groupname= factor(MEC3_byanimal$Groupname, levels = c('6wt', '8wt', '63x', '83x'))
  3822. CA1_byanimal$Groupname= factor(CA1_byanimal$Groupname, levels = c('6wt', '8wt', '63x', '83x'))
  3823. DG_byanimal$Groupname= factor(DG_byanimal$Groupname, levels = c('6wt', '8wt', '63x', '83x'))
  3824. CA3_byanimal$Groupname= factor(CA3_byanimal$Groupname, levels = c('6wt', '8wt', '63x', '83x'))
  3826. PV1 <- CellCountsbygroup_adjaxis(MEC2_byanimal, Avg, "PV+ Counts MEC2", expression(bold("Cells per" ~mm^2)), ymin= -0.25, ymax = 150)
  3827. PV1
  3829. print("PV MEC2 age x genotype anova")
  3830. General_AOVbyanim_ez(MEC2_byanimal, Avg, Age_Broad, Mouse)
  3831. General_byanim_holm(MEC2_byanimal, Avg, Age_Broad)
  3833. PV2 <-CellCountsbygroup_adjaxis(MEC3_byanimal, Avg, "PV+ Counts MEC3", expression(bold("Cells per" ~mm^2)), ymin= -0.25, ymax = 150)
  3834. PV2
  3836. print("PV MEC3 age x genotype anova")
  3837. General_AOVbyanim_ez(MEC3_byanimal, Avg, Age_Broad, Mouse)
  3838. General_byanim_holm(MEC3_byanimal, Avg, Age_Broad)
  3841. PV3 <- CellCountsbygroup_adjaxis(DG_byanimal, Avg, "PV+ Counts DG", expression(bold("Cells per" ~mm^2)), ymin= -0.25, ymax = 50)
  3842. PV3
  3843. print("PV DG age x genotype anova")
  3844. General_AOVbyanim_ez(DG_byanimal, Avg, Age_Broad, Mouse)
  3847. PV4 <- CellCountsbygroup_adjaxis(CA1_byanimal, Avg, "PV+ Counts CA1", expression(bold("Cells per" ~mm^2)), ymin= -0.25, ymax = 100)
  3848. PV4
  3849. print("PV CA1 age x genotype anova")
  3850. General_AOVbyanim_ez(CA1_byanimal, Avg, Age_Broad, Mouse)
  3851. General_byanim_holm(CA1_byanimal, Avg, Age_Broad)
  3853. PV5 <- CellCountsbygroup_adjaxis(CA3_byanimal, Avg, "PV+ Counts CA3", expression(bold("Cells per" ~mm^2)), ymin= -0.25, ymax = 150)
  3854. PV5
  3855. print("PV CA3 age x genotype anova")
  3856. General_AOVbyanim_ez(CA3_byanimal, Avg, Age_Broad, Mouse)
  3859. MEC3_byanimal_6mo <- subset(MEC3_byanimal, Age_Broad == '6')
  3860. MEC3_byanimal_8mo <- subset(MEC3_byanimal, Age_Broad == '8')
  3861. MEC2_byanimal_6mo <- subset(MEC2_byanimal, Age_Broad == '6')
  3862. MEC2_byanimal_8mo <- subset(MEC2_byanimal, Age_Broad == '8')
  3863. DG_byanimal_6mo <- subset(DG_byanimal, Age_Broad == '6')
  3864. DG_byanimal_8mo <- subset(DG_byanimal, Age_Broad == '8')
  3865. CA1_byanimal_6mo <- subset(CA1_byanimal, Age_Broad == '6')
  3866. CA1_byanimal_8mo <- subset(CA1_byanimal, Age_Broad == '8')
  3867. CA3_byanimal_6mo <- subset(CA3_byanimal, Age_Broad == '6')
  3868. CA3_byanimal_8mo <- subset(CA3_byanimal, Age_Broad == '8')
  3870. #Sex differences
  3871. print("PV MEC2 6 mo Genotype x Sex ANOVA")
  3872. General_AOVbyanim_ez(MEC2_byanimal_6mo, Avg, Sex, Mouse)
  3873. print("PV MEC3 6 mo Genotype x Sex ANOVA")
  3874. General_AOVbyanim_ez(MEC3_byanimal_6mo, Avg, Sex, Mouse)
  3875. print("PV MEC2 8 mo Genotype x Sex ANOVA")
  3876. General_AOVbyanim_ez(MEC2_byanimal_8mo, Avg, Sex, Mouse)
  3877. General_byanim_holm_sex(MEC2_byanimal_8mo, Avg, Sex)
  3878. print("PV MEC3 8 mo Genotype x Sex ANOVA")
  3879. General_AOVbyanim_ez(MEC3_byanimal_8mo, Avg, Sex, Mouse)
  3882. print("PV CA1 6 mo Genotype x Sex ANOVA")
  3883. General_AOVbyanim_ez(CA1_byanimal_6mo, Avg, Sex, Mouse)
  3884. print("PV DG 6 mo Genotype x Sex ANOVA")
  3885. General_AOVbyanim_ez(DG_byanimal_6mo, Avg, Sex, Mouse)
  3886. print("PV CA3 6 mo Genotype x Sex ANOVA")
  3887. General_AOVbyanim_ez(CA3_byanimal_6mo, Avg, Sex, Mouse)
  3888. print("PV CA1 8 mo Genotype x Sex ANOVA")
  3889. General_AOVbyanim_ez(CA1_byanimal_8mo, Avg, Sex, Mouse)
