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_imaging.dat", header = TRUE)
Data %>%
mutate(roi = row_number()) %>%
pivot_longer(cols = GluN1:Homer1c, names_to = "protein", values_to = "intensity") -> Data
Factor encoding
Data$mutation <- as.factor(Data$mutation)
Data$expt <- as.factor(Data$expt)
Data$protein <- as.factor(Data$protein)
Set mutation WT and protein Homer1c as reference levels
Data$mutation <- factor(Data$mutation, levels=c("NONE","WT","C436R","T531M","R518H","K669N","L812M"))
Data$protein <- factor(Data$protein, levels=c("Homer1c","GluN1"))
lmer settings
settings <- lmerControl(check.conv.singular = .makeCC(action = "ignore", tol = 1e-4), boundary.tol=0)
Fit a mixed linear model
# Initialize
variates <- c("intensity")
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(protein)-1)/2.5), 0.5),
grouping=interaction(roi, mutation)) %>%
mutate(mutation_protein = as.numeric(mutation)+(as.numeric(protein)-1)/2.5) %>%
ggplot(aes(x=mutation, y=!!sym(resp), group=grouping, color=protein)) +
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("#F8766D","#00BA38")) +
stat_summary(mapping = aes(x=mutation_protein,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_protein,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 <- ggplot(Data, aes(x=mutation, y=ratio, colour=mutation)) +
geom_sina(alpha=0.9, shape = 16) +
scale_color_manual(values=c("#D39200","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
# ("NONE","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)
None_vs_others <- c(-6,1,1,1,1,1,1)/7
WT_vs_Mutants <- c(0,-5,1,1,1,1,1)/6
GOF1_vs_LOF12 <- c(0,0,-2,-2,-2,3,3)/5
LOF1_vs_LOF2 <- c(0,0,-1,-1,2,0,0)/3
GOF1 <- c(0,0,0,0,0,-1,1)/2
LOF2 <- c(0,0,-1,1,0,0,0)/2
contr.orth <- cbind(None_vs_others, 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$mutation,"contrasts") %>%
as.data.frame() %>%
rownames_to_column(var = "mutation") %>%
knitr::kable(caption = sprintf("**Matrix of contrasts on mutation: %s**",resp), digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
print()
contrasts(Data$protein) <- rbind(-1,1)/2
attr(Data$protein,"contrasts") %>%
as.data.frame() %>%
rename(contrast = "V1") %>%
rownames_to_column(var = "protein") %>%
knitr::kable(caption = sprintf("**Matrix of contrasts on protein: %s**",resp), digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
print()
formula <- sprintf("log(%s) ~ mutation * protein + (1|expt/roi)", 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="roi:expt")
p4 <- infl %>%
mutate(influential = cooksd > 1.0) %>%
ggplot(aes(x=`roi:expt`,y=cooksd, color=influential)) +
geom_segment(aes(x=`roi:expt`, xend=`roi:expt`, 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))
# Calculated estimated marginal means, By default, emmeans uses Kenward-Roger's method for estimating the degrees of freedom
emm <- emmeans(model, ~ mutation * protein, data = Data, tran = 'log', type = 'response')
emm %>%
summary(calc = c(n = ".wgt.")) %>%
as.data.frame() %>%
