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_ko_nmdar.dat", header = TRUE)
# Implicit nesting (required for anovaBF)
Data %>%
mutate(genotype = as.factor(genotype)) %>%
mutate(animal = paste0(as.numeric(genotype),animal)) %>%
mutate(slice = paste0(animal,slice)) %>%
mutate(pair = paste0(slice,pair)) %>%
mutate(pair = factor(pair)) -> Data
Factor encoding
Data$genotype <- as.factor(Data$genotype)
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 genotype WT and transfection + as reference levels
Data$genotype <- factor(Data$genotype, ordered=TRUE, levels=c("WT","HET","HOM"))
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","rise","decay","dt50","charge")
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(genotype_jittered = jitter((as.numeric(genotype)+(as.numeric(transfection)-1)/2.5), 0.5),
grouping=interaction(pair, genotype)) %>%
mutate(genotype_transfection = as.numeric(genotype)+(as.numeric(transfection)-1)/2.5) %>%
ggplot(aes(x=genotype, y=!!sym(resp), group=grouping, color=transfection)) +
geom_blank() +
geom_line(aes(genotype_jittered), alpha=0.33) +
geom_point(aes(genotype_jittered), alpha=0.9, shape = 16) +
scale_color_manual(values=c("grey","#00BA38")) +
stat_summary(mapping = aes(x=genotype_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=genotype_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(genotype,pair,!!sym(resp)),names_from=transfection,values_from=!!sym(resp)) %>%
mutate(ratio = `+`/`-`) %>%
ggplot(aes(x=genotype, y=ratio, colour=genotype)) +
geom_sina(alpha=0.9, shape = 16) +
scale_color_manual(values=c("grey","#619CFF","#F8766D")) +
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 full model (with polynomial contrasts)
contrasts(Data$genotype) <- contr.poly(3) # needed for trend analysis
attr(Data$genotype,"contrasts") %>%
as.data.frame() %>%
rownames_to_column(var = "genotype") %>%
knitr::kable(caption = sprintf("**Matrix of contrasts on genotype: %s**",resp), digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
print()
contrasts(Data$transfection) <- contr.poly(2)
attr(Data$transfection,"contrasts") %>%
as.data.frame() %>%
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) ~ genotype * 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() +
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 ~ genotype * 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$BF
# Calculate polynomial contrasts and append them to the ANOVA summary table
# I go to the trouble of transforming the t-statistic (which is returned from the linear model)
# to an F statistic but they give identical p-values; I think this makes more sense and provides
# more consistency when splitting the source of variation into polynomial contrasts and
# presenting them in an ANOVA table
model_parameters(model, ci_method = "kenward", exponentiate = FALSE, effects = "fixed") %>%
filter(grepl(":",Parameter)) %>% # select interaction terms only
rename(Source = Parameter) %>% # set denominator degrees of freedom
mutate(Df = 1) %>% # set numerator degrees of freedom
add_column(BF = "") %>% # add empty column for Bayes factors
rename(Df.res = df_error) %>% # set denominator degrees of freedom
mutate(F = abs(t)^2) %>% # calculate F statistic
mutate(`Pr(>F)` = pf(F,Df,Df.res,lower.tail=FALSE)) %>% # calculate p value
dplyr::select(c(Source,F,Df,Df.res,`Pr(>F)`,BF)) %>% # select columns of interest for table
rbind(aov,.) %>% # row bind with anova table
mutate(`Pr(>F)` = afex::round_ps_apa(`Pr(>F)`)) %>% # format p values as APA style
knitr::kable(caption = sprintf("**ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into polynomial contrasts: %s**",resp), digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
add_indent(c(4:5)) %>% # add indentation to indicate source components
print()
# Calculate intraclass correlation coefficients (ICC) for the random effects
icc(model, by_group=TRUE, tolerance=0) %>%
as.data.frame() %>%
mutate(N = ngrps(model)) %>%
rbind(.,c("residual",1-sum(.$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, ~ genotype * 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, ~ genotype * 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 = 'genotype', 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)$r) %>%
mutate(z = atanh(r),
SE = 1/sqrt(n-3),
CI = sprintf("[%.2f, %.2f]",
LL = tanh(z - 1.96*SE),
UL = tanh(z + 1.96*SE))) %>%
dplyr::select(-c(ratio,SE,df,null,t.ratio,p.value,z)) %>%
knitr::kable(col.names = c("genotype",
"transfection",
"*n*",
"*r*",
"95% *CI*"),
caption = sprintf("**Standardized effect sizes (*r*) for contrasts: %s**",resp), digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
print()
# Joint tests
joint_tests(model,by="genotype") %>%
mutate(p.value = afex::round_ps_apa(p.value)) %>% # format p values as APA style
knitr::kable(caption = sprintf("**Joint (type III) tests by genotype: %s**",resp), digits = 2) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>%
print()
posthoc = TRUE
if (posthoc == TRUE) {
# p-values and maximum Bayes Factors for pairwise comparisons
# Westfall stepwise p-value adjustment to control FWER on p-values (using multcomp package)
# Chapter 4.1.2 in Bretz, F., Hothorn, T. and Westfall, P. (2011) Multiple Comparisons Using R. Taylor and Frances Group, LLC.
