# Kruskal-Wallis test = non-parametric multiple comparisons
#kruskal.test(interest1~Condition, data = meta)
#Wilcox test = non-parametric pairwise comparisons
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("0dpa", "1dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("0dpa", "3dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("0dpa", "7dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un0dpa", "un1dpa")))
meta <- neural[[]] %>% dplyr::select(Sample, Condition2, interest1)
meta <- meta[which(meta$Condition2 == "Uninjured"),]
#Statistics for module plot
atab <- aov(interest1~Sample, data = meta)
#Levene test for equal variances
leveneTest(interest1~Sample,  meta)
# Extract the residuals
aov_residuals <- residuals(object = atab)
# Run Shapiro-Wilk test for normality
shapiro.test(x = aov_residuals)
# Kruskal-Wallis test = non-parametric multiple comparisons
#kruskal.test(interest1~Condition, data = meta)
#Wilcox test = non-parametric pairwise comparisons
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un0dpa", "un1dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un0dpa", "un3dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un0dpa", "un7dpa")))
genes <- Features(neural)
genes_of_interest <- go_ids %>%
filter(TERM %in% c("neuron differentiation")) %>%
dplyr::select(gene)
genes_of_interest <- unique.data.frame(genes_of_interest)
genes_of_interest <- list(intersect(genes_of_interest$gene,genes))
neural <- AddModuleScore(neural, features = genes_of_interest, name = "interest")
# visualizing score on the UMAP
FeaturePlot_scCustom(neural, reduction = "umap",
pt.size = 1,
features = "interest1",
order = TRUE,
split.by = "Condition2",
num_columns = 2) &
scale_color_distiller(palette = "RdYlBu")
# cell division GO term over actual_cell_types and Sample
ggplot(neural@meta.data, aes(x = actual_cell_types, y = interest1, fill = Sample)) +
geom_boxplot(alpha = 0.1) +
theme(panel.background = element_blank(), axis.line = element_line(color = "black"), axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
# Figure 5C
Condpalette = c("Uninjured" = "white", "Regenerating" = "grey")
ggplot(neural@meta.data, aes(x = Sample, y = interest1, fill = Condition2, lwd = "black")) +
scale_fill_manual(values = Condpalette) +
geom_boxplot(lwd = 0.4, outlier.shape=NA) +
ylab("Cell Division") +
theme(panel.background = element_blank(),
axis.line = element_line(color = "black"),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
axis.title.x = element_blank()) +
NoLegend()
#ggsave("5C.png", width = 2, height = 2.5, units = c("in"), dpi = 300)
meta <- neural[[]] %>% dplyr::select(Sample, Condition2, interest1)
meta <- meta[which(meta$Condition2 == "Regenerating"),]
#Statistics for module plot
atab <- aov(interest1~Sample, data = meta)
#Levene test for equal variances
leveneTest(interest1~Sample,  meta)
# Extract the residuals
aov_residuals <- residuals(object = atab)
# Run Shapiro-Wilk test for normality
shapiro.test(x = aov_residuals)
# Kruskal-Wallis test = non-parametric multiple comparisons
#kruskal.test(interest1~Condition, data = meta)
#Wilcox test = non-parametric pairwise comparisons
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("0dpa", "1dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("0dpa", "3dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("0dpa", "7dpa")))
meta <- neural[[]] %>% dplyr::select(Sample, Condition2, interest1)
meta <- meta[which(meta$Condition2 == "Uninjured"),]
#Statistics for neuron_differentiation module plot
atab <- aov(interest1~Sample, data = meta)
#Levene test for equal variances
leveneTest(interest1~Sample,  meta)
# Extract the residuals
aov_residuals <- residuals(object = atab)
# Run Shapiro-Wilk test for normality
shapiro.test(x = aov_residuals)
# Kruskal-Wallis test = non-parametric multiple comparisons
#kruskal.test(interest1~Condition, data = meta)
#Wilcox test = non-parametric pairwise comparisons
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un0dpa", "un1dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un0dpa", "un3dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un0dpa", "un7dpa")))
genes <- Features(neural)
genes_of_interest <- go_ids %>%
filter(TERM %in% c("cell division")) %>%
dplyr::select(gene)
genes_of_interest <- unique.data.frame(genes_of_interest)
genes_of_interest <- list(intersect(genes_of_interest$gene,genes))
neural <- AddModuleScore(neural, features = genes_of_interest, name = "interest")