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 Homer1c-tdTomato fluorescence
emmeans(model, ~ mutation * protein, data = Data) %>%
as.data.frame() %>%
filter(protein == "Homer1c") %>%
dplyr::select(emmean) %>%
colMeans() %>%
exp() %>%
sprintf("**Overall average of %s for Homer1c-tdTomato fluorescence intensity**: %.2f",resp,.) %>%
print()
# Calculate GluN1/Homer1c ratios
emm.ratios <- contrast(emm, method = "trt.vs.ctrl", interaction = FALSE, by = 'mutation', adjust = "none")
emm.ratios %>%
confint() %>%
as.data.frame() %>%
relocate(df, .before = ratio) %>%
dplyr::select(-SE) %>%
knitr::kable(caption = sprintf("**Estimated marginal means with 95%% confidence intervals for GluN1/Homer1c ratios: %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_mutant_imaging",resp), width=5.5, height=3.5)
emm %>%
as.data.frame() %>%
mutate(mutation_protein = as.numeric(mutation)+(as.numeric(protein)-1)/2.5) -> emm_df
p1 <- Data %>%
mutate(mutation_jittered = jitter((as.numeric(mutation)+(as.numeric(protein)-1)/2.5), 0.5),
grouping=interaction(roi, mutation)) %>%
mutate(mutation_protein = as.numeric(mutation)+(as.numeric(protein)-1)/2.5) %>%
ggplot(aes(x=mutation, y=!!sym(resp), group=grouping, color=protein)) +
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=0.75) +
scale_color_manual(values=c("#F8766D","#00BA38")) +
scale_fill_manual(values=c("#F8766D","#00BA38")) +
geom_crossbar(data = emm_df,
aes(x=mutation_protein, y=response, ymin=`lower.CL`, ymax=`upper.CL`, fill=protein),
color="black", alpha=0.5, size=0.5, fatten=1, 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.ratios %>%
confint() %>%
as.data.frame() -> emm.ratios_df
p2 <- ggplot(Data, aes(x=mutation, y=ratio, colour=mutation)) +
geom_sina(alpha=0.6, shape=16, size=0.75, maxwidth=0.4) +
geom_crossbar(data = emm.ratios_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("#D39200","grey","#00C19F","#00B9E3","#619CFF","#DB72FB","#FF61C3")) +
scale_fill_manual(values=c("#D39200","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
setwd("..")
# Update model to remove the control (NONE) from the data set and contrasts since it will
# make the omnibus test statistic obsolete and isn't be part of our hypothesis test
Data %>%
filter(mutation!="NONE") -> droppedData
droppedData$mutation <- factor(droppedData$mutation, levels = levels(droppedData$mutation)[-1])
contr.orth <- contr.orth[,-1] # remove contrast for first column (contrast with level NONE)
contr.orth <- contr.orth[-1,] # remove contrast for first row (level NONE)
contrasts(droppedData$mutation) <- contr.orth
model <- update(model, data = droppedData)
# Calculate ANOVA table for the fitted model (Type III sum of squares)
car::Anova(model, type = 3, test.statistic = "F") %>% # Uses Kenward-Roger degrees of freedom
as.data.frame() %>%
rownames_to_column(var="Source") %>%
filter(Source != "(Intercept)") -> aov
# Calculate Bayes Factors for ANOVA and append them to the ANOVA data frame
# Inclusion Bayes Factor based on matched models (prior odds uniform-equal)
droppedData %>%