emm.interaction <- contrast(emm, method = "pairwise", interaction = TRUE, adjust = "none")
emm.interaction %>%
as.glht() %>%
summary(test = adjusted(type = "Westfall")) -> glht.out
emm.interaction %>%
as.data.frame() %>%
dplyr::select(-SE) %>%
mutate(p.adj = glht.out$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 (Westfall stepwise 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_ko_nmdar",resp), width=3.5, height=3.5)
emm %>%
as.data.frame() %>%
mutate(genotype_transfection = as.numeric(genotype)+(as.numeric(transfection)-1)/2.5) -> emm_df
p1 <- Data %>%
mutate(genotype_jittered = jitter((as.numeric(genotype)+(as.numeric(transfection)-1)/2.5), 0.5),
grouping=interaction(pair, genotype)) %>%
mutate(genotype_transfection = as.numeric(genotype)+(as.numeric(transfection)-1)/2.5) %>%
ggplot(aes(x=genotype, y=!!sym(resp), group=grouping, color=transfection)) +
geom_blank() +
geom_line(aes(genotype_jittered), alpha=0.3, color="grey", size=0.75) +
geom_point(aes(genotype_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=genotype_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(genotype,pair,!!sym(resp)),names_from=transfection,values_from=!!sym(resp)) %>%
mutate(ratio = `+`/`-`) %>%
ggplot(aes(x=genotype, y=ratio, colour=genotype)) +
geom_sina(alpha=0.6, shape=16, size=1.25, maxwidth=0.5) +
geom_crossbar(data = emm.transfection_df,
aes(x=genotype, y=ratio, ymin=`lower.CL`, ymax=`upper.CL`, fill=genotype),
color="black", alpha=0.5, size=0.5, fatten=1, width=0.8, inherit.aes=FALSE) +
scale_color_manual(values=c("grey","#619CFF","#F8766D")) +
scale_fill_manual(values=c("grey","#619CFF","#F8766D")) +
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 peak
Matrix of contrasts on genotype: peak
|
genotype
|
.L
|
.Q
|
|
WT
|
-0.71
|
0.41
|
|
HET
|
0.00
|
-0.82
|
|
HOM
|
0.71
|
0.41
|
Matrix of contrasts on transfection: peak
|
transfection
|
.L
|
|
-
|
-0.71
|
|
+
|
0.71
|
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into polynomial contrasts: peak
|
Source
|
F
|
Df
|
Df.res
|
Pr(>F)
|
BF
|
|
genotype
|
0.83
|
2
|
10.07
|
.465
|
0.461
|
|
transfection
|
25.46
|
1
|
96.00
|
<.001
|
5.05e+04
|
|
genotype:transfection
|
8.61
|
2
|
96.00
|
<.001
|
227
|
|
genotype.L:transfection.L
|
13.00
|
1
|
96.00
|
<.001
|
|
|
genotype.Q:transfection.L
|
3.76
|
1
|
96.00
|
.055
|
|
Intraclass correlation coefficients for random effects: peak
|
Group
|
ICC
|
N
|
|
pair:(slice:animal)
|
0.000
|
99
|
|
slice:animal
|
0.270
|
53
|
|
animal
|
0.293
|
14
|
|
residual
|
0.436
|
198
|
Estimated marginal means with 95% confidence intervals: peak
