Open code
rm(list = ls())
suppressWarnings(suppressMessages( {
library(brms)
library(beeswarm)
library(vioplot)
library(glmmTMB)
library(car)
library(cowplot)
library(ggplot2)
library(tidyverse)
} ) )Experimental treatment with edaravone in a mouse model of spinocerebellar ataxia 1
Data upload and defining functions
This page shows R code for the study Sucha et al. (2023, International Journal of Molecular Science).
Citation:
Sucha M, Benediktova S, Tichanek F, Jedlicka J, Kapl S, Jelinkova D, Purkartova Z, Tuma J, Kuncova J, Cendelin J. Experimental Treatment with Edaravone in a Mouse Model of Spinocerebellar Ataxia 1. International Journal of Molecular Sciences. 2023; 24(13):10689. https://doi.org/10.3390/ijms241310689
GitHub page: https://github.com/filip-tichanek/edaravonSCA1
1 Import of packages
2 Custom functions
2.1 run_model
Function to run and save, or load, Bayesian model. The function was written according to online proposal of Paul-Christian Bürkner
Open code
run_model <- function(expr, path, reuse = TRUE) {
path <- paste0(path, ".Rds")
if (reuse) {
fit <- suppressWarnings(try(readRDS(path), silent = TRUE))
}
if (is(fit, "try-error")) {
fit <- eval(expr)
saveRDS(fit, file = path)
}
fit
}2.2 prios
The function takes model (m) fitted with glmmTMB package and show prior with sca1 as reference group and the WT genotype as the 1st b regression parameter. The model m is based on previous data (Tichanek et al., 2020, Scientific Reports).
Open code
prios<- function(m){
pr<-summary(m)$coefficients$cond[2,c(1,2,4)]
if((pr[1]<0)&(pr[3]<0.05)){
mu<-(pr[1]+(pr[2]*1.96))
sigma<-max(abs(pr[1]-mu),abs(mu))*1.5}
else if((pr[1]>0)&(pr[3]<0.05)){
mu<-(pr[1]-pr[2]*1.96)
sigma<-max(abs(pr[1]-mu),abs(mu))*1.5}
else{
mu=0
sigma=0-abs(pr[1])*1.5}
return(c(mu,sigma))}2.3 mons_poste
The function takes data frame of posterior distributions of effect sizes (datf ) and value of zero effect (tres). There are two version: mons_poste6 is for visualizing posterior probabilities on limited space when there are 6 posterior distribution in a single column. mons_poste is used otherwise.
Open code
## mons_poste for visualisations of posterior distribution
mons_poste<-function(datf, tres){
xx<-1;xax<-data.frame();yax<-data.frame();cisl<-data.frame()
repeat{
xax[1:512,xx] <-density(datf[,xx])$x
yax[1:512,xx]<-density(datf[,xx])$y
vec<-data.frame(datf[,xx])
cisl[xx,1:7]<-sapply(vec,function(p) quantile(p,probs=c(0.25,0.75,0.025,0.975,0.001,0.999,0.5)))
xx<-xx+1;if(xx>dim(datf)[2]){break}}
axl<-data.frame(cbind(xax,yax))
dim(axl)
scaler<- max(xax) - min(xax)
uniscal<-round(log10(160/scaler))
xrange<- c(min(xax)-0.05*scaler,max(xax));xscaler=xrange[2]-xrange[1]
zpos<-c(seq(0,1,by=1/dim(datf)[2]))
ymax<-1/(max(yax)*1.8*dim(datf)[2])
par(mgp=c(1.6,0.55,0))
plot(NULL, xlim=c(xrange[1],xrange[2]),ylim=c(0,1),xlab="",ylab="",las=1, axes=FALSE)
xxs<-1
repeat{
x<-ste[xxs]
lines(c(x,x),c(0,1),col="grey70",lwd=0.7,lty=3)
xxs<-xxs+1;if(xxs>length(ste)){break}}
xx<-1
linop=0.05
repeat{
polygon(xax[,xx],(ymax*yax[,xx])+(zpos[xx])+linop,col=rgb(1,0.9,0.7),border="grey50")
polygon(xax[,xx],(ymax*yax[,xx])+(zpos[xx])+linop,col=rgb(0.82,0.82,0.82),border="NA")
ax<- axl[,c(xx,xx+dim(datf)[2])] [axl[,xx]>cisl[xx,3]&axl[,xx]<cisl[xx,4],]
polygon(c(ax[,1],rev(ax[,1])),
c((ymax*ax[,2])+(zpos[xx])+linop,
rep((zpos[xx])+linop,length(ax[,2]))
), col=rgb(1,0.88,0.46),border=NA)
lines(c(cisl[xx,3],cisl[xx,4]),c((zpos[xx])+linop,(zpos[xx])+linop),lend=1,lwd=3,col="grey10")
points(cisl[xx,7],zpos[xx]+linop,lend=2,pch=3,cex=2.2,lwd=1.5,col="grey10")
xx<-xx+1
if(xx>length(zpos)-1){break}}
lines(c(tres,tres),c(0,1),lty=1,col="blue4")
axis(side=1,las=1,cex.axis=1.1,
at=c(ste),pos=range[1],tck=tckk)
lines(c(min(xax)-scaler*0.05,max(xax)),c(range[1],range[1]))
xx<-1
repeat{
lines(c(xrange[1]+0.2*xscaler,xrange[1]+1*xscaler),
c(zpos[xx]+zpos[2]*0.88,zpos[xx]+zpos[2]*0.9),
lwd=22,col="white",lend=2)
text(xrange[1]+0.55*xscaler,zpos[xx]+zpos[2]*0.88,
bquote(.(round(cisl[xx,7],uniscal)) *
" (" *.(round(cisl[xx,3],uniscal)) *
" to "* .(round(cisl[xx,4],uniscal)) * ")")
,xpd=TRUE,cex=1,col=rgb(0.4, 0.1, 0))
pd<-p_dir(datf[,xx],tres)
pd2<- -round(log10(1-pd))+1
text(xrange[1]+0.9*xscaler,zpos[xx]+zpos[2]*0.6,
bquote(.(round(pd,pd2)))
,xpd=TRUE,cex=1.1,col=rgb(0.6, 0.1, 0.8))
xx=xx+1;if(xx>length(zpos)-1){break}}
text(mean(c(xrange[1],max(xax))),range[1]-0.12,paste(yla),xpd=T,cex=1.1)
}
## mons_poste6 for visualisations of 6 posterior distributions in a single column of the figure
mons_poste6<-function(datf, tres){
xx<-1;xax<-data.frame();yax<-data.frame();cisl<-data.frame()
repeat{
xax[1:512,xx] <-density(datf[,xx])$x
yax[1:512,xx]<-density(datf[,xx])$y
vec<-data.frame(datf[,xx])
cisl[xx,1:7]<-sapply(vec,function(p) quantile(p,probs=c(0.25,0.75,0.025,0.975,0.001,0.999,0.5)))
xx<-xx+1;if(xx>dim(datf)[2]){break}}
axl<-data.frame(cbind(xax,yax))
dim(axl)
scaler<- max(xax) - min(xax)
uniscal<-round(log10(160/scaler))
xrange<- c(min(xax)-0.05*scaler,max(xax));xscaler=xrange[2]-xrange[1]
zpos<-c(seq(0,1,by=1/dim(datf)[2]))
ymax<-1/(max(yax)*1.8*dim(datf)[2])
par(mgp=c(1.6,0.55,0))
plot(NULL, xlim=c(xrange[1],xrange[2]),ylim=c(0,1),xlab="",ylab="",las=1, axes=FALSE)
xxs<-1
repeat{
x<-ste[xxs]
lines(c(x,x),c(0,1),col="grey70",lwd=0.7,lty=3)
xxs<-xxs+1;if(xxs>length(ste)){break}}
xx<-1
linop=0.01
repeat{
polygon(xax[,xx],(ymax*yax[,xx])+(zpos[xx])+linop,col=rgb(1,0.9,0.7),border="grey50")
polygon(xax[,xx],(ymax*yax[,xx])+(zpos[xx])+linop,col=rgb(0.82,0.82,0.82),border="NA")
ax<- axl[,c(xx,xx+dim(datf)[2])] [axl[,xx]>cisl[xx,3]&axl[,xx]<cisl[xx,4],]
polygon(c(ax[,1],rev(ax[,1])),
