Clustering voltage-gated calcium channels in the auditory cortex suppresses impulse response

Exponential Decay Model Fit (CL data)

library(tidyverse)
library(ggpubr)
library(rstatix)
library(knitr)
library(kableExtra)
library(gridExtra)
library(nlstools)
library(plotly)
library(htmltools)

exp_model_fit <- function(df, group, msrmt){
  df <- df %>% 
  filter(
    Measurement == msrmt,
    Group == group)

  theta.0 <- min(df$PeakAmp) * 0.5  

  # Estimate the rest parameters using a linear model
  model.0 <- lm(log(PeakAmp - theta.0) ~ OrderofClick, data=df)  
  alpha.0 <- exp(coef(model.0)[1])
  beta.0 <- coef(model.0)[2]
  
  start <- list(alpha = alpha.0, beta = beta.0, theta = theta.0)
  
  model <- nls(PeakAmp ~ alpha * exp(beta * OrderofClick) + theta , data = df, start = start)
  return(model)
}

AVRECPeakCLST <- read_csv('../../Data/AVRECPeakCL.csv')

model_data <- AVRECPeakCLST %>% 
    filter(
        Measurement %in% c("preCL_1", "CL_1", "CL_2"),
        ClickFreq == 5,
        Layer == "All") %>%
    ungroup() %>% 
    mutate(
        Measurement = case_when(
        Measurement == "preCL_1" ~ "preLaser",
        TRUE ~ "postLaser"
    )) %>% 
    group_by(Group, Animal, OrderofClick, Measurement) %>% 
    summarize(PeakAmp = mean(PeakAmp, na.rm = TRUE)) %>% 
    # group_by(Group, OrderofClick, Measurement) %>% 
    # summarize(PeakAmp = mean(PeakAmp, na.rm = TRUE)) %>% 
    ungroup()

Model Fit

l <- htmltools::tagList()
i <- 1
for(group in c("KIC", "KIT", "KIV")){
  # group <- "KIT"
  
  cat("##", group)
  cat("\n\n")
  
  models <- list()
  for(msrmt in c("preLaser", "postLaser")) {
    models[[msrmt]] <- exp_model_fit(df = model_data, group = group, msrmt = msrmt)
  }
  
  summary(models$preLaser)$parameters %>% 
    kable(caption = "Pre-laser", full_width = F, digits = 3, escape = F) %>% 
    kable_classic_2(full_width = F) %>% 
    kable_styling() %>% 
    print()
  
  summary(models$postLaser)$parameters %>% 
    kable(caption = "Post-laser", full_width = F, digits = 3, escape = F) %>% 
    kable_classic_2(full_width = F) %>% 
    kable_styling() %>% 
    print()
  
  cat("\n\n")
  
  predicted_data <- data.frame(
    OrderofClick = seq(1,5,0.05), 
    Measurement = "preLaser") %>% 
    mutate(PeakAmp = predict(models$preLaser, list(OrderofClick = OrderofClick))) %>% 
    bind_rows(
      data.frame(
        OrderofClick = seq(1,5,0.05), 
        Measurement = "postLaser") %>% 
        mutate(PeakAmp = predict(models$postLaser, list(OrderofClick = OrderofClick)))
    )
    
  l[[i]] <- predicted_data %>% 
    plot_ly(
       x = ~OrderofClick, y = ~PeakAmp,
      color = ~Measurement,
      type = 'scatter', mode = 'lines',
      hovertemplate = paste(
        '<b>Fitted Data</b>',
        '<br><i>OrderofClick</i>: %{x}',
        '<br><i>PeakAmp</i>: %{y:.4f}')
    ) %>% 
    add_markers(
      data = model_data %>% filter(Group == group), hovertemplate = paste(
        '<b>Actual Data</b>',
        '<br><i>OrderofClick</i>: %{x}',
        '<br><i>PeakAmp</i>: %{y:.4f}')) %>% 
    layout(
      title = paste0("<b>Exponential Decay Model (", group, ")</b>"),
      xaxis = list(title = "<b>Order of Click</b>"),
      yaxis = list(title = "<b>Peak Amplitude</b>"),
      margin = list(t = 70))
  
  cat("\n\n")
  i <- i+1
}

KIC

Pre-laser

	Estimate	Std. Error	t value	Pr(>|t|)
alpha	0.002	0.003	0.685	0.497
beta	-1.180	1.533	-0.770	0.445
theta	0.001	0.000	5.010	0.000

Post-laser

	Estimate	Std. Error	t value	Pr(>|t|)
alpha	0.002	0.003	0.700	0.487
beta	-1.279	1.488	-0.859	0.395
theta	0.001	0.000	8.251	0.000

KIT

Pre-laser

	Estimate	Std. Error	t value	Pr(>|t|)
alpha	0.001	0.002	0.579	0.565
beta	-1.320	1.794	-0.736	0.466
theta	0.001	0.000	8.876	0.000

Post-laser

	Estimate	Std. Error	t value	Pr(>|t|)
alpha	0.001	0.001	0.692	0.492
beta	-1.051	1.541	-0.682	0.499
theta	0.001	0.000	7.232	0.000

KIV

Pre-laser

	Estimate	Std. Error	t value	Pr(>|t|)
alpha	0.002	0.001	1.353	0.186
beta	-0.951	0.806	-1.180	0.247
theta	0.001	0.000	7.047	0.000

Post-laser

	Estimate	Std. Error	t value	Pr(>|t|)
alpha	0.002	0.002	0.881	0.385
beta	-1.125	1.198	-0.939	0.355
theta	0.001	0.000	6.733	0.000

Plots

l