In RobWHickman/gettyR:

library(tidyverse)
library(gettyR)

dir <- "C:/Users/DHill/Desktop/robert_cell_sorting/sessions"
session <- "U-2020-07-30-s2"

data <- gettyR::open_mat(file.path(dir, session, dir(file.path(dir, session)) %>% .[grepl("[0-9]\\.mat", .)])) %>%
  mutate(cumulative_duration_ms = cumsum(duration),
         trial_start_time_ms = lag(cumulative_duration_ms, default = 0)) %>%
  select(-cumulative_duration_ms)

sorted_spikes <- gettyR::open_sorted_spikes(file.path(dir, session, 'sorted_spikes.mat')) %>%
  mutate(cell = paste0("cell", cell),
         trial = map_dbl(time, function(s) last(which(data$trial_start_time_ms < s)))) %>%
  rename(spike_time_ms = time)

data <- left_join(data, sorted_spikes, by = "trial") %>%
  mutate(trial_spike_time_ms = spike_time_ms - trial_start_time_ms) %>%
  tidyr::nest(sorted_spikes = c(spike_time_ms, trial_spike_time_ms, cell))

dirty_munging <- data %>%
  #filter(situation == 2) %>%
  select(trial, spikes, sorted_spikes) %>%
  pmap_df(gettyR::rough_match_spikes) %>%
  right_join(data, by = "trial")


sorted_spike_responses <- dirty_munging %>%
  unnest(addvals) %>%
  filter(names == "prv_reward_liquid") %>%
  mutate(win_trial = case_when(
    situation == 2 & lead(vals) >0 ~ "win",
    situation == 2 & lead(vals) == 0 ~ "lose"
  )) %>%
  select(trial, trial_start_time_ms, situation, bits, sorted_spikes, lag, Freq, win_trial) %>%
  unnest(bits) %>%
  filter(index == "upat") %>%
  unnest(sorted_spikes)

fr_sorted <- sorted_spike_responses %>%
  filter(situation == 4 & names == "budget_tap" & Freq > 2 & !is.na(vals)) %>%
  mutate(post_event_time_ms = trial_spike_time_ms - vals - lag) %>%
  filter(post_event_time_ms < 2000 & post_event_time_ms > -2000) %>%
  ggplot(aes(fill = cell)) +
  geom_histogram(aes(x = post_event_time_ms)) +
  geom_vline(xintercept = 0, colour = "red", linetype = "dashed", size = 2) +
  labs(title = "sorted spike responses to Fixation Cross",
       x = "post event time (/ms)",
       y = "spike count") +
  theme_minimal() +
  facet_wrap(~cell)

fix_cross_sorted <- sorted_spike_responses %>%
  filter(situation == 2 & names == "fixation_cross") %>%
  mutate(post_event_time_ms = trial_spike_time_ms - vals - lag) %>%
  filter(post_event_time_ms < 2000 & post_event_time_ms > -1000) %>%
  ggplot(aes(fill = cell)) +
  geom_histogram(aes(x = post_event_time_ms), binwidth = 25, alpha = 0.7) +
  geom_vline(xintercept = 0, colour = "red", linetype = "dashed", size = 2) +
  labs(title = "sorted spike responses to Fixation Cross",
       x = "post event time (/ms)",
       y = "spikes") +
  theme_minimal() +
  facet_wrap(~cell)

winlose_sorted <- sorted_spike_responses %>%
  filter(situation == 2 & names == "win_lose" & !is.na(win_trial)) %>%
  mutate(post_event_time_ms = trial_spike_time_ms - vals - lag) %>%
  filter(post_event_time_ms < 1000 & post_event_time_ms > -1000) %>%
  ggplot(aes(fill = cell)) +
  geom_histogram(aes(x = post_event_time_ms), binwidth = 25, alpha = 0.7) +
  geom_vline(xintercept = 0, colour = "red", linetype = "dashed", size = 2) +
  labs(title = "sorted spike responses to Win/Lose Reveal",
       x = "post event time (/ms)",
       y = "spikes") +
  theme_minimal() +
  facet_grid(win_trial~cell)

wc_windows <- 750
wilcoxons <- sorted_spike_responses %>%
  filter(situation == 2) %>%
  mutate(event_time_ms = trial_spike_time_ms - vals - lag) %>%
  mutate(event_ref = case_when(
    abs(event_time_ms) < wc_windows & event_time_ms < 0 ~ "pre",
    abs(event_time_ms) < wc_windows & event_time_ms > 0 ~ "post"
  )) %>%
  filter(!is.na(event_ref)) %>%
  group_by(cell, names, event_ref, trial) %>%
  summarise(spikes = n()) %>%
  pivot_wider(names_from = event_ref, values_from = spikes, values_fill = 0) %>%
  ungroup() %>%
  split(f = paste(.$cell, .$names)) %>%
  map_df(., function(s) {
    if(nrow(s) > 1) {
      test <- broom::tidy(wilcox.test(s$post, s$pre)) %>%
        mutate(cell = unique(s$cell),
               event = unique(s$names))
      return(test)
  } else {
    return(NULL)
  }
  }) %>%
  filter(event != "trial_onset")