c((ymax*ax[,2])+(zpos[xx])+linop,
rep((zpos[xx])+linop,length(ax[,2]))
), col=rgb(1,0.88,0.46),border=NA)
lines(c(cisl[xx,3],cisl[xx,4]),c((zpos[xx])+linop,(zpos[xx])+linop),lend=1,lwd=3,col="grey10")
points(cisl[xx,7],zpos[xx]+linop,lend=2,pch=3,cex=2.2,lwd=1.5,col="grey10")
xx<-xx+1
if(xx>length(zpos)-1){break}}
lines(c(tres,tres),c(0,1),lty=1,col="blue4")
axis(side=1,las=1,cex.axis=1.1,
at=c(ste),pos=0,tck=tckk,xpd=TRUE)
lines(c(min(xax)-scaler*0.05,max(xax)),c(range[1],range[1]))
xx<-1
repeat{
lines(c(xrange[1]+0.2*xscaler,xrange[1]+1*xscaler),
c(zpos[xx]+zpos[2]*0.88,zpos[xx]+zpos[2]*0.9),
lwd=22,col="white",lend=2)
text(xrange[1]+0.55*xscaler,zpos[xx]+zpos[2]*0.88,
bquote(.(round(cisl[xx,7],uniscal)) *
" (" *.(round(cisl[xx,3],uniscal)) *
" to "* .(round(cisl[xx,4],uniscal)) * ")")
,xpd=TRUE,cex=1.2,col=rgb(0.4, 0.1, 0))
pd<-p_dir(datf[,xx],tres)
pd2<- -round(log10(1-pd))+1
text(xrange[1]+0.9*xscaler,zpos[xx]+zpos[2]*0.6,
bquote(.(round(pd,pd2)))
,xpd=TRUE,cex=1.2,col=rgb(0.6, 0.1, 0.8))
xx=xx+1;if(xx>length(zpos)-1){break}}
text(mean(c(xrange[1],max(xax))),0-0.12,paste(yla),xpd=T,cex=1.1)
}2.4 p_dir
Takes data frame of posterior distribution of effect size (data) and value of zero effect (tres) and calculates probability of direction
Open code
p_dir<-function(data,tres){
1-((min(length(data[data>tres]),length(data[data<tres])))/(length(data)))
}2.5 Functions for data transformations
Open code
logit<-function(x){
log(x/(1-x))
}
inv.logit<-function(x){
exp(x)/(exp(x)+1)
}
asinTransform <- function(p) { asin(sqrt(p)) }
revAsin <- function(p){sin(p)^2}3 Colors definitions
Open code
alp=1;cola<-c(
rgb(0.7, 0.7, 0.25,alpha=alp),
rgb(0.55,0.55, 0.55,alpha=alp),
rgb(0.9,0,0,alpha=alp),
rgb(0.1, 0.1, 1,alpha=alp))
alp=0.2;colb<-c(
rgb(0.7, 0.7, 0.25,alpha=alp),
rgb(0.55,0.55, 0.55,alpha=alp),
rgb(0.9,0,0,alpha=alp),
rgb(0.1, 0.1, 1,alpha=alp))
alp=0.5;colc<-c(
rgb(0.7, 0.7, 0.25,alpha=alp),
rgb(0.55,0.55, 0.55,alpha=alp),
rgb(0.9,0,0,alpha=alp),
rgb(0.1, 0.1, 1,alpha=alp))
alp=0.4;cold<-c(
rgb(0.7, 0.7, 0.25,alpha=alp),
rgb(0.55,0.55, 0.55,alpha=alp),
rgb(0.9,0,0,alpha=alp),
rgb(0.1, 0.1, 1,alpha=alp))
cole=cola4 Data upload and wrangling
4.1 Behavioral data
Open code
## upload of data
behav<-read.csv("source_data/behav_dat.csv", stringsAsFactors = T)
## subject (id) as factor
behav$id <- as.factor(behav$id)
## defining factor 'group'
behav$group<-interaction(behav$treatment,behav$genotype)
behav$group<-factor(behav$group,levels=c('ctrl.wt','eda.wt','ctrl.sca','eda.sca'))
## defining numerical version of 'sex' variable
behav$sex_num<-ifelse(behav$sex=="f",-0.5,0.5)
## defining variable combining 'genotype', 'treatment' and 'sex' into single variable ('group_sex')
behav$group_sex<-interaction(behav$treatment,behav$genotype,behav$sex)
behav$group_sex<-factor(behav$group_sex, levels=c('ctrl.wt.f','ctrl.wt.m',
'eda.wt.f','eda.wt.m',
'ctrl.sca.f','ctrl.sca.m',
'eda.sca.f','eda.sca.m'))
## substetting data according to sex
behav_m<-subset(behav,behav$sex=="m")
behav_f<-subset(behav,behav$sex=="f")
## data summary
summary(behav) id treatment genotype sex sugar_water_ratio immobile_min
7 : 1 ctrl:40 sca:40 f:39 Min. :0.2292 Min. :0.000
9 : 1 eda :40 wt :40 m:41 1st Qu.:1.5702 1st Qu.:1.087
11 : 1 Median :2.7527 Median :2.243
12 : 1 Mean :2.8797 Mean :2.328
13 : 1 3rd Qu.:3.8438 3rd Qu.:3.270
14 : 1 Max. :7.8010 Max. :6.468
(Other):74
speed_95th distance_01 distance_02 distance_03
Min. :20.20 Min. : 163.4 Min. : 41.67 Min. : 77.03
1st Qu.:24.31 1st Qu.: 442.7 1st Qu.:271.55 1st Qu.: 280.81
Median :26.09 Median : 607.2 Median :437.44 Median : 443.64
Mean :26.45 Mean : 597.3 Mean :458.50 Mean : 457.20
3rd Qu.:28.34 3rd Qu.: 757.6 3rd Qu.:609.06 3rd Qu.: 583.79
Max. :34.47 Max. :1138.8 Max. :976.52 Max. :1023.29
distance_04 distance_05 distance_06 distance_07
Min. : 126.6 Min. : 71.11 Min. : 91.59 Min. : 93.38
1st Qu.: 343.7 1st Qu.:324.05 1st Qu.:302.85 1st Qu.:289.14
Median : 481.7 Median :445.53 Median :439.43 Median :406.29
Mean : 484.3 Mean :455.20 Mean :436.15 Mean :425.09
3rd Qu.: 585.9 3rd Qu.:591.39 3rd Qu.:551.27 3rd Qu.:567.86
Max. :1033.2 Max. :977.28 Max. :936.08 Max. :897.90
distance_08 distance_09 distance_10 inactivity_01
Min. : 42.79 Min. : 45.14 Min. : 49.13 Min. :0.00000
1st Qu.:262.38 1st Qu.:277.29 1st Qu.:284.32 1st Qu.:0.02981
Median :409.20 Median :405.23 Median :371.12 Median :0.07595
Mean :414.99 Mean :410.87 Mean :378.68 Mean :0.16112
3rd Qu.:554.42 3rd Qu.:535.16 3rd Qu.:469.50 3rd Qu.:0.25678
Max. :833.58 Max. :864.83 Max. :844.87 Max. :0.67222
inactivity_02 inactivity_03 inactivity_04 inactivity_05
Min. :0.006667 Min. :0.0120 Min. :0.0000 Min. :0.0000
1st Qu.:0.085667 1st Qu.:0.1591 1st Qu.:0.1560 1st Qu.:0.1518
Median :0.286667 Median :0.3150 Median :0.2627 Median :0.2913
Mean :0.340906 Mean :0.3586 Mean :0.3066 Mean :0.3444
3rd Qu.:0.546833 3rd Qu.:0.5478 3rd Qu.:0.4293 3rd Qu.:0.4732
Max. :0.998667 Max. :0.8967 Max. :0.8547 Max. :0.9380
inactivity_06 inactivity_07 inactivity_08 inactivity_09
Min. :0.03627 Min. :0.02333 Min. :0.0360 Min. :0.01533
1st Qu.:0.17717 1st Qu.:0.19265 1st Qu.:0.1871 1st Qu.:0.19083
Median :0.34167 Median :0.32833 Median :0.3410 Median :0.29600
Mean :0.35780 Mean :0.37851 Mean :0.3793 Mean :0.37241
3rd Qu.:0.50133 3rd Qu.:0.54083 3rd Qu.:0.5477 3rd Qu.:0.52167
Max. :0.89733 Max. :0.95733 Max. :0.9980 Max. :0.98467
inactivity_10 thigmo_propdist center_propdist center_avedist
Min. :0.04745 Min. :0.4038 Min. :0.01850 Min. :19.56
1st Qu.:0.21929 1st Qu.:0.6248 1st Qu.:0.04187 1st Qu.:21.63
Median :0.33402 Median :0.6651 Median :0.05092 Median :22.20