  3890. print("PV DG 8 mo Genotype x Sex ANOVA")
  3891. General_AOVbyanim_ez(DG_byanimal_8mo, Avg, Sex, Mouse)
  3892. print("PV CA3 8 mo Genotype x Sex ANOVA")
  3893. General_AOVbyanim_ez(CA3_byanimal_8mo, Avg, Sex, Mouse)
  3896. #Sample Sizes and Mean Ages
  3898. ###MEC2
  3899. print("Ns PV MEC2")
  3900. print("WT 6mo (M, F)")
  3901. sum(MEC2_byanimal_6mo$Genotype == "WT" & MEC2_byanimal_6mo$Age_Broad == '6')
  3902. sum(MEC2_byanimal_6mo$Genotype == "WT" & MEC2_byanimal_6mo$Age_Broad == '6' & MEC2_byanimal_6mo$Sex == 'M')
  3903. sum(MEC2_byanimal_6mo$Genotype == "WT" & MEC2_byanimal_6mo$Age_Broad == '6' & MEC2_byanimal_6mo$Sex == 'F')
  3905. print("WT 8mo (M, F)")
  3906. sum(MEC2_byanimal_8mo$Genotype == "WT" & MEC2_byanimal_8mo$Age_Broad == '8')
  3907. sum(MEC2_byanimal_8mo$Genotype == "WT" & MEC2_byanimal_8mo$Age_Broad == '8' & MEC2_byanimal_8mo$Sex == 'M')
  3908. sum(MEC2_byanimal_8mo$Genotype == "WT" & MEC2_byanimal_8mo$Age_Broad == '8' & MEC2_byanimal_8mo$Sex == 'F')
  3910. print("3xTg 6mo (M, F)")
  3911. sum(MEC2_byanimal_6mo$Genotype == "3xTg" & MEC2_byanimal_6mo$Age_Broad == '6')
  3912. sum(MEC2_byanimal_6mo$Genotype == "3xTg" & MEC2_byanimal_6mo$Age_Broad == '6' & MEC2_byanimal_6mo$Sex == 'M')
  3913. sum(MEC2_byanimal_6mo$Genotype == "3xTg" & MEC2_byanimal_6mo$Age_Broad == '6' & MEC2_byanimal_6mo$Sex == 'F')
  3915. print("3xTg 8mo (M, F)")
  3916. sum(MEC2_byanimal_8mo$Genotype == "3xTg" & MEC2_byanimal_8mo$Age_Broad == '8')
  3917. sum(MEC2_byanimal_8mo$Genotype == "3xTg" & MEC2_byanimal_8mo$Age_Broad == '8' & MEC2_byanimal_8mo$Sex == 'M')
  3918. sum(MEC2_byanimal_8mo$Genotype == "3xTg" & MEC2_byanimal_8mo$Age_Broad == '8' & MEC2_byanimal_8mo$Sex == 'F')
  3920. print("Age MEC2 6 mo")
  3921. MEC2_byanimal_6mo %>%
  3922. dplyr::group_by(Groupname) %>%
  3923. dplyr::summarise(mean = mean(Age),
  3924. sem = plotrix::std.error(Age),
  3925. min = min(Age),
  3926. max = max(Age))
  3929. print("Age MEC2 8 mo")
  3930. MEC2_byanimal_8mo %>%
  3931. dplyr::group_by(Groupname) %>%
  3932. dplyr::summarise(mean = mean(Age),
  3933. sem = plotrix::std.error(Age),
  3934. min = min(Age),
  3935. max = max(Age))
  3937. ###MEC3
  3938. print("Ns PV MEC3")
  3939. print("WT 6mo (M, F)")
  3940. sum(MEC3_byanimal_6mo$Genotype == "WT" & MEC3_byanimal_6mo$Age_Broad == '6')
  3941. sum(MEC3_byanimal_6mo$Genotype == "WT" & MEC3_byanimal_6mo$Age_Broad == '6' & MEC3_byanimal_6mo$Sex == 'M')
  3942. sum(MEC3_byanimal_6mo$Genotype == "WT" & MEC3_byanimal_6mo$Age_Broad == '6' & MEC3_byanimal_6mo$Sex == 'F')
  3944. print("WT 8mo (M, F)")
  3945. sum(MEC3_byanimal_8mo$Genotype == "WT" & MEC3_byanimal_8mo$Age_Broad == '8')
  3946. sum(MEC3_byanimal_8mo$Genotype == "WT" & MEC3_byanimal_8mo$Age_Broad == '8' & MEC3_byanimal_8mo$Sex == 'M')
  3947. sum(MEC3_byanimal_8mo$Genotype == "WT" & MEC3_byanimal_8mo$Age_Broad == '8' & MEC3_byanimal_8mo$Sex == 'F')
  3949. print("3xTg 6mo (M, F)")
  3950. sum(MEC3_byanimal_6mo$Genotype == "3xTg" & MEC3_byanimal_6mo$Age_Broad == '6')
  3951. sum(MEC3_byanimal_6mo$Genotype == "3xTg" & MEC3_byanimal_6mo$Age_Broad == '6' & MEC3_byanimal_6mo$Sex == 'M')
  3952. sum(MEC3_byanimal_6mo$Genotype == "3xTg" & MEC3_byanimal_6mo$Age_Broad == '6' & MEC3_byanimal_6mo$Sex == 'F')
  3954. print("3xTg 8mo (M, F)")
  3955. sum(MEC3_byanimal_8mo$Genotype == "3xTg" & MEC3_byanimal_8mo$Age_Broad == '8')
  3956. sum(MEC3_byanimal_8mo$Genotype == "3xTg" & MEC3_byanimal_8mo$Age_Broad == '8' & MEC3_byanimal_8mo$Sex == 'M')