hz <- 22000

trace <- gettyR::open_cell_trace(file) %>%
  group_by(cell, time) %>%
  mutate(time = time * 1000 / hz) %>%
  summarise(m = mean(value),
            sd = sd(value))

trace_plot <- ggplot(trace, aes(x = time, y = m)) + 
  geom_ribbon(aes(ymin = m - 1.96*sd, ymax = m + 1.96*sd), alpha = 0.5) +
  geom_line() +
  labs(title = "cell traces",
       x = "time (/ms)",
       y = "voltage change") +
  theme_minimal() +
  facet_wrap(~cell)

getty_spike_responses <- data %>%
  select(trial, trial_start_time_ms, situation, bits, spikes) %>%
  unnest(bits) %>%
  filter(index == "upat") %>%
  unnest(spikes) %>%
  select(-spike)

free_reward_getty <- getty_spike_responses %>%
  filter(situation == 4 & names == "budget_tap") %>%
  mutate(post_event_time_ms = trial_spike_time_ms - vals) %>%
  filter(post_event_time_ms < 2000 & post_event_time_ms > -2000) %>%
  ggplot() +
  geom_histogram(aes(x = post_event_time_ms)) +
  geom_vline(xintercept = 0, colour = "red", linetype = "dashed", size = 2) +
  labs(title = "getty spike responses to Free Reward",
       x = "post event time (/ms)",
       y = "spike count") +
  theme_minimal()

fix_cross_getty <- getty_spike_responses %>%
  filter(situation == 2 & names == "fixation_cross") %>%
  mutate(post_event_time_ms = trial_spike_time_ms - vals) %>%
  filter(post_event_time_ms < 2000 & post_event_time_ms > -2000) %>%
  ggplot() +
  geom_histogram(aes(x = post_event_time_ms), binwidth = 20) +
  geom_vline(xintercept = 0, colour = "red", linetype = "dashed", size = 2) +
  labs(title = "getty spike responses to Fixation Cross",
       x = "post event time (/ms)",
       y = "spike count") +
  theme_minimal()

win_lose_getty <- getty_spike_responses %>%
  filter(situation == 2 & names == "win_lose") %>%
  mutate(post_event_time_ms = trial_spike_time_ms - vals) %>%
  filter(post_event_time_ms < 2000 & post_event_time_ms > -2000) %>%
  ggplot() +
  geom_histogram(aes(x = post_event_time_ms)) +
  geom_vline(xintercept = 0, colour = "red", linetype = "dashed", size = 2) +
  labs(title = "getty spike responses to Winning Bids",
       x = "post event time (/ms)",
       y = "spike count") +
  theme_minimal()

sorted_spike_responses <- data %>%
  select(trial, trial_start_time_ms, situation, bits, sorted_spikes) %>%
  unnest(bits) %>%
  filter(index == "upat") %>%
  unnest(sorted_spikes)

free_reward_sorted <- sorted_spike_responses %>%
  filter(situation == 4 & names == "budget_tap") %>%
  mutate(post_event_time_ms = (spike_time_ms - trial_start_time_ms) - vals) %>%
  filter(post_event_time_ms < 2000 & post_event_time_ms > -2000) %>%
  ggplot() +
  geom_histogram(aes(x = post_event_time_ms)) +
  geom_vline(xintercept = 0, colour = "red", linetype = "dashed", size = 2) +
  labs(title = "getty spike responses to Free Reward",
       x = "post event time (/ms)",
       y = "spike count") +
  theme_minimal()

fix_cross_sorted <- sorted_spike_responses %>%
  filter(situation == 2 & names == "fixation_cross") %>%
  mutate(post_event_time_ms = (spike_time_ms - trial_start_time_ms) - vals) %>%
  filter(post_event_time_ms < 2000 & post_event_time_ms > -2000) %>%
  ggplot(aes(fill = cell)) +
  geom_histogram(aes(x = post_event_time_ms)) +
  geom_vline(xintercept = 0, colour = "red", linetype = "dashed", size = 2) +
  labs(title = "sorted spike responses to Fixation Cross",
       x = "post event time (/ms)",
       y = "spike count") +
  theme_minimal() +
  facet_wrap(~cell)

win_bid_sorted <- sorted_spike_responses %>%
  filter(situation == 4 & names == "win_lose") %>%
  mutate(post_event_time_ms = (spike_time_ms - trial_start_time_ms) - vals) %>%
  filter(post_event_time_ms < 2000 & post_event_time_ms > -2000) %>%
  ggplot(aes(fill = cell)) +
  geom_histogram(aes(x = post_event_time_ms)) +
  geom_vline(xintercept = 0, colour = "red", linetype = "dashed", size = 2) +
  labs(title = "sorted spike responses to Fixation Cross",
       x = "post event time (/ms)",
       y = "spike count") +
  theme_minimal() +
  facet_wrap(~cell)