Mean :0.38372 Mean :0.6722 Mean :0.05508 Mean :22.35
3rd Qu.:0.50036 3rd Qu.:0.7246 3rd Qu.:0.06769 3rd Qu.:22.91
Max. :0.97170 Max. :0.8462 Max. :0.12689 Max. :25.38
thigmo_proptime center_proptime center_avetime kurva_index
Min. :0.4626 Min. :0.00460 Min. :20.92 Min. :0.5181
1st Qu.:0.7107 1st Qu.:0.01755 1st Qu.:23.39 1st Qu.:0.8010
Median :0.7603 Median :0.02962 Median :24.25 Median :0.8348
Mean :0.7567 Mean :0.03372 Mean :24.17 Mean :0.8050
3rd Qu.:0.8259 3rd Qu.:0.04277 3rd Qu.:25.07 3rd Qu.:0.8471
Max. :0.9324 Max. :0.11440 Max. :26.92 Max. :0.8761
t_session1 t_session2 t_session3 t_session4
Min. : 0 Min. : 0.0 Min. : 0.00 Min. : 0.00
1st Qu.: 25 1st Qu.: 25.0 1st Qu.: 50.00 1st Qu.: 50.00
Median : 50 Median : 75.0 Median : 75.00 Median : 75.00
Mean : 45 Mean : 57.5 Mean : 65.31 Mean : 73.12
3rd Qu.: 50 3rd Qu.: 75.0 3rd Qu.:100.00 3rd Qu.:100.00
Max. :100 Max. :100.0 Max. :100.00 Max. :100.00
t_session5 t_session6 t_session7 t_session8
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 50.00 1st Qu.: 75.00 1st Qu.: 0.00 1st Qu.: 0.00
Median : 75.00 Median :100.00 Median : 0.00 Median : 25.00
Mean : 72.81 Mean : 79.38 Mean : 21.56 Mean : 29.38
3rd Qu.:100.00 3rd Qu.:100.00 3rd Qu.: 25.00 3rd Qu.: 50.00
Max. :100.00 Max. :100.00 Max. :100.00 Max. :100.00
t_session9 t_session10 t_session11 t_session12
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.0
1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 25.00 1st Qu.: 25.0
Median : 25.00 Median : 37.50 Median : 62.50 Median : 75.0
Mean : 36.25 Mean : 44.06 Mean : 54.69 Mean : 62.5
3rd Qu.: 56.25 3rd Qu.: 75.00 3rd Qu.:100.00 3rd Qu.:100.0
Max. :100.00 Max. :100.00 Max. :100.00 Max. :100.0
grip_strength1 grip_strength2 grip_strength3 grip_strength4
Min. :0.3000 Min. :0.4000 Min. :0.200 Min. :0.4000
1st Qu.:0.6000 1st Qu.:0.6000 1st Qu.:0.700 1st Qu.:0.7000
Median :0.8000 Median :0.8000 Median :0.800 Median :0.8000
Mean :0.8588 Mean :0.8912 Mean :0.890 Mean :0.8838
3rd Qu.:1.0000 3rd Qu.:1.0250 3rd Qu.:1.025 3rd Qu.:1.0000
Max. :2.3000 Max. :2.2000 Max. :1.800 Max. :2.0000
Run_Average_Speed_.cm.s._Mean Run_Average_Speed_.cm.s._SD RF_Stand_.s._Mean
Min. :14.68 Min. : 0.8289 Min. :0.07915
1st Qu.:22.18 1st Qu.: 3.8027 1st Qu.:0.13077
Median :25.87 Median : 6.3027 Median :0.14708
Mean :26.62 Mean : 6.9885 Mean :0.15151
3rd Qu.:30.30 3rd Qu.: 9.3582 3rd Qu.:0.16915
Max. :52.91 Max. :23.0817 Max. :0.23170
RF_Stand_.s._SD RF_Swing_.s._Mean RF_Swing_.s._SD
Min. :0.01581 Min. :0.07114 Min. :0.01008
1st Qu.:0.02915 1st Qu.:0.09591 1st Qu.:0.02074
Median :0.03888 Median :0.11475 Median :0.02617
Mean :0.04830 Mean :0.11564 Mean :0.02895
3rd Qu.:0.05049 3rd Qu.:0.13479 3rd Qu.:0.03419
Max. :0.22972 Max. :0.15467 Max. :0.11919
RF_SwingSpeed_.cm.s._Mean RF_SwingSpeed_.cm.s._SD RF_StrideLength_.cm._Mean
Min. : 40.51 Min. : 4.181 Min. :4.577
1st Qu.: 51.79 1st Qu.:12.798 1st Qu.:6.213
Median : 60.95 Median :19.938 Median :6.536
Mean : 63.92 Mean :20.830 Mean :6.586
3rd Qu.: 72.73 3rd Qu.:25.916 3rd Qu.:6.992
Max. :126.61 Max. :89.343 Max. :9.333
RF_StrideLength_.cm._SD RH_Stand_.s._Mean RH_Stand_.s._SD RH_Swing_.s._Mean
Min. :0.4189 Min. :0.06362 Min. :0.01950 Min. :0.09611
1st Qu.:0.6540 1st Qu.:0.10399 1st Qu.:0.02973 1st Qu.:0.13120
Median :0.8560 Median :0.11413 Median :0.03791 Median :0.15074
Mean :0.9353 Mean :0.11803 Mean :0.04896 Mean :0.15420
3rd Qu.:1.1820 3rd Qu.:0.13147 3rd Qu.:0.04936 3rd Qu.:0.17659
Max. :1.8803 Max. :0.20219 Max. :0.22393 Max. :0.23340
RH_Swing_.s._SD RH_SwingSpeed_.cm.s._Mean RH_SwingSpeed_.cm.s._SD
Min. :0.01389 Min. : 30.14 Min. : 3.870
1st Qu.:0.02491 1st Qu.: 39.70 1st Qu.: 9.748
Median :0.03708 Median : 46.43 Median :14.481
Mean :0.04255 Mean : 48.55 Mean :15.392
3rd Qu.:0.04938 3rd Qu.: 56.18 3rd Qu.:19.320
Max. :0.14919 Max. :102.56 Max. :82.964
RH_StrideLength_.cm._Mean RH_StrideLength_.cm._SD LF_Stand_.s._Mean
Min. :4.410 Min. :0.3320 Min. :0.07398
1st Qu.:6.136 1st Qu.:0.7018 1st Qu.:0.12925
Median :6.546 Median :0.9599 Median :0.14788
Mean :6.569 Mean :0.9924 Mean :0.15079
3rd Qu.:6.972 3rd Qu.:1.2440 3rd Qu.:0.16979
Max. :9.251 Max. :2.1190 Max. :0.24486
LF_Stand_.s._SD LF_Swing_.s._Mean LF_Swing_.s._SD
Min. :0.01193 Min. :0.07300 Min. :0.01121
1st Qu.:0.02843 1st Qu.:0.09842 1st Qu.:0.02051
Median :0.03947 Median :0.11568 Median :0.02687
Mean :0.04970 Mean :0.11573 Mean :0.02894
3rd Qu.:0.05618 3rd Qu.:0.13348 3rd Qu.:0.03468
Max. :0.23414 Max. :0.16516 Max. :0.09481
LF_SwingSpeed_.cm.s._Mean LF_SwingSpeed_.cm.s._SD LF_StrideLength_.cm._Mean
Min. : 40.91 Min. : 5.618 Min. :4.560
1st Qu.: 51.38 1st Qu.:12.835 1st Qu.:6.193
Median : 61.62 Median :21.106 Median :6.494
Mean : 63.36 Mean :19.924 Mean :6.579
3rd Qu.: 71.38 3rd Qu.:25.694 3rd Qu.:6.946
Max. :107.80 Max. :38.058 Max. :9.203
LF_StrideLength_.cm._SD LH_Stand_.s._Mean LH_Stand_.s._SD LH_Swing_.s._Mean
Min. :0.3907 Min. :0.04624 Min. :0.01361 Min. :0.09158
1st Qu.:0.6612 1st Qu.:0.09862 1st Qu.:0.02881 1st Qu.:0.13054
Median :0.8545 Median :0.11433 Median :0.03749 Median :0.15070
Mean :0.9522 Mean :0.11812 Mean :0.04887 Mean :0.15390
3rd Qu.:1.2388 3rd Qu.:0.13393 3rd Qu.:0.04974 3rd Qu.:0.17443
Max. :2.0050 Max. :0.20996 Max. :0.24937 Max. :0.23713
LH_Swing_.s._SD LH_SwingSpeed_.cm.s._Mean LH_SwingSpeed_.cm.s._SD
Min. :0.01654 Min. :27.88 Min. : 3.482
1st Qu.:0.02617 1st Qu.:39.56 1st Qu.: 9.196
Median :0.03271 Median :46.77 Median :14.154
Mean :0.04046 Mean :48.16 Mean :14.798
3rd Qu.:0.04863 3rd Qu.:56.68 3rd Qu.:18.462
Max. :0.16717 Max. :83.66 Max. :50.335
LH_StrideLength_.cm._Mean LH_StrideLength_.cm._SD StepSequence_CA_...
