GluN2A mutant rescue in grin2a knockout: EPSC properties

This document forms part of the data and code deposited at:
https://github.com/acp29/Elmasri_GRIN2A

Load package requirements

if (!require(package="tidyverse")) utils::install.packages("tidyverse")
library(tidyverse) 
if (!require(package="lme4")) utils::install.packages("lme4")
library(lme4)  
if (!require(package="HLMdiag")) utils::install.packages("HLMdiag")
library(HLMdiag) 
if (!require(package="parameters")) utils::install.packages("parameters")
library(parameters) 
if (!require(package="car")) utils::install.packages("car")
library(car)  
if (!require(package="performance")) utils::install.packages("performance")
library(performance)  
if (!require(package="BayesFactor")) utils::install.packages("BayesFactor")
library(BayesFactor) 
if (!require(package="bayestestR")) utils::install.packages("bayestestR")
library(bayestestR) 
if (!require(package="stats")) utils::install.packages("stats")
library(stats)
if (!require(package="pCalibrate")) utils::install.packages("pCalibrate")
library(pCalibrate)
if (!require(package="afex")) utils::install.packages("afex")
library(afex)
if (!require(package="emmeans")) utils::install.packages("emmeans")
library(emmeans)
if (!require(package="multcomp")) utils::install.packages("multcomp")
library(multcomp)
if (!require(package="knitr")) utils::install.packages("knitr")
library(knitr)
if (!require(package="kableExtra")) utils::install.packages("kableExtra")
library(kableExtra)
if (!require(package="ggplot2")) utils::install.packages("ggplot2")
library(ggplot2)
if (!require(package="qqplotr")) utils::install.packages("qqplotr")
library(qqplotr)
if (!require(package="gridExtra")) utils::install.packages("gridExtra")
library(gridExtra)
if (!require(package="ggforce")) utils::install.packages("ggforce")
library(ggforce)
if (!require(package="devEMF")) utils::install.packages("devEMF")
library(devEMF)
if (!require(package="effectsize")) utils::install.packages("effectsize")
library(effectsize)

Read text in from file

Data <- read.delim("../data/n2a_mutant.dat", header = TRUE)
# Implicit nesting (required for anovaBF)
Data %>% 
  mutate(mutation = as.factor(mutation)) %>% 
  mutate(animal = paste0(as.numeric(mutation),animal)) %>% 
  mutate(slice = paste0(animal,slice)) %>% 
  mutate(pair = paste0(slice,pair)) %>% 
  mutate(pair = factor(pair)) -> Data

Factor encoding

Data$mutation <- as.factor(Data$mutation)
Data$transfection <- as.factor(Data$transfection)
Data$animal <- as.factor(Data$animal)
Data$slice <- as.factor(Data$slice)
Data$pair <- as.factor(Data$pair)

Set mutation WT and transfection - as reference levels

Data$mutation <- factor(Data$mutation, levels=c("WT","C436R","T531M","R518H","K669N","L812M"))
Data$transfection <- factor(Data$transfection, levels=c("-","+"))

lmer settings

settings <- lmerControl(check.conv.singular = .makeCC(action = "ignore",  tol = 1e-4), boundary.tol=0)

Fit a mixed linear model

# Initialize
variates <- c("peaknmda","decaynmda","dt50nmda","chargenmda","peakampa","decayampa","dt50ampa","chargeampa")
l <- length(variates)

for (i in 1:l) {
  
variates[i] -> resp  

cat('\n\n\n# Analysis of',resp,'\n\n')

# Plot data
# colours selected from:
#  > library(scales)
#  > show_col(hue_pal()(9))
p1 <- Data %>%
    mutate(mutation_jittered = jitter((as.numeric(mutation)+(as.numeric(transfection)-1)/2.5), 0.5),
           grouping=interaction(pair, mutation)) %>%
    mutate(mutation_transfection = as.numeric(mutation)+(as.numeric(transfection)-1)/2.5) %>%
    ggplot(aes(x=mutation, y=!!sym(resp), group=grouping, color=transfection)) + 
    geom_blank() +
    geom_line(aes(mutation_jittered), alpha=0.2, color="grey") +
    geom_point(aes(mutation_jittered), alpha=0.4, shape=16) +
    scale_color_manual(values=c("grey","#00BA38")) +
    stat_summary(mapping = aes(x=mutation_transfection,y=!!sym(resp)), fun.data="median_hilow", fun.args = list(conf.int=0.5), geom="linerange", color="black", size=1.0,inherit.aes=FALSE) + 
    stat_summary(mapping = aes(x=mutation_transfection,y=!!sym(resp)), fun="median", geom="point", shape=21, fill="white", color="black", size=2.5, stroke=1, inherit.aes=FALSE) +
    ylab(resp) +
    ggtitle("a") +
    theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1),axis.line = element_line(colour="black"),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.border = element_blank(),
          panel.background = element_blank(),
          legend.title = element_blank(),
          legend.position = "top")
p2 <- Data %>% 
    pivot_wider(c(mutation,pair,!!sym(resp)),names_from=transfection,values_from=!!sym(resp)) %>% 
    mutate(ratio = `+`/`-`) %>%
    ggplot(aes(x=mutation, y=ratio, colour=mutation)) +
    geom_sina(alpha=0.9, shape = 16) + 
    scale_color_manual(values=c("grey","#00C19F","#00B9E3","#619CFF","#DB72FB","#FF61C3")) +
    stat_summary(fun.data="median_hilow", fun.args = list(conf.int=0.5), geom="linerange", color="black", size=1.0) + 
    stat_summary(fun="median", geom="point", shape=21, fill="white", color="black", size=2.5, stroke=1) +
    ylab("ratio") +
    ggtitle("b") +
    theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1),axis.line = element_line(colour="black"),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.border = element_blank(),
          panel.background = element_blank(),
          legend.position = "none")
grid.arrange(p1, p2, nrow = 1, ncol = 2, top=sprintf("Summary plots of the data for: %s\n",resp))

# Fit the model with planned contrasts and perform hypothesis testing
  
# Setup planned, orthogonal contrasts
# ("WT","NONE","C436R","T531M","R518H","K669N","L812M")
# According to exploratory factor and cluster analysis of published data:
# GOF1 is gain-of-function (Factor 1)
# LOF1 is loss-of-function (Factor 1)
# LOF2 is loss-of-function (Factor 2)
# Factor 1 (functional change, e.g. EC50[Glu])
# Factor 2 (expression change, e.g. surface expression and current density)
WT_vs_Mutants   <- c(-5,1,1,1,1,1)/6
GOF1_vs_LOF12   <- c(0,-2,-2,-2,3,3)/5
LOF1_vs_LOF2    <- c(0,-1,-1,2,0,0)/3
GOF1            <- c(0,0,0,0,-1,1)/2
LOF2            <- c(0,-1,1,0,0,0)/2
contr.orth <- cbind(WT_vs_Mutants, GOF1_vs_LOF12, LOF1_vs_LOF2, LOF2, GOF1)
rownames(contr.orth) <- levels(Data$mutation)