|
genotype
|
transfection
|
df
|
response
|
n
|
lower.CL
|
upper.CL
|
|
WT
|
-
|
10.29
|
83.78
|
33
|
56.93
|
123.30
|
|
HET
|
-
|
15.96
|
63.93
|
29
|
44.96
|
90.92
|
|
HOM
|
-
|
9.95
|
81.10
|
37
|
55.20
|
119.16
|
|
WT
|
+
|
10.29
|
74.90
|
33
|
50.89
|
110.23
|
|
HET
|
+
|
15.96
|
57.06
|
29
|
40.13
|
81.15
|
|
HOM
|
+
|
9.95
|
46.77
|
37
|
31.83
|
68.72
|
[1] “
Overall average of peak for untransfected neurons: 75.74”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: peak
|
contrast
|
genotype
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
(+) / (-)
|
WT
|
96
|
0.89
|
0.75
|
1.07
|
|
(+) / (-)
|
HET
|
96
|
0.89
|
0.74
|
1.08
|
|
(+) / (-)
|
HOM
|
96
|
0.58
|
0.49
|
0.68
|
95% confidence intervals for contrasts: peak
|
genotype_trt.vs.ctrl
|
transfection_trt.vs.ctrl
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
HET / WT
|
(+) / (-)
|
96
|
1.00
|
0.77
|
1.29
|
|
HOM / WT
|
(+) / (-)
|
96
|
0.65
|
0.51
|
0.82
|
Standardized effect sizes (r) for contrasts: peak
|
genotype
|
transfection
|
n
|
r
|
95% CI
|
|
HET / WT
|
(+) / (-)
|
99
|
0.00
|
[-0.20, 0.20]
|
|
HOM / WT
|
(+) / (-)
|
99
|
-0.35
|
[-0.51, -0.16]
|
Joint (type III) tests by genotype: peak
|
model term
|
genotype
|
df1
|
df2
|
F.ratio
|
p.value
|
|
transfection
|
WT
|
1
|
96
|
1.61
|
.208
|
|
transfection
|
HET
|
1
|
96
|
1.45
|
.231
|
|
transfection
|
HOM
|
1
|
96
|
43.49
|
<.001
|
## Note: df set to 96
Hypothesis testing on interaction parameters (Westfall stepwise p-value adjustment): peak
|
genotype_pairwise
|
transfection_pairwise
|
ratio
|
df
|
null
|
t.ratio
|
p.value
|
p.adj
|
maxBF
|
|
WT / HET
|
(-) / (+)
|
1.00
|
96
|
1
|
-0.01
|
.990
|
.990
|
1
|
|
WT / HOM
|
(-) / (+)
|
0.65
|
96
|
1
|
-3.61
|
<.001
|
.001
|
38
|
|
HET / HOM
|
(-) / (+)
|
0.65
|
96
|
1
|
-3.47
|
<.001
|
.001
|
38
|
Analysis of rise
Matrix of contrasts on genotype: rise
|
genotype
|
.L
|
.Q
|
|
WT
|
-0.71
|
0.41
|
|
HET
|
0.00
|
-0.82
|
|
HOM
|
0.71
|
0.41
|
Matrix of contrasts on transfection: rise
|
transfection
|
.L
|
|
-
|
-0.71
|
|
+
|
0.71
|
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into polynomial contrasts: rise
|
Source
|
F
|
Df
|
Df.res
|
Pr(>F)
|
BF
|
|
genotype
|
1.50
|
2
|
9.69
|
.270
|
0.538
|
|
transfection
|
83.79
|
1
|
96.00
|
<.001
|
1.15e+10
|
|
genotype:transfection
|
44.21
|
2
|
96.00
|
<.001
|
2.33e+13
|
|
genotype.L:transfection.L
|
81.48
|
1
|
96.00
|
<.001
|
|
|
genotype.Q:transfection.L
|
5.55
|
1
|
96.00
|
.021
|
|
Intraclass correlation coefficients for random effects: rise
|
Group
|
ICC
|
N
|
|