Min. :4.147 Min. :0.4121 Min. : 0.000
1st Qu.:6.143 1st Qu.:0.6944 1st Qu.: 8.761
Median :6.511 Median :0.9291 Median :17.192
Mean :6.579 Mean :1.0366 Mean :20.322
3rd Qu.:6.974 3rd Qu.:1.2609 3rd Qu.:30.076
Max. :9.582 Max. :3.6256 Max. :76.471
StepSequence_CB_... StepSequence_AA_... StepSequence_AB_...
Min. : 0.000 Min. : 0.000 Min. : 2.703
1st Qu.: 9.531 1st Qu.: 0.000 1st Qu.:33.333
Median :20.256 Median : 5.644 Median :53.590
Mean :20.621 Mean : 8.108 Mean :50.658
3rd Qu.:28.145 3rd Qu.:12.708 3rd Qu.:68.460
Max. :58.333 Max. :44.118 Max. :97.500
StepSequence_RA_... StepSequence_RB_... StepSequence_RegularityIndex_...
Min. :0.0000 Min. :0.0000 Min. : 84.79
1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.: 97.03
Median :0.0000 Median :0.0000 Median : 98.53
Mean :0.1567 Mean :0.1219 Mean : 97.72
3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.: 99.22
Max. :3.0303 Max. :4.5455 Max. :100.00
BOS_FrontPaws_Mean_.cm. BOS_HindPaws_Mean_.cm.
Min. :0.8644 Min. :1.440
1st Qu.:1.0828 1st Qu.:2.057
Median :1.1568 Median :2.195
Mean :1.1650 Mean :2.208
3rd Qu.:1.2679 3rd Qu.:2.296
Max. :1.5753 Max. :2.794
OtherStatistics_Average_Speed_Mean OtherStatistics_Average_Speed_SD
Min. :14.65 Min. : 0.9671
1st Qu.:21.75 1st Qu.: 3.6794
Median :25.57 Median : 6.1019
Mean :26.37 Mean : 6.8192
3rd Qu.:29.95 3rd Qu.: 8.7676
Max. :53.44 Max. :22.9035
PrintPositions_RightPaws_Mean_.cm. PrintPositions_LeftPaws_Mean_.cm.
Min. :-0.3018 Min. :-0.0500
1st Qu.: 0.3222 1st Qu.: 0.3398
Median : 0.5345 Median : 0.5257
Mean : 0.5662 Mean : 0.5822
3rd Qu.: 0.7295 3rd Qu.: 0.7493
Max. : 1.7968 Max. : 1.9000
Support_Zero_... Support_Single_... Support_Diagonal_... Support_Girdle_...
Min. :0.00000 Min. : 1.923 Min. :44.21 Min. : 1.010
1st Qu.:0.00000 1st Qu.: 6.516 1st Qu.:64.69 1st Qu.: 3.150
Median :0.09702 Median :10.448 Median :71.77 Median : 4.720
Mean :0.31129 Mean :12.583 Mean :69.30 Mean : 5.128
3rd Qu.:0.33881 3rd Qu.:17.385 3rd Qu.:75.03 3rd Qu.: 6.896
Max. :5.58659 Max. :34.358 Max. :82.80 Max. :14.171
Support_Lateral_... Support_Three_... Support_Four_... group
Min. :0.2107 Min. : 0.2793 Min. : 0.0000 ctrl.wt :20
1st Qu.:1.1572 1st Qu.: 4.6113 1st Qu.: 0.0000 eda.wt :20
Median :1.8449 Median : 7.3846 Median : 0.2879 ctrl.sca:20
Mean :2.0836 Mean : 9.1610 Mean : 1.4289 eda.sca :20
3rd Qu.:2.7938 3rd Qu.:12.4493 3rd Qu.: 1.0040
Max. :7.1279 Max. :25.6497 Max. :20.3458
sex_num group_sex
Min. :-0.5000 ctrl.wt.m :11
1st Qu.:-0.5000 eda.wt.f :10
Median : 0.5000 eda.wt.m :10
Mean : 0.0125 ctrl.sca.f:10
3rd Qu.: 0.5000 ctrl.sca.m:10
Max. : 0.5000 eda.sca.f :10
(Other) :19 4.2 Rotarod data
Open code
## upload
rotarod<-read.csv("source_data/rotarod_data.csv",stringsAsFactors = T)
## data summary
summary(rotarod) id treatment genotype sex day session
Min. : 7.00 ctrl:960 sca:960 f:936 Min. :1 Min. :1.00
1st Qu.: 31.75 eda :960 wt :960 m:984 1st Qu.:1 1st Qu.:1.75
Median : 73.00 Median :2 Median :2.50
Mean : 72.70 Mean :2 Mean :2.50
3rd Qu.:107.50 3rd Qu.:3 3rd Qu.:3.25
Max. :149.00 Max. :3 Max. :4.00
rot_latency pre_post
Min. : 2.30 post:960
1st Qu.: 94.97 pre :960
Median :126.90
Mean :127.79
3rd Qu.:157.60
Max. :299.20 4.3 Data from Tichanek et al., 2020, SciRep
Open code
date_scirep<- read.csv("source_data/scirep_2020_data.csv",sep=";",stringsAsFactors = T)
young_scirep <- subset(date_scirep,date_scirep$age_cohort=="sca1_w06"|date_scirep$age_cohort=="sca1_w10")
young_scirep$genotype<-relevel(young_scirep$genotype,ref="tg+")
## data summary
summary(young_scirep) id genotype age_cohort born experiment_start
355 : 1 tg+:30 sca1_w06:29 23.03.2018: 6 02.07.2018: 9
356 : 1 w :25 sca1_w10:26 20.05.2018: 5 05.03.2018: 8
363 : 1 sca1_w17: 0 07.09.2018: 4 17.09.2018: 8
364 : 1 sca1_w26: 0 08.11.2017: 4 22.01.2018: 7
365 : 1 19.01.2018: 4 22.10.2018: 7
366 : 1 22.01.2018: 4 04.06.2018: 6
(Other):49 (Other) :28 (Other) :10
age_in_start_.weeks. first_weight_.mg. pre_death_weight_.mg. epm_falls
Min. : 5.400 Min. :14.84 Min. :17.54 Min. :0
1st Qu.: 6.100 1st Qu.:20.00 1st Qu.:21.21 1st Qu.:0
Median : 6.400 Median :21.20 Median :22.54 Median :0
Mean : 8.145 Mean :21.94 Mean :22.98 Mean :0
