OSCR

Calbindin stratifies midbrain dopaminergic neurons governing distinct aspects of locomotion.

Code ↔ Paper

6 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 6 matches
  1. [1] § Results › Silencing of CALB1− or CALB1+ DA Neurons Uncovers Dissociable Roles in Motor Learning but Shared Contributions to Voluntary Movement. ↔ Open Field Analysis/DREADD/Open Field Analysis_DREADD_TimeCentre_Batch1.R, lines 164–224 · score 0.61 · Games Howell, post hoc, way ANOVA, fDIO, mCherry, mice
  2. [2] § Results › DA Transients in the DLS May Contribute to Movement Vigor, but Are Neither Necessary Nor Sufficient to Command Their Execution. ↔ Fiberphotometry/Fiberphotometry_DA peaks to locomotor bouts analysis.R, lines 1353–1394 · score 0.54 · locomotor bouts, dLight, fDIO, transitorily, mCherry, peak
  3. [3] § Results › DA Transients in the DLS May Contribute to Movement Vigor, but Are Neither Necessary Nor Sufficient to Command Their Execution. ↔ Fiberphotometry/Fiberphotometry_DA peaks to locomotor bouts analysis.R, lines 1353–1394 · score 0.54 · pre DCZ, forcing locomotion, hM4Di, mice
  4. [4] § Results › Non-Cell-Autonomous Loss of CALB1− DA Neurons Following CALB1+ Ablation Coincides With Local Inflammation. ↔ Striatum analysis/Striatum analysis_taCasp3_Dat-Cre.R, lines 630–684 · score 0.52 · Dat ires Cre, post hoc, way ANOVA, Tukey, striatum, striatal
  5. [5] § Results › DA Transients in the DLS May Contribute to Movement Vigor, but Are Neither Necessary Nor Sufficient to Command Their Execution. ↔ Fiberphotometry/Fiberphotometry_DA peaks analysis.R, lines 322–387 · score 0.52 · DCZ injection, dLight, fDIO, intervals, mCherry, SD
  6. [6] § Results › CALB1− or CALB1+ DA Neurons’ Bilateral Ablation Yields Distinct Impairments on Weight Loss and Motor Learning, but Shared Deficits on Voluntary Movements. ↔ Rotarod Analysis.R, lines 550–602 · score 0.52 · Games Howell, taCasp3, mixed, slope, Latency, day