# visualizing score on the UMAP
FeaturePlot_scCustom(neural, reduction = "umap",
pt.size = 1,
features = "interest1",
order = TRUE,
split.by = "Condition2",
num_columns = 2) &
scale_color_distiller(palette = "RdYlBu")
# cell division GO term over actual_cell_types and Sample
ggplot(neural@meta.data, aes(x = actual_cell_types, y = interest1, fill = Sample)) +
geom_boxplot(alpha = 0.1) +
theme(panel.background = element_blank(), axis.line = element_line(color = "black"), axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
# Figure 5C
Condpalette = c("Uninjured" = "white", "Regenerating" = "grey")
ggplot(neural@meta.data, aes(x = Sample, y = interest1, fill = Condition2, lwd = "black")) +
scale_fill_manual(values = Condpalette) +
geom_boxplot(lwd = 0.4, outlier.shape=NA) +
ylab("Cell Division") +
theme(panel.background = element_blank(),
axis.line = element_line(color = "black"),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
axis.title.x = element_blank()) +
NoLegend()
#ggsave("5C.png", width = 2, height = 2.5, units = c("in"), dpi = 300)
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un0dpa", "0dpa")))
meta <- neural[[]] %>% dplyr::select(Sample, Condition2, interest1)
meta <- meta[which(meta$Condition2 == "Uninjured"),]
#Statistics for neuron_differentiation module plot
atab <- aov(interest1~Sample, data = meta)
#Levene test for equal variances
leveneTest(interest1~Sample,  meta)
# Extract the residuals
aov_residuals <- residuals(object = atab)
# Run Shapiro-Wilk test for normality
shapiro.test(x = aov_residuals)
# Kruskal-Wallis test = non-parametric multiple comparisons
#kruskal.test(interest1~Condition, data = meta)
#Wilcox test = non-parametric pairwise comparisons
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un0dpa", "0dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un0dpa", "3dpa")))
meta <- neural[[]] %>% dplyr::select(Sample, Condition2, interest1)
#meta <- meta[which(meta$Condition2 == "Uninjured"),]
#Statistics for neuron_differentiation module plot
atab <- aov(interest1~Sample, data = meta)
#Levene test for equal variances
leveneTest(interest1~Sample,  meta)
# Extract the residuals
aov_residuals <- residuals(object = atab)
# Run Shapiro-Wilk test for normality
shapiro.test(x = aov_residuals)
meta <- neural[[]] %>% dplyr::select(Sample, Condition2, interest1)
#meta <- meta[which(meta$Condition2 == "Uninjured"),]
#Statistics for neuron_differentiation module plot
atab <- aov(interest1~Sample, data = meta)
#Levene test for equal variances
leveneTest(interest1~Sample,  meta)
# Extract the residuals
aov_residuals <- residuals(object = atab)
# Run Shapiro-Wilk test for normality
shapiro.test(x = aov_residuals)
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un0dpa", "0dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un0dpa", "3dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un0dpa", "7dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un0dpa", "1dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un1dpa", "un3dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un1dpa", "un7dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un1dpa", "0dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un1dpa", "1dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un1dpa", "3dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un1dpa", "7dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un3dpa", "un7dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("0dpa", "un7dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un1dpa", "un3dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("1dpa", "un3dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("3dpa", "un3dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("7dpa", "un3dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("0dpa", "un3dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("1dpa", "un7dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("3dpa", "un7dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("7dpa", "un7dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("1dpa", "3dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("1dpa", "7dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("3dpa", "7dpa")))
genes <- Features(neural)
genes_of_interest <- go_ids %>%
filter(TERM %in% c("neuron differentiation")) %>%
dplyr::select(gene)
genes_of_interest <- unique.data.frame(genes_of_interest)