  3957. sum(MEC3_byanimal_8mo$Genotype == "3xTg" & MEC3_byanimal_8mo$Age_Broad == '8' & MEC3_byanimal_8mo$Sex == 'F')
  3959. print("Age MEC3 6 mo")
  3960. MEC3_byanimal_6mo %>%
  3961. dplyr::group_by(Groupname) %>%
  3962. dplyr::summarise(mean = mean(Age),
  3963. sem = plotrix::std.error(Age),
  3964. min = min(Age),
  3965. max = max(Age))
  3968. print("Age MEC3 8 mo")
  3969. MEC3_byanimal_8mo %>%
  3970. dplyr::group_by(Groupname) %>%
  3971. dplyr::summarise(mean = mean(Age),
  3972. sem = plotrix::std.error(Age),
  3973. min = min(Age),
  3974. max = max(Age))
  3976. ###CA1
  3977. print("Ns PV CA1")
  3978. print("WT 6mo (M, F)")
  3979. sum(CA1_byanimal_6mo$Genotype == "WT" & CA1_byanimal_6mo$Age_Broad == '6')
  3980. sum(CA1_byanimal_6mo$Genotype == "WT" & CA1_byanimal_6mo$Age_Broad == '6' & CA1_byanimal_6mo$Sex == 'M')
  3981. sum(CA1_byanimal_6mo$Genotype == "WT" & CA1_byanimal_6mo$Age_Broad == '6' & CA1_byanimal_6mo$Sex == 'F')
  3983. print("WT 8mo (M, F)")
  3984. sum(CA1_byanimal_8mo$Genotype == "WT" & CA1_byanimal_8mo$Age_Broad == '8')
  3985. sum(CA1_byanimal_8mo$Genotype == "WT" & CA1_byanimal_8mo$Age_Broad == '8' & CA1_byanimal_8mo$Sex == 'M')
  3986. sum(CA1_byanimal_8mo$Genotype == "WT" & CA1_byanimal_8mo$Age_Broad == '8' & CA1_byanimal_8mo$Sex == 'F')
  3988. print("3xTg 6mo (M, F)")
  3989. sum(CA1_byanimal_6mo$Genotype == "3xTg" & CA1_byanimal_6mo$Age_Broad == '6')
  3990. sum(CA1_byanimal_6mo$Genotype == "3xTg" & CA1_byanimal_6mo$Age_Broad == '6' & CA1_byanimal_6mo$Sex == 'M')
  3991. sum(CA1_byanimal_6mo$Genotype == "3xTg" & CA1_byanimal_6mo$Age_Broad == '6' & CA1_byanimal_6mo$Sex == 'F')
  3993. print("3xTg 8mo (M, F)")
  3994. sum(CA1_byanimal_8mo$Genotype == "3xTg" & CA1_byanimal_8mo$Age_Broad == '8')
  3995. sum(CA1_byanimal_8mo$Genotype == "3xTg" & CA1_byanimal_8mo$Age_Broad == '8' & CA1_byanimal_8mo$Sex == 'M')
  3996. sum(CA1_byanimal_8mo$Genotype == "3xTg" & CA1_byanimal_8mo$Age_Broad == '8' & CA1_byanimal_8mo$Sex == 'F')
  3998. print("Age CA1 6 mo")
  3999. CA1_byanimal_6mo %>%
  4000. dplyr::group_by(Groupname) %>%
  4001. dplyr::summarise(mean = mean(Age),
  4002. sem = plotrix::std.error(Age),
  4003. min = min(Age),
  4004. max = max(Age))
  4007. print("Age CA1 8 mo")
  4008. CA1_byanimal_8mo %>%
  4009. dplyr::group_by(Groupname) %>%
  4010. dplyr::summarise(mean = mean(Age),
  4011. sem = plotrix::std.error(Age),
  4012. min = min(Age),
  4013. max = max(Age))
  4016. ###DG
  4017. print("Ns PV DG")
  4018. print("WT 6mo (M, F)")
  4019. sum(DG_byanimal_6mo$Genotype == "WT" & DG_byanimal_6mo$Age_Broad == '6')
  4020. sum(DG_byanimal_6mo$Genotype == "WT" & DG_byanimal_6mo$Age_Broad == '6' & DG_byanimal_6mo$Sex == 'M')
  4021. sum(DG_byanimal_6mo$Genotype == "WT" & DG_byanimal_6mo$Age_Broad == '6' & DG_byanimal_6mo$Sex == 'F')
  4023. print("WT 8mo (M, F)")
  4024. sum(DG_byanimal_8mo$Genotype == "WT" & DG_byanimal_8mo$Age_Broad == '8')
  4025. sum(DG_byanimal_8mo$Genotype == "WT" & DG_byanimal_8mo$Age_Broad == '8' & DG_byanimal_8mo$Sex == 'M')
  4026. sum(DG_byanimal_8mo$Genotype == "WT" & DG_byanimal_8mo$Age_Broad == '8' & DG_byanimal_8mo$Sex == 'F')
  4028. print("3xTg 6mo (M, F)")
  4029. sum(DG_byanimal_6mo$Genotype == "3xTg" & DG_byanimal_6mo$Age_Broad == '6')
  4030. sum(DG_byanimal_6mo$Genotype == "3xTg" & DG_byanimal_6mo$Age_Broad == '6' & DG_byanimal_6mo$Sex == 'M')
  4031. sum(DG_byanimal_6mo$Genotype == "3xTg" & DG_byanimal_6mo$Age_Broad == '6' & DG_byanimal_6mo$Sex == 'F')
  4033. print("3xTg 8mo (M, F)")
  4034. sum(DG_byanimal_8mo$Genotype == "3xTg" & DG_byanimal_8mo$Age_Broad == '8')