test <- data %>%
  select(trial, situation, bits, spikes) %>%
  filter(situation == 4) %>%
  unnest(bits) %>%
  filter(index == "upat" & names == "budget_tap") %>%
  unnest(spikes) %>%
  mutate(post_event_time = case_when(
    !is.na(vals) ~ spike_time - vals,
    TRUE ~ spike_time
  )) %>%
  mutate(rewarded = case_when(
    !is.na(vals) ~ "free reward",
    TRUE ~ "no free reward"
  )) %>%
  filter(post_event_time > -2000 & post_event_time < 2000)

fr1 <- test %>%
  ggplot() +
  geom_rect(aes(xmin = post_event_time, xmax = post_event_time + 4, ymin = trial, ymax = trial + 1)) +
  geom_vline(xintercept = 0, colour = "red", linetype = "dashed", size = 2) +
  theme_minimal() +
  facet_wrap(~rewarded, scales = "free_x")

fr2 <- test %>%
  ggplot() +
  geom_histogram(aes(x = post_event_time)) +
  geom_vline(xintercept = 0, colour = "red", linetype = "dashed", size = 2) +
  theme_minimal() +
  facet_wrap(~rewarded, scales = "free_x")

fr3 <- data %>%
  select(trial, trial_start_time, situation, bits, sorted_spikes) %>%
  filter(situation == 4) %>%
  unnest(bits) %>%
  filter(index == "upat" & names == "budget_tap" &!is.na(vals)) %>%
  mutate(fr_trial = 1:n()) %>%
  tidyr::unnest(sorted_spikes) %>%
  mutate(trial_spike_time = time - trial_start_time) %>%
  mutate(post_event_time = (trial_spike_time * 1000) - vals) %>%
  filter(post_event_time > -2000 & post_event_time < 2000) %>%
  ggplot(aes(fill = cell)) +
  geom_rect(aes(xmin = post_event_time, xmax = post_event_time + 6, ymin = fr_trial, ymax = fr_trial + 1), colour = "black") +
  geom_vline(xintercept = 0, colour = "red", linetype = "dashed", size = 2) +
  labs(title = "sorted cell responses to Free Reward",
       y = "free reward trial",
       x = "post reward time (/ms)") +
  theme_minimal()

fr4 <- data %>%
  select(trial, trial_start_time, situation, bits, sorted_spikes) %>%
  filter(situation == 4) %>%
  unnest(bits) %>%
  filter(index == "upat" & names == "budget_tap" &!is.na(vals)) %>%
  mutate(fr_trial = 1:n()) %>%
  tidyr::unnest(sorted_spikes) %>%
  mutate(trial_spike_time = time - trial_start_time) %>%
  mutate(post_event_time = (trial_spike_time * 1000) - vals) %>%
  filter(post_event_time > -2000 & post_event_time < 2000) %>%
  ggplot(aes(fill = cell)) +
geom_histogram(aes(x = post_event_time)) +  geom_vline(xintercept = 0, colour = "red", linetype = "dashed", size = 2) +
  geom_vline(xintercept = 0, colour = "red", linetype = "dashed", size = 2) +
  labs(title = "sorted cell responses to Free Reward",
       y = "free reward trial",
       x = "post reward time (/ms)") +
  theme_minimal() +
  facet_wrap(~cell)

fc1 <- data %>%
  select(trial, trial_start_time, situation, bits, sorted_spikes) %>%
  filter(situation == 2) %>%
  unnest(bits) %>%
  filter(index == "upat" & names == "fixation_cross" &!is.na(vals)) %>%
  tidyr::unnest(sorted_spikes) %>%
  mutate(trial_spike_time = time - trial_start_time) %>%
  mutate(post_event_time = (trial_spike_time * 1000) - vals) %>%
  ggplot() +
  geom_rect(aes(xmin = post_event_time, xmax = post_event_time + 4, ymin = trial, ymax = trial + 1)) +
  geom_vline(xintercept = 0, colour = "red", linetype = "dashed", size = 2) +
  theme_minimal() +
  facet_wrap(~cell)

fc1 <- data %>%
  select(trial, trial_start_time, situation, bits, sorted_spikes) %>%
  filter(situation == 2) %>%
  unnest(bits) %>%
  filter(index == "upat" & names == "fixation_cross" &!is.na(vals)) %>%
  tidyr::unnest(sorted_spikes) %>%
  mutate(trial_spike_time = time - trial_start_time) %>%
  mutate(post_event_time = (trial_spike_time * 1000) - vals) %>%
  filter(post_event_time > -2000 & post_event_time < 2000) %>%
  ggplot() +
geom_histogram(aes(x = post_event_time)) +  geom_vline(xintercept = 0, colour = "red", linetype = "dashed", size = 2) +
  theme_minimal() +
  facet_wrap(~cell)

RobWHickman/gettyR documentation built on Sept. 16, 2020, 10:54 p.m.