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

R · 1,624 lines · 67 KB · no license · 2 matches

  1. library(ggplot2)
  2. library(tidyverse)
  3. library(data.table)
  4. library(ggpubr)
  5. library(tidyplots)
  6. library(data.table)
  7. library(DescTools)
  8. library(magick)
  9. library(imager)
  10. library(lattice)
  11. library(viridis)
  12. #library(paletteer)
  13. library(gplots)
  14. library(sommer)
  15. library(patchwork)
  16. library(gganimate)
  17. library(lubridate)
  18. library(ggpointdensity)
  19. library(stringr)
  20. library(scales)
  21. library(purrr)
  22. library(gglinedensity)
  23. library(ggExtra)
  24. library(WRS2)
  25. library(rstatix)
  26. #-------------------------------------------------------------------------------
  27. ################################################################################
  28. ########################## LOADING BODY MOTION TRACES ########################
  29. ################################################################################
  31. setwd("C:/Users/cyril/OneDrive - McGill University (1)/Documents/PhD/Raw data/Fibrephotometry/dLight_DCZinj5min/BodyTraces_csv")
  33. #Import the body traces as a list
  34. csv_files <- list.files(pattern = "\\.csv$")
  36. Body_motion <- list()
  37. for (n in csv_files) {
  38. Body_motion[[n]] <- read.csv(n)
  39. Body_motion[[n]]$Time <- Body_motion[[n]]$frame_idx * 0.0333333333333333 #Note add a column to provide the timescale to fibrephotometry, resolution is 0,0333 sec. per frame
  40. }
  42. names(Body_motion) <- unlist(strsplit(csv_files, split = ".analysis.csv"))
  44. #-------------------------------------------------------------------------------
  45. #Load the image
  46. #image_filenames <- list.files(pattern="*.png")
  47. #img <- readPNG(image_filenames[1])
  48. #h <- nrow(img)
  49. #w <- ncol(img)
  50. #windows(width = 10, height = 10 * (h/w))
  51. #par(mar = c(0,0,0,0), xaxs = "i", yaxs = "i")
  52. #plot(1, type = "n", xlim = c(0, w), ylim = c(h, 0), asp = 1, axes = FALSE)
  53. #rasterImage(img, 0, h, w, 0)
  55. #Place two dots in corners
  56. #pts <- locator(2)
  58. #Show the points
  59. #points(pts$x, pts$y, col = "red", pch = 19, cex = 1.5)
  61. #Extract the distance in pixels
  62. #x1 <- pts$x[1]
  63. #y1 <- pts$y[1]
  64. #x2 <- pts$x[2]
  65. #y2 <- pts$y[2]
  67. #Pixel_Distance <- sqrt((x2-x1)^2+(y2-y1)^2)
  68. #Resolution <- 39.5/Pixel_Distance
  69. #Resolution
  71. #Note: The value I got for one pixel is equivalent to 0.04483225, or 0.448mm
  72. Resolution <- 0.448
  74. #-------------------------------------------------------------------------------
  75. #Quality control
  76. Torso <- list()
  77. for (mouseID in names(Body_motion)) {
  78. Torso[[mouseID]] <- Body_motion[[mouseID]]
  80. #Remove the rows has unlablled torso points
  81. Torso[[mouseID]] <- Torso[[mouseID]][!is.na(Torso[[mouseID]]$torso.x),]
  82. Torso[[mouseID]] <- Torso[[mouseID]][!is.na(Torso[[mouseID]]$torso.y),]
  84. #Remove the rows that has no torso score
  85. Torso[[mouseID]] <- Torso[[mouseID]][!is.na(Torso[[mouseID]]$torso.score),]
  87. #Remove the torso points tnat has a confidence score < 0.8
  88. Torso[[mouseID]] <- Torso[[mouseID]][Torso[[mouseID]]$torso.score>0.8,]
  90. #Convert the x y coordinates into mm scale
  91. Torso[[mouseID]][,c("head.x", "head.y", "torso.x", "torso.y", "tail_base.x", "tail_base.y")] <- Torso[[mouseID]][,c("head.x", "head.y", "torso.x", "torso.y", "tail_base.x", "tail_base.y")] * Resolution
  93. #Remove the track column
  94. Torso[[mouseID]]$track <- NULL
  95. }
  97. #------------------------------------------------------------------------
  98. #Downsample the data to the time resolution
  99. time_resolution <- 0.1 #Time resolution in second
  101. for (mouseID in names(Torso)) {
  102. print(mouseID)
  103. Ref_frames <- seq(from = 0, to = 2100, by = time_resolution)
  104. Closest_frames <- Closest(Torso[[mouseID]]$Time, Ref_frames, which = FALSE, na.rm = FALSE)
  106. New_frames <- c()
  107. for (x in 1:length(Closest_frames)) {
  108. New_frames[x] <- Closest_frames[[x]]
  109. }
  111. New_frames <- unique(New_frames) #Some frames have been assigned in double, so need to take only the unique ones
  113. Frame_indices <- c()
  114. for (x in 1:length(New_frames)) {
  115. Frame_indices[x] <- which(Torso[[mouseID]]$Time == New_frames[x])
  116. }
  118. #Subsample the frames
  119. Torso[[mouseID]] <- Torso[[mouseID]][Frame_indices,]
  121. }
  123. #-------------------------------------------------------------------------------
  124. #Compute the values to velocity
  125. velocity <- function(X1, X2, Y1, Y2, T1, T2) {
  126. speed <- (sqrt((X2 - X1)^2 + (Y2-Y1)^2))/(T2-T1)
  127. return (speed)
  128. }
  130. for (mouseID in names(Torso)) {
  132. print(mouseID)
  133. Torso.velocity <- c()
  135. for (x in 2:nrow(Torso[[mouseID]])) {
  136. Torso.velocity[x] <- velocity(X1 = Torso[[mouseID]]$torso.x[x-1],
  137. X2 = Torso[[mouseID]]$torso.x[x],
  138. Y1 = Torso[[mouseID]]$torso.y[x-1],
  139. Y2 = Torso[[mouseID]]$torso.y[x],
  140. T1 = Torso[[mouseID]]$Time[x-1],
  141. T2 = Torso[[mouseID]]$Time[x])
  142. }
  144. Torso[[mouseID]]$Torso.velocity <- Torso.velocity
  146. }
  148. #------------------------------------------------------------------------
  149. #Rearrange the torso list keeping the sleap_data as a separate element
  150. Torso_New <- list()
  152. for (mouseID in names(Torso)) {
  153. Torso_New[[mouseID]][["Sleap_data"]] <- Torso[[mouseID]]
  154. }
  156. Torso <- Torso_New
  157. Torso_New <- NULL
  159. #------------------------------------------------------------------------
  160. # Extract the bouts
  161. #Note: Bouts will be defined as speed is defined as an occurence where the speed is suddenly increased >25mm/s for at least 600ms
  163. #Set the threshold for speed and time
  164. Speed_Threshold <- 25 #in mm/s
  165. Time_Threshold <- 0.6 #in sec.
  167. #Extract the bouts
  169. for (mouseID in names(Torso)) {
  171. #Identify the index having a velocity value higher than the time threshold
  172. idx <- which(Torso[[mouseID]][["Sleap_data"]]$Torso.velocity > Speed_Threshold)
  173. grp <- cumsum(c(1, diff(idx) != 1)) #Identify index that are seperated by more than one value
  174. Potential_bouts <- split(idx, grp)
  176. Bouts_Start_Index <- c()
  177. Bouts_End_Index <- c()
  179. for (n in 1:length(Potential_bouts)) {
  181. Start_Index <- Potential_bouts[[n]][1]
  182. End_Index <- Potential_bouts[[n]][length(Potential_bouts[[n]])]
  184. #Filter for the bouts that are longer than the time threshold
  185. if (Torso[[mouseID]][["Sleap_data"]]$Time[End_Index] - Torso[[mouseID]][["Sleap_data"]]$Time[Start_Index] > Time_Threshold) {
  186. Bouts_Start_Index[n] <- Start_Index - 1 #Adding one index prior occurence which has a speed > threshold
  187. Bouts_End_Index[n] <- End_Index + 1 #Adding one index post bout occurence which has a speed > threshold
  188. }
  189. }
  191. #Remove the NA generated
  192. Bouts_Start_Index <- Bouts_Start_Index[!is.na(Bouts_Start_Index)]
  193. Bouts_End_Index <- Bouts_End_Index[!is.na(Bouts_End_Index)]
  195. #Store the bouts index
  196. Torso[[mouseID]][["Bouts_Index"]] <- data.frame(Start_Index = Bouts_Start_Index, End_Index = Bouts_End_Index)
  198. #Generate a list to store the bouts
  199. Torso[[mouseID]][["Bouts"]] <- list()
  201. for (Bout_n in 1:nrow(Torso[[mouseID]][["Bouts_Index"]])) {
  202. Torso[[mouseID]][["Bouts"]][[Bout_n]] <- Torso[[mouseID]][["Sleap_data"]][Torso[[mouseID]][["Bouts_Index"]]$Start_Index[Bout_n]:Torso[[mouseID]][["Bouts_Index"]]$End_Index[Bout_n],]
  203. }
  204. }
  206. #------------------------------------------------------------------------
  207. #Save the RDS file
  208. setwd("C:/Users/cyril/OneDrive - McGill University (1)/Documents/PhD/Raw data/Fibrephotometry/dLight_DCZinj5min/Output/Body_motion")
  209. saveRDS(Torso, file = "Body_motion.rds")
  211. #------------------------------------------------------------------------
  212. #Integrate the body motion and DA traces together
  213. Body_motion <- readRDS("C:/Users/cyril/OneDrive - McGill University (1)/Documents/PhD/Raw data/Fibrephotometry/dLight_DCZinj5min/Output/Body_motion/Body_motion.rds")
  215. #Filter the speed of K0331 at the moment where the cable got unpluged
  216. Body_motion[["K0331"]][["Sleap_data"]] <- Body_motion[["K0331"]][["Sleap_data"]] |>
  217. dplyr::filter(!between(Time, 1640, 1680))
  219. Body_motion[["K0331"]][["Sleap_data"]] <- Body_motion[["K0331"]][["Sleap_data"]] |>
  220. dplyr::filter(!between(Time, 1820, 1860))
  222. intervals <- list(c(1640, 1680), c(1820, 1860))
  224. Body_motion[["K0331"]][["Bouts"]] <- Filter(function(bout) {
  225. bout_time <- bout$Time
  227. is_bad <- any(sapply(intervals, function(inter) {
  228. any(bout_time >= inter[1] & bout_time <= inter[2])
  229. }))
  231. return(!is_bad)
  232. }, Body_motion[["K0331"]][["Bouts"]])
  235. #Import the traces as a list
  237. setwd("C:/Users/cyril/OneDrive - McGill University (1)/Documents/PhD/Raw data/Fibrephotometry/dLight_DCZinj5min/dLightTraces/DFF/LowBF3Hz_IRLS300sec")
  238. #csv_files <- list.files(pattern = "\\.csv$")
  239. #Traces <- list()
  240. #for (n in 1:length(csv_files)) {
  241. # Traces[[n]] <- read.csv(csv_files[n])
  242. #}
  243. #names(Traces) <- unlist(strsplit(csv_files, split = "_DFF_0000.csv"))
  245. Traces <- readRDS("C:/Users/cyril/OneDrive - McGill University (1)/Documents/PhD/Raw data/Fibrephotometry/dLight_DCZinj5min/Output/Peaks analysis/LOESS_Traces.rds")
  246. names(Traces) <- unlist(strsplit(names(Traces), split = "_DFF_0000.csv"))
  248. #Add the DA trace per mouse
  249. for (mouseID in names(Body_motion)) {
  250. Body_motion[[mouseID]][["dLight_DFF"]] <- Traces[[mouseID]]
  251. }
  253. #There is a mistake of labelling to one of these ID, I will correct it later
  254. #Body_motion[["K0861"]][["dLight_DFF"]] <- Traces[["J0861"]]
  255. Body_motion[["K0871"]][["dLight_DFF"]] <- Traces[["J0871"]]
  257. #Add a AIN01 column that correspond to DFF
  258. for (mouseID in names(Body_motion)) {
  259. Body_motion[[mouseID]][["dLight_DFF"]]$AIN01 <- Body_motion[[mouseID]][["dLight_DFF"]]$DFF * 100
  260. }
  262. #Generate a column where the average trace generated by loess is substracted from the deltaF/F
  263. for (mouseID in names(Body_motion)) {
  264. Body_motion[[mouseID]][["dLight_DFF"]]$DFF_LOESSsub <- Body_motion[[mouseID]][["dLight_DFF"]]$AIN01 - Body_motion[[mouseID]][["dLight_DFF"]]$loess0.1
  265. }
  267. #------------------------------------------------------------------------
  268. #Generate an average trace for dLight prior and post-bout occurence and bout peak-speed
  270. Plot_PeakSpeed <- function(curve_to_timeloc, n_Seconds, min_time, max_time, title, ylim) {
  271. curve_col_index <- which(colnames(Body_motion[[mouseID]][["dLight_DFF"]]) == curve_to_timeloc)