pair:(slice:animal)
|
0.045
|
99
|
|
slice:animal
|
0.491
|
53
|
|
animal
|
0.146
|
14
|
|
residual
|
0.318
|
198
|
Estimated marginal means with 95% confidence intervals: rise
|
genotype
|
transfection
|
df
|
response
|
n
|
lower.CL
|
upper.CL
|
|
WT
|
-
|
9.43
|
3.74
|
33
|
3.40
|
4.11
|
|
HET
|
-
|
16.68
|
3.73
|
29
|
3.40
|
4.09
|
|
HOM
|
-
|
9.10
|
3.61
|
37
|
3.28
|
3.96
|
|
WT
|
+
|
9.43
|
3.74
|
33
|
3.40
|
4.12
|
|
HET
|
+
|
16.68
|
4.00
|
29
|
3.65
|
4.39
|
|
HOM
|
+
|
9.10
|
4.71
|
37
|
4.29
|
5.18
|
[1] “
Overall average of rise for untransfected neurons: 3.69”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: rise
|
contrast
|
genotype
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
(+) / (-)
|
WT
|
96
|
1.00
|
0.96
|
1.05
|
|
(+) / (-)
|
HET
|
96
|
1.07
|
1.03
|
1.12
|
|
(+) / (-)
|
HOM
|
96
|
1.31
|
1.26
|
1.36
|
95% confidence intervals for contrasts: rise
|
genotype_trt.vs.ctrl
|
transfection_trt.vs.ctrl
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
HET / WT
|
(+) / (-)
|
96
|
1.07
|
1.01
|
1.14
|
|
HOM / WT
|
(+) / (-)
|
96
|
1.30
|
1.23
|
1.38
|
Standardized effect sizes (r) for contrasts: rise
|
genotype
|
transfection
|
n
|
r
|
95% CI
|
|
HET / WT
|
(+) / (-)
|
99
|
0.22
|
[0.02, 0.40]
|
|
HOM / WT
|
(+) / (-)
|
99
|
0.68
|
[0.55, 0.77]
|
Joint (type III) tests by genotype: rise
|
model term
|
genotype
|
df1
|
df2
|
F.ratio
|
p.value
|
|
transfection
|
WT
|
1
|
96
|
0.01
|
.921
|
|
transfection
|
HET
|
1
|
96
|
9.67
|
.002
|
|
transfection
|
HOM
|
1
|
96
|
175.62
|
<.001
|
## Note: df set to 96
Hypothesis testing on interaction parameters (Westfall stepwise p-value adjustment): rise
|
genotype_pairwise
|
transfection_pairwise
|
ratio
|
df
|
null
|
t.ratio
|
p.value
|
p.adj
|
maxBF
|
|
WT / HET
|
(-) / (+)
|
1.07
|
96
|
1
|
2.20
|
.030
|
.030
|
3.48
|
|
WT / HOM
|
(-) / (+)
|
1.30
|
96
|
1
|
9.03
|
<.001
|
<.001
|
4.18e+11
|
|
HET / HOM
|
(-) / (+)
|
1.22
|
96
|
1
|
6.46
|
<.001
|
<.001
|
4.35e+06
|
Analysis of decay
Matrix of contrasts on genotype: decay
|
genotype
|
.L
|
.Q
|
|
WT
|
-0.71
|
0.41
|
|
HET
|
0.00
|
-0.82
|
|
HOM
|
0.71
|
0.41
|
Matrix of contrasts on transfection: decay
|
transfection
|
.L
|
|
-
|
-0.71
|
|
+
|
0.71
|
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into polynomial contrasts: decay
|
Source
|
F
|
Df
|
Df.res
|
Pr(>F)
|
BF
|
|
genotype
|
4.91
|
2
|
9.9
|
.033
|
1.97
|
|
transfection
|
156.82
|
1
|
96.0
|
<.001
|
8.68e+13
|
|
genotype:transfection
|
76.23
|
2
|
96.0
|
<.001
|
4.67e+19
|
|
genotype.L:transfection.L
|
140.11
|
1
|
96.0
|
<.001
|
