PFC-total stathmin
data=read.csv(“D:/RESEARCH-2/STATISTIC/Analyse/WB/stathmin/p1f5.csv”) attach(data) head(data) names(data) summary(data) data\(Group=as.factor(data\)Group) data\(g1=as.factor(data\)g1) data\(g2=as.factor(data\)g2) data\(Region=as.factor(data\)Region) shapiro.test(PFC) library(FSA) Sum = Summarize(PFC ~ group, data=data, digits=3) Sum\(se = Sum\)sd / sqrt(Sum\(n) Sum\)se = signif(Sum$se, digits=3) Sum
library(DescTools) fm1 <- aov(PFC ~ g2,data=data) summary(fm1) PostHocTest(fm1, method = “bonferroni”) PostHocTest(fm1, method = “hsd”) PostHocTest(fm1, method = “scheffe”) DunnettTest(PFC ~ g2,data=data) PostHocTest(fm1, method = “duncan”) PostHocTest(fm1, method = “lsd”) PostHocTest(fm1, method = “newmankeuls”)
model = lm(PFC ~ group + genotype,data = data) anova(model) #Interaction: model = lm(PFC ~ group*genotype,data = data) anova(model) # Posthoc library(emmeans) marginal = emmeans(model,pairwise ~ group,adjust=“tukey”)
marginal = emmeans(model,pairwise ~ group:genotype,adjust=“tukey”)
### Tukey-adjusted comparisons marginal$contrasts
library(ggplot2) p = ggplot(data=data, aes(x=Region, y=Phospho.Stathmin, fill=Group)) p = p + stat_summary(fun.y = mean, geom = “bar”, position = “dodge”) + stat_summary(fun.data = mean_cl_normal,geom = “errorbar”,position = position_dodge(width = 0.9),width = 0.2) p = p + labs(title=“Western Blot”)+ylab(“Phospho stathmin/B-actin expression”)+xlab(“”)+scale_x_discrete(name=“Region”) p= p+ annotate(“text”,x=1.15,y=0.95,label=“p=0.037 p=0.055”) p
p = ggplot(data=data, aes(x=Region, y=Phospho.Stathmin, fill=Group)) p = p + stat_summary(fun.y = mean, geom = “bar”, position = “dodge”) + stat_summary(fun.data = mean_cl_normal,geom = “errorbar”,position = position_dodge(width = 0.9),width = 0.2) p = p + labs(title=“Locomotion behavior”)+ylab(“Locomotion time (s)”)+xlab(“”)+scale_x_discrete(name=“Time”) p = p + geom_jitter(aes(color=Region), size=1.5, alpha=.2) p
p = ggplot(data=data, aes(x=time, y=TZ25, fill=Group)) p = p + stat_summary(fun.y = mean, geom = “bar”, position = “dodge”) + stat_summary(fun.data = mean_cl_normal,geom = “errorbar”,position = position_dodge(width = 0.9),width = 0.2) p = p + labs(title=“Locomotion behavior”)+ylab(“Time spent in center zone 25% (s)”)+xlab(“”)+scale_x_discrete(name=“Time”) p = p + geom_jitter(aes(color=Time), size=1.5, alpha=.2) p
p = ggplot(data=data, aes(x=time, y=TZ50, fill=Group)) p = p + stat_summary(fun.y = mean, geom = “bar”, position = “dodge”) + stat_summary(fun.data = mean_cl_normal,geom = “errorbar”,position = position_dodge(width = 0.9),width = 0.2) p = p + labs(title=“Locomotion behavior”)+ylab(“Time spent in center zone 50% (cm)”)+xlab(“”)+scale_x_discrete(name=“Time”) p = p + geom_jitter(aes(color=Time), size=1.5, alpha=.2) p
library(ggplot2) p = ggplot(data=data, aes(x=g1, y=PFC, fill=genotype)) p = p + stat_summary(fun.y = mean, geom = “bar”, position = “dodge”) + stat_summary(fun.data = mean_cl_normal,geom = “errorbar”,position = position_dodge(width = 0.9),width = 0.2) p = p + labs(title=“Western blot-PFC”)+ylab(“Total DARPP-32/b-actin”)+xlab(“”)+scale_x_discrete(name=“Group”) p
table(data\(group, data\)genotype) install.packages(“devtools”) install.packages(“ggpubr”) library(“ggpubr”) ggline(data, x = “group”, y = “AMY”, color = “genotype”, add = c(“mean_se”, “dotplot”), palette = c(“#00AFBB”, “#E7B800”))
p = interaction.plot(x.factor = data\(group, trace.factor = data\)genotype, response = data$PFC, fun = mean,type = “b”, legend = TRUE, xlab = “Group”, ylab=“Total DARPP-32/b-actin”,pch=c(1,19), col = c(“#00AFBB”, “#E7B800”))
library(DescTools)
PT = NemenyiTest(x = data\(PFC,g = data\)group,dist=“tukey”) PT = pairwise.wilcox.test(data\(PFC,data\)group,p.adjust.method=“none”) PT = dunnTest(AMY ~ group,data=data,method=“bh”)
library(ggplot2) p = ggplot(data=data, aes(x=g1, y=PFC, fill=genotype)) p = p + geom_boxplot(aes(fill=genotype), alpha=1) p = p + geom_jitter(aes(color=genotype), size=1.5, alpha=.2) p + geom_boxplot(outlier.colour = “red”) p = p + labs(title=“Western blot-PFC”)+ylab(“Total DARPP-32/b-actin”)+xlab(“”)+scale_x_discrete(name=“Group”) p
boxplot(PFC~group*genotype, data=data)