# Check that contrasts are indeed orthogonal
contr.orth %>%
  cor() %>%
  knitr::kable(caption = sprintf("**All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()
contr.orth %>%
  colSums() %>%
  as.data.frame() %>%
  rename(.,'sum' = '.') %>%
  knitr::kable(caption = sprintf("**Sum of each orthogonal contrast should be zero: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

# Fit model (with orthogonal contrasts)
contrasts(Data$mutation) <- contr.orth 
attr(Data$mutation,"contrasts") %>%
  as.data.frame() %>%
  rownames_to_column(var = "mutation") %>% 
  knitr::kable(caption = sprintf("**Matrix of contrasts on mutation: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()
contrasts(Data$transfection) <- rbind(-1,1)/2
attr(Data$transfection,"contrasts") %>% 
  as.data.frame() %>%
  rename(contrast = "V1") %>%
  rownames_to_column(var = "transfection") %>% 
  mutate_at("transfection", str_replace_all, pattern = "\\+", replacement = "\\\\+")  %>% 
  mutate_at("transfection", str_replace_all, pattern = "\\-", replacement = "\\\\-")  %>% 
  knitr::kable(caption = sprintf("**Matrix of contrasts on transfection: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print() 
formula <- sprintf("log(%s) ~ mutation * transfection + (1|animal/slice/pair)", resp)
model <- lme4::lmer(formula, data = Data, REML = TRUE, control = settings, na.action = "na.fail") 

# Checking model assumptions
resid = residuals(model)
n = length(resid)
stdev = sqrt((n-1)/n) * sd(resid) # standard deviation with denominator n
std_resid = resid/stdev
p1 <- ggplot(Data, aes(x = fitted(model), y = std_resid)) +
  geom_point() +
  ggtitle("a") +
  xlab("Fitted values") + ylab("Standardized Residuals") +
  geom_hline(yintercept = 0) +
  geom_quantile(formula=y~x, color="#619CFF", size=1) +
  geom_smooth(method="loess", formula = y ~ x, color="#F8766D", size=1, se=FALSE)
p2 <- ggplot(Data, aes(x = std_resid)) +
  geom_histogram(aes(y=..density..), binwidth = 0.9*n^(-1/5), fill="#619CFF", alpha=0.33)  +
  geom_density(kernel="gaussian", alpha=0, color="#619CFF", size=1) +
  ggtitle("b") +
  xlab("Standardized Residuals") + ylab("Density") +
  geom_vline(xintercept = 0) +
  geom_function(fun = dnorm, args = list(mean=0, sd=1), col = "#F8766D", size = 1)
p3 <- ggplot(Data, aes(sample = std_resid)) +
  geom_qq_band(distribution = "norm", bandType = "ts", mapping = aes(fill = "TS"), fill="#619CFF", alpha = 0.33) +
  stat_qq() + 
  stat_qq_line(color="#F8766D",size=1) +
  ggtitle("c") +
  xlab("Normal Quantiles") + ylab("Sample Quantiles") 
infl <- hlm_influence(model, level="pair:(slice:animal)")
p4 <- infl %>% 
  mutate(influential = cooksd > 1.0) %>% 
  ggplot(aes(x=`pair:(slice:animal)`,y=cooksd, color=influential)) + 
  geom_segment(aes(x=`pair:(slice:animal)`, xend=`pair:(slice:animal)`, y=0, yend=cooksd)) + 
  geom_point() + 
  ylab("Cook's distance") +
  scale_color_manual(values=c("#619CFF","#F8766D")) + 
  ggtitle("d") +
  theme(axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        legend.position = "none",
        panel.background = element_rect(color="#EBEBEB"),
        panel.grid = element_blank(),
        panel.grid.minor.y = element_line(color = "white", size=0.25),
        panel.grid.major.y = element_line(color = "white", size=0.5),
        axis.line = element_blank(),
        axis.line.x = element_line(size = 0.5, colour = "black"))
grid.arrange(p1, p2, p3, p4, nrow=2, ncol=2, top=sprintf("Plots of standardized model residuals and Cook's distances: %s\n",resp))

# Calculate ANOVA table for the fitted model (Type III sum of squares) 
car::Anova(model, type = 3, test.statistic = "F") %>%       # Uses Kenward-Roger degrees of freedom
  as.data.frame() %>%
  rownames_to_column(var="Source") %>%
  filter(Source != "(Intercept)") -> aov

# Calculate Bayes Factors for ANOVA and append them to the ANOVA data frame
# Inclusion Bayes Factor based on matched models (prior odds uniform-equal)
Data %>% 
  mutate(logresp = log(!!sym(resp))) %>%
  as.data.frame() -> Data
set.seed(123456)
anovaBF(logresp ~ mutation * transfection + animal + slice + pair, 
                 whichRandom = c("animal","slice","pair"), 
                 whichModels = "withmain", 
                 iterations = 20000,
                 data = Data) %>%
  bayesfactor_inclusion(match_models = TRUE) %>% 
  as.data.frame() %>% 
  na.omit() %>%    # removes the (nuisance) random factors
  mutate(BF = exp(log_BF)) %>%
  mutate_at("BF", formatC, format='g',digits = 3) %>% 
  dplyr::select(BF) %>% 
  unlist() -> aov$BF

# Calculate orthogonal contrasts and append them to the ANOVA summary table
# I go to the trouble of transforming the t-statistic (which is returned from the linear model) 
# to an F statistic but they give identical p-values; I think this makes more sense and provides 
# more consistency when splitting the source of variation into orthogonal contrasts and presenting 
# them in an ANOVA table (eg. like with summary.aov or orthogonal contrasts in SAS)
model_parameters(model, ci_method = "kenward", exponentiate = TRUE, effects = "fixed") %>%  
  filter(grepl(":",Parameter)) %>%                          # select interaction terms only
  rename(Source = Parameter) %>%                            # set denominator degrees of freedom
  mutate(Df = 1) %>%                                        # set numerator degrees of freedom
  add_column(BF = "")  %>%                                  # add empty column for Bayes factors
  rename(Df.res = df_error) %>%                             # set denominator degrees of freedom
  mutate(F = abs(t)^2) %>%                                  # calculate F statistic
  mutate(`Pr(>F)` = pf(F,Df,Df.res,lower.tail=FALSE)) %>%   # calculate p value
  dplyr::select(c(Source,F,Df,Df.res,`Pr(>F)`,BF)) %>%      # select columns of interest for table
  rbind(aov,.) %>%                                          # row bind with anova table
  mutate(`Pr(>F)` = afex::round_ps_apa(`Pr(>F)`)) %>%       # format p values as APA style
  knitr::kable(caption = sprintf("**ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  add_indent(c(4:8)) %>%                                    # add indentation to indicate source components
  print()

# Calculate intraclass correlation coefficients (ICC) for the random effects
icc(model, by_group=TRUE, tolerance=0) %>% 
  as.data.frame() %>% 
  mutate(N = ngrps(model)) %>%
  rbind(.,c("residual",1-sum(.$ICC),nobs(model))) %>%
  mutate(ICC = as.numeric(ICC)) %>%                               
  knitr::kable(caption = sprintf("**Intraclass correlation coefficients for random effects: %s**",resp), digits = 3) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