genes_of_interest <- list(intersect(genes_of_interest$gene,genes))
neural <- AddModuleScore(neural, features = genes_of_interest, name = "interest")
# visualizing score on the UMAP
FeaturePlot_scCustom(neural, reduction = "umap",
pt.size = 1,
features = "interest1",
order = TRUE,
split.by = "Condition2",
num_columns = 2) &
scale_color_distiller(palette = "RdYlBu")
# cell division GO term over actual_cell_types and Sample
ggplot(neural@meta.data, aes(x = actual_cell_types, y = interest1, fill = Sample)) +
geom_boxplot(alpha = 0.1) +
theme(panel.background = element_blank(), axis.line = element_line(color = "black"), axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
# Figure 5C
Condpalette = c("Uninjured" = "white", "Regenerating" = "grey")
ggplot(neural@meta.data, aes(x = Sample, y = interest1, fill = Condition2, lwd = "black")) +
scale_fill_manual(values = Condpalette) +
geom_boxplot(lwd = 0.4, outlier.shape=NA) +
ylab("Cell Division") +
theme(panel.background = element_blank(),
axis.line = element_line(color = "black"),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
axis.title.x = element_blank()) +
NoLegend()
#ggsave("5D.png", width = 2, height = 2.5, units = c("in"), dpi = 300)
meta <- neural[[]] %>% dplyr::select(Sample, Condition2, interest1)
#meta <- meta[which(meta$Condition2 == "Regenerating"),]
#Statistics for module plot
atab <- aov(interest1~Sample, data = meta)
#Levene test for equal variances
leveneTest(interest1~Sample,  meta)
# Extract the residuals
aov_residuals <- residuals(object = atab)
# Run Shapiro-Wilk test for normality
shapiro.test(x = aov_residuals)
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un0dpa", "0dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un0dpa", "1dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un0dpa", "3dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un0dpa", "7dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un1dpa", "un3dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un1dpa", "un7dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un1dpa", "0dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un1dpa", "1dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un1dpa", "3dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un1dpa", "7dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un3dpa", "un7dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un3dpa", "0dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un3dpa", "1dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un3dpa", "3dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un3dpa", "7dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un7dpa", "0dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un7dpa", "1dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un7dpa", "3dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("un7dpa", "7dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("0dpa", "7dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("1dpa", "3dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("1dpa", "7dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("1dpa", "3dpa")))
pairwise_wilcox_test(meta, interest1~Sample, comparisons = list(c("3dpa", "7dpa")))
knitr::opts_chunk$set(echo = TRUE)
#install.packages("tidyverse")
#install.packages("rstatix")
#install.packages("ggpubr")
library(tidyverse)
library(dplyr)
library(readr)
library(ggridges)
library(rstatix)
library(ggpubr)
library(car)
neutotal <- read_csv("Figure_5E,H.csv")
neutotal %>%
filter(X > 0) %>%
group_by(Age, Label, Date, Delay) %>%
reframe(N = n()) %>%
group_by(Age) %>%
summarize(avg_neu = mean(N), se = sd(N)/sqrt(n())) %>%
ggplot(aes(x = Age, y = avg_neu)) + geom_col() + geom_errorbar(aes(x = Age, ymin = avg_neu - se, ymax = avg_neu + se), width = 0.4) + xlab(NULL) + ylab(expression(paste("Avg. Number of Neurons in the Regenerated Tissue"))) + theme(panel.background = element_rect(fill = "white"), panel.grid.major.y = element_line(color = "grey"), panel.grid.major.x = element_blank())
# for revisions
neu2 <- neutotal %>%
filter(X > 0) %>%
group_by(Age, Label, Date, Delay) %>%
reframe(N = n())
neutotal
install.packages("ggbeeswarm")
library(ggbeeswarm)
ggplot(neu2, aes(x = Age, y = N)) +
geom_boxplot(outliers = FALSE, fill = "white", color = "darkgrey") +
geom_beeswarm(cex = 2,
method = "center",
preserve.data.axis=TRUE,
size = 0.4,
color = "black") +
xlab(NULL) +