|
|
genotype.Q:transfection.L
|
9.90
|
1
|
96.0
|
.002
|
|
Intraclass correlation coefficients for random effects: decay
|
Group
|
ICC
|
N
|
|
pair:(slice:animal)
|
0.144
|
99
|
|
slice:animal
|
0.259
|
53
|
|
animal
|
0.247
|
14
|
|
residual
|
0.350
|
198
|
Estimated marginal means with 95% confidence intervals: decay
|
genotype
|
transfection
|
df
|
response
|
n
|
lower.CL
|
upper.CL
|
|
WT
|
-
|
9.90
|
83.36
|
33
|
67.72
|
102.62
|
|
HET
|
-
|
15.68
|
91.08
|
29
|
75.08
|
110.48
|
|
HOM
|
-
|
9.49
|
86.41
|
37
|
70.27
|
106.25
|
|
WT
|
+
|
9.90
|
85.08
|
33
|
69.11
|
104.73
|
|
HET
|
+
|
15.68
|
111.82
|
29
|
92.18
|
135.63
|
|
HOM
|
+
|
9.49
|
182.18
|
37
|
148.15
|
224.03
|
[1] “
Overall average of decay for untransfected neurons: 86.89”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: decay
|
contrast
|
genotype
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
(+) / (-)
|
WT
|
96
|
1.02
|
0.93
|
1.11
|
|
(+) / (-)
|
HET
|
96
|
1.23
|
1.12
|
1.35
|
|
(+) / (-)
|
HOM
|
96
|
2.11
|
1.94
|
2.29
|
95% confidence intervals for contrasts: decay
|
genotype_trt.vs.ctrl
|
transfection_trt.vs.ctrl
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
HET / WT
|
(+) / (-)
|
96
|
1.20
|
1.06
|
1.37
|
|
HOM / WT
|
(+) / (-)
|
96
|
2.07
|
1.83
|
2.33
|
Standardized effect sizes (r) for contrasts: decay
|
genotype
|
transfection
|
n
|
r
|
95% CI
|
|
HET / WT
|
(+) / (-)
|
99
|
0.28
|
[0.09, 0.45]
|
|
HOM / WT
|
(+) / (-)
|
99
|
0.77
|
[0.68, 0.84]
|
Joint (type III) tests by genotype: decay
|
model term
|
genotype
|
df1
|
df2
|
F.ratio
|
p.value
|
|
transfection
|
WT
|
1
|
96
|
0.21
|
.649
|
|
transfection
|
HET
|
1
|
96
|
18.62
|
<.001
|
|
transfection
|
HOM
|
1
|
96
|
314.11
|
<.001
|
## Note: df set to 96
Hypothesis testing on interaction parameters (Westfall stepwise p-value adjustment): decay
|
genotype_pairwise
|
transfection_pairwise
|
ratio
|
df
|
null
|
t.ratio
|
p.value
|
p.adj
|
maxBF
|
|
WT / HET
|
(-) / (+)
|
1.20
|
96
|
1
|
2.84
|
.006
|
.006
|
12.7
|
|
WT / HOM
|
(-) / (+)
|
2.07
|
96
|
1
|
11.84
|
<.001
|
<.001
|
4.6e+13
|
|
HET / HOM
|
(-) / (+)
|
1.72
|
96
|
1
|
8.52
|
<.001
|
<.001
|
5.59e+10
|
Analysis of dt50
Matrix of contrasts on genotype: dt50
|
genotype
|
.L
|
.Q
|
|
WT
|
-0.71
|
0.41
|
|
HET
|
0.00
|
-0.82
|
|
HOM
|
0.71
|
0.41
|
Matrix of contrasts on transfection: dt50
|
transfection
|
.L
|
|
-
|
-0.71
|
|
+
|
0.71
|
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into polynomial contrasts: dt50
|
Source
|
F
|
Df
|
Df.res
|
Pr(>F)
|
BF
|
|
genotype
|
3.97
|
2
|
10.59
|
.052
|
2.22
|