  4035. sum(DG_byanimal_8mo$Genotype == "3xTg" & DG_byanimal_8mo$Age_Broad == '8' & DG_byanimal_8mo$Sex == 'M')
  4036. sum(DG_byanimal_8mo$Genotype == "3xTg" & DG_byanimal_8mo$Age_Broad == '8' & DG_byanimal_8mo$Sex == 'F')
  4038. print("Age DG 6 mo")
  4039. DG_byanimal_6mo %>%
  4040. dplyr::group_by(Groupname) %>%
  4041. dplyr::summarise(mean = mean(Age),
  4042. sem = plotrix::std.error(Age),
  4043. min = min(Age),
  4044. max = max(Age))
  4047. print("Age DG 8 mo")
  4048. DG_byanimal_8mo %>%
  4049. dplyr::group_by(Groupname) %>%
  4050. dplyr::summarise(mean = mean(Age),
  4051. sem = plotrix::std.error(Age),
  4052. min = min(Age),
  4053. max = max(Age))
  4055. ###CA3
  4056. print("Ns PV CA3")
  4057. print("WT 6mo (M, F)")
  4058. sum(CA3_byanimal_6mo$Genotype == "WT" & CA3_byanimal_6mo$Age_Broad == '6')
  4059. sum(CA3_byanimal_6mo$Genotype == "WT" & CA3_byanimal_6mo$Age_Broad == '6' & CA3_byanimal_6mo$Sex == 'M')
  4060. sum(CA3_byanimal_6mo$Genotype == "WT" & CA3_byanimal_6mo$Age_Broad == '6' & CA3_byanimal_6mo$Sex == 'F')
  4062. print("WT 8mo (M, F)")
  4063. sum(CA3_byanimal_8mo$Genotype == "WT" & CA3_byanimal_8mo$Age_Broad == '8')
  4064. sum(CA3_byanimal_8mo$Genotype == "WT" & CA3_byanimal_8mo$Age_Broad == '8' & CA3_byanimal_8mo$Sex == 'M')
  4065. sum(CA3_byanimal_8mo$Genotype == "WT" & CA3_byanimal_8mo$Age_Broad == '8' & CA3_byanimal_8mo$Sex == 'F')
  4067. print("3xTg 6mo (M, F)")
  4068. sum(CA3_byanimal_6mo$Genotype == "3xTg" & CA3_byanimal_6mo$Age_Broad == '6')
  4069. sum(CA3_byanimal_6mo$Genotype == "3xTg" & CA3_byanimal_6mo$Age_Broad == '6' & CA3_byanimal_6mo$Sex == 'M')
  4070. sum(CA3_byanimal_6mo$Genotype == "3xTg" & CA3_byanimal_6mo$Age_Broad == '6' & CA3_byanimal_6mo$Sex == 'F')
  4072. print("3xTg 8mo (M, F)")
  4073. sum(CA3_byanimal_8mo$Genotype == "3xTg" & CA3_byanimal_8mo$Age_Broad == '8')
  4074. sum(CA3_byanimal_8mo$Genotype == "3xTg" & CA3_byanimal_8mo$Age_Broad == '8' & CA3_byanimal_8mo$Sex == 'M')
  4075. sum(CA3_byanimal_8mo$Genotype == "3xTg" & CA3_byanimal_8mo$Age_Broad == '8' & CA3_byanimal_8mo$Sex == 'F')
  4077. print("Age CA3 6 mo")
  4078. CA3_byanimal_6mo %>%
  4079. dplyr::group_by(Groupname) %>%
  4080. dplyr::summarise(mean = mean(Age),
  4081. sem = plotrix::std.error(Age),
  4082. min = min(Age),
  4083. max = max(Age))
  4086. print("Age CA3 8 mo")
  4087. CA3_byanimal_8mo %>%
  4088. dplyr::group_by(Groupname) %>%
  4089. dplyr::summarise(mean = mean(Age),
  4090. sem = plotrix::std.error(Age),
  4091. min = min(Age),
  4092. max = max(Age))
  4095. #REPEAT FOR NEUN
  4097. CellCounts<- read_xlsx("NeuN_IHC_MEC_final.xlsx")
  4100. CellCounts <- mutate(CellCounts, Genotype = case_when(Genotype == '3x-Tg' ~ '3xTg',
  4101. Genotype == '3xTg' ~ '3xTg',
  4102. Genotype == 'WT' ~ 'WT'))
  4104. CellCounts$Genotype = factor(CellCounts$Genotype, levels = c('WT', '3xTg'))
  4106. CellCounts <- mutate(CellCounts, Age_Broad = ifelse(Age < 7.5, "6",
  4107. ifelse(Age > 7.5 & Age < 10, "8", "NA")))
  4110. CellCounts <- mutate(CellCounts, Groupname = case_when(Genotype == 'WT' & Age_Broad == '6' ~ '6wt',
  4111. Genotype == '3xTg' & Age_Broad == '6' ~ '63x',
  4112. Genotype == 'WT' & Age_Broad == '8' ~ '8wt',
  4113. Genotype == '3xTg' & Age_Broad == '8' ~ '83x'
  4114. ))
  4116. #removed slices with 0 cells labeled due to poor staining caused by experimental errors - mostly in wave 2
  4118. MEC2_Counts <- subset(CellCounts, Subregion == 'MEC2')
  4119. MEC3_Counts <- subset(CellCounts, Subregion == 'MEC3')