mutate(logresp = log(!!sym(resp))) %>%
as.data.frame() -> droppedData
set.seed(123456)
anovaBF(logresp ~ mutation * protein + expt,
whichRandom = "expt",
whichModel = "withmain",
iterations = 20000,
data = droppedData) %>%
bayesfactor_inclusion(match_models = TRUE) %>%
as.data.frame() %>%
na.omit() %>% # removes the (nuisance) random factors
mutate(BF = exp(log_BF)) %>%
mutate_at("BF", formatC, format='g',digits = 3) %>%
dplyr::select(BF) %>%
unlist() -> aov$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(.$ICC),nobs(model))) %>%
mutate(ICC = as.numeric(ICC)) %>%
knitr::kable(caption = sprintf("**Intraclass correlation coefficients for random effects: %s**",resp), digits = 3) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
print()
# 95% confidence intervals for interaction contrasts
emm <- emmeans(model, ~ mutation * protein, data = droppedData, tran = 'log', type = 'response')
emm.interaction <- contrast(emm, method = "trt.vs.ctrl", interaction = TRUE, adjust = "none")
emm.interaction %>%
confint() %>%
relocate(df, .before = ratio) %>%
dplyr::select(-SE) %>%
knitr::kable(caption = sprintf("**95%% confidence intervals for contrasts: %s**",resp), digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
print()
# Standardized effect sizes (*r*) for interaction contrasts
# Methods used the same as this server: https://easystats4u.shinyapps.io/statistic2effectsize/
emm.interaction %>%
as.data.frame() %>%
mutate(n = df+nrow(.)+1) %>%
mutate(r = t_to_r(t.ratio, df)$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",
"Protein",
"*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()
}
}
Analysis of intensity
All off-diagonal elements in correlation matrix of orthogonal contrasts should be zero: intensity
|
|
None_vs_others
|
WT_vs_Mutants
|
GOF1_vs_LOF12
|
LOF1_vs_LOF2
|
LOF2
|
GOF1
|
|
None_vs_others
|
1
|
0
|
0
|
0
|
0
|
0
|
|
WT_vs_Mutants
|
0
|
1
|
0
|
0
|
0
|
0
|
|
GOF1_vs_LOF12
|
0
|
0
|
1
|
0
|
0
|
0
|
|
LOF1_vs_LOF2
|
0
|
0
|
0
|
1
|
0
|
0
|
|
LOF2
|
0
|
0
|
0
|
0
|
1
|
0
|
|
GOF1
|
0
|
0
|
0
|
0
|
0
|
1
|
Sum of each orthogonal contrast should be zero: intensity
|
|
sum
|
|
None_vs_others
|
0
|
|
WT_vs_Mutants
|
0
|
|
GOF1_vs_LOF12
|
0
|
|
LOF1_vs_LOF2
|
0
|
|
LOF2
|
0
|
|
GOF1
|
0
|
Matrix of contrasts on mutation: intensity
|
mutation
|
None_vs_others
|
WT_vs_Mutants
|
GOF1_vs_LOF12
|
LOF1_vs_LOF2
|
LOF2
|
GOF1
|
|
NONE
|
-0.86
|
0.00
|
0.0
|
0.00
|
0.0
|
0.0
|
|
WT
|
0.14
|
-0.83
|
0.0
|
0.00
|
0.0
|
0.0
|
|
C436R
|
0.14
|
0.17
|
-0.4
|
-0.33
|
-0.5
|
0.0
|
|
T531M
|
0.14
|
0.17
|
-0.4
|
-0.33
|
0.5
|
0.0
|
|
R518H
|
0.14
|
0.17
|
-0.4
|
0.67
|
0.0
|
0.0
|
|
K669N
|
0.14
|
0.17
|
0.6
|
0.00
|
0.0
|
-0.5
|
|
L812M
|
0.14
|
0.17
|
0.6
|
0.00
|
0.0
|
0.5
|
Matrix of contrasts on protein: intensity
|
protein
|
contrast
|
|
Homer1c
|
-0.5
|
|
GluN1
|
0.5
|
Estimated marginal means with 95% confidence intervals: intensity
|
mutation
|
protein
|
df
|
response
|
n
|
lower.CL
|
upper.CL
|
|
NONE
|
Homer1c
|
14.55
|
137.45
|
81