3rd Qu.:10.400 3rd Qu.:24.10 3rd Qu.:24.82 3rd Qu.:0
Max. :10.900 Max. :29.00 Max. :30.46 Max. :0
epm_distance epm_close1_dur epm_close1_fre epm_close1_to1
Min. : 718.7 Min. : 19.60 Min. : 3.000 Min. : 0.00
1st Qu.:1314.8 1st Qu.: 67.36 1st Qu.: 7.000 1st Qu.: 0.92
Median :1604.6 Median : 84.72 Median : 9.000 Median : 9.52
Mean :1680.2 Mean : 94.79 Mean : 8.745 Mean : 18.88
3rd Qu.:1922.7 3rd Qu.:117.88 3rd Qu.:10.000 3rd Qu.: 19.96
Max. :3371.4 Max. :219.68 Max. :16.000 Max. :112.40
epm_close2_dur epm_close2_fre epm_close2_to1 epm_open1_dur
Min. : 19.52 Min. : 2.000 Min. : 0.00 Min. : 0.00
1st Qu.: 53.44 1st Qu.: 5.000 1st Qu.: 7.36 1st Qu.: 0.00
Median : 77.68 Median : 7.000 Median : 16.48 Median : 2.56
Mean : 81.71 Mean : 8.036 Mean : 39.19 Mean :10.16
3rd Qu.:105.48 3rd Qu.:11.000 3rd Qu.: 28.28 3rd Qu.:18.52
Max. :158.64 Max. :21.000 Max. :268.72 Max. :48.32
epm_open1_fre epm_open1_to1 epm_open2_dur epm_open2_fre
Min. :0.000 Min. : 0.00 Min. : 0.00 Min. :0.000
1st Qu.:0.000 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.:0.000
Median :1.000 Median : 0.00 Median : 7.04 Median :1.000
Mean :1.382 Mean : 57.91 Mean :10.64 Mean :1.345
3rd Qu.:2.000 3rd Qu.:102.28 3rd Qu.:18.80 3rd Qu.:2.000
Max. :7.000 Max. :312.08 Max. :54.48 Max. :7.000
epm_open2_to1 of.distance of.corner of.center.dist
Min. : 0.00 Min. :1637 Min. :0.4050 Min. :22.44
1st Qu.: 0.00 1st Qu.:3156 1st Qu.:0.5382 1st Qu.:26.19
Median : 41.44 Median :4247 Median :0.5906 Median :27.33
Mean : 75.10 Mean :4359 Mean :0.5993 Mean :27.35
3rd Qu.:128.12 3rd Qu.:5382 3rd Qu.:0.6497 3rd Qu.:28.53
Max. :343.68 Max. :9380 Max. :0.8418 Max. :31.25
of.on.edges_narrow of.distance_on_edges_narrow olm_testing1 olm_stable1
Min. :0.6445 Min. :0.5743 Min. : 0.00 Min. : 0.0
1st Qu.:0.8035 1st Qu.:0.7122 1st Qu.:19.50 1st Qu.:19.5
Median :0.8738 Median :0.7731 Median :31.00 Median :30.0
Mean :0.8563 Mean :0.7683 Mean :30.64 Mean :30.6
3rd Qu.:0.9160 3rd Qu.:0.8329 3rd Qu.:40.00 3rd Qu.:41.0
Max. :0.9758 Max. :0.9425 Max. :64.00 Max. :62.0
olm_testing2 olm_stable2 olm_testing3 olm_stable3 olm_testing4
Min. : 0.00 Min. : 0.00 Min. : 2.00 Min. : 1.00 Min. : 0.0
1st Qu.: 6.00 1st Qu.: 6.00 1st Qu.:11.50 1st Qu.:11.50 1st Qu.: 6.0
Median :13.00 Median :17.00 Median :18.00 Median :19.00 Median :11.0
Mean :19.42 Mean :17.45 Mean :21.58 Mean :20.45 Mean :15.8
3rd Qu.:33.50 3rd Qu.:26.00 3rd Qu.:28.50 3rd Qu.:27.50 3rd Qu.:25.0
Max. :64.00 Max. :58.00 Max. :60.00 Max. :62.00 Max. :46.0
olm_stable4 startle_max startle_lat_max pre_max
Min. : 0.00 Min. : 6.06 Min. : 54.21 Min. : 3.170
1st Qu.: 4.00 1st Qu.:14.79 1st Qu.: 73.49 1st Qu.: 7.277
Median :11.00 Median :18.18 Median : 80.43 Median : 9.760
Mean :14.53 Mean :18.56 Mean : 82.68 Mean :10.265
3rd Qu.:23.50 3rd Qu.:21.52 3rd Qu.: 89.20 3rd Qu.:12.070
Max. :51.00 Max. :36.98 Max. :118.31 Max. :24.550
NA's :3 NA's :3 NA's :3
pre_lat_max digi_12_hind_Stance_Stride digi_12_hind_Stride_Length
Min. : 62.17 Min. :72.72 Min. :3.350
1st Qu.: 81.56 1st Qu.:75.45 1st Qu.:3.917
Median : 91.11 Median :77.75 Median :4.150
Mean : 97.19 Mean :77.91 Mean :4.213
3rd Qu.:108.23 3rd Qu.:79.70 3rd Qu.:4.450
Max. :177.92 Max. :88.20 Max. :5.975
NA's :3 NA's :6 NA's :6
digi_12_hind_abs.Paw_Angle. digi_12_hind_Stance_Width
Min. :10.00 Min. :1.500
1st Qu.:16.93 1st Qu.:2.000
Median :19.82 Median :2.200
Mean :19.33 Mean :2.162
3rd Qu.:21.65 3rd Qu.:2.300
Max. :27.80 Max. :2.600
NA's :6 NA's :6
digi_12_hind_Stride_length_CV digi_12_hind_Stance_Width_CV
Min. : 4.735 Min. : 2.88
1st Qu.:12.080 1st Qu.: 8.65
Median :15.130 Median :16.44
Mean :15.864 Mean :19.55
3rd Qu.:18.733 3rd Qu.:27.26
Max. :35.560 Max. :64.68
NA's :6 NA's :6
digi_12_fore_Stance_Stride digi_12_fore_Stride_Length
Min. :60.80 Min. :3.000
1st Qu.:67.60 1st Qu.:3.717
Median :70.05 Median :4.050
Mean :69.61 Mean :4.021
3rd Qu.:71.75 3rd Qu.:4.250
Max. :74.83 Max. :5.675
NA's :6 NA's :6
digi_12_fore_Stride_Length_CV digi_18_hind_Stance_Stride
Min. : 8.08 Min. :65.83
1st Qu.:20.84 1st Qu.:70.80
Median :25.36 Median :72.98
Mean :26.48 Mean :73.95
3rd Qu.:32.53 3rd Qu.:76.55
Max. :48.99 Max. :83.90
NA's :6 NA's :4
digi_18_hind_Stride_Length digi_18_hind_abs.Paw_Angle.