# Calculated estimated marginal means, By default, emmeans uses Kenward-Roger's method for estimating the degrees of freedom
emm <- emmeans(model, ~ mutation * transfection, data = Data, tran = 'log', type = 'response')
emm %>% 
  summary(calc = c(n = ".wgt.")) %>%
  as.data.frame() %>%
  mutate_at("transfection", str_replace_all, pattern = "\\+", replacement = "\\\\+")  %>% 
  mutate_at("transfection", str_replace_all, pattern = "\\-", replacement = "\\\\-")  %>% 
  relocate(df, .before = response) %>%
  dplyr::select(-SE) %>%
  knitr::kable(caption = sprintf("**Estimated marginal means with 95%% confidence intervals: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

# Calculate overall average for untransfected neurons
emmeans(model, ~ mutation * transfection, data = Data) %>%
  as.data.frame() %>%
  filter(transfection == "-") %>%
  dplyr::select(emmean) %>% 
  colMeans() %>%
  exp() %>%
  sprintf("**Overall average of %s for untransfected neurons**: %.2f",resp,.) %>%
  print()

# Calculate transfected/untransfected ratios
emm.transfection <- contrast(emm, method = "trt.vs.ctrl", interaction = FALSE, by = 'mutation', adjust = "none")
emm.transfection %>%
  confint() %>%
  as.data.frame() %>%
  relocate(df, .before = ratio) %>%
  dplyr::select(-SE) %>%
  knitr::kable(caption = sprintf("**Estimated marginal means with 95%% confidence intervals for transfected/untransfected ratios: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

# 95% confidence intervals for interaction contrasts
emm.interaction <- contrast(emm, method = "trt.vs.ctrl", interaction = TRUE, adjust = "none")
emm.interaction %>%
  confint() %>% 
  relocate(df, .before = ratio) %>%
  dplyr::select(-SE) %>%
  knitr::kable(caption = sprintf("**95%% confidence intervals for contrasts: %s**",resp), digits = 2) %>% 
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

# Standardized effect sizes (*r*) for interaction contrasts
# Methods used the same as this server: https://easystats4u.shinyapps.io/statistic2effectsize/
emm.interaction %>% 
    as.data.frame() %>% 
    mutate(n = df+nrow(.)+1) %>% 
    mutate(r = t_to_r(t.ratio, df)$r) %>% 
    mutate(z = atanh(r),
           SE = 1/sqrt(n-3),
           CI = sprintf("[%.2f, %.2f]",
                        LL = tanh(z - 1.96*SE),
                        UL = tanh(z + 1.96*SE))) %>%
    dplyr::select(-c(ratio,SE,df,null,t.ratio,p.value,z)) %>%
    knitr::kable(col.names = c("mutation",
                               "transfection",
                               "*n*",
                               "*r*",
                               "95% *CI*"),
                 caption = sprintf("**Standardized effect sizes (*r*) for contrasts: %s**",resp), digits = 2) %>% 
    kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
    print()

posthoc = FALSE

if (posthoc == TRUE) {
  
# p-values and maximum Bayes Factors for interaction contrasts 
# Dunnett's step-down adjustment to control FWER on p-values (using multcomp package)
# Chapter 4.1.2 in Bretz, F., Hothorn, T. and Westfall, P. (2011) Multiple Comparisons Using R. Taylor and Frances Group, LLC.
emm.interaction %>%   
    as.glht() %>%
    summary(test = adjusted(type = "free")) -> glht.out  
emm.interaction %>%   
  as.data.frame() %>%
  dplyr::select(-SE) %>%
  mutate(p.adj = glht.out$test$pvalues) %>%
  mutate(p.adj = sapply(p.adj,max,.Machine$double.eps)) %>%
  mutate(maxBF = 1/pCalibrate(p.adj,"exploratory")) %>%
  mutate_at("maxBF", formatC, format='g',digits = 3) %>%
  mutate(p.value = afex::round_ps_apa(p.value)) %>%
  mutate(p.adj = afex::round_ps_apa(p.adj)) %>%
  knitr::kable(caption = sprintf("**Hypothesis testing on interaction parameters (Dunnett's step-down p-value adjustment): %s**",resp), digits = 2) %>%
  kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
  print()

}
# Replot data with 95% confidence intervals
emf(sprintf("../img/%s_%s.emf","n2a_mutants",resp), width=5, height=3.5)
emm %>%
    as.data.frame() %>%
    mutate(mutation_transfection = as.numeric(mutation)+(as.numeric(transfection)-1)/2.5) -> emm_df
p1 <- Data %>%
    mutate(mutation_jittered = jitter((as.numeric(mutation)+(as.numeric(transfection)-1)/2.5), 0.5),
           grouping=interaction(pair, mutation)) %>%
    mutate(mutation_transfection = as.numeric(mutation)+(as.numeric(transfection)-1)/2.5) %>%
    ggplot(aes(x=mutation, y=!!sym(resp), group=grouping, color=transfection)) + 
    geom_blank() +
    geom_line(aes(mutation_jittered), alpha=0.3, color="grey", size=0.75) +
    geom_point(aes(mutation_jittered), alpha=0.6, shape = 16, size=1.25) +
    scale_color_manual(values=c("grey","#00BA38")) +
    scale_fill_manual(values=c("grey","#00BA38")) +
    geom_crossbar(data = emm_df, 
                    aes(x=mutation_transfection, y=response, ymin=`lower.CL`, ymax=`upper.CL`, fill=transfection), 
                    color="black", alpha=0.5, size=0.5, fatten=1, width=0.3, inherit.aes=FALSE) + 
    ylab(resp) +
    theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1),axis.line = element_line(colour="black"),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.border = element_blank(),
          panel.background = element_blank(),
          legend.title = element_blank(),
          legend.position = c(0.5, 1.06),
          legend.direction = "horizontal",
          text = element_text(size=14))
emm.transfection %>%
    confint() %>%
    as.data.frame() -> emm.transfection_df
p2 <- Data %>% 
    pivot_wider(c(mutation,pair,!!sym(resp)),names_from=transfection,values_from=!!sym(resp)) %>% 
    mutate(ratio = `+`/`-`) %>%
    ggplot(aes(x=mutation, y=ratio, colour=mutation)) +
    geom_sina(alpha=0.6, shape=16, size=1.25, maxwidth=0.5) + 
    geom_crossbar(data = emm.transfection_df, 
                           aes(x=mutation, y=ratio, ymin=`lower.CL`, ymax=`upper.CL`, fill=mutation), 
                    color="black", alpha=0.5, size=0.5, fatten=1, width=0.8, inherit.aes=FALSE) +
    scale_colour_manual(values=c("grey","#00C19F","#00B9E3","#619CFF","#DB72FB","#FF61C3")) +
    scale_fill_manual(values=c("grey","#00C19F","#00B9E3","#619CFF","#DB72FB","#FF61C3")) +
    ylab("ratio") +
    theme(axis.text.x = element_text(angle = 45, vjust=1, hjust=1), axis.line = element_line(colour="black"),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          panel.border = element_blank(),
          panel.background = element_blank(),
          legend.position = "none",
          text=element_text(size=14))
grid.arrange(p1, p2, layout_matrix=rbind(c(1,2)), top=sprintf("Summary plots of the data with 95%% confidence intervals: %s\n",resp))
dev.off() #turn off device and finalize file