|
transfection
|
415.87
|
1
|
96.00
|
<.001
|
5.31e+19
|
|
genotype:transfection
|
231.23
|
2
|
96.00
|
<.001
|
2.36e+43
|
|
genotype.L:transfection.L
|
416.89
|
1
|
96.00
|
<.001
|
|
|
genotype.Q:transfection.L
|
37.39
|
1
|
96.00
|
<.001
|
|
Intraclass correlation coefficients for random effects: dt50
|
Group
|
ICC
|
N
|
|
pair:(slice:animal)
|
0.081
|
99
|
|
slice:animal
|
0.070
|
53
|
|
animal
|
0.633
|
14
|
|
residual
|
0.216
|
198
|
Estimated marginal means with 95% confidence intervals: dt50
|
genotype
|
transfection
|
df
|
response
|
n
|
lower.CL
|
upper.CL
|
|
WT
|
-
|
10.45
|
32.33
|
33
|
24.18
|
43.22
|
|
HET
|
-
|
12.93
|
39.77
|
29
|
31.09
|
50.87
|
|
HOM
|
-
|
10.31
|
32.39
|
37
|
24.24
|
43.29
|
|
WT
|
+
|
10.45
|
32.78
|
33
|
24.52
|
43.82
|
|
HET
|
+
|
12.93
|
50.60
|
29
|
39.55
|
64.73
|
|
HOM
|
+
|
10.31
|
90.71
|
37
|
67.88
|
121.22
|
[1] “
Overall average of dt50 for untransfected neurons: 34.66”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: dt50
|
contrast
|
genotype
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
(+) / (-)
|
WT
|
96
|
1.01
|
0.94
|
1.09
|
|
(+) / (-)
|
HET
|
96
|
1.27
|
1.18
|
1.37
|
|
(+) / (-)
|
HOM
|
96
|
2.80
|
2.62
|
3.00
|
95% confidence intervals for contrasts: dt50
|
genotype_trt.vs.ctrl
|
transfection_trt.vs.ctrl
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
HET / WT
|
(+) / (-)
|
96
|
1.26
|
1.13
|
1.39
|
|
HOM / WT
|
(+) / (-)
|
96
|
2.76
|
2.50
|
3.05
|
Standardized effect sizes (r) for contrasts: dt50
|
genotype
|
transfection
|
n
|
r
|
95% CI
|
|
HET / WT
|
(+) / (-)
|
99
|
0.4
|
[0.22, 0.55]
|
|
HOM / WT
|
(+) / (-)
|
99
|
0.9
|
[0.86, 0.93]
|
Joint (type III) tests by genotype: dt50
|
model term
|
genotype
|
df1
|
df2
|
F.ratio
|
p.value
|
|
transfection
|
WT
|
1
|
96
|
0.14
|
.707
|
|
transfection
|
HET
|
1
|
96
|
38.96
|
<.001
|
|
transfection
|
HOM
|
1
|
96
|
908.24
|
<.001
|
## Note: df set to 96
Hypothesis testing on interaction parameters (Westfall stepwise p-value adjustment): dt50
|
genotype_pairwise
|
transfection_pairwise
|
ratio
|
df
|
null
|
t.ratio
|
p.value
|
p.adj
|
maxBF
|
|
WT / HET
|
(-) / (+)
|
1.26
|
96
|
1
|
4.30
|
<.001
|
<.001
|
874
|
|
WT / HOM
|
(-) / (+)
|
2.76
|
96
|
1
|
20.42
|
<.001
|
<.001
|
4.6e+13
|
|
HET / HOM
|
(-) / (+)
|
2.20
|
96
|
1
|
15.30
|
<.001
|
<.001
|
4.6e+13
|
Analysis of charge
Matrix of contrasts on genotype: charge
|
genotype
|
.L
|
.Q
|
|
WT
|
-0.71
|
0.41
|
|
HET
|
0.00
|
-0.82
|
|
HOM
|
0.71
|
0.41
|
Matrix of contrasts on transfection: charge
|
transfection
|
.L
|
|
-
|
-0.71
|
|
+
|
0.71