Min. :4.117 Min. :11.10
1st Qu.:4.875 1st Qu.:16.40
Median :5.225 Median :18.62
Mean :5.192 Mean :18.10
3rd Qu.:5.458 3rd Qu.:20.07
Max. :6.200 Max. :21.45
NA's :4 NA's :4
digi_18_hind_Stance_Width digi_18_hind_Stride_length_CV
Min. :1.400 Min. : 5.712
1st Qu.:2.000 1st Qu.: 8.743
Median :2.100 Median :10.715
Mean :2.088 Mean :11.687
3rd Qu.:2.233 3rd Qu.:13.119
Max. :2.533 Max. :26.785
NA's :4 NA's :4
digi_18_hind_Stance_Width_CV digi_18_fore_Stance_Stride
Min. : 4.330 Min. :59.30
1st Qu.: 8.518 1st Qu.:64.11
Median :13.480 Median :65.75
Mean :17.364 Mean :66.10
3rd Qu.:23.138 3rd Qu.:68.17
Max. :41.740 Max. :74.35
NA's :4 NA's :4
digi_18_fore_Stride_Length digi_18_fore_Stride_Length_CV rotarod1_1
Min. :4.133 Min. :11.99 Min. : 5.00
1st Qu.:4.721 1st Qu.:16.95 1st Qu.: 55.00
Median :5.175 Median :21.22 Median : 87.00
Mean :5.087 Mean :22.43 Mean : 91.57
3rd Qu.:5.383 3rd Qu.:27.35 3rd Qu.:129.50
Max. :6.050 Max. :52.23 Max. :200.00
NA's :4 NA's :4
rotarod1_2 rotarod1_3 rotarod1_4 rotarod1_5
Min. : 18.0 Min. : 14.0 Min. : 48.0 Min. : 58.0
1st Qu.: 69.5 1st Qu.: 88.5 1st Qu.: 90.0 1st Qu.: 88.0
Median :102.0 Median :114.0 Median :120.0 Median :130.0
Mean :107.3 Mean :129.6 Mean :125.9 Mean :132.9
3rd Qu.:143.0 3rd Qu.:162.0 3rd Qu.:146.0 3rd Qu.:166.5
Max. :272.0 Max. :328.0 Max. :300.0 Max. :283.0
rotarod2_1 rotarod2_2 rotarod2_3 rotarod2_4
Min. : 59.0 Min. : 33.0 Min. : 66.0 Min. : 71.0
1st Qu.: 97.0 1st Qu.:115.5 1st Qu.:108.5 1st Qu.:109.0
Median :138.0 Median :139.0 Median :136.0 Median :143.0
Mean :147.8 Mean :150.5 Mean :155.0 Mean :159.8
3rd Qu.:185.5 3rd Qu.:178.5 3rd Qu.:191.5 3rd Qu.:203.5
Max. :288.0 Max. :354.0 Max. :358.0 Max. :322.0
rotarod2_5 rotarod3_1 rotarod3_2 rotarod3_3
Min. : 44.0 Min. : 41.0 Min. : 25.0 Min. : 55.0
1st Qu.:119.0 1st Qu.:104.0 1st Qu.:119.0 1st Qu.:123.0
Median :144.0 Median :148.0 Median :164.0 Median :153.0
Mean :154.7 Mean :156.1 Mean :167.7 Mean :170.3
3rd Qu.:192.5 3rd Qu.:189.5 3rd Qu.:213.5 3rd Qu.:202.0
Max. :264.0 Max. :322.0 Max. :325.0 Max. :365.0
rotarod3_4 rotarod3_5 rotarod4_1 rotarod4_2
Min. : 51.0 Min. : 51.0 Min. : 46.0 Min. : 43.0
1st Qu.:122.5 1st Qu.:112.5 1st Qu.:114.0 1st Qu.:102.5
Median :151.0 Median :160.0 Median :161.0 Median :171.0
Mean :155.3 Mean :163.9 Mean :167.0 Mean :162.7
3rd Qu.:178.5 3rd Qu.:199.5 3rd Qu.:201.5 3rd Qu.:207.0
Max. :317.0 Max. :387.0 Max. :312.0 Max. :319.0
rotarod4_3 rotarod4_4 rotarod4_5 rotarod5_1
Min. : 48.0 Min. : 61.0 Min. : 45.0 Min. : 22.0
1st Qu.:112.5 1st Qu.:116.5 1st Qu.:110.5 1st Qu.:118.5
Median :166.0 Median :169.0 Median :157.0 Median :154.0
Mean :171.0 Mean :171.9 Mean :159.7 Mean :160.9
3rd Qu.:217.5 3rd Qu.:208.0 3rd Qu.:192.5 3rd Qu.:208.5
Max. :360.0 Max. :376.0 Max. :355.0 Max. :325.0
rotarod5_2 rotarod5_3 rotarod5_4 rotarod5_5
Min. : 27.0 Min. : 44.0 Min. : 66.0 Min. : 47.0
1st Qu.:100.0 1st Qu.:105.0 1st Qu.:108.0 1st Qu.:127.5
Median :139.0 Median :161.0 Median :150.0 Median :168.0
Mean :149.9 Mean :154.9 Mean :156.4 Mean :168.0
3rd Qu.:185.5 3rd Qu.:203.0 3rd Qu.:191.5 3rd Qu.:207.5
Max. :382.0 Max. :296.0 Max. :369.0 Max. :367.0
mwm_probe_trial mwm_lat_2_7 mwm_lat_8_9 mwm_non_mowing_1_7
Min. :0.0000 Min. :29.61 Min. : 8.355 Min. : 2.158
1st Qu.:0.0929 1st Qu.:42.07 1st Qu.:20.310 1st Qu.:24.243
Median :0.1657 Median :49.59 Median :34.980 Median :51.793
Mean :0.1636 Mean :48.40 Mean :35.180 Mean :45.454
3rd Qu.:0.2162 3rd Qu.:55.80 3rd Qu.:50.062 3rd Qu.:62.864
Max. :0.5720 Max. :60.10 Max. :60.120 Max. :86.477
t_maze1 t_maze2 t_maze3 t_maze4
Min. : 0 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 50 1st Qu.: 10.00 1st Qu.: 0.00 1st Qu.: 0.00
Median : 70 Median : 30.00 Median : 10.00 Median : 0.00
Mean : 62 Mean : 34.73 Mean : 23.27 Mean : 13.27
3rd Qu.: 80 3rd Qu.: 55.00 3rd Qu.: 30.00 3rd Qu.: 15.00
Max. :100 Max. :100.00 Max. :100.00 Max. :100.00
t_maze5 t_maze6 t_maze7 t_maze8
Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 10.00
1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 70.00
Median : 0.00 Median : 0.000 Median : 0.000 Median : 90.00
Mean :10.36 Mean : 9.818 Mean : 6.727 Mean : 80.55
3rd Qu.:10.00 3rd Qu.:10.000 3rd Qu.:10.000 3rd Qu.:100.00
Max. :60.00 Max. :90.000 Max. :90.000 Max. :100.00
t_maze9 t_maze10 t_maze11 fst5_freqency_immobile
Min. : 0 Min. : 0.00 Min. : 0.00 Min. : 0.0
1st Qu.: 40 1st Qu.: 10.00 1st Qu.: 0.00 1st Qu.: 7.5
Median : 80 Median : 40.00 Median : 20.00 Median :12.0
Mean : 66 Mean : 45.09 Mean : 36.55 Mean :11.2
3rd Qu.: 95 3rd Qu.: 80.00 3rd Qu.: 70.00 3rd Qu.:14.0
Max. :100 Max. :100.00 Max. :100.00 Max. :32.0
fst5_first fst_immobility1 fst_immobility2 fst_immobility3
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 2.44 1st Qu.: 3.00 1st Qu.: 0.56 1st Qu.: 7.76
Median : 10.08 Median :10.72 Median :15.04 Median :27.76
Mean : 36.04 Mean :14.94 Mean :19.65 Mean :25.97
3rd Qu.: 26.44 3rd Qu.:25.48 3rd Qu.:36.24 3rd Qu.:37.20
Max. :360.00 Max. :48.80 Max. :60.00 Max. :60.00
fst_immobility4 fst_immobility5 fst_immobility6 brain_weight
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. :316.0
1st Qu.: 5.96 1st Qu.: 7.84 1st Qu.: 9.64 1st Qu.:360.5
Median :22.96 Median :29.20 Median :25.92 Median :376.0
Mean :26.90 Mean :27.03 Mean :27.33 Mean :380.3
3rd Qu.:47.56 3rd Qu.:42.36 3rd Qu.:41.16 3rd Qu.:395.5
Max. :60.00 Max. :60.00 Max. :60.00 Max. :443.0
cb_mol cb_granular DG_ML_volume ca_srlm
Min. :2.000 Min. :1.775 Min. :2.291 Min. :3.782
1st Qu.:2.674 1st Qu.:2.406 1st Qu.:2.644 1st Qu.:4.678
Median :2.822 Median :2.501 Median :2.843 Median :4.950
Mean :2.824 Mean :2.509 Mean :2.901 Mean :5.022
3rd Qu.:3.015 3rd Qu.:2.632 3rd Qu.:3.164 3rd Qu.:5.480
Max. :4.170 Max. :3.860 Max. :3.616 Max. :5.905
NA's :19 NA's :19 NA's :19 NA's :19
hypoglossal_nuclues_volume.mm3. parietal_cortex_thickness.mm.
Min. : NA Min. : NA
1st Qu.: NA 1st Qu.: NA
Median : NA Median : NA
Mean :NaN Mean :NaN
3rd Qu.: NA 3rd Qu.: NA
Max. : NA Max. : NA
NA's :55 NA's :55
ca_oriens_pyramidale.mm3. dg_hilus.mm3. dg_granular.mm3.