  4122. #reformat data to get dataframes with values by animal instead of by slice
  4124. MEC2_Counts <- mutate(MEC2_Counts, Slice= as.factor(Slice))
  4125. MEC2_byanimal <- MEC2_Counts %>%
  4126. dplyr::select(Mouse, Genotype, Groupname, Sex, Age_Broad, Age, Wave, Cage, Slice, CellsPerSqmm)
  4127. MEC2_byanimal<- dcast(MEC2_byanimal, Mouse + Genotype + Groupname + Sex + Age_Broad + Age + Wave + Cage ~ Slice, na.rm=TRUE)
  4128. MEC2_byanimal <- mutate(MEC2_byanimal, Avg = rowMeans(dplyr::select(MEC2_byanimal,'1','2','3','4','5','6'), na.rm =TRUE))
  4130. MEC3_Counts <- mutate(MEC3_Counts, Slice= as.factor(Slice))
  4131. MEC3_byanimal <- MEC3_Counts %>%
  4132. dplyr::select(Mouse, Genotype, Groupname, Sex, Age_Broad, Age, Wave, Cage, Slice, CellsPerSqmm)
  4133. MEC3_byanimal<- dcast(MEC3_byanimal, Mouse + Genotype + Groupname + Sex + Age_Broad + Age + Wave + Cage ~ Slice, na.rm=TRUE)
  4134. MEC3_byanimal <- mutate(MEC3_byanimal, Avg = rowMeans(dplyr::select(MEC3_byanimal,'1','2','3','4','5','6'), na.rm =TRUE))
  4137. MEC2_byanimal$Groupname= factor(MEC2_byanimal$Groupname, levels = c('6wt', '8wt', '63x', '83x'))
  4138. MEC3_byanimal$Groupname= factor(MEC3_byanimal$Groupname, levels = c('6wt', '8wt', '63x', '83x'))
  4141. NeuN1 <- CellCountsbygroup_adjaxis(MEC2_byanimal, Avg, "NeuN+ Counts MEC2", expression(bold("Cells per" ~mm^2)), ymin= -0.25, ymax = 800)
  4142. NeuN1
  4143. print("NeuN MEC2 age x genotype anova")
  4144. General_AOVbyanim_ez(MEC2_byanimal, Avg, Age_Broad, Mouse)
  4145. General_byanim_holm(MEC2_byanimal, Avg, Age_Broad)
  4147. NeuN2 <- CellCountsbygroup_adjaxis(MEC3_byanimal, Avg, "NeuN+ Counts MEC3", expression(bold("Cells per" ~mm^2)), ymin= -0.25, ymax = 800)
  4148. NeuN2
  4149. print("NeuN MEC3 age x genotype anova")
  4150. General_AOVbyanim_ez(MEC3_byanimal, Avg, Age_Broad, Mouse)
  4151. General_byanim_holm(MEC3_byanimal, Avg, Age_Broad)
  4154. #Sex diffs
  4155. MEC3_byanimal_6mo <- subset(MEC3_byanimal, Age_Broad == '6')
  4156. MEC3_byanimal_8mo <- subset(MEC3_byanimal, Age_Broad == '8')
  4157. MEC2_byanimal_6mo <- subset(MEC2_byanimal, Age_Broad == '6')
  4158. MEC2_byanimal_8mo <- subset(MEC2_byanimal, Age_Broad == '8')
  4160. print("NeuN MEC2 6 mo Genotype x Sex ANOVA")
  4161. General_AOVbyanim_ez(MEC2_byanimal_6mo, Avg, Sex, Mouse)
  4162. print("NeuN MEC3 6 mo Genotype x Sex ANOVA")
  4163. General_AOVbyanim_ez(MEC3_byanimal_6mo, Avg, Sex, Mouse)
  4164. print("NeuN MEC2 8 mo Genotype x Sex ANOVA")
  4165. General_AOVbyanim_ez(MEC2_byanimal_8mo, Avg, Sex, Mouse)
  4166. print("NeuN MEC3 8 mo Genotype x Sex ANOVA")
  4167. General_AOVbyanim_ez(MEC3_byanimal_8mo, Avg, Sex, Mouse)
  4170. #Sample Sizes and Mean Ages
  4172. ###MEC2
  4173. print("Ns NeuN MEC2")
  4174. print("WT 6mo (M, F)")
  4175. sum(MEC2_byanimal_6mo$Genotype == "WT" & MEC2_byanimal_6mo$Age_Broad == '6')
  4176. sum(MEC2_byanimal_6mo$Genotype == "WT" & MEC2_byanimal_6mo$Age_Broad == '6' & MEC2_byanimal_6mo$Sex == 'M')
  4177. sum(MEC2_byanimal_6mo$Genotype == "WT" & MEC2_byanimal_6mo$Age_Broad == '6' & MEC2_byanimal_6mo$Sex == 'F')
  4179. print("WT 8mo (M, F)")
  4180. sum(MEC2_byanimal_8mo$Genotype == "WT" & MEC2_byanimal_8mo$Age_Broad == '8')
  4181. sum(MEC2_byanimal_8mo$Genotype == "WT" & MEC2_byanimal_8mo$Age_Broad == '8' & MEC2_byanimal_8mo$Sex == 'M')
  4182. sum(MEC2_byanimal_8mo$Genotype == "WT" & MEC2_byanimal_8mo$Age_Broad == '8' & MEC2_byanimal_8mo$Sex == 'F')
  4184. print("3xTg 6mo (M, F)")