}

Analysis of peaknmda

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: peaknmda

	WT_vs_Mutants	GOF1_vs_LOF12	LOF1_vs_LOF2	LOF2	GOF1
WT_vs_Mutants	1	0	0	0	0
GOF1_vs_LOF12	0	1	0	0	0
LOF1_vs_LOF2	0	0	1	0	0
LOF2	0	0	0	1	0
GOF1	0	0	0	0	1

Sum of each orthogonal contrast should be zero: peaknmda

	sum
WT_vs_Mutants	0
GOF1_vs_LOF12	0
LOF1_vs_LOF2	0
LOF2	0
GOF1	0

Matrix of contrasts on mutation: peaknmda

mutation	WT_vs_Mutants	GOF1_vs_LOF12	LOF1_vs_LOF2	LOF2	GOF1
WT	-0.83	0.0	0.00	0.0	0.0
C436R	0.17	-0.4	-0.33	-0.5	0.0
T531M	0.17	-0.4	-0.33	0.5	0.0
R518H	0.17	-0.4	0.67	0.0	0.0
K669N	0.17	0.6	0.00	0.0	-0.5
L812M	0.17	0.6	0.00	0.0	0.5

Matrix of contrasts on transfection: peaknmda

transfection	contrast
-	-0.5
+	0.5

ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: peaknmda

Source	F	Df	Df.res	Pr(>F)	BF
mutation	0.72	5	19.8	.615	0.472
transfection	78.02	1	119.0	<.001	7.94e+11
mutation:transfection	0.87	5	119.0	.502	0.0838
mutationWT_vs_Mutants:transfection1	2.47	1	119.0	.118
mutationGOF1_vs_LOF12:transfection1	1.88	1	119.0	.173
mutationLOF1_vs_LOF2:transfection1	0.25	1	119.0	.617
mutationLOF2:transfection1	0.00	1	119.0	.992
mutationGOF1:transfection1	0.14	1	119.0	.711

Intraclass correlation coefficients for random effects: peaknmda

Group	ICC	N
pair:(slice:animal)	0.245	125
slice:animal	0.224	78
animal	0.369	27
residual	0.161	250

Estimated marginal means with 95% confidence intervals: peaknmda

mutation	transfection	df	response	n	lower.CL	upper.CL
WT	-	17.63	145.50	18	69.13	306.22
C436R	-	29.14	106.83	17	59.12	193.01
T531M	-	19.64	178.67	17	83.60	381.88
R518H	-	22.44	113.63	17	57.88	223.07
K669N	-	19.51	88.64	29	49.28	159.44
L812M	-	21.18	137.25	27	78.80	239.05
WT	+	17.63	115.06	18	54.67	242.16
C436R	+	29.14	67.30	17	37.25	121.59
T531M	+	19.64	112.75	17	52.75	240.98
R518H	+	22.44	66.56	17	33.91	130.67
K669N	+	19.51	60.66	29	33.73	109.12
L812M	+	21.18	98.65	27	56.64	171.82

[1] “Overall average of peaknmda for untransfected neurons: 125.13”

Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: peaknmda

contrast	mutation	df	ratio	lower.CL	upper.CL
(+) / (-)	WT	119	0.79	0.63	1.00
(+) / (-)	C436R	119	0.63	0.50	0.80
(+) / (-)	T531M	119	0.63	0.50	0.80
(+) / (-)	R518H	119	0.59	0.46	0.74
(+) / (-)	K669N	119	0.68	0.57	0.82
(+) / (-)	L812M	119	0.72	0.60	0.87

95% confidence intervals for contrasts: peaknmda

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	df	ratio	lower.CL	upper.CL
C436R / WT	(+) / (-)	119	0.80	0.57	1.11
T531M / WT	(+) / (-)	119	0.80	0.57	1.11
R518H / WT	(+) / (-)	119	0.74	0.53	1.03
K669N / WT	(+) / (-)	119	0.87	0.65	1.16
L812M / WT	(+) / (-)	119	0.91	0.68	1.22

Standardized effect sizes (r) for contrasts: peaknmda

mutation	transfection	n	r	95% CI
C436R / WT	(+) / (-)	125	-0.12	[-0.29, 0.05]
T531M / WT	(+) / (-)	125	-0.12	[-0.29, 0.05]
R518H / WT	(+) / (-)	125	-0.16	[-0.33, 0.01]
K669N / WT	(+) / (-)	125	-0.09	[-0.26, 0.09]
L812M / WT	(+) / (-)	125	-0.06	[-0.23, 0.12]

Analysis of decaynmda

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: decaynmda

	WT_vs_Mutants	GOF1_vs_LOF12	LOF1_vs_LOF2	LOF2	GOF1
WT_vs_Mutants	1	0	0	0	0
GOF1_vs_LOF12	0	1	0	0	0
LOF1_vs_LOF2	0	0	1	0	0
LOF2	0	0	0	1	0
GOF1	0	0	0	0	1

Sum of each orthogonal contrast should be zero: decaynmda

	sum
WT_vs_Mutants	0
GOF1_vs_LOF12	0
LOF1_vs_LOF2	0
LOF2	0
GOF1	0

Matrix of contrasts on mutation: decaynmda

mutation	WT_vs_Mutants	GOF1_vs_LOF12	LOF1_vs_LOF2	LOF2	GOF1
WT	-0.83	0.0	0.00	0.0	0.0
C436R	0.17	-0.4	-0.33	-0.5	0.0
T531M	0.17	-0.4	-0.33	0.5	0.0
R518H	0.17	-0.4	0.67	0.0	0.0
K669N	0.17	0.6	0.00	0.0	-0.5
L812M	0.17	0.6	0.00	0.0	0.5

Matrix of contrasts on transfection: decaynmda

transfection	contrast
-	-0.5
+	0.5

ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: decaynmda

Source	F	Df	Df.res	Pr(>F)	BF
mutation	3.06	5	19.28	.034	1.11
transfection	179.57	1	119.00	<.001	3.11e+22
mutation:transfection	12.09	5	119.00	<.001	3.43e+08
mutationWT_vs_Mutants:transfection1	37.09	1	119.00	<.001
mutationGOF1_vs_LOF12:transfection1	28.34	1	119.00	<.001
mutationLOF1_vs_LOF2:transfection1	0.13	1	119.00	.723
mutationLOF2:transfection1	0.23	1	119.00	.633
mutationGOF1:transfection1	0.12	1	119.00	.733

Intraclass correlation coefficients for random effects: decaynmda

Group	ICC	N
pair:(slice:animal)	0.044	125
slice:animal	0.118	78
animal	0.158	27
residual	0.680	250

Estimated marginal means with 95% confidence intervals: decaynmda

mutation	transfection	df	response	n	lower.CL	upper.CL
WT	-	22.80	103.13	18	83.82	126.90
C436R	-	50.01	90.07	17	75.20	107.88
T531M	-	25.76	97.89	17	79.13	121.08
R518H	-	34.13	91.10	17	74.89	110.81
K669N	-	25.42	87.30	29	74.08	102.87
L812M	-	29.56	86.00	27	73.29	100.92
WT	+	22.80	100.62	18	81.77	123.81
C436R	+	50.01	176.46	17	147.33	211.34
T531M	+	25.76	203.51	17	164.52	251.74
R518H	+	34.13	176.94	17	145.47	215.23
K669N	+	25.42	117.76	29	99.93	138.77
L812M	+	29.56	119.93	27	102.19	140.74