|
122.20
|
154.60
|
|
WT
|
Homer1c
|
20.04
|
132.73
|
57
|
117.15
|
150.39
|
|
C436R
|
Homer1c
|
67.17
|
130.23
|
20
|
110.06
|
154.10
|
|
T531M
|
Homer1c
|
30.41
|
122.46
|
41
|
106.47
|
140.85
|
|
R518H
|
Homer1c
|
37.86
|
129.90
|
33
|
112.22
|
150.38
|
|
K669N
|
Homer1c
|
60.51
|
138.69
|
22
|
117.73
|
163.39
|
|
L812M
|
Homer1c
|
76.53
|
88.23
|
18
|
74.22
|
104.88
|
|
NONE
|
GluN1
|
14.55
|
22.94
|
81
|
20.39
|
25.80
|
|
WT
|
GluN1
|
20.04
|
41.69
|
57
|
36.80
|
47.24
|
|
C436R
|
GluN1
|
67.17
|
22.83
|
20
|
19.29
|
27.01
|
|
T531M
|
GluN1
|
30.41
|
28.73
|
41
|
24.98
|
33.05
|
|
R518H
|
GluN1
|
37.86
|
39.42
|
33
|
34.05
|
45.63
|
|
K669N
|
GluN1
|
60.51
|
47.40
|
22
|
40.24
|
55.84
|
|
L812M
|
GluN1
|
76.53
|
20.62
|
18
|
17.35
|
24.51
|
[1] “
Overall average of intensity for Homer1c-tdTomato fluorescence intensity: 124.46”
Estimated marginal means with 95% confidence intervals for GluN1/Homer1c ratios: intensity
|
contrast
|
mutation
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
GluN1 / Homer1c
|
NONE
|
265
|
0.17
|
0.15
|
0.18
|
|
GluN1 / Homer1c
|
WT
|
265
|
0.31
|
0.28
|
0.35
|
|
GluN1 / Homer1c
|
C436R
|
265
|
0.18
|
0.15
|
0.21
|
|
GluN1 / Homer1c
|
T531M
|
265
|
0.23
|
0.21
|
0.26
|
|
GluN1 / Homer1c
|
R518H
|
265
|
0.30
|
0.26
|
0.35
|
|
GluN1 / Homer1c
|
K669N
|
265
|
0.34
|
0.29
|
0.40
|
|
GluN1 / Homer1c
|
L812M
|
265
|
0.23
|
0.19
|
0.28
|
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into orthogonal contrasts: intensity
|
Source
|
F
|
Df
|
Df.res
|
Pr(>F)
|
BF
|
|
mutation
|
21.02
|
5
|
126.1
|
<.001
|
3.52e+17
|
|
protein
|
1901.81
|
1
|
185.0
|
<.001
|
2.05e+131
|
|
mutation:protein
|
10.29
|
5
|
185.0
|
<.001
|
4.82e+04
|
|
mutationWT_vs_Mutants:protein1
|
12.71
|
1
|
185.0
|
<.001
|
|
|
mutationGOF1_vs_LOF12:protein1
|
6.90
|
1
|
185.0
|
.009
|
|
|
mutationLOF1_vs_LOF2:protein1
|
21.65
|
1
|
185.0
|
<.001
|
|
|
mutationLOF2:protein1
|
7.45
|
1
|
185.0
|
.007
|
|
|
mutationGOF1:protein1
|
9.32
|
1
|
185.0
|
.003
|
|
Intraclass correlation coefficients for random effects: intensity
|
Group
|
ICC
|
N
|
|
roi:expt
|
0.187
|
191
|
|
expt
|
0.094
|
8
|
|
residual
|
0.719
|
382
|
95% confidence intervals for contrasts: intensity
|
mutation_trt.vs.ctrl
|
protein_trt.vs.ctrl
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
C436R / WT
|
GluN1 / Homer1c
|
185
|
0.56
|
0.46
|
0.68
|
|
T531M / WT
|
GluN1 / Homer1c
|
185
|
0.75
|
0.64
|
0.88
|
|
R518H / WT
|
GluN1 / Homer1c
|
185
|
0.97
|
0.82
|
1.14
|
|
K669N / WT
|
GluN1 / Homer1c
|
185
|
1.09
|
0.90
|
1.32
|
|
L812M / WT
|
GluN1 / Homer1c
|
185
|
0.74
|
0.60
|
0.92
|
Standardized effect sizes (r) for contrasts: intensity
|
mutation
|
Protein
|
N
|
r
|
95% CI
|
|
C436R / WT
|
GluN1 / Homer1c
|
191
|
-0.39
|
[-0.50, -0.26]
|
|
T531M / WT
|
GluN1 / Homer1c
|
191
|
-0.26
|
[-0.39, -0.12]
|
|
R518H / WT
|
GluN1 / Homer1c
|
191
|
-0.03
|
[-0.17, 0.11]
|
|
K669N / WT
|
GluN1 / Homer1c
|
191
|
0.06
|
[-0.08, 0.20]
|
|
L812M / WT
|
GluN1 / Homer1c
|
191
|
-0.20
|
[-0.33, -0.06]
|