  4185. sum(MEC2_byanimal_6mo$Genotype == "3xTg" & MEC2_byanimal_6mo$Age_Broad == '6')
  4186. sum(MEC2_byanimal_6mo$Genotype == "3xTg" & MEC2_byanimal_6mo$Age_Broad == '6' & MEC2_byanimal_6mo$Sex == 'M')
  4187. sum(MEC2_byanimal_6mo$Genotype == "3xTg" & MEC2_byanimal_6mo$Age_Broad == '6' & MEC2_byanimal_6mo$Sex == 'F')
  4189. print("3xTg 8mo (M, F)")
  4190. sum(MEC2_byanimal_8mo$Genotype == "3xTg" & MEC2_byanimal_8mo$Age_Broad == '8')
  4191. sum(MEC2_byanimal_8mo$Genotype == "3xTg" & MEC2_byanimal_8mo$Age_Broad == '8' & MEC2_byanimal_8mo$Sex == 'M')
  4192. sum(MEC2_byanimal_8mo$Genotype == "3xTg" & MEC2_byanimal_8mo$Age_Broad == '8' & MEC2_byanimal_8mo$Sex == 'F')
  4194. print("Age MEC2 6 mo")
  4195. MEC2_byanimal_6mo %>%
  4196. dplyr::group_by(Groupname) %>%
  4197. dplyr::summarise(mean = mean(Age),
  4198. sem = plotrix::std.error(Age),
  4199. min = min(Age),
  4200. max = max(Age))
  4203. print("Age MEC2 8 mo")
  4204. MEC2_byanimal_8mo %>%
  4205. dplyr::group_by(Groupname) %>%
  4206. dplyr::summarise(mean = mean(Age),
  4207. sem = plotrix::std.error(Age),
  4208. min = min(Age),
  4209. max = max(Age))
  4211. ###MEC3
  4212. print("Ns NeuN MEC3")
  4213. print("WT 6mo (M, F)")
  4214. sum(MEC3_byanimal_6mo$Genotype == "WT" & MEC3_byanimal_6mo$Age_Broad == '6')
  4215. sum(MEC3_byanimal_6mo$Genotype == "WT" & MEC3_byanimal_6mo$Age_Broad == '6' & MEC3_byanimal_6mo$Sex == 'M')
  4216. sum(MEC3_byanimal_6mo$Genotype == "WT" & MEC3_byanimal_6mo$Age_Broad == '6' & MEC3_byanimal_6mo$Sex == 'F')
  4218. print("WT 8mo (M, F)")
  4219. sum(MEC3_byanimal_8mo$Genotype == "WT" & MEC3_byanimal_8mo$Age_Broad == '8')
  4220. sum(MEC3_byanimal_8mo$Genotype == "WT" & MEC3_byanimal_8mo$Age_Broad == '8' & MEC3_byanimal_8mo$Sex == 'M')
  4221. sum(MEC3_byanimal_8mo$Genotype == "WT" & MEC3_byanimal_8mo$Age_Broad == '8' & MEC3_byanimal_8mo$Sex == 'F')
  4223. print("3xTg 6mo (M, F)")
  4224. sum(MEC3_byanimal_6mo$Genotype == "3xTg" & MEC3_byanimal_6mo$Age_Broad == '6')
  4225. sum(MEC3_byanimal_6mo$Genotype == "3xTg" & MEC3_byanimal_6mo$Age_Broad == '6' & MEC3_byanimal_6mo$Sex == 'M')
  4226. sum(MEC3_byanimal_6mo$Genotype == "3xTg" & MEC3_byanimal_6mo$Age_Broad == '6' & MEC3_byanimal_6mo$Sex == 'F')
  4228. print("3xTg 8mo (M, F)")
  4229. sum(MEC3_byanimal_8mo$Genotype == "3xTg" & MEC3_byanimal_8mo$Age_Broad == '8')
  4230. sum(MEC3_byanimal_8mo$Genotype == "3xTg" & MEC3_byanimal_8mo$Age_Broad == '8' & MEC3_byanimal_8mo$Sex == 'M')
  4231. sum(MEC3_byanimal_8mo$Genotype == "3xTg" & MEC3_byanimal_8mo$Age_Broad == '8' & MEC3_byanimal_8mo$Sex == 'F')
  4233. print("Age MEC3 6 mo")
  4234. MEC3_byanimal_6mo %>%
  4235. dplyr::group_by(Groupname) %>%
  4236. dplyr::summarise(mean = mean(Age),
  4237. sem = plotrix::std.error(Age),
  4238. min = min(Age),
  4239. max = max(Age))
  4242. print("Age MEC3 8 mo")
  4243. MEC3_byanimal_8mo %>%
  4244. dplyr::group_by(Groupname) %>%
  4245. dplyr::summarise(mean = mean(Age),
  4246. sem = plotrix::std.error(Age),
  4247. min = min(Age),
  4248. max = max(Age))
  4252. plot_list = list(PV1, PV2, PV3, PV4, PV5)
  4253. ID = 1
  4255. for (p in plot_list) {
  4256. ggsave(
  4257. p,
  4258. filename=paste("PVcounts",ID,".svg",sep=""),
  4259. width = 5,
  4260. height = 5,
  4261. dpi = 600)
  4262. ID = ID + 1
  4263. }
  4265. plot_list = list(NeuN1, NeuN2)
  4266. ID = 1
  4268. for (p in plot_list) {
  4269. ggsave(
  4270. p,
  4271. filename=paste("NeuNcounts",ID,".svg",sep=""),
  4272. width = 5,
  4273. height = 5,
  4274. dpi = 600)
  4275. ID = ID + 1
  4276. }
  4281. ```