[1] “Overall average of decaynmda for untransfected neurons: 92.39”

Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: decaynmda

contrast	mutation	df	ratio	lower.CL	upper.CL
(+) / (-)	WT	119	0.98	0.82	1.16
(+) / (-)	C436R	119	1.96	1.65	2.33
(+) / (-)	T531M	119	2.08	1.75	2.47
(+) / (-)	R518H	119	1.94	1.63	2.31
(+) / (-)	K669N	119	1.35	1.18	1.54
(+) / (-)	L812M	119	1.39	1.21	1.60

95% confidence intervals for contrasts: decaynmda

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	df	ratio	lower.CL	upper.CL
C436R / WT	(+) / (-)	119	2.01	1.58	2.56
T531M / WT	(+) / (-)	119	2.13	1.67	2.72
R518H / WT	(+) / (-)	119	1.99	1.56	2.54
K669N / WT	(+) / (-)	119	1.38	1.11	1.71
L812M / WT	(+) / (-)	119	1.43	1.15	1.78

Standardized effect sizes (r) for contrasts: decaynmda

mutation	transfection	n	r	95% CI
C436R / WT	(+) / (-)	125	0.46	[0.31, 0.59]
T531M / WT	(+) / (-)	125	0.49	[0.35, 0.61]
R518H / WT	(+) / (-)	125	0.46	[0.31, 0.59]
K669N / WT	(+) / (-)	125	0.26	[0.09, 0.42]
L812M / WT	(+) / (-)	125	0.28	[0.11, 0.44]

Analysis of dt50nmda

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: dt50nmda

	WT_vs_Mutants	GOF1_vs_LOF12	LOF1_vs_LOF2	LOF2	GOF1
WT_vs_Mutants	1	0	0	0	0
GOF1_vs_LOF12	0	1	0	0	0
LOF1_vs_LOF2	0	0	1	0	0
LOF2	0	0	0	1	0
GOF1	0	0	0	0	1

Sum of each orthogonal contrast should be zero: dt50nmda

	sum
WT_vs_Mutants	0
GOF1_vs_LOF12	0
LOF1_vs_LOF2	0
LOF2	0
GOF1	0

Matrix of contrasts on mutation: dt50nmda

mutation	WT_vs_Mutants	GOF1_vs_LOF12	LOF1_vs_LOF2	LOF2	GOF1
WT	-0.83	0.0	0.00	0.0	0.0
C436R	0.17	-0.4	-0.33	-0.5	0.0
T531M	0.17	-0.4	-0.33	0.5	0.0
R518H	0.17	-0.4	0.67	0.0	0.0
K669N	0.17	0.6	0.00	0.0	-0.5
L812M	0.17	0.6	0.00	0.0	0.5

Matrix of contrasts on transfection: dt50nmda

transfection	contrast
-	-0.5
+	0.5

ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: dt50nmda

Source	F	Df	Df.res	Pr(>F)	BF
mutation	6.33	5	18.73	.001	6.59
transfection	439.09	1	119.00	<.001	2.77e+39
mutation:transfection	17.03	5	119.00	<.001	1.5e+11
mutationWT_vs_Mutants:transfection1	49.74	1	119.00	<.001
mutationGOF1_vs_LOF12:transfection1	29.18	1	119.00	<.001
mutationLOF1_vs_LOF2:transfection1	2.60	1	119.00	.110
mutationLOF2:transfection1	4.77	1	119.00	.031
mutationGOF1:transfection1	5.00	1	119.00	.027

Intraclass correlation coefficients for random effects: dt50nmda

Group	ICC	N
pair:(slice:animal)	0.148	125
slice:animal	0.223	78
animal	0.073	27
residual	0.556	250

Estimated marginal means with 95% confidence intervals: dt50nmda

mutation	transfection	df	response	n	lower.CL	upper.CL
WT	-	19.92	42.66	18	35.65	51.04
C436R	-	52.32	40.44	17	34.29	47.70
T531M	-	24.57	45.34	17	37.58	54.70
R518H	-	35.25	42.41	17	35.60	50.53
K669N	-	24.43	37.67	29	32.60	43.54
L812M	-	26.65	37.11	27	32.23	42.74
WT	+	19.92	48.66	18	40.67	58.22
C436R	+	52.32	86.56	17	73.39	102.09
T531M	+	24.57	122.73	17	101.72	148.08
R518H	+	35.25	87.87	17	73.75	104.69
K669N	+	24.43	56.59	29	48.97	65.40
L812M	+	26.65	67.24	27	58.39	77.44

[1] “Overall average of dt50nmda for untransfected neurons: 40.84”

Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: dt50nmda

contrast	mutation	df	ratio	lower.CL	upper.CL
(+) / (-)	WT	119	1.14	0.99	1.32
(+) / (-)	C436R	119	2.14	1.84	2.49
(+) / (-)	T531M	119	2.71	2.33	3.15
(+) / (-)	R518H	119	2.07	1.78	2.41
(+) / (-)	K669N	119	1.50	1.34	1.69
(+) / (-)	L812M	119	1.81	1.61	2.04

95% confidence intervals for contrasts: dt50nmda

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	df	ratio	lower.CL	upper.CL
C436R / WT	(+) / (-)	119	1.88	1.52	2.31
T531M / WT	(+) / (-)	119	2.37	1.92	2.93
R518H / WT	(+) / (-)	119	1.82	1.47	2.24
K669N / WT	(+) / (-)	119	1.32	1.09	1.59
L812M / WT	(+) / (-)	119	1.59	1.31	1.92

Standardized effect sizes (r) for contrasts: dt50nmda

mutation	transfection	n	r	95% CI
C436R / WT	(+) / (-)	125	0.48	[0.33, 0.60]
T531M / WT	(+) / (-)	125	0.60	[0.47, 0.70]
R518H / WT	(+) / (-)	125	0.46	[0.31, 0.59]
K669N / WT	(+) / (-)	125	0.26	[0.09, 0.42]
L812M / WT	(+) / (-)	125	0.41	[0.25, 0.54]

Analysis of chargenmda

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: chargenmda

	WT_vs_Mutants	GOF1_vs_LOF12	LOF1_vs_LOF2	LOF2	GOF1
WT_vs_Mutants	1	0	0	0	0
GOF1_vs_LOF12	0	1	0	0	0
LOF1_vs_LOF2	0	0	1	0	0
LOF2	0	0	0	1	0
GOF1	0	0	0	0	1

Sum of each orthogonal contrast should be zero: chargenmda

	sum
WT_vs_Mutants	0
GOF1_vs_LOF12	0
LOF1_vs_LOF2	0
LOF2	0
GOF1	0

Matrix of contrasts on mutation: chargenmda

mutation	WT_vs_Mutants	GOF1_vs_LOF12	LOF1_vs_LOF2	LOF2	GOF1
WT	-0.83	0.0	0.00	0.0	0.0
C436R	0.17	-0.4	-0.33	-0.5	0.0
T531M	0.17	-0.4	-0.33	0.5	0.0
R518H	0.17	-0.4	0.67	0.0	0.0
K669N	0.17	0.6	0.00	0.0	-0.5
L812M	0.17	0.6	0.00	0.0	0.5