|
ANOVA table (Type III Wald F tests with Kenward-Roger df) and Bayes factors for fixed effects with interaction source split into polynomial contrasts: charge
|
Source
|
F
|
Df
|
Df.res
|
Pr(>F)
|
BF
|
|
genotype
|
0.09
|
2
|
10.09
|
.911
|
0.372
|
|
transfection
|
0.01
|
1
|
96.00
|
.938
|
0.157
|
|
genotype:transfection
|
1.21
|
2
|
96.00
|
.303
|
0.277
|
|
genotype.L:transfection.L
|
2.39
|
1
|
96.00
|
.125
|
|
|
genotype.Q:transfection.L
|
0.01
|
1
|
96.00
|
.907
|
|
Intraclass correlation coefficients for random effects: charge
|
Group
|
ICC
|
N
|
|
pair:(slice:animal)
|
0.009
|
99
|
|
slice:animal
|
0.317
|
53
|
|
animal
|
0.312
|
14
|
|
residual
|
0.362
|
198
|
Estimated marginal means with 95% confidence intervals: charge
|
genotype
|
transfection
|
df
|
response
|
n
|
lower.CL
|
upper.CL
|
|
WT
|
-
|
10.01
|
8.79
|
33
|
5.49
|
14.06
|
|
HET
|
-
|
15.44
|
7.56
|
29
|
4.94
|
11.58
|
|
HOM
|
-
|
9.73
|
7.91
|
37
|
4.95
|
12.64
|
|
WT
|
+
|
10.01
|
8.01
|
33
|
5.01
|
12.82
|
|
HET
|
+
|
15.44
|
7.52
|
29
|
4.91
|
11.52
|
|
HOM
|
+
|
9.73
|
8.84
|
37
|
5.53
|
14.12
|
[1] “
Overall average of charge for untransfected neurons: 8.07”
Estimated marginal means with 95% confidence intervals for transfected/untransfected ratios: charge
|
contrast
|
genotype
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
(+) / (-)
|
WT
|
96
|
0.91
|
0.75
|
1.10
|
|
(+) / (-)
|
HET
|
96
|
0.99
|
0.81
|
1.22
|
|
(+) / (-)
|
HOM
|
96
|
1.12
|
0.93
|
1.34
|
95% confidence intervals for contrasts: charge
|
genotype_trt.vs.ctrl
|
transfection_trt.vs.ctrl
|
df
|
ratio
|
lower.CL
|
upper.CL
|
|
HET / WT
|
(+) / (-)
|
96
|
1.09
|
0.83
|
1.44
|
|
HOM / WT
|
(+) / (-)
|
96
|
1.22
|
0.94
|
1.59
|
Standardized effect sizes (r) for contrasts: charge
|
genotype
|
transfection
|
n
|
r
|
95% CI
|
|
HET / WT
|
(+) / (-)
|
99
|
0.06
|
[-0.14, 0.26]
|
|
HOM / WT
|
(+) / (-)
|
99
|
0.16
|
[-0.04, 0.34]
|
Joint (type III) tests by genotype: charge
|
model term
|
genotype
|
df1
|
df2
|
F.ratio
|
p.value
|
|
transfection
|
WT
|
1
|
96
|
0.94
|
.335
|
|
transfection
|
HET
|
1
|
96
|
0.00
|
.960
|
|
transfection
|
HOM
|
1
|
96
|
1.50
|
.223
|
## Note: df set to 96
Hypothesis testing on interaction parameters (Westfall stepwise p-value adjustment): charge
|
genotype_pairwise
|
transfection_pairwise
|
ratio
|
df
|
null
|
t.ratio
|
p.value
|
p.adj
|
maxBF
|
|
WT / HET
|
(-) / (+)
|
1.09
|
96
|
1
|
0.63
|
.533
|
.533
|
1
|
|
WT / HOM
|
(-) / (+)
|
1.22
|
96
|
1
|
1.55
|
.125
|
.274
|
1.04
|
|
HET / HOM
|
(-) / (+)
|
1.12
|
96
|
1
|
0.85
|
.397
|
.397
|
1
|