Vetere_2026_AllPlotsAndStats.Rmd at commit 66e5072, under GPL-3.0 · at the source

Overview

Authors: Lauren M. Vetere1, Angelina M. Galas1, Nick Vaughan1, Cassidy Kohler1, Yu Feng1, Zoé Christenson Wick1, Paul A. Philipsberg1, Olga Liobimova1, Kathryn E. Gordon1, Antonio Fernandez-Ruiz2, Denise J. Cai1, Tristan Shuman1,3
ORCID iDs: Tristan Shuman
  1. Icahn School of Medicine at Mount Sinai, New York, NY, USA
  2. Cornell University, Ithaca, NY, USA
  3. Lead contact
Institutions: Icahn School of Medicine at Mount Sinai (United States); Cornell University (United States)
Journal: Cell reports, volume 45, issue 7, article 117646
Dates: published online 13 July 2026; in print 28 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.celrep.2026.117646 · PMID 42441402 · PMCID PMC13502474 · OpenAlex W4406546687
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.01.15.633171
Status: code verified
Categories: mouse (organism), Alzheimer's / dementia (population), systems (subfield)
Keywords: Hippocampus, Electrophysiology, Learning and memory, Alzheimer's disease, Entorhinal Cortex, Phase-locking, Theta Oscillations, Cp: Neuroscience, Circuit Desynchronization
MeSH: Alzheimer Disease*, Entorhinal Cortex*, Hippocampus*, Memory Disorders*, Animals, Disease Models, Animal, Male, Mice, Mice, Transgenic, Neurons, Theta Rhythm (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NINDS NIH HHS (R01 NS136590, F32 NS116416, R03 NS111493, F31 NS134301, R01 NS116357); NIA NIH HHS (RF1 AG072497, T32 AG049688, R01 AG072497, F31 AG069496); National Institute of Neurological Disorders and Stroke; NIMH NIH HHS (DP2 MH122399, R01 MH120162, R56 MH132959); National Institute of Mental Health; Simons Foundation; National Institute on Aging; American Epilepsy Society
Citations: not cited yet (Europe PMC); 161 references in the paper
Research resources: Rabbit anti-NeuN RRID:AB_10807945, Alexa Goat Anti-Rabbit 488 RRID:AB_143165, Rabbit anti-Amyloid-Beta RRID:AB_2056585, Rabbit anti-PV RRID:AB_2173898, Alexa Goat Anti-Rabbit 555 RRID:AB_2535850

Abstract

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 14 matches between paragraphs and lines of code.

ZachPenn/CellCounting

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 6de8105d8945c2e88cb6cfdfc10c1198e7235b1e, 23 August 2026
Languages: Jupyter (4), Python (1)
Size: 7 files, 5 scripts
Software Heritage: not archived
Found in: the text, “Immunohistochemistry”
Holds: README, 4 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (3 files), NumPy (3 files), OpenCV (3 files), pandas (3 files), scikit-image (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
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At the source: github.com/ZachPenn/CellCounting

buzsakilab/buzcode

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Evidence: files inventoried
Commit: 0969ddf7f55ccaca8c71969bee4b21f310840047, 27 August 2026
Languages: MATLAB (1980), C (29), C/C++ (15), Jupyter (5), C++ (1)
Size: 3,062 files, 2,030 scripts
Software Heritage: archived
Found in: the text, “Analysis of local field potential and single-uni”
Holds: README, license file, tests, documentation, 4 notebooks
Not found: CITATION.cff, environment file, continuous integration
Tools: EEGLAB (172 files), Statistics and Machine Learning Toolbox (97 files), Chronux (74 files), Signal Processing Toolbox (64 files), CircStat (41 files), Image Processing Toolbox (12 files), FieldTrip (9 files), Matplotlib (5 files), NumPy (5 files), Optimization Toolbox (4 files), Parallel Computing Toolbox (4 files), Neurodata Without Borders (PyNWB, MatNWB) (3 files), SciPy (3 files), boundedline (2 files), scikit-image (2 files), export_fig (1 file), Curve Fitting Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
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2,000 files
At the source: github.com/buzsakilab/buzcode