Matrix of contrasts on transfection: chargenmda

transfection	contrast
-	-0.5
+	0.5

ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: chargenmda

Source	F	Df	Df.res	Pr(>F)	BF
mutation	1.05	5	19.48	.420	0.547
transfection	0.00	1	119.00	.991	0.141
mutation:transfection	2.13	5	119.00	.067	0.632
mutationWT_vs_Mutants:transfection1	4.18	1	119.00	.043
mutationGOF1_vs_LOF12:transfection1	4.26	1	119.00	.041
mutationLOF1_vs_LOF2:transfection1	2.47	1	119.00	.119
mutationLOF2:transfection1	0.17	1	119.00	.682
mutationGOF1:transfection1	0.32	1	119.00	.572

Intraclass correlation coefficients for random effects: chargenmda

Group	ICC	N
pair:(slice:animal)	0.326	125
slice:animal	0.225	78
animal	0.267	27
residual	0.183	250

Estimated marginal means with 95% confidence intervals: chargenmda

mutation	transfection	df	response	n	lower.CL	upper.CL
WT	-	16.95	18.79	18	8.32	42.43
C436R	-	31.70	10.29	17	5.26	20.11
T531M	-	19.35	22.68	17	9.84	52.31
R518H	-	23.37	13.54	17	6.40	28.68
K669N	-	19.22	9.45	29	4.95	18.01
L812M	-	20.99	16.78	27	9.06	31.07
WT	+	16.95	14.29	18	6.33	32.26
C436R	+	31.70	12.69	17	6.49	24.81
T531M	+	19.35	30.58	17	13.26	70.51
R518H	+	23.37	13.02	17	6.15	27.56
K669N	+	19.22	9.01	29	4.72	17.17
L812M	+	20.99	14.54	27	7.85	26.92

[1] “Overall average of chargenmda for untransfected neurons: 14.53”

Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: chargenmda

contrast	mutation	df	ratio	lower.CL	upper.CL
(+) / (-)	WT	119	0.76	0.57	1.02
(+) / (-)	C436R	119	1.23	0.91	1.67
(+) / (-)	T531M	119	1.35	1.00	1.82
(+) / (-)	R518H	119	0.96	0.71	1.30
(+) / (-)	K669N	119	0.95	0.76	1.20
(+) / (-)	L812M	119	0.87	0.68	1.10

95% confidence intervals for contrasts: chargenmda

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	df	ratio	lower.CL	upper.CL
C436R / WT	(+) / (-)	119	1.62	1.06	2.47
T531M / WT	(+) / (-)	119	1.77	1.16	2.70
R518H / WT	(+) / (-)	119	1.26	0.83	1.93
K669N / WT	(+) / (-)	119	1.25	0.86	1.82
L812M / WT	(+) / (-)	119	1.14	0.78	1.67

Standardized effect sizes (r) for contrasts: chargenmda

mutation	transfection	n	r	95% CI
C436R / WT	(+) / (-)	125	0.20	[0.03, 0.37]
T531M / WT	(+) / (-)	125	0.24	[0.07, 0.40]
R518H / WT	(+) / (-)	125	0.10	[-0.08, 0.27]
K669N / WT	(+) / (-)	125	0.11	[-0.07, 0.28]
L812M / WT	(+) / (-)	125	0.06	[-0.11, 0.24]

Analysis of peakampa

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: peakampa

	WT_vs_Mutants	GOF1_vs_LOF12	LOF1_vs_LOF2	LOF2	GOF1
WT_vs_Mutants	1	0	0	0	0
GOF1_vs_LOF12	0	1	0	0	0
LOF1_vs_LOF2	0	0	1	0	0
LOF2	0	0	0	1	0
GOF1	0	0	0	0	1

Sum of each orthogonal contrast should be zero: peakampa

	sum
WT_vs_Mutants	0
GOF1_vs_LOF12	0
LOF1_vs_LOF2	0
LOF2	0
GOF1	0

Matrix of contrasts on mutation: peakampa

mutation	WT_vs_Mutants	GOF1_vs_LOF12	LOF1_vs_LOF2	LOF2	GOF1
WT	-0.83	0.0	0.00	0.0	0.0
C436R	0.17	-0.4	-0.33	-0.5	0.0
T531M	0.17	-0.4	-0.33	0.5	0.0
R518H	0.17	-0.4	0.67	0.0	0.0
K669N	0.17	0.6	0.00	0.0	-0.5
L812M	0.17	0.6	0.00	0.0	0.5

Matrix of contrasts on transfection: peakampa

transfection	contrast
-	-0.5
+	0.5

ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: peakampa

Source	F	Df	Df.res	Pr(>F)	BF
mutation	2.66	5	19.95	.053	2.14
transfection	2.38	1	119.00	.125	1.26
mutation:transfection	1.49	5	119.00	.199	0.276
mutationWT_vs_Mutants:transfection1	0.04	1	119.00	.839
mutationGOF1_vs_LOF12:transfection1	7.23	1	119.00	.008
mutationLOF1_vs_LOF2:transfection1	0.15	1	119.00	.700
mutationLOF2:transfection1	0.05	1	119.00	.819
mutationGOF1:transfection1	0.01	1	119.00	.928

Intraclass correlation coefficients for random effects: peakampa

Group	ICC	N
pair:(slice:animal)	0.266	125
slice:animal	0.000	78
animal	0.403	27
residual	0.331	250

Estimated marginal means with 95% confidence intervals: peakampa

mutation	transfection	df	response	n	lower.CL	upper.CL
WT	-	20.13	9.85	18	5.24	18.50
C436R	-	31.09	14.82	17	9.05	24.29
T531M	-	20.59	14.09	17	7.48	26.55
R518H	-	23.70	16.74	17	9.51	29.46
K669N	-	20.47	34.06	29	20.86	55.62
L812M	-	23.83	28.93	27	18.11	46.20
WT	+	20.13	8.81	18	4.69	16.56
C436R	+	31.09	16.25	17	9.92	26.62
T531M	+	20.59	14.73	17	7.82	27.75
R518H	+	23.70	16.73	17	9.51	29.44
K669N	+	20.47	26.26	29	16.08	42.89
L812M	+	23.83	21.98	27	13.76	35.11

[1] “Overall average of peakampa for untransfected neurons: 17.99”

Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: peakampa

contrast	mutation	df	ratio	lower.CL	upper.CL
(+) / (-)	WT	119	0.89	0.68	1.18
(+) / (-)	C436R	119	1.10	0.82	1.46
(+) / (-)	T531M	119	1.05	0.78	1.39
(+) / (-)	R518H	119	1.00	0.75	1.33
(+) / (-)	K669N	119	0.77	0.62	0.96
(+) / (-)	L812M	119	0.76	0.60	0.96