  272. Bouts <- list()
  274. for (mouseID in names(Body_motion)) {
  276. Bouts[[mouseID]] <- list()
  277. Bouts[[mouseID]][["Bouts"]] <- list()
  279. for (Bout_n in seq_along(Body_motion[[mouseID]][["Bouts"]])) {
  280. start_time <- Body_motion[[mouseID]][["Bouts"]][[Bout_n]]$Time[1]
  282. if (start_time > min_time && start_time < max_time) {
  283. Bouts[[mouseID]][["Bouts"]][[length(Bouts[[mouseID]][["Bouts"]]) + 1]] <- Body_motion[[mouseID]][["Bouts"]][[Bout_n]]
  284. }
  285. }
  286. }
  289. #Time loc these bouts few seconds before and after to their occurence
  290. #n_Seconds <- 2
  292. for (mouseID in names(Bouts)) {
  294. print(paste("Time loc:", mouseID))
  297. Bouts[[mouseID]][["Time_Range"]] <- list()
  299. for (Bout_n in seq_along(Bouts[[mouseID]][["Bouts"]])) {
  300. Bout <- Bouts[[mouseID]][["Bouts"]][[Bout_n]]
  301. Start_Time <- Bout$Time[1]
  302. Pre_Time <- Start_Time - n_Seconds
  303. Post_Time <- Start_Time + n_Seconds
  304. Peak_Time <- Bout[which.max(Bout$Time),]$Time
  305. Peak_Pre_Time <- Peak_Time - n_Seconds
  306. Peak_Post_Time <- Peak_Time + n_Seconds
  308. Bouts[[mouseID]][["Time_Range"]][[Bout_n]] <- data.frame(Start_Time = Start_Time,
  309. Pre_Time = Pre_Time,
  310. Post_Time = Post_Time,
  311. Peak_Time = Peak_Time,
  312. Peak_Pre_Time = Peak_Pre_Time,
  313. Peak_Post_Time = Peak_Post_Time)
  314. }
  315. }
  318. #Time loc the dLight traces
  319. for (mouseID in names(Bouts)) {
  321. print(paste("Time loc step2:", mouseID))
  323. Bouts[[mouseID]][["TimeLoc_dLight(BoutStart)"]] <- list()
  324. Bouts[[mouseID]][["TimeLoc_dLight(PeakSpeed)"]] <- list()
  326. for (Bout_n in seq_along(Bouts[[mouseID]][["Bouts"]])) {
  327. Start_Time <- Bouts[[mouseID]][["Time_Range"]][[Bout_n]]$Start_Time
  328. Pre_Time <- Bouts[[mouseID]][["Time_Range"]][[Bout_n]]$Pre_Time
  329. Post_Time <- Bouts[[mouseID]][["Time_Range"]][[Bout_n]]$Post_Time
  330. Peak_Time <- Bouts[[mouseID]][["Time_Range"]][[Bout_n]]$Peak_Time
  331. Peak_Pre_Time <- Bouts[[mouseID]][["Time_Range"]][[Bout_n]]$Peak_Pre_Time
  332. Peak_Post_Time <- Bouts[[mouseID]][["Time_Range"]][[Bout_n]]$Peak_Post_Time
  334. dLight_DFF <- Body_motion[[mouseID]][["dLight_DFF"]]
  335. dLight_DFF <- dLight_DFF[dLight_DFF$Time > Pre_Time & dLight_DFF$Time < Post_Time,]
  336. dLight_DFF$Time_Loc <- dLight_DFF$Time - Start_Time
  337. Bouts[[mouseID]][["TimeLoc_dLight(BoutStart)"]][[Bout_n]] <- dLight_DFF
  339. dLight_DFF <- Body_motion[[mouseID]][["dLight_DFF"]]
  340. dLight_DFF <- dLight_DFF[dLight_DFF$Time > Peak_Pre_Time & dLight_DFF$Time < Peak_Post_Time,]
  341. dLight_DFF$Time_Loc <- dLight_DFF$Time - Peak_Time
  342. Bouts[[mouseID]][["TimeLoc_dLight(PeakSpeed)"]][[Bout_n]] <- dLight_DFF
  344. }
  345. }
  347. #Since the T times for Time loc are not exactly the same, interpolate the deltaF/F values at consensus times
  348. for (mouseID in names(Bouts)) {
  350. print(paste("Interpolation:", mouseID))
  353. Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(BoutStart)"]] <- list()
  354. Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(PeakSpeed)"]] <- list()
  356. for (Bout_n in seq_along(Bouts[[mouseID]][["Bouts"]])) {
  358. Time <- seq(from = -n_Seconds, to = n_Seconds, by = 0.02)
  359. data <- Bouts[[mouseID]][["TimeLoc_dLight(BoutStart)"]][[Bout_n]]
  360. Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(BoutStart)"]][[Bout_n]] <- as.data.frame(approx(x = data$Time_Loc, y = data[,curve_col_index], xout = Time))
  361. colnames(Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(BoutStart)"]][[Bout_n]]) <- c("Time", "dLight_DFF")
  363. Time <- seq(from = -n_Seconds, to = n_Seconds, by = 0.02)
  364. data <- Bouts[[mouseID]][["TimeLoc_dLight(PeakSpeed)"]][[Bout_n]]
  365. Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(PeakSpeed)"]][[Bout_n]] <- as.data.frame(approx(x = data$Time_Loc, y = data[,curve_col_index], xout = Time))
  366. colnames(Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(PeakSpeed)"]][[Bout_n]]) <- c("Time", "dLight_DFF")
  367. }
  368. }
  370. #Combine all the interpolated traces
  371. All_traces <- list()
  372. counter <- 1
  373. for (mouseID in names(Bouts)) {
  374. for (Bout_n in seq_along(Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(BoutStart)"]])) {
  375. All_traces[[counter]] <- Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(BoutStart)"]][[Bout_n]]
  376. All_traces[[counter]]$TraceID <- as.character(rep(counter, times = nrow(All_traces[[counter]])))
  377. All_traces[[counter]]$mouseID <- rep(mouseID, times = nrow(All_traces[[counter]]))
  378. counter <- counter + 1
  379. }
  380. }
  382. from_BoutStart <- bind_rows(All_traces)
  383. from_BoutStart <- from_BoutStart %>%
  384. mutate(
  385. Group = case_when(
  386. mouseID == "J0777" ~ "fDIO-mCherry",
  387. mouseID == "J0782" ~ "fDIO-mCherry",
  388. mouseID == "J0859" ~ "fDIO-mCherry",
  389. mouseID == "K0871" ~ "fDIO-mCherry",
  391. mouseID == "J0779" ~ "CoffFon-hM4Di",
  392. mouseID == "J0784" ~ "CoffFon-hM4Di",
  393. mouseID == "K0331" ~ "CoffFon-hM4Di",
  394. mouseID == "K0994" ~ "CoffFon-hM4Di",
  395. mouseID == "K0861" ~ "CoffFon-hM4Di",
  397. mouseID == "K0029" ~ "ConFon-hM4Di",
  398. mouseID == "K0863" ~ "ConFon-hM4Di",
  399. mouseID == "K0865" ~ "ConFon-hM4Di",
  400. mouseID == "K0990" ~ "ConFon-hM4Di"
  401. )
  402. )
  404. All_traces <- list()
  405. counter <- 1
  406. for (mouseID in names(Bouts)) {
  407. for (Bout_n in seq_along(Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(PeakSpeed)"]])) {
  408. All_traces[[counter]] <- Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(PeakSpeed)"]][[Bout_n]]
  409. All_traces[[counter]]$TraceID <- as.character(rep(counter, times = nrow(All_traces[[counter]])))
  410. All_traces[[counter]]$mouseID <- rep(mouseID, times = nrow(All_traces[[counter]]))
  411. counter <- counter + 1
  412. }
  413. }
  415. from_PeakSpeed <- bind_rows(All_traces)
  416. from_PeakSpeed <- from_PeakSpeed %>%
  417. mutate(
  418. Group = case_when(
  419. mouseID == "J0777" ~ "fDIO-mCherry",
  420. mouseID == "J0782" ~ "fDIO-mCherry",
  421. mouseID == "J0859" ~ "fDIO-mCherry",
  422. mouseID == "K0871" ~ "fDIO-mCherry",
  424. mouseID == "J0779" ~ "CoffFon-hM4Di",
  425. mouseID == "J0784" ~ "CoffFon-hM4Di",
  426. mouseID == "K0331" ~ "CoffFon-hM4Di",
  427. mouseID == "K0994" ~ "CoffFon-hM4Di",
  428. mouseID == "K0861" ~ "CoffFon-hM4Di",
  430. mouseID == "K0029" ~ "ConFon-hM4Di",
  431. mouseID == "K0863" ~ "ConFon-hM4Di",
  432. mouseID == "K0865" ~ "ConFon-hM4Di",
  433. mouseID == "K0990" ~ "ConFon-hM4Di"
  434. )
  435. )
  437. plot <- from_PeakSpeed |>
  438. tidyplot(x = Time, y = dLight_DFF, color = Group) |>
  439. add_mean_line() |>
  440. add_ci95_ribbon() |>
  441. adjust_title(title) |>
  442. adjust_x_axis_title("Time from peak speed (sec.)") |>
  443. adjust_x_axis(limits = c(-2, 1.5), breaks = seq(from = -10, to = 10, by = 0.5)) |>
  444. adjust_y_axis(limits = ylim, breaks = seq(from = -10, to = 10, by = 1)) |>
  445. adjust_y_axis_title("dLight dF/F (%)") #|>
  447. ggsave(paste(title, "_PeakSpeed.pdf", sep = ""),
  448. width = 4,
  449. height = 4,
  450. plot = plot)
  452. plot
  454. #Collect the number of bouts per mouse
  455. nBouts <- c()
  456. for (mouseID_n in 1:length(Bouts)) {
  457. nBouts[mouseID_n] <- length(Bouts[[mouseID_n]][["Bouts"]])
  458. }
  460. nBouts_df <- data.frame(mouseID = names(Bouts), nBouts = nBouts)
  461. nBouts_df <- nBouts_df %>%
  462. mutate(
  463. Group = case_when(
  464. mouseID == "J0777" ~ "fDIO-mCherry",
  465. mouseID == "J0782" ~ "fDIO-mCherry",
  466. mouseID == "J0859" ~ "fDIO-mCherry",
  467. mouseID == "K0871" ~ "fDIO-mCherry",
  469. mouseID == "J0779" ~ "CoffFon-hM4Di",
  470. mouseID == "J0784" ~ "CoffFon-hM4Di",
  471. mouseID == "K0331" ~ "CoffFon-hM4Di",
  472. mouseID == "K0994" ~ "CoffFon-hM4Di",
  473. mouseID == "K0861" ~ "CoffFon-hM4Di",
  475. mouseID == "K0029" ~ "ConFon-hM4Di",
  476. mouseID == "K0863" ~ "ConFon-hM4Di",
  477. mouseID == "K0865" ~ "ConFon-hM4Di",
  478. mouseID == "K0990" ~ "ConFon-hM4Di"
  479. )
  480. )
  482. print(nBouts_df)
  483. write.csv(nBouts_df, paste(title, "nBouts.csv", sep = "_")) #save the data to CSV
  485. nBouts_df_pergroup <- aggregate(nBouts ~ Group, data = nBouts_df, FUN = sum, na.rm = TRUE)
  486. print(nBouts_df_pergroup)
  487. write.csv(nBouts_df_pergroup, paste(title, "nBoutsperGroup.csv", sep = "_")) #save the data to CSV
  490. }
  492. Plot_BoutStart <- function(curve_to_timeloc, n_Seconds, ylim, min_time, max_time, title ) {
  493. curve_col_index <- which(colnames(Body_motion[[mouseID]][["dLight_DFF"]]) == curve_to_timeloc)
  494. Bouts <- list()
  496. for (mouseID in names(Body_motion)) {
  497. Bouts[[mouseID]] <- list()
  498. Bouts[[mouseID]][["Bouts"]] <- list()
  500. for (Bout_n in seq_along(Body_motion[[mouseID]][["Bouts"]])) {
  501. start_time <- Body_motion[[mouseID]][["Bouts"]][[Bout_n]]$Time[1]
  503. if (start_time > min_time && start_time < max_time) {
  504. Bouts[[mouseID]][["Bouts"]][[length(Bouts[[mouseID]][["Bouts"]]) + 1]] <- Body_motion[[mouseID]][["Bouts"]][[Bout_n]]
  505. }
  506. }
  507. }
  510. #Time loc these bouts few seconds before and after to their occurence
  511. #n_Seconds <- 2
  513. for (mouseID in names(Bouts)) {
  514. Bouts[[mouseID]][["Time_Range"]] <- list()
  516. for (Bout_n in seq_along(Bouts[[mouseID]][["Bouts"]])) {
  517. Bout <- Bouts[[mouseID]][["Bouts"]][[Bout_n]]
  518. Start_Time <- Bout$Time[1]
  519. Pre_Time <- Start_Time - n_Seconds
  520. Post_Time <- Start_Time + n_Seconds
  521. Peak_Time <- Bout[which.max(Bout$Time),]$Time
  522. Peak_Pre_Time <- Peak_Time - n_Seconds
  523. Peak_Post_Time <- Peak_Time + n_Seconds
  525. Bouts[[mouseID]][["Time_Range"]][[Bout_n]] <- data.frame(Start_Time = Start_Time,
  526. Pre_Time = Pre_Time,
  527. Post_Time = Post_Time,
  528. Peak_Time = Peak_Time,
  529. Peak_Pre_Time = Peak_Pre_Time,
  530. Peak_Post_Time = Peak_Post_Time)
  531. }
  532. }
  535. #Time loc the dLight traces
  536. for (mouseID in names(Bouts)) {
  537. Bouts[[mouseID]][["TimeLoc_dLight(BoutStart)"]] <- list()
  538. Bouts[[mouseID]][["TimeLoc_dLight(PeakSpeed)"]] <- list()
  540. for (Bout_n in seq_along(Bouts[[mouseID]][["Bouts"]])) {
  541. Start_Time <- Bouts[[mouseID]][["Time_Range"]][[Bout_n]]$Start_Time
  542. Pre_Time <- Bouts[[mouseID]][["Time_Range"]][[Bout_n]]$Pre_Time
  543. Post_Time <- Bouts[[mouseID]][["Time_Range"]][[Bout_n]]$Post_Time
  544. Peak_Time <- Bouts[[mouseID]][["Time_Range"]][[Bout_n]]$Peak_Time
  545. Peak_Pre_Time <- Bouts[[mouseID]][["Time_Range"]][[Bout_n]]$Peak_Pre_Time
  546. Peak_Post_Time <- Bouts[[mouseID]][["Time_Range"]][[Bout_n]]$Peak_Post_Time
  548. dLight_DFF <- Body_motion[[mouseID]][["dLight_DFF"]]
  549. dLight_DFF <- dLight_DFF[dLight_DFF$Time > Pre_Time & dLight_DFF$Time < Post_Time,]