Min. : NA Min. : NA Min. : NA
1st Qu.: NA 1st Qu.: NA 1st Qu.: NA
Median : NA Median : NA Median : NA
Mean :NaN Mean :NaN Mean :NaN
3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA
Max. : NA Max. : NA Max. : NA
NA's :55 NA's :55 NA's :55 4.4 Mitochondrial respiration data
Open code
## data upload
mitochondria <- read.csv("source_data/mitochondria.csv", stringsAsFactors = T)
## data summary
summary(mitochondria) id.mito id sex genotype treatment normalisation
EM1 : 2 Min. : 1.0 f:160 sca:156 ctrl:160 cs:168
EM10 : 2 1st Qu.: 42.0 m:176 wt :180 eda :176 mg:168
EM100 : 2 Median : 62.5
EM101 : 2 Mean : 59.9
EM102 : 2 3rd Qu.: 83.0
EM103 : 2 Max. :122.0
(Other):324
region Sample M_plus_G D
cerebellum :168 Min. : 0.000 Min. : 2.189 Min. : 6.016
hippocampus:168 1st Qu.: 1.098 1st Qu.: 5.487 1st Qu.: 17.671
Median : 3.463 Median : 10.116 Median : 36.264
Mean : 97.432 Mean : 314.904 Mean :1035.382
3rd Qu.:173.009 3rd Qu.: 604.561 3rd Qu.:1970.509
Max. :748.771 Max. :1250.561 Max. :5641.065
NA's :15 NA's :15 NA's :15
c P S Fccp
Min. : 7.535 Min. : 8.744 Min. : 23.76 Min. : 38.28
1st Qu.: 19.584 1st Qu.: 27.571 1st Qu.: 61.34 1st Qu.: 85.44
Median : 36.438 Median : 53.599 Median : 101.43 Median : 133.04
Mean :1114.776 Mean :1597.370 Mean : 3502.48 Mean : 4832.88
3rd Qu.:2144.681 3rd Qu.:3066.020 3rd Qu.: 6774.85 3rd Qu.: 9543.67
Max. :5750.210 Max. :9222.498 Max. :14690.52 Max. :18087.47
NA's :15 NA's :15 NA's :15 NA's :15
Rot Ama As.Tm Azd
Min. : 12.99 Min. : -0.0816 Min. : 87.4 Min. : 22.71
1st Qu.: 33.28 1st Qu.: 0.0000 1st Qu.: 216.0 1st Qu.: 39.97
Median : 50.32 Median : 0.0000 Median : 450.1 Median : 106.01
Mean :1875.01 Mean : 5.6830 Mean :13458.6 Mean : 2697.27
3rd Qu.:3680.76 3rd Qu.: 0.0000 3rd Qu.:24566.5 3rd Qu.: 4519.46
Max. :6385.13 Max. :356.3343 Max. :65239.8 Max. :18978.24
NA's :15 NA's :15 NA's :15 NA's :15
C_iv b out out_susp
Min. : 58.08 Mode:logical Min. :1 Mode:logical
1st Qu.: 177.66 NA's:336 1st Qu.:1 NA's:336
Median : 348.20 Median :1
Mean :10761.30 Mean :1
3rd Qu.:20475.88 3rd Qu.:1
Max. :49012.00 Max. :1
NA's :15 NA's :325 4.5 Calbindin immunofluorescence intensity
Open code
## upload data of intensity in molecular layer
calb <- read.csv("source_data/calbindin.csv", sep = ';', header = TRUE, stringsAsFactors = T)
## subject as factor
calb$id <- as.factor(calb$id)
## slice as factor, nested in subject
calb$slice_id <- interaction(calb$id, calb$slice)
## defining groups
calb$group<-interaction(calb$treatment,calb$genotype)
calb$group<-factor(calb$group,levels=c('ctrl.wt','ctrl.sca','eda.sca'))
## sex as numerical variable, scale to -0.5 and 0.5 values
calb$sex_num<-ifelse(calb$sex=="f",-0.5,0.5)
## defining factor combining 'group' and 'sex'
calb$group_sex<-interaction(calb$treatment,calb$genotype,calb$sex)
calb$group_sex<-factor(calb$group_sex, levels=c('ctrl.wt.f','ctrl.wt.m',
'ctrl.sca.f','ctrl.sca.m',
'eda.sca.f','eda.sca.m'))
## upload of calbindin intensities in granular layer (GL)
calbgran <- read.csv("source_data/calbgran.csv", sep = ';', header = TRUE, stringsAsFactors = T)
calbgran$id <- as.factor(calbgran$id)
calbgran$slice_id <- interaction(calbgran$id, calbgran$slice)
## there are two GL measurements neighouring one molecular layer measurement.
## Lets delete one to simplify combining of both datasets
calbgran <- calbgran %>% filter(slice_id != '13.9' | order != 'F')
calb <- calb %>% mutate(
mean_gran = calbgran$mean)
calb$group <- factor(calb$group)
## data summary
summary(calb) id treatment genotype sex slice order
15 : 80 ctrl:596 sca:580 f:443 Min. : 1.000 A:140
29 : 78 eda :282 wt :298 m:435 1st Qu.: 4.000 B:129
9 : 77 Median : 8.000 C:162
36 : 77 Mean : 7.548 D:161
20 : 76 3rd Qu.:11.000 E:150
11 : 73 Max. :15.000 F:136
(Other):417
CbRegion.vermis_hemispheres. mean area slice_id
hemispheres:610 Min. :13.97 Min. : 46036 29.1 : 6
vermis :268 1st Qu.:30.03 1st Qu.:192086 15.2 : 6
Median :35.36 Median :297077 19.2 : 6
Mean :36.53 Mean :312565 29.2 : 6
3rd Qu.:41.88 3rd Qu.:419212 13.3 : 6
Max. :62.38 Max. :868452 15.3 : 6
(Other):842
group sex_num group_sex mean_gran
ctrl.wt :298 Min. :-0.500000 ctrl.wt.f :151 Min. : 5.286
ctrl.sca:298 1st Qu.:-0.500000 ctrl.wt.m :147 1st Qu.:12.983
eda.sca :282 Median :-0.500000 ctrl.sca.f:156 Median :16.065
Mean :-0.004556 ctrl.sca.m:142 Mean :16.313
3rd Qu.: 0.500000 eda.sca.f :136 3rd Qu.:19.148
Max. : 0.500000 eda.sca.m :146 Max. :33.583
NA's :10 4.6 PSA-NCAM data
Open code
## upload data
psa <- read.csv('source_data/psa_ncam.csv', stringsAsFactors = T)
## upload data for connecting psa table with metadata
histid <- read.csv('source_data/hist_id.csv', stringsAsFactors = T)
## connecting both tables
psa <- left_join(psa, histid, by = 'hist_id')
## removing missing data
psa <- subset(psa, psa$psa_ncam != 'X' & psa$psa_ncam != '')
psa$psa_ncam <- as.numeric(as.character(psa$psa_ncam))
## defining psa_ncam and numerical variable
psa_nc <- subset(psa, grepl('control', as.character(psa$slice)) == FALSE)
## defning factor of slice order
psa_nc$slice <- factor(psa_nc$slice)
## slice order as numerical variable
psa_nc$slice_n <- as.numeric(as.character(psa_nc$slice))
## data summary
summary(psa_nc) hist_id slice side part.ID structure
Min. :6367 3 : 96 l:459 :640 CA1 SLM :225
1st Qu.:6370 6 : 96 p:459 1 :206 CA4 pyr :212
Median :6377 7 : 96 2 : 60 DG-ML :245
Mean :6385 8 : 96 3 : 4 DG PL hilus:236
3rd Qu.:6401 9 : 93 control: 8
Max. :6417 4 : 92
(Other):349
area psa_ncam id treat genotype sex
13459 : 2 Min. : 5.536 Min. : 9.00 ctrl:611 sca:592 f:450