95% confidence intervals for contrasts: peakampa

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	df	ratio	lower.CL	upper.CL
C436R / WT	(+) / (-)	119	1.22	0.82	1.83
T531M / WT	(+) / (-)	119	1.17	0.78	1.75
R518H / WT	(+) / (-)	119	1.12	0.75	1.67
K669N / WT	(+) / (-)	119	0.86	0.60	1.23
L812M / WT	(+) / (-)	119	0.85	0.59	1.22

Standardized effect sizes (r) for contrasts: peakampa

mutation	transfection	n	r	95% CI
C436R / WT	(+) / (-)	125	0.09	[-0.09, 0.26]
T531M / WT	(+) / (-)	125	0.07	[-0.11, 0.24]
R518H / WT	(+) / (-)	125	0.05	[-0.13, 0.22]
K669N / WT	(+) / (-)	125	-0.08	[-0.25, 0.10]
L812M / WT	(+) / (-)	125	-0.08	[-0.25, 0.10]

Analysis of decayampa

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: decayampa

	WT_vs_Mutants	GOF1_vs_LOF12	LOF1_vs_LOF2	LOF2	GOF1
WT_vs_Mutants	1	0	0	0	0
GOF1_vs_LOF12	0	1	0	0	0
LOF1_vs_LOF2	0	0	1	0	0
LOF2	0	0	0	1	0
GOF1	0	0	0	0	1

Sum of each orthogonal contrast should be zero: decayampa

	sum
WT_vs_Mutants	0
GOF1_vs_LOF12	0
LOF1_vs_LOF2	0
LOF2	0
GOF1	0

Matrix of contrasts on mutation: decayampa

mutation	WT_vs_Mutants	GOF1_vs_LOF12	LOF1_vs_LOF2	LOF2	GOF1
WT	-0.83	0.0	0.00	0.0	0.0
C436R	0.17	-0.4	-0.33	-0.5	0.0
T531M	0.17	-0.4	-0.33	0.5	0.0
R518H	0.17	-0.4	0.67	0.0	0.0
K669N	0.17	0.6	0.00	0.0	-0.5
L812M	0.17	0.6	0.00	0.0	0.5

Matrix of contrasts on transfection: decayampa

transfection	contrast
-	-0.5
+	0.5

ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: decayampa

Source	F	Df	Df.res	Pr(>F)	BF
mutation	0.98	5	20.26	.454	0.518
transfection	1.01	1	119.00	.317	0.57
mutation:transfection	3.36	5	119.00	.007	8.87
mutationWT_vs_Mutants:transfection1	0.75	1	119.00	.389
mutationGOF1_vs_LOF12:transfection1	13.88	1	119.00	<.001
mutationLOF1_vs_LOF2:transfection1	1.03	1	119.00	.312
mutationLOF2:transfection1	0.10	1	119.00	.753
mutationGOF1:transfection1	1.73	1	119.00	.191

Intraclass correlation coefficients for random effects: decayampa

Group	ICC	N
pair:(slice:animal)	0.070	125
slice:animal	0.197	78
animal	0.522	27
residual	0.211	250

Estimated marginal means with 95% confidence intervals: decayampa

mutation	transfection	df	response	n	lower.CL	upper.CL
WT	-	19.17	7.20	18	3.66	14.14
C436R	-	27.19	7.07	17	4.22	11.83
T531M	-	20.67	13.12	17	6.61	26.03
R518H	-	22.30	7.07	17	3.87	12.91
K669N	-	20.54	4.71	29	2.77	8.00
L812M	-	21.99	5.78	27	3.53	9.48
WT	+	19.17	8.18	18	4.16	16.06
C436R	+	27.19	5.91	17	3.53	9.89
T531M	+	20.67	11.53	17	5.81	22.88
R518H	+	22.30	6.97	17	3.82	12.72
K669N	+	20.54	5.44	29	3.20	9.24
L812M	+	21.99	7.85	27	4.79	12.87

[1] “Overall average of decayampa for untransfected neurons: 7.10”

Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: decayampa

contrast	mutation	df	ratio	lower.CL	upper.CL
(+) / (-)	WT	119	1.14	0.92	1.41
(+) / (-)	C436R	119	0.84	0.67	1.04
(+) / (-)	T531M	119	0.88	0.70	1.10
(+) / (-)	R518H	119	0.99	0.79	1.23
(+) / (-)	K669N	119	1.15	0.97	1.37
(+) / (-)	L812M	119	1.36	1.14	1.62

95% confidence intervals for contrasts: decayampa

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	df	ratio	lower.CL	upper.CL
C436R / WT	(+) / (-)	119	0.74	0.54	1.00
T531M / WT	(+) / (-)	119	0.77	0.57	1.05
R518H / WT	(+) / (-)	119	0.87	0.64	1.18
K669N / WT	(+) / (-)	119	1.02	0.77	1.34
L812M / WT	(+) / (-)	119	1.20	0.91	1.58

Standardized effect sizes (r) for contrasts: decayampa

mutation	transfection	n	r	95% CI
C436R / WT	(+) / (-)	125	-0.18	[-0.34, -0.00]
T531M / WT	(+) / (-)	125	-0.15	[-0.32, 0.03]
R518H / WT	(+) / (-)	125	-0.08	[-0.26, 0.09]
K669N / WT	(+) / (-)	125	0.01	[-0.17, 0.19]
L812M / WT	(+) / (-)	125	0.12	[-0.06, 0.29]

Analysis of dt50ampa

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: dt50ampa

	WT_vs_Mutants	GOF1_vs_LOF12	LOF1_vs_LOF2	LOF2	GOF1
WT_vs_Mutants	1	0	0	0	0
GOF1_vs_LOF12	0	1	0	0	0
LOF1_vs_LOF2	0	0	1	0	0
LOF2	0	0	0	1	0
GOF1	0	0	0	0	1

Sum of each orthogonal contrast should be zero: dt50ampa

	sum
WT_vs_Mutants	0
GOF1_vs_LOF12	0
LOF1_vs_LOF2	0
LOF2	0
GOF1	0

Matrix of contrasts on mutation: dt50ampa

mutation	WT_vs_Mutants	GOF1_vs_LOF12	LOF1_vs_LOF2	LOF2	GOF1
WT	-0.83	0.0	0.00	0.0	0.0
C436R	0.17	-0.4	-0.33	-0.5	0.0
T531M	0.17	-0.4	-0.33	0.5	0.0
R518H	0.17	-0.4	0.67	0.0	0.0
K669N	0.17	0.6	0.00	0.0	-0.5
L812M	0.17	0.6	0.00	0.0	0.5

Matrix of contrasts on transfection: dt50ampa

transfection	contrast
-	-0.5
+	0.5

ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: dt50ampa

Source	F	Df	Df.res	Pr(>F)	BF
mutation	1.86	5	19.55	.147	0.767
transfection	1.49	1	119.00	.225	0.189
mutation:transfection	1.04	5	119.00	.397	0.151
mutationWT_vs_Mutants:transfection1	0.04	1	119.00	.836
mutationGOF1_vs_LOF12:transfection1	3.91	1	119.00	.050
mutationLOF1_vs_LOF2:transfection1	0.36	1	119.00	.551
mutationLOF2:transfection1	0.70	1	119.00	.405
mutationGOF1:transfection1	0.05	1	119.00	.819