  550. dLight_DFF$Time_Loc <- dLight_DFF$Time - Start_Time
  551. Bouts[[mouseID]][["TimeLoc_dLight(BoutStart)"]][[Bout_n]] <- dLight_DFF
  553. dLight_DFF <- Body_motion[[mouseID]][["dLight_DFF"]]
  554. dLight_DFF <- dLight_DFF[dLight_DFF$Time > Peak_Pre_Time & dLight_DFF$Time < Peak_Post_Time,]
  555. dLight_DFF$Time_Loc <- dLight_DFF$Time - Peak_Time
  556. Bouts[[mouseID]][["TimeLoc_dLight(PeakSpeed)"]][[Bout_n]] <- dLight_DFF
  558. }
  559. }
  561. #Since the T times for Time loc are not exactly the same, interpolate the deltaF/F values at consensus times
  562. for (mouseID in names(Bouts)) {
  564. Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(BoutStart)"]] <- list()
  565. Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(PeakSpeed)"]] <- list()
  567. for (Bout_n in seq_along(Bouts[[mouseID]][["Bouts"]])) {
  569. Time <- seq(from = -n_Seconds, to = n_Seconds, by = 0.02)
  570. data <- Bouts[[mouseID]][["TimeLoc_dLight(BoutStart)"]][[Bout_n]]
  571. Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(BoutStart)"]][[Bout_n]] <- as.data.frame(approx(x = data$Time_Loc, y = data[,curve_col_index], xout = Time))
  572. colnames(Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(BoutStart)"]][[Bout_n]]) <- c("Time", "dLight_DFF")
  574. Time <- seq(from = -n_Seconds, to = n_Seconds, by = 0.02)
  575. data <- Bouts[[mouseID]][["TimeLoc_dLight(PeakSpeed)"]][[Bout_n]]
  576. Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(PeakSpeed)"]][[Bout_n]] <- as.data.frame(approx(x = data$Time_Loc, y = data[,curve_col_index], xout = Time))
  577. colnames(Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(PeakSpeed)"]][[Bout_n]]) <- c("Time", "dLight_DFF")
  578. }
  579. }
  581. Bouts[[mouseID]][["TimeLoc_dLight(BoutStart)"]]
  583. #Combine all the interpolated traces
  584. All_traces <- list()
  585. counter <- 1
  586. for (mouseID in names(Bouts)) {
  587. for (Bout_n in seq_along(Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(BoutStart)"]])) {
  588. All_traces[[counter]] <- Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(BoutStart)"]][[Bout_n]]
  589. All_traces[[counter]]$TraceID <- as.character(rep(counter, times = nrow(All_traces[[counter]])))
  590. All_traces[[counter]]$mouseID <- rep(mouseID, times = nrow(All_traces[[counter]]))
  591. counter <- counter + 1
  592. }
  593. }
  595. from_BoutStart <- bind_rows(All_traces)
  596. from_BoutStart <- from_BoutStart %>%
  597. mutate(
  598. Group = case_when(
  599. mouseID == "J0777" ~ "fDIO-mCherry",
  600. mouseID == "J0782" ~ "fDIO-mCherry",
  601. mouseID == "J0859" ~ "fDIO-mCherry",
  602. mouseID == "K0871" ~ "fDIO-mCherry",
  604. mouseID == "J0779" ~ "CoffFon-hM4Di",
  605. mouseID == "J0784" ~ "CoffFon-hM4Di",
  606. mouseID == "K0331" ~ "CoffFon-hM4Di",
  607. mouseID == "K0994" ~ "CoffFon-hM4Di",
  608. mouseID == "K0861" ~ "CoffFon-hM4Di",
  610. mouseID == "K0029" ~ "ConFon-hM4Di",
  611. mouseID == "K0863" ~ "ConFon-hM4Di",
  612. mouseID == "K0865" ~ "ConFon-hM4Di",
  613. mouseID == "K0990" ~ "ConFon-hM4Di"
  614. )
  615. )
  617. All_traces <- list()
  618. counter <- 1
  619. for (mouseID in names(Bouts)) {
  620. for (Bout_n in seq_along(Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(PeakSpeed)"]])) {
  621. All_traces[[counter]] <- Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(PeakSpeed)"]][[Bout_n]]
  622. All_traces[[counter]]$TraceID <- as.character(rep(counter, times = nrow(All_traces[[counter]])))
  623. All_traces[[counter]]$mouseID <- rep(mouseID, times = nrow(All_traces[[counter]]))
  624. counter <- counter + 1
  625. }
  626. }
  628. from_PeakSpeed <- bind_rows(All_traces)
  629. from_PeakSpeed <- from_PeakSpeed %>%
  630. mutate(
  631. Group = case_when(
  632. mouseID == "J0777" ~ "fDIO-mCherry",
  633. mouseID == "J0782" ~ "fDIO-mCherry",
  634. mouseID == "J0859" ~ "fDIO-mCherry",
  635. mouseID == "K0871" ~ "fDIO-mCherry",
  637. mouseID == "J0779" ~ "CoffFon-hM4Di",
  638. mouseID == "J0784" ~ "CoffFon-hM4Di",
  639. mouseID == "K0331" ~ "CoffFon-hM4Di",
  640. mouseID == "K0994" ~ "CoffFon-hM4Di",
  641. mouseID == "K0861" ~ "CoffFon-hM4Di",
  643. mouseID == "K0029" ~ "ConFon-hM4Di",
  644. mouseID == "K0863" ~ "ConFon-hM4Di",
  645. mouseID == "K0865" ~ "ConFon-hM4Di",
  646. mouseID == "K0990" ~ "ConFon-hM4Di"
  647. )
  648. )
  651. plot <- from_BoutStart |>
  652. tidyplot(x = Time, y = dLight_DFF, color = Group) |>
  653. add_mean_line() |>
  654. add_ci95_ribbon() |>
  655. adjust_title(title) |>
  656. adjust_x_axis_title("Time from bout onset (sec.)") |>
  657. adjust_x_axis(limits = c(-10, 10), breaks = seq(from = -10, to = 10, by = 1)) |>
  658. adjust_y_axis(limits = ylim, breaks = seq(from = -10, to = 10, by = 1)) |>
  659. adjust_y_axis_title("dLight dF/F (%)") #|>
  661. ggsave(paste(title, "_BoutOnset.pdf", sep = ""),
  662. width = 4,
  663. height = 4,
  664. plot = plot)
  666. plot
  668. #Collect the number of bouts per mouse
  669. nBouts <- c()
  670. for (mouseID_n in 1:length(Bouts)) {
  671. nBouts[mouseID_n] <- length(Bouts[[mouseID_n]][["Bouts"]])
  672. }
  674. nBouts_df <- data.frame(mouseID = names(Bouts), nBouts = nBouts)
  675. nBouts_df <- nBouts_df %>%
  676. mutate(
  677. Group = case_when(
  678. mouseID == "J0777" ~ "fDIO-mCherry",
  679. mouseID == "J0782" ~ "fDIO-mCherry",
  680. mouseID == "J0859" ~ "fDIO-mCherry",
  681. mouseID == "K0871" ~ "fDIO-mCherry",
  683. mouseID == "J0779" ~ "CoffFon-hM4Di",
  684. mouseID == "J0784" ~ "CoffFon-hM4Di",
  685. mouseID == "K0331" ~ "CoffFon-hM4Di",
  686. mouseID == "K0994" ~ "CoffFon-hM4Di",
  687. mouseID == "K0861" ~ "CoffFon-hM4Di",
  689. mouseID == "K0029" ~ "ConFon-hM4Di",
  690. mouseID == "K0863" ~ "ConFon-hM4Di",
  691. mouseID == "K0865" ~ "ConFon-hM4Di",
  692. mouseID == "K0990" ~ "ConFon-hM4Di"
  693. )
  694. )
  696. print(nBouts_df)
  697. write.csv(nBouts_df, paste(title, "nBouts.csv", sep = "_")) #save the data to CSV
  699. nBouts_df_pergroup <- aggregate(nBouts ~ Group, data = nBouts_df, FUN = sum, na.rm = TRUE)
  700. print(nBouts_df_pergroup)
  701. write.csv(nBouts_df_pergroup, paste(title, "nBoutsperGroup.csv", sep = "_")) #save the data to CSV
  704. }
  706. setwd("C:/Users/cyril/OneDrive - McGill University (1)/Documents/PhD/Raw data/Fibrephotometry/dLight_DCZinj5min/Output/Peak to Bout Correlation")
  707. Plot_PeakSpeed(curve_to_timeloc = "AIN01", n_Seconds = 5, ylim = c(-10,7), min_time = 5, max_time = 300, title = "Pre-DCZ")
  708. Plot_PeakSpeed(curve_to_timeloc = "AIN01", n_Seconds = 5, ylim = c(-10,7), min_time = 500, max_time = 1500, title = "Post-DCZ")
  709. Plot_PeakSpeed(curve_to_timeloc = "AIN01", n_Seconds = 5, ylim = c(-10,7), min_time = 1500, max_time = 2100, title = "Pushed")
  710. Plot_PeakSpeed(curve_to_timeloc = "DFF_LOESSsub", n_Seconds = 2, ylim = c(-6,5), min_time = 5, max_time = 300, title = "Pre-DCZ_SubstractedfromLOESS")
  711. Plot_PeakSpeed(curve_to_timeloc = "DFF_LOESSsub", n_Seconds = 2, ylim = c(-6,5), min_time = 500, max_time = 1500, title = "Post-DCZ_SubstractedfromLOESS")
  712. Plot_PeakSpeed(curve_to_timeloc = "DFF_LOESSsub", n_Seconds = 2, ylim = c(-6,5), min_time = 1500, max_time = 2090, title = "Pushed_SubstractedfromLOESS")
  713. Plot_PeakSpeed(curve_to_timeloc = "DFF_LOESSsub", n_Seconds = 10, ylim = c(-6,5), min_time = 5, max_time = 300, title = "Pre-DCZ_SubstractedfromLOESS_10sec")
  714. Plot_PeakSpeed(curve_to_timeloc = "DFF_LOESSsub", n_Seconds = 10, ylim = c(-6,5), min_time = 500, max_time = 1500, title = "Post-DCZ_SubstractedfromLOESS_10sec")
  715. Plot_PeakSpeed(curve_to_timeloc = "DFF_LOESSsub", n_Seconds = 10, ylim = c(-6,5), min_time = 1500, max_time = 2090, title = "Pushed_SubstractedfromLOESS_10sec")
  717. Plot_BoutStart(curve_to_timeloc = "AIN01", n_Seconds = 3, ylim = c(-10,7), min_time = 5, max_time = 300, title = "Pre-DCZ Injection")
  718. Plot_BoutStart(curve_to_timeloc = "AIN01", n_Seconds = 3, ylim = c(-10,7), min_time = 500, max_time = 1500, title = "Post-DCZ Injection")
  719. Plot_BoutStart(curve_to_timeloc = "AIN01", n_Seconds = 3, ylim = c(-10,7), min_time = 1500, max_time = 2100, title = "Pushed")
  721. #------------------------------------------------------------------------
  722. #Generate an average trace per mouse for dLight prior and post-bout occurence and bout peak-speed
  723. Extract_Bouts_TimeLoc <- function(n_Seconds, min_time, max_time) {
  725. Bouts <- list()
  727. for (mouseID in names(Body_motion)) {
  728. Bouts[[mouseID]] <- list()
  729. Bouts[[mouseID]][["Bouts"]] <- list()
  731. for (Bout_n in seq_along(Body_motion[[mouseID]][["Bouts"]])) {
  732. start_time <- Body_motion[[mouseID]][["Bouts"]][[Bout_n]]$Time[1]
  734. if (start_time > min_time && start_time < max_time) {
  735. Bouts[[mouseID]][["Bouts"]][[length(Bouts[[mouseID]][["Bouts"]]) + 1]] <- Body_motion[[mouseID]][["Bouts"]][[Bout_n]]
  736. }
  737. }
  738. }
  741. #Time loc these bouts few seconds before and after to their occurence
  742. #n_Seconds <- 2
  744. for (mouseID in names(Bouts)) {
  745. Bouts[[mouseID]][["Time_Range"]] <- list()
  747. for (Bout_n in seq_along(Bouts[[mouseID]][["Bouts"]])) {
  748. Bout <- Bouts[[mouseID]][["Bouts"]][[Bout_n]]
  749. Start_Time <- Bout$Time[1]
  750. Pre_Time <- Start_Time - n_Seconds
  751. Post_Time <- Start_Time + n_Seconds
  752. Peak_Time <- Bout[which.max(Bout$Time),]$Time
  753. Peak_Pre_Time <- Peak_Time - n_Seconds
  754. Peak_Post_Time <- Peak_Time + n_Seconds
  756. Bouts[[mouseID]][["Time_Range"]][[Bout_n]] <- data.frame(Start_Time = Start_Time,
  757. Pre_Time = Pre_Time,
  758. Post_Time = Post_Time,
  759. Peak_Time = Peak_Time,
  760. Peak_Pre_Time = Peak_Pre_Time,
  761. Peak_Post_Time = Peak_Post_Time)
  762. }
  763. }
  766. #Time loc the dLight traces
  767. for (mouseID in names(Bouts)) {
  768. Bouts[[mouseID]][["TimeLoc_dLight(BoutStart)"]] <- list()
  769. Bouts[[mouseID]][["TimeLoc_dLight(PeakSpeed)"]] <- list()
  771. for (Bout_n in seq_along(Bouts[[mouseID]][["Bouts"]])) {
  772. Start_Time <- Bouts[[mouseID]][["Time_Range"]][[Bout_n]]$Start_Time
  773. Pre_Time <- Bouts[[mouseID]][["Time_Range"]][[Bout_n]]$Pre_Time
  774. Post_Time <- Bouts[[mouseID]][["Time_Range"]][[Bout_n]]$Post_Time
  775. Peak_Time <- Bouts[[mouseID]][["Time_Range"]][[Bout_n]]$Peak_Time
  776. Peak_Pre_Time <- Bouts[[mouseID]][["Time_Range"]][[Bout_n]]$Peak_Pre_Time
  777. Peak_Post_Time <- Bouts[[mouseID]][["Time_Range"]][[Bout_n]]$Peak_Post_Time
  779. dLight_DFF <- Body_motion[[mouseID]][["dLight_DFF"]]
  780. dLight_DFF <- dLight_DFF[dLight_DFF$Time > Pre_Time & dLight_DFF$Time < Post_Time,]
  781. dLight_DFF$Time_Loc <- dLight_DFF$Time - Start_Time
  782. Bouts[[mouseID]][["TimeLoc_dLight(BoutStart)"]][[Bout_n]] <- dLight_DFF
  784. dLight_DFF <- Body_motion[[mouseID]][["dLight_DFF"]]
  785. dLight_DFF <- dLight_DFF[dLight_DFF$Time > Peak_Pre_Time & dLight_DFF$Time < Peak_Post_Time,]