14699 : 2 1st Qu.: 9.943 1st Qu.:13.00 eda :307 wt :326 m:468
15859 : 2 Median :12.116 Median :19.00
16851 : 2 Mean :12.739 Mean :21.23
17199 : 2 3rd Qu.:14.751 3rd Qu.:29.00
20008 : 2 Max. :34.074 Max. :37.00
(Other):906
slice_n
Min. : 1.000
1st Qu.: 4.000
Median : 6.000
Mean : 6.158
3rd Qu.: 9.000
Max. :14.000
4.7 Hippocampal volume data
Open code
## upload data
hp_vol <- read.csv('source_data/hip_vol.csv', stringsAsFactors = T)
## defining 'group'
hp_vol$group<-interaction(hp_vol$treatment,hp_vol$genotype)
hp_vol$group<-factor(hp_vol$group,levels=c('ctrl.wt','ctrl.sca','eda.sca'))
## rescaling 'sex'
hp_vol$sex_num<-ifelse(hp_vol$sex=="f",-0.5,0.5)
## defining factor combining 'group' and 'sex'
hp_vol$group_sex<-interaction(hp_vol$treatment,hp_vol$genotype,hp_vol$sex)
hp_vol$group_sex<-factor(hp_vol$group_sex, levels=c('ctrl.wt.f','ctrl.wt.m',
'ctrl.sca.f','ctrl.sca.m',
'eda.sca.f','eda.sca.m'))
## data summary
summary(hp_vol) id treatment genotype sex side volume group
Min. : 9.00 ctrl:16 sca:16 f:12 l:12 Min. :1.184 ctrl.wt :8
1st Qu.:14.50 eda : 8 wt : 8 m:12 p:12 1st Qu.:1.688 ctrl.sca:8
Median :19.50 Median :1.899 eda.sca :8
Mean :21.75 Mean :1.827
3rd Qu.:29.50 3rd Qu.:2.011
Max. :37.00 Max. :2.303
sex_num group_sex
Min. :-0.5 ctrl.wt.f :4
1st Qu.:-0.5 ctrl.wt.m :4
Median : 0.0 ctrl.sca.f:4
Mean : 0.0 ctrl.sca.m:4
3rd Qu.: 0.5 eda.sca.f :4
Max. : 0.5 eda.sca.m :4 4.8 Cerebellar molecular layer volume data
Open code
## data upload
cbml <- read.csv('source_data/cbml.csv', stringsAsFactors = T)
## defining 'group'
cbml$group<-interaction(cbml$treatment,cbml$genotype)
cbml$group<-factor(cbml$group,levels=c('ctrl.wt','ctrl.sca','eda.sca'))
## rescaling 'sex' factor
cbml$sex_num<-ifelse(cbml$sex=="f",-0.5,0.5)
## defining 'group_sex'
cbml$group_sex<-interaction(cbml$treatment,cbml$genotype,cbml$sex)
cbml$group_sex<-factor(cbml$group_sex, levels=c('ctrl.wt.f','ctrl.wt.m',
'ctrl.sca.f','ctrl.sca.m',
'eda.sca.f','eda.sca.m'))
## subject as factor
cbml$id <- factor(cbml$id)
## rename the outcome variable
cbml$volume <- cbml$cb_ml
## data summary
summary(cbml) id treatment genotype sex cb_ml group
9 :1 ctrl:8 sca:8 f:6 Min. :16.56 ctrl.wt :4
11 :1 eda :4 wt :4 m:6 1st Qu.:18.22 ctrl.sca:4
13 :1 Median :18.56 eda.sca :4
15 :1 Mean :18.89
18 :1 3rd Qu.:19.47
19 :1 Max. :21.64
(Other):6
sex_num group_sex volume
Min. :-0.5 ctrl.wt.f :2 Min. :16.56
1st Qu.:-0.5 ctrl.wt.m :2 1st Qu.:18.22
Median : 0.0 ctrl.sca.f:2 Median :18.56
Mean : 0.0 ctrl.sca.m:2 Mean :18.89
3rd Qu.: 0.5 eda.sca.f :2 3rd Qu.:19.47
Max. : 0.5 eda.sca.m :2 Max. :21.64
4.9 Citrate synthase activity data
Open code
## data upload
csyn <- read.csv('source_data/citrate_synt.csv', stringsAsFactors = T)
## defining 'group'
csyn$group<-interaction(csyn$treatment,csyn$genotype)
csyn$group<-factor(csyn$group,levels=c('ctrl.wt','eda.wt','ctrl.sca','eda.sca'))
## subject as factor
csyn$id <- factor(csyn$id)
## data summary
summary(csyn) id sex genotype treatment tissue cs
1 : 2 F:71 sca:79 ctrl:80 cerebellum :77 Min. :0.006127
2 : 2 M:83 wt :75 eda :74 hippocampus:77 1st Qu.:0.009835
3 : 2 Median :0.011520
4 : 2 Mean :0.012113
5 : 2 3rd Qu.:0.014216
6 : 2 Max. :0.023529
(Other):142
mereni group
a:77 ctrl.wt :40
b:77 eda.wt :35
ctrl.sca:40
eda.sca :39
5 Saving the environment
Save the R environment to load data in other scripts
Open code
if(T){save.image(file='source_data/myEnvironment.RData')} else {print('Env NOT SAVED NOW!')}6 Session info
Open code
sessionInfo()R version 4.2.2 (2022-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0 dplyr_1.1.0
[5] purrr_1.0.1 readr_2.1.4 tidyr_1.3.0 tibble_3.1.8
[9] tidyverse_2.0.0 ggplot2_3.4.1 cowplot_1.1.1 car_3.1-1
[13] carData_3.0-5 glmmTMB_1.1.5 vioplot_0.4.0 zoo_1.8-11
[17] sm_2.2-5.7.1 beeswarm_0.4.0 brms_2.18.0 Rcpp_1.0.10
loaded via a namespace (and not attached):
[1] TH.data_1.1-2 minqa_1.2.5 colorspace_2.1-0
[4] ellipsis_0.3.2 estimability_1.4.1 markdown_1.5
[7] base64enc_0.1-3 rstudioapi_0.14 farver_2.1.1
[10] rstan_2.26.16 DT_0.27 fansi_1.0.4
[13] mvtnorm_1.1-3 bridgesampling_1.1-2 codetools_0.2-19
[16] splines_4.2.2 knitr_1.42 shinythemes_1.2.0
[19] bayesplot_1.10.0 projpred_2.6.0 jsonlite_1.8.4
[22] nloptr_2.0.3 shiny_1.7.4 compiler_4.2.2
[25] emmeans_1.8.6 backports_1.4.1 Matrix_1.5-3
[28] fastmap_1.1.1 cli_3.6.0 later_1.3.0
[31] htmltools_0.5.4 prettyunits_1.1.1 tools_4.2.2
[34] igraph_1.4.1 coda_0.19-4 gtable_0.3.1
[37] glue_1.6.2 reshape2_1.4.4 posterior_1.4.0
[40] V8_4.2.2 vctrs_0.5.2 nlme_3.1-162
[43] crosstalk_1.2.0 tensorA_0.36.2 xfun_0.37
[46] ps_1.7.2 lme4_1.1-31 timechange_0.2.0
[49] mime_0.12 miniUI_0.1.1.1 lifecycle_1.0.3
[52] gtools_3.9.4 MASS_7.3-58.2 scales_1.2.1
[55] colourpicker_1.2.0 hms_1.1.2 promises_1.2.0.1
[58] Brobdingnag_1.2-9 parallel_4.2.2 sandwich_3.0-2
[61] inline_0.3.19 TMB_1.9.2 shinystan_2.6.0
[64] gamm4_0.2-6 yaml_2.3.7 curl_5.0.0
[67] gridExtra_2.3 loo_2.5.1 StanHeaders_2.26.16
[70] stringi_1.7.12 dygraphs_1.1.1.6 checkmate_2.1.0
[73] boot_1.3-28.1 pkgbuild_1.4.0 rlang_1.1.1
[76] pkgconfig_2.0.3 matrixStats_0.63.0 distributional_0.3.1
[79] evaluate_0.20 lattice_0.20-45 rstantools_2.2.0
[82] htmlwidgets_1.6.1 processx_3.8.0 tidyselect_1.2.0
[85] plyr_1.8.8 magrittr_2.0.3 R6_2.5.1
[88] generics_0.1.3 multcomp_1.4-25 withr_2.5.0
[91] pillar_1.8.1 mgcv_1.8-41 xts_0.13.0
[94] survival_3.5-3 abind_1.4-5 crayon_1.5.2
[97] utf8_1.2.3 tzdb_0.3.0 rmarkdown_2.23
[100] grid_4.2.2 callr_3.7.3 threejs_0.3.3
[103] digest_0.6.31 xtable_1.8-4 numDeriv_2016.8-1.1
[106] httpuv_1.6.9 RcppParallel_5.1.7 stats4_4.2.2
[109] munsell_0.5.0 shinyjs_2.1.0