Intraclass correlation coefficients for random effects: dt50ampa

Group	ICC	N
pair:(slice:animal)	0.007	125
slice:animal	0.278	78
animal	0.239	27
residual	0.477	250

Estimated marginal means with 95% confidence intervals: dt50ampa

mutation	transfection	df	response	n	lower.CL	upper.CL
WT	-	19.37	5.28	18	3.73	7.46
C436R	-	37.90	3.56	17	2.67	4.76
T531M	-	22.68	6.26	17	4.38	8.95
R518H	-	27.30	4.10	17	2.97	5.64
K669N	-	22.47	3.37	29	2.56	4.44
L812M	-	24.31	3.86	27	2.97	5.02
WT	+	19.37	4.92	18	3.48	6.95
C436R	+	37.90	3.30	17	2.47	4.40
T531M	+	22.68	5.10	17	3.57	7.29
R518H	+	27.30	3.85	17	2.80	5.31
K669N	+	22.47	3.62	29	2.75	4.77
L812M	+	24.31	4.03	27	3.10	5.24

[1] “Overall average of dt50ampa for untransfected neurons: 4.30”

Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: dt50ampa

contrast	mutation	df	ratio	lower.CL	upper.CL
(+) / (-)	WT	119	0.93	0.76	1.15
(+) / (-)	C436R	119	0.93	0.75	1.15
(+) / (-)	T531M	119	0.81	0.66	1.01
(+) / (-)	R518H	119	0.94	0.76	1.17
(+) / (-)	K669N	119	1.07	0.91	1.26
(+) / (-)	L812M	119	1.04	0.88	1.24

95% confidence intervals for contrasts: dt50ampa

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	df	ratio	lower.CL	upper.CL
C436R / WT	(+) / (-)	119	0.99	0.74	1.34
T531M / WT	(+) / (-)	119	0.87	0.65	1.18
R518H / WT	(+) / (-)	119	1.01	0.75	1.36
K669N / WT	(+) / (-)	119	1.15	0.88	1.50
L812M / WT	(+) / (-)	119	1.12	0.86	1.46

Standardized effect sizes (r) for contrasts: dt50ampa

mutation	transfection	n	r	95% CI
C436R / WT	(+) / (-)	125	0.00	[-0.18, 0.17]
T531M / WT	(+) / (-)	125	-0.08	[-0.25, 0.10]
R518H / WT	(+) / (-)	125	0.00	[-0.17, 0.18]
K669N / WT	(+) / (-)	125	0.10	[-0.08, 0.27]
L812M / WT	(+) / (-)	125	0.08	[-0.10, 0.25]

Analysis of chargeampa

All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: chargeampa

	WT_vs_Mutants	GOF1_vs_LOF12	LOF1_vs_LOF2	LOF2	GOF1
WT_vs_Mutants	1	0	0	0	0
GOF1_vs_LOF12	0	1	0	0	0
LOF1_vs_LOF2	0	0	1	0	0
LOF2	0	0	0	1	0
GOF1	0	0	0	0	1

Sum of each orthogonal contrast should be zero: chargeampa

	sum
WT_vs_Mutants	0
GOF1_vs_LOF12	0
LOF1_vs_LOF2	0
LOF2	0
GOF1	0

Matrix of contrasts on mutation: chargeampa

mutation	WT_vs_Mutants	GOF1_vs_LOF12	LOF1_vs_LOF2	LOF2	GOF1
WT	-0.83	0.0	0.00	0.0	0.0
C436R	0.17	-0.4	-0.33	-0.5	0.0
T531M	0.17	-0.4	-0.33	0.5	0.0
R518H	0.17	-0.4	0.67	0.0	0.0
K669N	0.17	0.6	0.00	0.0	-0.5
L812M	0.17	0.6	0.00	0.0	0.5

Matrix of contrasts on transfection: chargeampa

transfection	contrast
-	-0.5
+	0.5

ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: chargeampa

Source	F	Df	Df.res	Pr(>F)	BF
mutation	3.87	5	18.71	.014	2.78
transfection	0.81	1	119.00	.371	0.236
mutation:transfection	0.23	5	119.00	.951	0.0328
mutationWT_vs_Mutants:transfection1	0.28	1	119.00	.599
mutationGOF1_vs_LOF12:transfection1	0.30	1	119.00	.586
mutationLOF1_vs_LOF2:transfection1	0.09	1	119.00	.769
mutationLOF2:transfection1	0.16	1	119.00	.685
mutationGOF1:transfection1	0.22	1	119.00	.639

Intraclass correlation coefficients for random effects: chargeampa

Group	ICC	N
pair:(slice:animal)	0.454	125
slice:animal	0.033	78
animal	0.101	27
residual	0.413	250

Estimated marginal means with 95% confidence intervals: chargeampa

mutation	transfection	df	response	n	lower.CL	upper.CL
WT	-	19.30	0.62	18	0.42	0.92
C436R	-	46.13	0.84	17	0.59	1.20
T531M	-	21.41	1.33	17	0.89	1.99
R518H	-	30.44	0.86	17	0.59	1.25
K669N	-	20.86	1.19	29	0.87	1.63
L812M	-	25.13	1.47	27	1.07	2.00
WT	+	19.30	0.63	18	0.43	0.94
C436R	+	46.13	0.79	17	0.55	1.12
T531M	+	21.41	1.36	17	0.91	2.03
R518H	+	30.44	0.80	17	0.55	1.17
K669N	+	20.86	1.04	29	0.76	1.42
L812M	+	25.13	1.38	27	1.01	1.88

[1] “Overall average of chargeampa for untransfected neurons: 1.01”

Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: chargeampa

contrast	mutation	df	ratio	lower.CL	upper.CL
(+) / (-)	WT	119	1.02	0.77	1.34
(+) / (-)	C436R	119	0.94	0.71	1.25
(+) / (-)	T531M	119	1.02	0.77	1.35
(+) / (-)	R518H	119	0.93	0.70	1.23
(+) / (-)	K669N	119	0.87	0.70	1.08
(+) / (-)	L812M	119	0.94	0.75	1.18

95% confidence intervals for contrasts: chargeampa

mutation_trt.vs.ctrl	transfection_trt.vs.ctrl	df	ratio	lower.CL	upper.CL
C436R / WT	(+) / (-)	119	0.92	0.62	1.37
T531M / WT	(+) / (-)	119	1.00	0.68	1.49
R518H / WT	(+) / (-)	119	0.92	0.62	1.36
K669N / WT	(+) / (-)	119	0.86	0.60	1.22
L812M / WT	(+) / (-)	119	0.92	0.65	1.32

Standardized effect sizes (r) for contrasts: chargeampa

mutation	transfection	n	r	95% CI
C436R / WT	(+) / (-)	125	-0.04	[-0.21, 0.14]
T531M / WT	(+) / (-)	125	0.00	[-0.17, 0.18]
R518H / WT	(+) / (-)	125	-0.04	[-0.21, 0.14]
K669N / WT	(+) / (-)	125	-0.08	[-0.25, 0.10]
L812M / WT	(+) / (-)	125	-0.04	[-0.21, 0.14]