  786. dLight_DFF$Time_Loc <- dLight_DFF$Time - Peak_Time
  787. Bouts[[mouseID]][["TimeLoc_dLight(PeakSpeed)"]][[Bout_n]] <- dLight_DFF
  789. }
  790. }
  792. #Since the T times for Time loc are not exactly the same, interpolate the deltaF/F values at consensus times
  793. for (mouseID in names(Bouts)) {
  795. Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(BoutStart)"]] <- list()
  796. Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(PeakSpeed)"]] <- list()
  798. for (Bout_n in seq_along(Bouts[[mouseID]][["Bouts"]])) {
  800. Time <- seq(from = -n_Seconds, to = n_Seconds, by = 0.02)
  801. data <- Bouts[[mouseID]][["TimeLoc_dLight(BoutStart)"]][[Bout_n]]
  802. Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(BoutStart)"]][[Bout_n]] <- as.data.frame(approx(x = data$Time_Loc, y = data[,curve_col_index], xout = Time))
  803. colnames(Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(BoutStart)"]][[Bout_n]]) <- c("Time", "dLight_DFF")
  805. Time <- seq(from = -n_Seconds, to = n_Seconds, by = 0.02)
  806. data <- Bouts[[mouseID]][["TimeLoc_dLight(PeakSpeed)"]][[Bout_n]]
  807. Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(PeakSpeed)"]][[Bout_n]] <- as.data.frame(approx(x = data$Time_Loc, y = data[,curve_col_index], xout = Time))
  808. colnames(Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(PeakSpeed)"]][[Bout_n]]) <- c("Time", "dLight_DFF")
  809. }
  810. }
  812. return(Bouts)
  814. }
  816. #Generate a function for plotting average deltaF/F curve per mouse and not per bout
  817. Plot_Average_Trace <- function(Object, Time_Loc_List, Title, x_Title) {
  819. Bouts <- Object
  821. for (mouseID in names(Bouts)) {
  823. if (length(Bouts[[mouseID]][["Bouts"]]) > 0) {
  825. #Extract all the traces and store them in a dataframe by binding the columns
  826. Traces <- Bouts[[mouseID]][[Time_Loc_List]] %>%
  827. map(~ .x["dLight_DFF"]) %>%
  828. bind_cols()
  830. #Average the traces together at each T time
  831. Average_dLightDFF_Trace <- rowMeans(Traces)
  832. Traces <- NULL
  833. Time <- Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(PeakSpeed)"]][[1]]$Time
  834. Bouts[[mouseID]][["AverageTrace_dLightDFF(PeakSpeed)"]] <- data.frame(Time = Time,
  835. Average_dLightDFF_Trace = Average_dLightDFF_Trace,
  836. mouseID = rep(mouseID, times = length(Time)))
  837. }
  838. }
  840. Average_Traces <- Bouts %>%
  841. map(~ .x[["AverageTrace_dLightDFF(PeakSpeed)"]]) %>%
  842. bind_rows(.id = "mouseID")
  844. #Add a group column
  845. Average_Traces <- Average_Traces %>%
  846. mutate(
  847. Group = case_when(
  848. mouseID == "J0777" ~ "fDIO-mCherry",
  849. mouseID == "J0782" ~ "fDIO-mCherry",
  850. mouseID == "J0859" ~ "fDIO-mCherry",
  851. mouseID == "K0871" ~ "fDIO-mCherry",
  853. mouseID == "J0779" ~ "CoffFon-hM4Di",
  854. mouseID == "J0784" ~ "CoffFon-hM4Di",
  855. mouseID == "K0331" ~ "CoffFon-hM4Di",
  856. mouseID == "K0994" ~ "CoffFon-hM4Di",
  857. mouseID == "K0861" ~ "CoffFon-hM4Di",
  859. mouseID == "K0029" ~ "ConFon-hM4Di",
  860. mouseID == "K0863" ~ "ConFon-hM4Di",
  861. mouseID == "K0865" ~ "ConFon-hM4Di",
  862. mouseID == "K0990" ~ "ConFon-hM4Di"
  863. )
  864. )
  866. plot <- Average_Traces |>
  867. tidyplot(x = Time, y = Average_dLightDFF_Trace, color = Group) |>
  868. add_mean_line() |>
  869. add_ci95_ribbon() |>
  870. adjust_title(Title) |>
  871. adjust_x_axis_title(x_Title) |>
  872. #adjust_x_axis(limits = c(0.5, 5.5), breaks = seq(from = 0.5, to = 5, by = 1)) |>
  873. adjust_x_axis(limits = c(-1.5, 1), breaks = seq(from = -1.5, to = 1, by = 0.5)) |>
  874. adjust_y_axis(limits = c(-12, 12), breaks = seq(from = -10, to = 10, by = 2.5)) |>
  875. adjust_y_axis_title("dLight dF/F (%)")
  877. #Save the plot
  878. ggsave(
  879. filename = paste(Title, ".pdf", sep = ""),
  880. plot = plot, # Saves the last plot displayed
  881. #device = "png", # Essential for transparency
  882. bg = "transparent", # Removes the white background
  883. width = 6, # Width in inches
  884. height = 5, # Height in inches
  885. units = "in",
  886. dpi = 300, # High resolution for publication
  887. limitsize = TRUE
  888. )
  889. }
  891. setwd("C:/Users/cyril/OneDrive - McGill University (1)/Documents/PhD/Raw data/Fibrephotometry/dLight_DCZinj5min/Output/Peak to Bout Correlation")
  893. Plot_Average_Trace(Object = Extract_Bouts_TimeLoc(n_Seconds = 1.5,
  894. min_time = 5,
  895. max_time = 300),
  896. Time_Loc_List = "Interpolated_TimeLoc_dLight(BoutStart)",
  897. Title = "AverageTrace_PreDCZ_BoutOnset_PerMouse",
  898. x_Title = "Time from bout occurence (sec.)")
  900. Plot_Average_Trace(Object = Extract_Bouts_TimeLoc(n_Seconds = 1.5,
  901. min_time = 500,
  902. max_time = 1500),
  903. Time_Loc_List = "Interpolated_TimeLoc_dLight(BoutStart)",
  904. Title = "AverageTrace_PostDCZ_BoutOnset_PerMouse",
  905. x_Title = "Time from bout occurence (sec.)")
  907. Plot_Average_Trace(Object = Extract_Bouts_TimeLoc(n_Seconds = 1.5,
  908. min_time = 1500,
  909. max_time = 2100),
  910. Time_Loc_List = "Interpolated_TimeLoc_dLight(BoutStart)",
  911. Title = "AverageTrace_PostDCZ(ForcedLocomotion)_BoutOnset_PerMouse",
  912. x_Title = "Time from bout occurence (sec.)")
  914. Plot_Average_Trace(Object = Extract_Bouts_TimeLoc(n_Seconds = 1.5,
  915. min_time = 5,
  916. max_time = 300),
  917. Time_Loc_List = "Interpolated_TimeLoc_dLight(PeakSpeed)",
  918. Title = "AverageTrace_PreDCZ_BoutPeakSpeed_PerMouse",
  919. x_Title = "Time from peak speed (sec.)")
  921. Plot_Average_Trace(Object = Extract_Bouts_TimeLoc(n_Seconds = 1.5,
  922. min_time = 500,
  923. max_time = 1500),
  924. Time_Loc_List = "Interpolated_TimeLoc_dLight(PeakSpeed)",
  925. Title = "AverageTrace_PostDCZ_BoutPeakSpeed_PerMouse",
  926. x_Title = "Time from peak speed (sec.)")
  928. Plot_Average_Trace(Object = Extract_Bouts_TimeLoc(n_Seconds = 1.5,
  929. min_time = 1500,
  930. max_time = 2100),
  931. Time_Loc_List = "Interpolated_TimeLoc_dLight(PeakSpeed)",
  932. Title = "AverageTrace_PostDCZ(ForcedLocomotion)_BoutPeakSpeed_PerMouse",
  933. x_Title = "Time from peak speed (sec.)")
  936. #------------------------------------------------------------------------
  937. #Plot density line
  938. Density_Line <- function(Object, Time_Loc_List, Group, nBins, Color_Option, Title, x_Title, Density_Range){
  940. Bouts <- Object
  941. All_traces <- list()
  942. counter <- 1
  943. for (mouseID in names(Bouts)) {
  944. for (Bout_n in seq_along(Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(PeakSpeed)"]])) {
  945. All_traces[[counter]] <- Bouts[[mouseID]][["Interpolated_TimeLoc_dLight(PeakSpeed)"]][[Bout_n]]
  946. All_traces[[counter]]$TraceID <- as.character(rep(counter, times = nrow(All_traces[[counter]])))
  947. All_traces[[counter]]$mouseID <- rep(mouseID, times = nrow(All_traces[[counter]]))
  948. counter <- counter + 1
  949. }
  950. }
  952. from_BoutStart <- bind_rows(All_traces)
  953. from_BoutStart <- from_BoutStart %>%
  954. mutate(
  955. Group = case_when(
  956. mouseID == "J0777" ~ "fDIO-mCherry",
  957. mouseID == "J0782" ~ "fDIO-mCherry",
  958. mouseID == "J0859" ~ "fDIO-mCherry",
  959. mouseID == "K0871" ~ "fDIO-mCherry",
  961. mouseID == "J0779" ~ "CoffFon-hM4Di",
  962. mouseID == "J0784" ~ "CoffFon-hM4Di",
  963. mouseID == "K0331" ~ "CoffFon-hM4Di",
  964. mouseID == "K0994" ~ "CoffFon-hM4Di",
  965. mouseID == "K0861" ~ "CoffFon-hM4Di",
  967. mouseID == "K0029" ~ "ConFon-hM4Di",
  968. mouseID == "K0863" ~ "ConFon-hM4Di",
  969. mouseID == "K0865" ~ "ConFon-hM4Di",
  970. mouseID == "K0990" ~ "ConFon-hM4Di"
  971. )
  972. )
  974. All_traces <- list()
  975. counter <- 1
  976. for (mouseID in names(Bouts)) {
  977. for (Bout_n in seq_along(Bouts[[mouseID]][[Time_Loc_List]])) {
  978. All_traces[[counter]] <- Bouts[[mouseID]][[Time_Loc_List]][[Bout_n]]
  979. All_traces[[counter]]$TraceID <- as.character(rep(counter, times = nrow(All_traces[[counter]])))
  980. All_traces[[counter]]$mouseID <- rep(mouseID, times = nrow(All_traces[[counter]]))
  981. counter <- counter + 1
  982. }
  983. }
  985. All_traces <- bind_rows(All_traces)
  986. All_traces <- All_traces %>%
  987. mutate(
  988. Group = case_when(
  989. mouseID == "J0777" ~ "fDIO-mCherry",
  990. mouseID == "J0782" ~ "fDIO-mCherry",
  991. mouseID == "J0859" ~ "fDIO-mCherry",
  992. mouseID == "K0871" ~ "fDIO-mCherry",
  994. mouseID == "J0779" ~ "CoffFon-hM4Di",
  995. mouseID == "J0784" ~ "CoffFon-hM4Di",
  996. mouseID == "K0331" ~ "CoffFon-hM4Di",
  997. mouseID == "K0994" ~ "CoffFon-hM4Di",
  998. mouseID == "K0861" ~ "CoffFon-hM4Di",
  1000. mouseID == "K0029" ~ "ConFon-hM4Di",
  1001. mouseID == "K0863" ~ "ConFon-hM4Di",
  1002. mouseID == "K0865" ~ "ConFon-hM4Di",
  1003. mouseID == "K0990" ~ "ConFon-hM4Di"
  1004. )
  1005. )
  1007. #Rename the traceID
  1008. All_traces$TraceID <- paste(All_traces$mouseID,All_traces$TraceID, sep = "_")
  1010. #Subset the group to plot
  1011. All_traces <- All_traces[All_traces$Group == Group,]
  1013. #Plot
  1014. plot <- ggplot(data = All_traces, aes(x = Time, y = dLight_DFF, group = TraceID)) +
  1015. stat_line_density(aes(color = after_stat(density), fill = after_stat(density)),
  1016. bins = nBins, drop = FALSE, na.rm = TRUE) +
  1017. #scale_color_viridis_c(option = "magma") +
  1018. scale_fill_viridis_c(option = Color_Option, limits = Density_Range, values = c(0,0.5,1)) +
  1019. scale_x_continuous(limits = c(-2,2), expand = c(0, 0), breaks = seq(from = -2, to = 2, by = 0.5)) +
  1020. scale_y_continuous(limits = c(-25,25), expand = c(0, 0), breaks = seq(from = -20, to = 20 , by = 5)) +
  1021. theme_classic() +
  1022. labs(
  1023. title = Title, # Main title
  1024. x = x_Title, # X-axis label
  1025. y = "dLight deltaF/F (%)", # Y-axis label
  1026. ) +
  1027. theme(plot.title = element_text(size = 12, hjust = 0.5))
  1029. ggsave(
  1030. filename = paste(Title, "_DensityLine.png", sep = ""),
  1031. plot = plot, # Saves the last plot displayed
  1032. #device = "png", # Essential for transparency
  1033. bg = "transparent", # Removes the white background
  1034. width = 6, # Width in inches
  1035. height = 5, # Height in inches
  1036. units = "in",
  1037. dpi = 1200, # High resolution for publication
  1038. limitsize = TRUE)
  1039. }
  1041. setwd("C:/Users/cyril/OneDrive - McGill University (1)/Documents/PhD/Raw data/Fibrephotometry/dLight_DCZinj5min/Output/Peak to Bout Correlation")
  1042. Density_Line(Object = Extract_Bouts_TimeLoc(n_Seconds = 2,
  1043. min_time = 5,
  1044. max_time = 300),
  1045. Time_Loc_List = "Interpolated_TimeLoc_dLight(PeakSpeed)",
  1046. Group = "fDIO-mCherry",
  1047. nBins = 100,
  1048. Color_Option = "turbo",
  1049. Title = "PreDCZ_fDIO-mCherry",
  1050. x_Title = "Time from bout peak speed (sec.)",
  1051. Density_Range = NULL)
  1053. Density_Line(Object = Extract_Bouts_TimeLoc(n_Seconds = 2,
