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_dko_mutant_nmdar.dat", header = TRUE)
# Implicit nesting (required for anovaBF)
Data %>%
filter(!grepl('DKO|DKODQP|WT_2B', mutation)) %>%
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("peak")
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=0.33, color="grey") +
geom_point(aes(mutation_jittered), alpha=0.9, 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","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)/3
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) <- contr.sum(2)/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 <- 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() +
scale_color_manual(values=c("#619CFF","#F8766D")) +
ylab("Cook's distance") +
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_dko_mutant_nmdar",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_color_manual(values=c("grey","#00C19F","#00B9E3","#619CFF","#DB72FB","#FF61C3")) +
scale_fill_manual(values=c("grey","#00C19F","#00B9E3","#619CFF","#DB72FB","#FF61C3")) +
ylab("ratio") +
ylim(0,1.4) +
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 peak
All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: peak
|
|
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: peak
|
|
sum
|
|
WT_vs_Mutants
|
0
|
|
GOF1_vs_LOF12
|
0
|
|
LOF1_vs_LOF2
|
0
|
|
LOF2
|
0
|
|
GOF1
|
0
|
Matrix of contrasts on mutation: peak
|
mutation
|
WT_vs_Mutants
|
GOF1_vs_LOF12
|
LOF1_vs_LOF2
|
LOF2
|
GOF1
|
|
WT
|
-0.83
|
0.0
|
0.00
|
0.00
|
0.0
|
|
C436R
|
0.17
|
-0.4
|
-0.33
|
-0.33
|
0.0
|
|
T531M
|
0.17
|
-0.4
|
-0.33
|
0.33
|
0.0
|
|
R518H
|
0.17
|
-0.4
|
0.67
|
0.00
|
0.0
|
|
K669N
|
0.17
|
0.6
|
0.00
|
0.00
|
-0.5
|
|
L812M
|
0.17
|
0.6
|
0.00
|
0.00
|
0.5
|
Matrix of contrasts on transfection: peak
|
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: peak
|
Source
|
F
|
Df
|
Df.res
|
Pr(>F)
|
BF
|
|
mutation
|
7.89
|
5
|
8.8
|
.004
|
1.97
|
|
transfection
|
433.89
|
1
|
87.0
|
<.001
|
4.16e+36
|
|
mutation:transfection
|
11.35
|
5
|
87.0
|
<.001
|
3.88e+06
|
|
mutationWT_vs_Mutants:transfection1
|
31.22
|
1
|
87.0
|
<.001
|
|
|
mutationGOF1_vs_LOF12:transfection1
|
25.94
|
1
|
87.0
|
<.001
|
|
|
mutationLOF1_vs_LOF2:transfection1
|
0.01
|
1
|
87.0
|
.926
|
|
|
mutationLOF2:transfection1
|
0.14
|
1
|
87.0
|
.712
|
|
|
mutationGOF1:transfection1
|
0.00
|
1
|
87.0
|
.979
|
|
Intraclass correlation coefficients for random effects: peak
|
Group
|
ICC
|
N
|
|
pair:(slice:animal)
|
0.409
|
93
|
|
slice:animal
|
0.001
|
54
|
|
animal
|
0.000
|
16
|
|
residual
|
0.590
|
186
|
Estimated marginal means with 95% confidence intervals: peak
|
mutation
|
transfection
|
df
|
response
|
n
|
lower.CL
|
upper.CL
|
|
WT
|
-
|
27.83
|
127.74
|
14
|
100.13
|
162.95
|
|
C436R
|
-
|
15.54
|
127.90
|
19
|
102.58
|
159.47
|
|
T531M
|
-
|
31.10
|
92.64
|
9
|
68.58
|
125.14
|
|
R518H
|
-
|
15.72
|
102.62
|
13
|
78.80
|
133.64
|
|
K669N
|
-
|
10.12
|
120.50
|
21
|
96.77
|
150.05
|
|
L812M
|
-
|
16.18
|
106.55
|
17
|
84.59
|
134.21
|
|
WT
|
+
|
27.83
|
83.58
|
14
|
65.52
|
106.62
|
|
C436R
|
+
|
15.54
|
29.35
|
19
|
23.54
|
36.60
|
|
T531M
|
+
|
31.10
|
22.84
|
9
|
16.91
|
30.86
|
|
R518H
|
+
|
15.72
|
24.79
|
13
|
19.04
|
32.28
|
|
K669N
|
+
|
10.12
|
50.50
|
21
|
40.56
|
62.89
|
|
L812M
|
+
|
16.18
|
44.84
|
17
|
35.60
|
56.47
|
[1] “
Overall average of peak for untransfected neurons: 112.19”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: peak
|
contrast
|
mutation
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
(+) / (-)
|
WT
|
87
|
0.65
|
0.51
|
0.84
|
|
(+) / (-)
|
C436R
|
87
|
0.23
|
0.18
|
0.29
|
|
(+) / (-)
|
T531M
|
87
|
0.25
|
0.18
|
0.34
|
|
(+) / (-)
|
R518H
|
87
|
0.24
|
0.19
|
0.31
|
|
(+) / (-)
|
K669N
|
87
|
0.42
|
0.34
|
0.52
|
|
(+) / (-)
|
L812M
|
87
|
0.42
|
0.33
|
0.53
|
95% confidence intervals for contrasts: peak
|
mutation_trt.vs.ctrl
|
transfection_trt.vs.ctrl
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
C436R / WT
|
(+) / (-)
|
87
|
0.35
|
0.25
|
0.49
|
|
T531M / WT
|
(+) / (-)
|
87
|
0.38
|
0.25
|
0.57
|
|
R518H / WT
|
(+) / (-)
|
87
|
0.37
|
0.26
|
0.53
|
|
K669N / WT
|
(+) / (-)
|
87
|
0.64
|
0.46
|
0.89
|
|
L812M / WT
|
(+) / (-)
|
87
|
0.64
|
0.46
|
0.91
|
Standardized effect sizes (r) for contrasts: peak
|
mutation
|
transfection
|
n
|
r
|
95% CI
|
|
C436R / WT
|
(+) / (-)
|
93
|
-0.55
|
[-0.68, -0.40]
|
|
T531M / WT
|
(+) / (-)
|
93
|
-0.46
|
[-0.60, -0.28]
|
|
R518H / WT
|
(+) / (-)
|
93
|
-0.50
|
[-0.64, -0.33]
|
|
K669N / WT
|
(+) / (-)
|
93
|
-0.28
|
[-0.46, -0.08]
|
|
L812M / WT
|
(+) / (-)
|
93
|
-0.26
|
[-0.44, -0.06]
|