Zenodo 20314327

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: CircStat (32 files), Statistics and Machine Learning Toolbox (17 files), Signal Processing Toolbox (12 files), Chronux (5 files), broom (1 file), car (1 file), data.table (1 file), emmeans (1 file), lme4 (1 file), lmerTest (1 file), multcomp (1 file), reshape2 (1 file), rstatix (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
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107 files
At the source: doi.org/10.5281/zenodo.20314327

shumanlab/vetere-et-al-2026-cell-reports

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 66e5072810e0f0e6f73db6d65309d1ae083ddd0c, 19 May 2026
Languages: MATLAB (104), R (1)
Size: 165 files, 105 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, 1 notebook
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: CircStat (32 files), Statistics and Machine Learning Toolbox (17 files), Signal Processing Toolbox (12 files), Chronux (5 files), broom (1 file), car (1 file), data.table (1 file), emmeans (1 file), lme4 (1 file), lmerTest (1 file), multcomp (1 file), reshape2 (1 file), rstatix (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
107 files
At the source: github.com/ShumanLab/Vetere-et-al-2026-Cell-Reports/tree/Vetere-et-al-2026-Cell-Reports

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

Tracing map

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What the map holds:

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

Data

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

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:

  • it points to the authors' code: Zenodo 20314327
  • it says that the data are available on request
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Read it in the paper: doi.org/10.1016/j.celrep.2026.117646.

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Cell Press
  • Authors: added Tristan Shuman (0000-0003-2310-6142); removed Tristan Shuman

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 9 keywords, 11 MeSH terms, 8 funders, 161 references, 5 RRIDs.

Cite

This paper

Vetere, L. M., Galas, A. M., Vaughan, N., Kohler, C., Feng, Y., Wick, Z. C., Philipsberg, P. A., Liobimova, O., Gordon, K. E., Fernandez-Ruiz, A., Cai, D. J., & Shuman, T. (2026). Medial entorhinal-hippocampal desynchronization parallels the emergence of memory impairment in a mouse model of Alzheimer's disease pathology. Cell reports, 45(7), 117646. https://doi.org/10.1016/j.celrep.2026.117646

BibTeX

@article{vetere2026medial,
author = {Vetere, Lauren M. and Galas, Angelina M. and Vaughan, Nick and Kohler, Cassidy and Feng, Yu and Wick, Zoé Christenson and Philipsberg, Paul A. and Liobimova, Olga and Gordon, Kathryn E. and Fernandez-Ruiz, Antonio and Cai, Denise J. and Shuman, Tristan},
title = {{Medial entorhinal-hippocampal desynchronization parallels the emergence of memory impairment in a mouse model of Alzheimer's disease pathology}},
journal = {Cell reports},
year = {2026},
month = jul,
volume = {45},
number = {7},
pages = {117646},
publisher = {Cell Press},
issn = {2211-1247},
doi = {10.1016/j.celrep.2026.117646},
url = {https://doi.org/10.1016/j.celrep.2026.117646},
pmid = {42441402},
pmcid = {PMC13502474}
}

RIS

TY - JOUR
AU - Vetere, Lauren M.
AU - Galas, Angelina M.
AU - Vaughan, Nick
AU - Kohler, Cassidy
AU - Feng, Yu
AU - Wick, Zoé Christenson
AU - Philipsberg, Paul A.
AU - Liobimova, Olga
AU - Gordon, Kathryn E.
AU - Fernandez-Ruiz, Antonio
AU - Cai, Denise J.
AU - Shuman, Tristan
TI - Medial entorhinal-hippocampal desynchronization parallels the emergence of memory impairment in a mouse model of Alzheimer's disease pathology
T2 - Cell reports
J2 - Cell Rep
PY - 2026
DA - 2026/07/13
VL - 45
IS - 7
SP - 117646
SN - 2211-1247
PB - Cell Press
DO - 10.1016/j.celrep.2026.117646
UR - https://doi.org/10.1016/j.celrep.2026.117646
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.celrep.2026.117646",
"type": "article-journal",
"title": "Medial entorhinal-hippocampal desynchronization parallels the emergence of memory impairment in a mouse model of Alzheimer's disease pathology",
"container-title": "Cell reports",
"author": [
{
"family": "Vetere",
"given": "Lauren M."
},
{
"family": "Galas",
"given": "Angelina M."
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{
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{
"family": "Kohler",
"given": "Cassidy"
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{
"family": "Feng",
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},
{
"family": "Wick",
"given": "Zoé Christenson"
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{
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"given": "Paul A."
},
{
"family": "Liobimova",
"given": "Olga"
},
{
"family": "Gordon",
"given": "Kathryn E."
},
{
"family": "Fernandez-Ruiz",
"given": "Antonio"
},
{
"family": "Cai",
"given": "Denise J."
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{
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"given": "Tristan"
}
],
"container-title-short": "Cell Rep",
"volume": "45",
"issue": "7",
"page": "117646",
"DOI": "10.1016/j.celrep.2026.117646",
"PMID": "42441402",
"PMCID": "PMC13502474",
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}
}

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

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