  1054. min_time = 500,
  1055. max_time = 1500),
  1056. Time_Loc_List = "Interpolated_TimeLoc_dLight(PeakSpeed)",
  1057. Group = "fDIO-mCherry",
  1058. nBins = 100,
  1059. Color_Option = "turbo",
  1060. Title = "PostDCZ_fDIO-mCherry",
  1061. x_Title = "Time from bout peak speed (sec.)",
  1062. Density_Range = NULL)
  1064. Density_Line(Object = Extract_Bouts_TimeLoc(n_Seconds = 2,
  1065. min_time = 1500,
  1066. max_time = 2100),
  1067. Time_Loc_List = "Interpolated_TimeLoc_dLight(PeakSpeed)",
  1068. Group = "fDIO-mCherry",
  1069. nBins = 100,
  1070. Color_Option = "turbo",
  1071. Title = "PostDCZ(Forced)_fDIO-mCherry",
  1072. x_Title = "Time from bout peak speed (sec.)",
  1073. Density_Range = NULL)
  1075. Density_Line(Object = Extract_Bouts_TimeLoc(n_Seconds = 2,
  1076. min_time = 5,
  1077. max_time = 300),
  1078. Time_Loc_List = "Interpolated_TimeLoc_dLight(PeakSpeed)",
  1079. Group = "CoffFon-hM4Di",
  1080. nBins = 100,
  1081. Color_Option = "turbo",
  1082. Title = "PreDCZ_CoffFon-hM4Di",
  1083. x_Title = "Time from bout peak speed (sec.)",
  1084. Density_Range = NULL)
  1086. Density_Line(Object = Extract_Bouts_TimeLoc(n_Seconds = 2,
  1087. min_time = 500,
  1088. max_time = 1500),
  1089. Time_Loc_List = "Interpolated_TimeLoc_dLight(PeakSpeed)",
  1090. Group = "CoffFon-hM4Di",
  1091. nBins = 100,
  1092. Color_Option = "turbo",
  1093. Title = "PostDCZ_CoffFon-hM4Di",
  1094. x_Title = "Time from bout peak speed (sec.)",
  1095. Density_Range = NULL)
  1097. Density_Line(Object = Extract_Bouts_TimeLoc(n_Seconds = 2,
  1098. min_time = 1500,
  1099. max_time = 2100),
  1100. Time_Loc_List = "Interpolated_TimeLoc_dLight(PeakSpeed)",
  1101. Group = "CoffFon-hM4Di",
  1102. nBins = 100,
  1103. Color_Option = "turbo",
  1104. Title = "PostDCZ(Forced)_CoffFon-hM4Di",
  1105. x_Title = "Time from bout peak speed (sec.)",
  1106. Density_Range = NULL)
  1108. Density_Line(Object = Extract_Bouts_TimeLoc(n_Seconds = 2,
  1109. min_time = 5,
  1110. max_time = 300),
  1111. Time_Loc_List = "Interpolated_TimeLoc_dLight(PeakSpeed)",
  1112. Group = "ConFon-hM4Di",
  1113. nBins = 100,
  1114. Color_Option = "turbo",
  1115. Title = "PreDCZ_ConFon-hM4Di",
  1116. x_Title = "Time from bout peak speed (sec.)",
  1117. Density_Range = NULL)
  1119. Density_Line(Object = Extract_Bouts_TimeLoc(n_Seconds = 2,
  1120. min_time = 500,
  1121. max_time = 1500),
  1122. Time_Loc_List = "Interpolated_TimeLoc_dLight(PeakSpeed)",
  1123. Group = "ConFon-hM4Di",
  1124. nBins = 100,
  1125. Color_Option = "turbo",
  1126. Title = "PostDCZ_ConFon-hM4Di",
  1127. x_Title = "Time from bout peak speed (sec.)",
  1128. Density_Range = NULL)
  1130. Density_Line(Object = Extract_Bouts_TimeLoc(n_Seconds = 2,
  1131. min_time = 1500,
  1132. max_time = 2100),
  1133. Time_Loc_List = "Interpolated_TimeLoc_dLight(PeakSpeed)",
  1134. Group = "ConFon-hM4Di",
  1135. nBins = 100,
  1136. Color_Option = "turbo",
  1137. Title = "PostDCZ(Forced)_ConFon-hM4Di",
  1138. x_Title = "Time from bout peak speed (sec.)",
  1139. Density_Range = NULL)
  1141. #------------------------------------------------------------------------
  1142. #Correlate the velocity and dLight DFF at every T times
  1143. velocity_dLightDFF_datapoints <- list()
  1145. for (mouseID in names(Body_motion)) {
  1146. #Extract the velocity and dLight_DFF data for each frame
  1147. velocity_df <- Body_motion[[mouseID]][["Sleap_data"]][,c("Time", "Torso.velocity")]
  1148. dLightDFF_df <- Body_motion[[mouseID]][["dLight_DFF"]]
  1150. #Approximate velocity on each T time of the dLight data
  1151. approximation <- approx(x = velocity_df$Time, y = velocity_df$Torso.velocity, xout = dLightDFF_df$Time)
  1152. dLightDFF_df$Torso.velocity <- approximation$y
  1154. #Add a column for the mouseID
  1155. dLightDFF_df$mouseID <- rep(mouseID, times = nrow(dLightDFF_df))
  1157. #Add the dataframe to the list
  1158. velocity_dLightDFF_datapoints[[mouseID]] <- dLightDFF_df
  1160. #Clean the objects generated
  1161. velocity_df <- NULL
  1162. dLightDFF_df <- NULL
  1163. approximation <- NULL
  1164. }
  1166. #Stack the dataframes
  1167. velocity_dLightDFF_datapoints <- dplyr::bind_rows(velocity_dLightDFF_datapoints)
  1169. #Add a group column
  1170. velocity_dLightDFF_datapoints <- velocity_dLightDFF_datapoints %>%
  1171. mutate(
  1172. Group = case_when(
  1173. mouseID == "J0777" ~ "fDIO-mCherry",
  1174. mouseID == "J0782" ~ "fDIO-mCherry",
  1175. mouseID == "J0859" ~ "fDIO-mCherry",
  1176. mouseID == "K0871" ~ "fDIO-mCherry",
  1178. mouseID == "J0779" ~ "CoffFon-hM4Di",
  1179. mouseID == "J0784" ~ "CoffFon-hM4Di",
  1180. mouseID == "K0331" ~ "CoffFon-hM4Di",
  1181. mouseID == "K0994" ~ "CoffFon-hM4Di",
  1182. mouseID == "K0861" ~ "CoffFon-hM4Di",
  1184. mouseID == "K0029" ~ "ConFon-hM4Di",
  1185. mouseID == "K0863" ~ "ConFon-hM4Di",
  1186. mouseID == "K0865" ~ "ConFon-hM4Di",
  1187. mouseID == "K0990" ~ "ConFon-hM4Di"
  1188. )
  1189. )
  1192. Velocity_DFF_DensityPlot <- function(Start_time,
  1193. End_time,
  1194. Group,
  1195. Title,
  1196. density_limits
  1197. ) {
  1198. Toplot <- velocity_dLightDFF_datapoints[velocity_dLightDFF_datapoints$Time > Start_time &
  1199. velocity_dLightDFF_datapoints$Time < End_time &
  1200. velocity_dLightDFF_datapoints$Group == Group,]
  1202. plot <- ggplot(Toplot, mapping = aes(x = Torso.velocity, y = AIN01)) +
  1203. #eom_point(alpha = 1/10) +
  1204. geom_pointdensity(size = 0.1,
  1205. adjust = 5) +
  1206. scale_color_viridis(option = "magma",
  1207. limits = density_limits,
  1208. oob = scales::squish, #Prevents the color from disappearing if saturated
  1209. alpha = 1) +
  1210. xlim(0,1000) +
  1211. ylim(-8,12) +
  1212. theme_classic(base_size = 10) +
  1213. #geom_hline(yintercept = 0) +
  1214. #geom_vline(xintercept = 0) +
  1215. labs(
  1216. title = Title, # Main title
  1217. x = "Velocity (mm/sec.)", # X-axis label
  1218. y = "dLight deltaF/F (%)", # Y-axis label
  1219. ) +
  1220. theme(plot.title = element_text(size = 10, hjust = 0.5)) + #Centre the title
  1221. annotate("text", x = 800, y = 12, label = paste(as.character(nrow(Toplot)), "Frames"), color = "gray40", size = 3) #Add the number of frames
  1223. #Save the plot
  1224. ggsave(
  1225. filename = paste(Title, "_Velocity&dLightDFF_DensityPlot.png", sep = ""),
  1226. plot = plot, # Saves the last plot displayed
  1227. #device = "png", # Essential for transparency
  1228. bg = "transparent", # Removes the white background
  1229. width = 6, # Width in inches
  1230. height = 5, # Height in inches
  1231. units = "in",
  1232. dpi = 300, # High resolution for publication
  1233. limitsize = TRUE
  1234. )
  1236. }
  1238. setwd("C:/Users/cyril/OneDrive - McGill University (1)/Documents/PhD/Raw data/Fibrephotometry/dLight_DCZinj5min/Output/Peak to Bout Correlation")
  1239. Velocity_DFF_DensityPlot(Start_time = 5,
  1240. End_time = 300,
  1241. Group = "fDIO-mCherry",
  1242. Title = "fDIO-mCherry (PreDCZ)",
  1243. density_limits = c(0, 0.005)
  1244. )
  1246. Velocity_DFF_DensityPlot(Start_time = 500,
  1247. End_time = 1500,
  1248. Group = "fDIO-mCherry",
  1249. Title = "fDIO-mCherry (PostDCZ)",
  1250. density_limits = c(0, 0.005)
  1251. )
  1253. Velocity_DFF_DensityPlot(Start_time = 1500,
  1254. End_time = 2100,
  1255. Group = "fDIO-mCherry",
  1256. Title = "fDIO-mCherry (PostDCZ + Forced Locomotion)",
  1257. density_limits = c(0, 0.005)
  1258. )
  1260. Velocity_DFF_DensityPlot(Start_time = 5,
  1261. End_time = 300,
  1262. Group = "CoffFon-hM4Di",
  1263. Title = "CoffFon-hM4Di (PreDCZ)",
  1264. density_limits = c(0, 0.005)
  1265. )
  1267. Velocity_DFF_DensityPlot(Start_time = 500,
  1268. End_time = 1500,
  1269. Group = "CoffFon-hM4Di",
  1270. Title = "CoffFon-hM4Di (PostDCZ)",
  1271. density_limits = c(0, 0.005)
  1272. )
  1274. Velocity_DFF_DensityPlot(Start_time = 1500,
  1275. End_time = 2100,
  1276. Group = "CoffFon-hM4Di",
  1277. Title = "CoffFon-hM4Di (PostDCZ + Forced Locomotion)",
  1278. density_limits = c(0, 0.005)
  1279. )
  1281. Velocity_DFF_DensityPlot(Start_time = 5,
  1282. End_time = 300,
  1283. Group = "ConFon-hM4Di",
  1284. Title = "ConFon-hM4Di (PreDCZ)",
  1285. density_limits = c(0, 0.005)
  1286. )
  1288. Velocity_DFF_DensityPlot(Start_time = 500,
  1289. End_time = 1500,
  1290. Group = "ConFon-hM4Di",
  1291. Title = "ConFon-hM4Di (PostDCZ)",
  1292. density_limits = c(0, 0.005)
  1293. )
  1295. Velocity_DFF_DensityPlot(Start_time = 1500,
  1296. End_time = 2100,
  1297. Group = "ConFon-hM4Di",
  1298. Title = "ConFon-hM4Di (PostDCZ + Forced Locomotion)",
  1299. density_limits = c(0, 0.005)
  1300. )
  1302. #------------------------------------------------------------------------
  1303. #Plot sum deltaF/F and velocity per 10 sec., labelling PreDCZ, Transition, PostDCZ, PostDCZ+Forced
  1304. #Interpolate dLight deltaDFF
  1305. for (mouseID in names(Body_motion)) {
  1306. Body_motion[[mouseID]][["Sleap_data"]]$dLightDFF <- approx(Body_motion[[mouseID]][["dLight_DFF"]]$Time,
  1307. Body_motion[[mouseID]][["dLight_DFF"]]$AIN01,
  1308. Body_motion[[mouseID]][["Sleap_data"]]$Time)$y
  1309. Body_motion[[mouseID]][["Velocity/dLight"]] <- data.frame(Time = Body_motion[[mouseID]][["Sleap_data"]]$Time,
  1310. Velocity = Body_motion[[mouseID]][["Sleap_data"]]$Torso.velocity,
  1311. dLightDFF = Body_motion[[mouseID]][["Sleap_data"]]$dLightDFF)
  1312. }
  1314. #Assign the time values to PreDCZ, Transition, PostDCZ, PostDCZ_Forced
  1315. for (mouseID in names(Body_motion)) {
  1316. Body_motion[[mouseID]][["Velocity/dLight"]] <- Body_motion[[mouseID]][["Velocity/dLight"]] %>%
  1317. mutate(
  1318. Period = case_when(
  1319. Time >= 0 & Time < 300 ~ "PreDCZ",
  1320. Time >= 300 & Time < 500 ~ "Transition",
  1321. Time >= 500 & Time < 1500 ~ "PostDCZ",
  1322. Time >= 1500 & Time < 2100 ~ "PostDCZ (Forced Locomotion)"
  1323. )
  1324. )
  1325. }
  1328. #Assign the bins
  1329. for (mouseID in names(Body_motion)) {
  1330. bin_time <- 1 #in sec.
  1331. nbins <- 2100 / bin_time
  1333. Body_motion[[mouseID]][["Velocity/dLight"]]$BinID <- cut(Body_motion[[mouseID]][["Velocity/dLight"]]$Time,
  1334. breaks = seq(from = 0, to = 2100, by = bin_time),
  1335. labels = 1:nbins)
  1337. }
  1339. #Average the dLight and velocity values inside the bins
  1340. for (mouseID in names(Body_motion)) {
  1341. temp1 <- Body_motion[[mouseID]][["Velocity/dLight"]] %>%
  1342. group_by(BinID) %>%
  1343. summarize(mean_velocity = mean(Velocity))
  1345. temp2 <- Body_motion[[mouseID]][["Velocity/dLight"]] %>%
  1346. group_by(BinID) %>%
  1347. summarize(SD_dLightDFF = sd(dLightDFF))
  1349. Body_motion[[mouseID]][["Mean_Velocity/dLight"]] <- data.frame(BinID = temp1$BinID,
  1350. mean_velocity = temp1$mean_velocity,
  1351. SD_dLightDFF = temp2$SD_dLightDFF)
  1353. #Reassign the period time
