https://github.com/latereshko/Tereshko_neuron_cilia
Tip revision: a975cce55d21d925d6a60157710638e2c54372f4 authored by latereshko on 02 February 2021, 03:35:55 UTC
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Tip revision: a975cce
Fig_3_v6.R
library(tidyverse)
library(ggpubr)
library(ggbeeswarm)
library(patchwork)
library(dplyr)
library(FSA)
###################
#plotting functions
###################
###################
bees_bars <- function(fillcol) {
list(stat_summary(geom = "bar", fun = mean,
aes(fill = {{ fillcol }}),
width = 0.75, alpha = 1),
geom_quasirandom(aes(colour = {{ fillcol }}),
shape = 16, size=0.8, width = 0.15, alpha = 1),
stat_summary(geom = "errorbar",
fun.data = mean_se, width = 0.5),
scale_y_continuous(expand = c(0,0)),
scale_fill_manual(values = cols),
scale_colour_manual(values = dots),
theme_pubr(),
theme(legend.position = "none",
axis.title.x = element_blank(),
axis.text.x = element_text(size=10, colour="black"),
axis.text.y = element_text(size=10, colour="black"),
axis.ticks = element_line(colour="black",size=0.5))
)
}
###################
###################
###################
###################
#Figure 3A Absolute value amplitudes of mEPSCs
###################
###################
mEPSC_AVG<-read.csv(file.choose(), header=TRUE)
cols <- c("CTL" = "grey51","shARL13b_1"= "midnightblue","shARL13b_2" = 'deepskyblue3')
dots <- c("CTL" = "grey80", "shARL13b_1" = 'blue2',"shARL13b_2" = 'deepskyblue')
mEPSC_AVG %>% ggplot(aes(x = Treatment, y = AVG_AmpY)) + bees_bars(fillcol = Treatment) +
coord_cartesian(ylim = c(0,20)) + ylab("Abs. amplitude (pA)")
#look at data
ggqqplot(mEPSC_AVG,"AVG_AmpY",facet.by = "Treatment")
ggdensity(mEPSC_AVG,"AVG_AmpY",color = "Treatment",palette = cols)
#Dunn Kruskal-Wallis multiple comparison
dunnTest(AVG_AmpY ~ Treatment,
data=mEPSC_AVG,
method="bh")
###################
###################
# Figure 3B_C mini CDFs
###################
###################
imi <-read.csv(file.choose(), header=TRUE)
first22_imi <- imi %>% group_by(Cell,Treatment,Dissociation) %>% slice(1:22)
CHECKminiperCell <-first22_imi %>% group_by(Cell,Treatment, Dissociation) %>% summarise(minicount=n())
first22_imi %>%
ggplot(aes(Abs_AmpY, colour = Treatment)) +
stat_ecdf(size=0.75) +
stat_ecdf(geom = "line", size = 0.1) +
scale_y_continuous(expand = c(0,0)) +
coord_cartesian(ylim = c(0,1.05))+
theme_classic() +
xlab("IMI (mS) ") +
ylab("Cumulative Probability") +
scale_color_manual(values=c('grey51','midnightblue','deepskyblue3'))
first22_imi %>%
ggplot(aes(IMI_Manual, colour = Treatment)) +
stat_ecdf(size=0.75) +
stat_ecdf(geom = "line", size = 0.1) +
scale_x_continuous(expand = c(0,0)) +
scale_y_continuous(expand = c(0,0)) +
theme_classic() +
xlab("IMI (mS) ") +
ylab("Cumulative Probability") +
scale_color_manual(values=c('grey51','midnightblue','deepskyblue3'))
kruskal.test(Abs_AmpY ~ Treatment, data = first22_imi)
dunnTest(Abs_AmpY ~ Treatment,
data=first22_imi,
method="bonferroni")
kruskal.test(IMI_Manual ~ Treatment, data = first22_imi)
dunnTest(IMI_Manual ~ Treatment,
data=first22_imi,
method="bonferroni")
###################
###################
# Figure 1D,E Passive properties
###################
###################
AVG<-read.csv(file.choose(), header=TRUE)
AVG %>% ggplot(aes(x = Treatment, y = AVG_Vm)) + bees_bars(fillcol = Treatment) +
coord_cartesian(ylim = c(0,-75)) + ylab("Vm")
AVG %>% ggplot(aes(x = Treatment, y = AVG_Rin)) + bees_bars(fillcol = Treatment) +
coord_cartesian(ylim = c(0,590)) + ylab("Rin")
#look at data
ggqqplot(AVG,"AVG_Vm",facet.by = "Treatment")
ggdensity(AVG,"AVG_Vm",color = "Treatment",palette = cols)
ggqqplot(AVG,"AVG_Rin",facet.by = "Treatment")
ggdensity(AVG,"AVG_Rin",color = "Treatment",palette = cols)
#test
#Dunn Kruskal-Wallis multiple comparison
dunnTest(AVG_Rin ~ Treatment,
data=AVG,
method="bh")
#Dunn Kruskal-Wallis multiple comparison
dunnTest(AVG_Vm ~ Treatment,
data=AVG,
method="bh")