  1354. Body_motion[[mouseID]][["Mean_Velocity/dLight"]]$BinID <- as.numeric(Body_motion[[mouseID]][["Mean_Velocity/dLight"]]$BinID)
  1355. Body_motion[[mouseID]][["Mean_Velocity/dLight"]] <- Body_motion[[mouseID]][["Mean_Velocity/dLight"]] %>%
  1356. mutate(
  1357. Period = case_when(
  1358. BinID >= 0 & BinID < 300/bin_time ~ "PreDCZ",
  1359. BinID >= 300/bin_time & BinID < 500/bin_time ~ "Transition",
  1360. BinID >= 500/bin_time & BinID < 1500/bin_time ~ "PostDCZ",
  1361. BinID >= 1500/bin_time & BinID <= 2100/bin_time ~ "PostDCZ (Forced Locomotion)")
  1362. )
  1364. #Add the mouseID
  1365. Body_motion[[mouseID]][["Mean_Velocity/dLight"]]$mouseID <- rep(mouseID, times = nrow(Body_motion[[mouseID]][["Mean_Velocity/dLight"]]))
  1367. }
  1369. #Stack the dataframes together
  1370. Binned_velocity_dLightDFF <- Body_motion %>%
  1371. map(~ .x[["Mean_Velocity/dLight"]]) %>%
  1372. bind_rows()
  1374. #Add group column
  1375. Binned_velocity_dLightDFF <- Binned_velocity_dLightDFF %>%
  1376. mutate(
  1377. Group = case_when(
  1378. mouseID == "J0777" ~ "fDIO-mCherry",
  1379. mouseID == "J0782" ~ "fDIO-mCherry",
  1380. mouseID == "J0859" ~ "fDIO-mCherry",
  1381. mouseID == "K0871" ~ "fDIO-mCherry",
  1383. mouseID == "J0779" ~ "CoffFon-hM4Di",
  1384. mouseID == "J0784" ~ "CoffFon-hM4Di",
  1385. mouseID == "K0331" ~ "CoffFon-hM4Di",
  1386. mouseID == "K0994" ~ "CoffFon-hM4Di",
  1387. mouseID == "K0861" ~ "CoffFon-hM4Di",
  1389. mouseID == "K0029" ~ "ConFon-hM4Di",
  1390. mouseID == "K0863" ~ "ConFon-hM4Di",
  1391. mouseID == "K0865" ~ "ConFon-hM4Di",
  1392. mouseID == "K0990" ~ "ConFon-hM4Di"
  1393. )
  1394. )
  1396. dLightSDvsMeanVelocity_PerTime <- function(Group) {
  1397. Toplot <- Binned_velocity_dLightDFF[Binned_velocity_dLightDFF$Group == Group,]
  1398. Toplot <- Toplot[sample(nrow(Toplot)), ] #Randomize the rows appearance
  1400. #Remove the NAs
  1401. Toplot <- Toplot[!is.na(Toplot$Period),]
  1402. Toplot$Time <- Toplot$BinID*bin_time
  1403. ggplot(Toplot, mapping = aes(x = mean_velocity, y = SD_dLightDFF, color = Time)) +
  1404. theme_classic() +
  1405. xlim(0,300) +
  1406. ylim(0,6) +
  1407. labs(
  1408. title = Group, # Main title
  1409. x = "Velocity Average (mm/s per 5s)", # X-axis label
  1410. y = "dLight deltaF/F Standard Deviation (per 5s)", # Y-axis label
  1411. ) +
  1412. theme(plot.title = element_text(size = 12, hjust = 0.5)) + #Centre the title
  1413. geom_point(alpha = 0.9, size = 1) +
  1414. scale_color_viridis_c(option = "turbo", values = c(0, 0.2, 0.3, 0.7, 1), breaks = c(0, 300, 500, 1500, 2100))
  1416. }
  1418. setwd("C:/Users/cyril/OneDrive - McGill University (1)/Documents/PhD/Raw data/Fibrephotometry/dLight_DCZinj5min/Output/Peak to Bout Correlation")
  1419. dLightSDvsMeanVelocity_PerTime(Group = "fDIO-mCherry")
  1420. dLightSDvsMeanVelocity_PerTime(Group = "CoffFon-hM4Di")
  1421. dLightSDvsMeanVelocity_PerTime(Group = "ConFon-hM4Di")
  1426. dLightSDvsMeanVelocity_PerPeriod <- function(Group) {
  1427. Toplot <- Binned_velocity_dLightDFF[Binned_velocity_dLightDFF$Group == Group,]
  1428. Toplot <- Toplot[sample(nrow(Toplot)), ] #Randomize the rows appearance
  1430. #Remove the NAs
  1431. Toplot <- Toplot[!is.na(Toplot$Period),]
  1432. Toplot$Time <- Toplot$BinID*bin_time
  1434. #Transform the Period to factor so they appear in the temporal order
  1435. Toplot$Period <- factor(Toplot$Period, levels = c("PreDCZ", "Transition", "PostDCZ", "PostDCZ (Forced Locomotion)"))
  1436. color <- viridis(100, option = "turbo")
  1438. #Discard the transition period
  1439. Toplot <- Toplot %>%
  1440. dplyr::filter(Period %in% c("PreDCZ", "PostDCZ", "PostDCZ (Forced Locomotion)"))
  1443. p <- ggplot(Toplot, mapping = aes(x = mean_velocity, y = SD_dLightDFF, color = Period)) +
  1444. theme_classic() +
  1445. stat_ellipse(type = "norm", linetype = "dashed", linewidth = 1.5, level = 0.95) +
  1446. xlim(-100,250) +
  1447. ylim(-1,6) +
  1448. labs(
  1449. title = Group, # Main title
  1450. x = "Velocity Average (mm/s)", # X-axis label
  1451. y = "dLight deltaF/F Standard Deviation", # Y-axis label
  1452. ) +
  1453. theme(plot.title = element_text(size = 12, hjust = 0.5), #Centre the title
  1454. legend.position = "bottom", # Move legend to bottom
  1455. legend.direction = "vertical" # Make it lay out side-by-side
  1456. ) +
  1457. geom_point(alpha = 0.9, size = 0.5) +
  1458. #scale_color_viridis_d(option = "cividis")
  1459. scale_color_manual(values = c("PreDCZ" = color[5],
  1460. "PostDCZ" = color[64],
  1461. "PostDCZ (Forced Locomotion)" = color[98])) +
  1462. theme(legend.position = "none")
  1464. ggMarginal(p, type = "density", groupFill = TRUE, alpha = 0.99)
  1466. }
  1468. dLightSDvsMeanVelocity_PerPeriod(Group = "fDIO-mCherry")
  1469. dLightSDvsMeanVelocity_PerPeriod(Group = "CoffFon-hM4Di")
  1470. dLightSDvsMeanVelocity_PerPeriod(Group = "ConFon-hM4Di")
  1473. #------------------------------------------------------------------------
  1474. #Plot average velocity and dLightDFF across the phases
  1475. #First remove the rows with NA
  1476. for (mouseID in names(Body_motion)) {
  1477. Body_motion[[mouseID]][["Velocity/dLight"]] <- na.omit(Body_motion[[mouseID]][["Velocity/dLight"]])
  1478. }
  1480. #Then compute the average velocity per period
  1481. for (mouseID in names(Body_motion)) {
  1482. temp1 <- Body_motion[[mouseID]][["Velocity/dLight"]] %>%
  1483. group_by(Period) %>%
  1484. summarize(mean_velocity = mean(Velocity))
  1486. temp2 <- Body_motion[[mouseID]][["Velocity/dLight"]] %>%
  1487. group_by(Period) %>%
  1488. summarize(SD_dLightDFF = sd(dLightDFF))
  1490. Body_motion[[mouseID]][["Mean_Velocity/dLight"]] <- data.frame(Period = temp1$Period,
  1491. mean_velocity = temp1$mean_velocity,
  1492. SD_dLightDFF = temp2$SD_dLightDFF)
  1493. }
  1495. #Add the mouseID
  1496. for (mouseID in names(Body_motion)) {
  1497. Body_motion[[mouseID]][["Mean_Velocity/dLight"]]$mouseID <- rep(mouseID, times = nrow(Body_motion[[mouseID]][["Mean_Velocity/dLight"]]))
  1498. }
  1501. #Stack the dataframes together
  1502. Mean_velocity_dLightDFF <- Body_motion %>%
  1503. map(~ .x[["Mean_Velocity/dLight"]]) %>%
  1504. bind_rows()
  1506. #Add group column
  1507. Mean_velocity_dLightDFF <- Mean_velocity_dLightDFF %>%
  1508. mutate(
  1509. Group = case_when(
  1510. mouseID == "J0777" ~ "fDIO-mCherry",
  1511. mouseID == "J0782" ~ "fDIO-mCherry",
  1512. mouseID == "J0859" ~ "fDIO-mCherry",
  1513. mouseID == "K0871" ~ "fDIO-mCherry",
  1515. mouseID == "J0779" ~ "CoffFon-hM4Di",
  1516. mouseID == "J0784" ~ "CoffFon-hM4Di",
  1517. mouseID == "K0331" ~ "CoffFon-hM4Di",
  1518. mouseID == "K0994" ~ "CoffFon-hM4Di",
  1519. mouseID == "K0861" ~ "CoffFon-hM4Di",
  1521. mouseID == "K0029" ~ "ConFon-hM4Di",
  1522. mouseID == "K0863" ~ "ConFon-hM4Di",
  1523. mouseID == "K0865" ~ "ConFon-hM4Di",
  1524. mouseID == "K0990" ~ "ConFon-hM4Di"
  1525. )
  1526. )
  1528. #Plot
  1529. library(ggpubr)
  1530. Mean_velocity_dLightDFF$Period <- factor(Mean_velocity_dLightDFF$Period, levels = c("PreDCZ", "Transition", "PostDCZ", "PostDCZ (Forced Locomotion)"))
  1531. Mean_velocity_dLightDFF$Group <- factor(Mean_velocity_dLightDFF$Group, levels = c("fDIO-mCherry", "CoffFon-hM4Di", "ConFon-hM4Di"))
  1533. #Remove the transition time
  1534. Mean_velocity_dLightDFF <- Mean_velocity_dLightDFF %>%
  1535. dplyr::filter(!Period %in% "Transition")
  1537. #Specify the sex
  1538. Mean_velocity_dLightDFF <- Mean_velocity_dLightDFF %>%
  1539. mutate(Sex = case_when(
  1540. mouseID == "J0782" ~ "M",
  1541. mouseID == "J0859" ~ "M",
  1542. mouseID == "J0777" ~ "F",
  1543. mouseID == "J0784" ~ "M",
  1544. mouseID == "J0779" ~ "F",
  1545. mouseID == "J0782" ~ "M",
  1546. mouseID == "K0029" ~ "F",
  1547. mouseID == "K0992" ~ "M",
  1548. mouseID == "K0871" ~ "F",
  1549. mouseID == "K0331" ~ "M",
  1550. mouseID == "K0994" ~ "M",
  1551. mouseID == "K0863" ~ "M",
  1552. mouseID == "K0865" ~ "M",
  1553. mouseID == "J0868" ~ "F",
  1554. mouseID == "K0990" ~ "F",
  1555. mouseID == "K0861" ~ "F"
  1557. ))
  1560. Mean_velocity_dLightDFF |>
  1561. tidyplot(x = Period, y = mean_velocity, color = Group) |>
  1562. add_mean_bar(alpha = 0.3) |>
  1563. add_sem_errorbar() |>
  1564. add_data_points_beeswarm(data = filter_rows(Sex == "M"), shape = 16, color = "black") |>
  1565. add_data_points_beeswarm(data = filter_rows(Sex == "F"), shape = 1, color = "black") |>
  1566. add_test_asterisks(hide_info = TRUE) |>
  1567. adjust_y_axis(title = "Mean Velocity (mm/s)")
  1570. Mean_velocity_dLightDFF |>
  1571. tidyplot(x = Period, y = SD_dLightDFF, color = Group) |>
  1572. add_mean_bar(alpha = 0.3) |>
  1573. add_sem_errorbar() |>
  1574. add_data_points_beeswarm(data = filter_rows(Sex == "M"), shape = 16, color = "black") |>
  1575. add_data_points_beeswarm(data = filter_rows(Sex == "F"), shape = 1, color = "black") |>
  1576. add_test_asterisks(hide_info = TRUE) |>
  1577. adjust_y_axis(title = "dLight deltaF/F Standard Deviation")
  1579. #Run two-way ANOVA
  1581. #####Velocity##########
  1583. #Check assumption
  1584. #Normality
  1585. ggqqplot(Mean_velocity_dLightDFF, "mean_velocity", ggtheme = theme_bw()) +
  1586. facet_grid(Group ~ Period)
  1587. #Note: All the points fall approximately along the reference line, for each cell. So we can assume normality of the data.
  1589. #Homogeneity of variance assumption
  1590. Mean_velocity_dLightDFF %>% levene_test(mean_velocity ~ Group*Period)
  1591. #The Levene’s test not significant so we can assume homogeneity
  1593. #ANOVA
  1594. res.aov <- Mean_velocity_dLightDFF %>% anova_test(mean_velocity ~ Group*Period)
  1595. res.aov
  1596. #ANOVA has a significant interaction, so I will proceed to Tukey pairwise comparison
  1597. pwc <- Mean_velocity_dLightDFF %>%
  1598. group_by(Period) %>%
  1599. tukey_hsd(mean_velocity ~ Group)
  1601. write.csv(pwc, "MeanVelocity_PerPeriod_twowayANOVATukeyHSD.csv")
  1603. #####deltaF/F##########
  1605. #Check assumption
  1606. #Normality
  1607. ggqqplot(Mean_velocity_dLightDFF, "SD_dLightDFF", ggtheme = theme_bw()) +
  1608. facet_grid(Group ~ Period)
  1609. #Note: All the points fall approximately along the reference line, for each cell. So we can assume normality of the data.
  1611. #Homogeneity of variance assumption
  1612. Mean_velocity_dLightDFF %>% levene_test(SD_dLightDFF ~ Group*Period)
  1613. #The Levene’s test not significant so we can assume homogeneity
  1615. #ANOVA
  1616. res.aov <- Mean_velocity_dLightDFF %>% anova_test(SD_dLightDFF ~ Group*Period)
  1617. res.aov
  1618. #ANOVA has a significant interaction, so I will proceed to Tukey pairwise comparison
  1619. pwc <- Mean_velocity_dLightDFF %>%
  1620. group_by(Period) %>%
  1621. tukey_hsd(SD_dLightDFF ~ Group)
  1623. write.csv(pwc, "SDdeltaFF_PerPeriod_twowayANOVATukeyHSD.csv")

Fiberphotometry_DA peaks to locomotor bouts analysis.R at commit 3484e62, no license · at the source

Overview

Authors: Cyril Bolduc1, Cameron Oram1, Skylar Donovan1, Haleigh Bach1, Martha Liu1, Rafaëlle Marier1, Morgan Sharpe1, Siqi Liu1, Cédric Campeau1, Carl Duncan Spencer1, Sarah A. Martin1, Rajeshwar Awatramani2, Jean-François Poulin1
ORCID iDs: Cyril Bolduc, Cameron Oram, Skylar Donovan, Martha Liu, Morgan Sharpe, Siqi Liu, Cédric Campeau, Carl Duncan Spencer, Rajeshwar Awatramani, Jean-François Poulin
  1. Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal H3A 2B4, Quebec
  2. Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611
Institutions: McGill University (Canada); Northwestern University (United States)
Journal: Proceedings of the National Academy of Sciences of the United States of America, volume 123, issue 35, article e2613593123
Dates: received 23 April 2026; accepted 21 July 2026; published online 24 August 2026; in print 1 September 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1073/pnas.2613593123 · PMID 42636380 · PMCID PMC13535173 · OpenAlex W4412182866
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.07.07.663227
Status: code verified
Categories: mouse (organism), Parkinson's (population)
Keywords: dopamine subtypes, bradykinesia, basal ganglia, fiberphotometry, exploration
MeSH: Calbindin 1*, Calbindins*, Dopaminergic Neurons*, Locomotion*, Mesencephalon*, Animals, Dopamine, Male, Mice (* major topic)
Journal subjects: Biological Sciences, Neuroscience
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Parkinson Canada (NA); Fonds de recherche du Québec (NA); Canadian Institutes of Health Research (NA, PJT-183760)
Citations: cited by 1 paper (Europe PMC); 89 references in the paper

Abstract

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

Repository

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

poulin-lab/Bolduc-et-al-2026_Calb1-paper

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 3484e623569f44179457e7cd42c27665a4524815, 25 June 2026
Languages: R (37)
Size: 39 files, 37 scripts
Software Heritage: not archived
Found in: “Data, Materials, and Software Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (37 files), data.table (36 files), ggplot2 (36 files), ggpubr (36 files), rstatix (11 files), emmeans (1 file), lme4 (1 file), patchwork (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
38 files
At the source: github.com/poulin-lab/Bolduc-et-al-2026_Calb1-paper.git

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

Tracing map

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

What the map holds:

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

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

Data

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

Code and data availability statement

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

Read it in the paper: doi.org/10.1073/pnas.2613593123.

Versions

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

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 5 keywords, 9 MeSH terms, 3 funders, 88 references.

Cite

This paper

Bolduc, C., Oram, C., Donovan, S., Bach, H., Liu, M., Marier, R., Sharpe, M., Liu, S., Campeau, C., Spencer, C. D., Martin, S. A., Awatramani, R., & Poulin, J.-F. (2026). Calbindin stratifies midbrain dopaminergic neurons governing distinct aspects of locomotion. Proceedings of the National Academy of Sciences of the United States of America, 123(35), e2613593123. https://doi.org/10.1073/pnas.2613593123

BibTeX

@article{bolduc2026calbindin,
author = {Bolduc, Cyril and Oram, Cameron and Donovan, Skylar and Bach, Haleigh and Liu, Martha and Marier, Rafaëlle and Sharpe, Morgan and Liu, Siqi and Campeau, Cédric and Spencer, Carl Duncan and Martin, Sarah A. and Awatramani, Rajeshwar and Poulin, Jean-François},
title = {{Calbindin stratifies midbrain dopaminergic neurons governing distinct aspects of locomotion}},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = aug,
volume = {123},
number = {35},
pages = {e2613593123},
publisher = {National Academy of Sciences},
issn = {0027-8424},
doi = {10.1073/pnas.2613593123},
url = {https://doi.org/10.1073/pnas.2613593123},
pmid = {42636380},
pmcid = {PMC13535173}
}

RIS

TY - JOUR
AU - Bolduc, Cyril
AU - Oram, Cameron
AU - Donovan, Skylar
AU - Bach, Haleigh
AU - Liu, Martha
AU - Marier, Rafaëlle
AU - Sharpe, Morgan
AU - Liu, Siqi
AU - Campeau, Cédric
AU - Spencer, Carl Duncan
AU - Martin, Sarah A.
AU - Awatramani, Rajeshwar
AU - Poulin, Jean-François
TI - Calbindin stratifies midbrain dopaminergic neurons governing distinct aspects of locomotion
T2 - Proceedings of the National Academy of Sciences of the United States of America
J2 - Proc Natl Acad Sci U S A
PY - 2026
DA - 2026/08/24
VL - 123
IS - 35
SP - e2613593123
SN - 0027-8424
PB - National Academy of Sciences
DO - 10.1073/pnas.2613593123
UR - https://doi.org/10.1073/pnas.2613593123
LA - en
ER -

CSL-JSON

{
"id": "10.1073/pnas.2613593123",
"type": "article-journal",
"title": "Calbindin stratifies midbrain dopaminergic neurons governing distinct aspects of locomotion",
"container-title": "Proceedings of the National Academy of Sciences of the United States of America",
"author": [
{
"family": "Bolduc",
"given": "Cyril"
},
{
"family": "Oram",
"given": "Cameron"
},
{
"family": "Donovan",
"given": "Skylar"
},
{
"family": "Bach",
"given": "Haleigh"
},
{
"family": "Liu",
"given": "Martha"
},
{
"family": "Marier",
"given": "Rafaëlle"
},
{
"family": "Sharpe",
"given": "Morgan"
},
{
"family": "Liu",
"given": "Siqi"
},
{
"family": "Campeau",
"given": "Cédric"
},
{
"family": "Spencer",
"given": "Carl Duncan"
},
{
"family": "Martin",
"given": "Sarah A."
},
{
"family": "Awatramani",
"given": "Rajeshwar"
},
{
"family": "Poulin",
"given": "Jean-François"
}
],
"container-title-short": "Proc Natl Acad Sci U S A",
"volume": "123",
"issue": "35",
"page": "e2613593123",
"DOI": "10.1073/pnas.2613593123",
"PMID": "42636380",
"PMCID": "PMC13535173",
"ISSN": "0027-8424",
"publisher": "National Academy of Sciences",
"URL": "https://doi.org/10.1073/pnas.2613593123",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
24
]
]
}
}

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

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41467-026-75194-3 [code]
Leucine-rich repeat kinase 2 impairs the release sites of Parkinson's disease vulnerable dopamine axons.
Journal: Nature communications
In common: ggplot2, tidyverse, Parkinson's, mouse, 17 references, author Rajeshwar Awatramani
[2] doi:10.1038/s41467-026-73994-1 [code]
Prediction error correlates in the striosome-dopamine circuit emerge from information gain.
Journal: Nature communications
In common: 16 references
[3] doi:10.1016/j.celrep.2026.117298 [code]
Midbrain endocannabinoids actuate dopamine-based action selection.
Journal: Cell reports
In common: emmeans, lme4, data.table, 3 other tools, mouse, 6 references
[4] doi:10.1016/j.isci.2026.117130 [code]
Region-specific weighting of sensory intensity and reward prediction error by dopamine signals.
Journal: iScience
In common: 8 references
[5] doi:10.1126/sciadv.adq8840 [code]
Astrocytes mediate the dopaminergic modulation of tonic GABAergic signaling in substantia nigra.
Journal: Science advances
In common: mouse, 4 references, author Rajeshwar Awatramani
[6] doi:10.3390/ijms27135713 [code]
Chronic Administration of Marinobufagenin in Mice Causes Hyperlocomotion and Decrease in Anxiety by Altering Monoamine Turnover Unaccompanied by Motor Deficits or Oxidative Stress.
Journal: International journal of molecular sciences
In common: rstatix, emmeans, lme4, 5 other tools, mouse
[7] doi:10.1126/sciadv.aec9291 [code]
Computational mechanisms of perception in autism revealed using games inspired by rodent operant tasks.
Journal: Science advances
In common: rstatix, emmeans, lme4, 5 other tools
[8] doi:10.1093/bioinformatics/btag592 [code]
Network-based stratification of allele-specific expression reveals patient subgroups in Huntington's disease.
Journal: Bioinformatics (Oxford, England)
In common: rstatix, emmeans, lme4, 5 other tools
[9] doi:10.1038/s41467-026-74753-y [code]
A human-specific microRNA controls the timing of excitatory synaptogenesis.
Journal: Nature communications
In common: rstatix, emmeans, lme4, 5 other tools
[10] doi:10.1038/s41398-026-04141-z [code]
Effects on hippocampal activity following novel 5-HT4 receptor agonism in unmedicated patients with depression: the RESTAND study.
Journal: Translational psychiatry
In common: rstatix, emmeans, lme4, 5 other tools

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.