ylab(expression(paste("Avg. Number of Neurons in the Regenerated Tissue"))) +
theme(panel.background = element_blank(),
axis.line = element_line(color = "black"),
axis.text = element_text(color = "black"),
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
#ggsave("5E.png", width = 3, height = 2, units = c("in"), dpi = 300)
neu3$N <- as.vector(neu3$N)
neutotal %>%
filter(X > 0, Delay == "24hours") %>%
group_by(injection, Label, Delay, Date) %>%
summarize(brdupos = mean(BrdU)*100) %>%
group_by(injection) %>%
summarize(avg_neu = mean(brdupos), sd = sd(brdupos)) %>%
ggplot(aes(x = injection, y = avg_neu)) + geom_col() + geom_errorbar(aes(x = injection, ymin = avg_neu - sd, ymax = avg_neu + sd), width = 0.4) + ylim(-5, 10) + xlab(NULL) + ylab("% BrdU+ Hu+ Cells/Hu+ Cells") + theme(panel.background = element_rect(fill = "white"), panel.grid.major.y = element_line(color = "grey"), panel.grid.major.x = element_blank()) + scale_x_discrete(labels=c("0d1d" = "0d - 2d", "1d2d" = "1d - 3d", "2d3d" = "2d-4d", "3d4d" = "3d-5d", "4d5d" = "4d-6d", "5d6d" = "5d-7d", "6d7d" = "6d-8d"))
neutotal %>%
filter(X > 0, Delay == "48hours") %>%
group_by(injection, Label, Delay, Date) %>%
summarize(N = sum(BrdU)) %>%
group_by(injection, Delay) %>%
summarize(avg_neu = mean(N), sd = sd(N)/sqrt(n())) %>%
ggplot(aes(x = injection, y = avg_neu)) + geom_col() + geom_errorbar(aes(x = injection, ymin = avg_neu - sd, ymax = avg_neu + sd), width = 0.4) + xlab(NULL) + ylab("BrdU+ Hu+ Cells") + theme(panel.background = element_rect(fill = "white"), panel.grid.major.y = element_line(color = "grey"), panel.grid.major.x = element_blank()) + scale_x_discrete(labels=c("0d1d" = "0d - 2d", "1d2d" = "1d - 3d", "2d3d" = "2d-4d", "3d4d" = "3d-5d", "4d5d" = "4d-6d", "5d6d" = "5d-7d", "6d7d" = "6d-8d"))
neutotal %>%
filter(X > 0, Delay == "48hours") %>%
group_by(injection, Label, Delay, Date) %>%
summarize(brdupos = mean(BrdU)*100) %>%
group_by(injection) %>%
summarize(avg_neu = mean(brdupos), sd = sd(brdupos)/sqrt(n())) %>%
ggplot(aes(x = injection, y = avg_neu)) + geom_col() + geom_errorbar(aes(x = injection, ymin = avg_neu - sd, ymax = avg_neu + sd), width = 0.4) + xlab(NULL) + ylab("% BrdU+ Hu+ Cells/Hu+ Cells") + theme(panel.background = element_rect(fill = "white"), panel.grid.major.y = element_line(color = "grey"), panel.grid.major.x = element_blank()) + scale_x_discrete(labels=c("0d1d" = "0d - 2d", "1d2d" = "1d - 3d", "2d3d" = "2d-4d", "3d4d" = "3d-5d", "4d5d" = "4d-6d", "5d6d" = "5d-7d", "6d7d" = "6d-8d"))
# for revisions
neu3 <- neutotal %>%
filter(X > 0, Delay == "48hours") %>%
group_by(injection, Label, Delay, Date) %>%
summarize(brdupos = mean(BrdU)*100) %>%
group_by(injection)
neu3
ggplot(neu3, aes(x = injection, y = avg_neu)) +
geom_col() +
geom_errorbar(aes(x = injection, ymin = avg_neu - sd, ymax = avg_neu + sd), width = 0.4) +
xlab(NULL) +
ylab("% BrdU+ Hu+ Cells/Hu+ Cells") +
theme(panel.background = element_rect(fill = "white"), panel.grid.major.y = element_line(color = "grey"), panel.grid.major.x = element_blank()) +
scale_x_discrete(labels=c("0d1d" = "0d - 2d", "1d2d" = "1d - 3d", "2d3d" = "2d-4d", "3d4d" = "3d-5d", "4d5d" = "4d-6d", "5d6d" = "5d-7d", "6d7d" = "6d-8d"))
atab <- aov(brdupos~injection, data = neutotal %>%
filter(X > 0, Delay == "48hours") %>%
group_by(injection, Label, Delay, Date) %>%
summarize(brdupos = mean(BrdU)*100) %>%
ungroup())
#Levene test for equal variances
leveneTest(brdupos~injection, data = neutotal %>%
filter(X > 0, Delay == "48hours") %>%
group_by(injection, Label, Delay, Date) %>%
summarize(brdupos = mean(BrdU)*100) %>%
ungroup())
# Extract the residuals
aov_residuals <- residuals(object = atab)
# Run Shapiro-Wilk test for normality
shapiro.test(x = aov_residuals)
#Wilcox test for non-parametric comparisons
wilcox_test <- neutotal %>%
filter(X > 0, Delay == "48hours") %>%
group_by(injection, Label, Delay, Date) %>%
summarize(brdupos = mean(BrdU)*100) %>%
ungroup() %>%
pairwise_wilcox_test(brdupos~injection, ref.group = "0d1d")
neutotal
#Wilcox test for non-parametric comparisons
wilcox_test <- neutotal %>%
filter(X > 0, Delay == "48hours") %>%
group_by(injection, Label, Delay, Date) %>%
summarize(brdupos = mean(BrdU)*100) %>%
ungroup() %>%
pairwise_wilcox_test(brdupos~injection, ref.group = "0d1d")
neutotal
atab <- aov(brdupos~injection, data = neutotal %>%
filter(X > 0, Delay == "48hours") %>%
group_by(injection, Label, Delay, Date) %>%
summarize(brdupos = mean(BrdU)*100) %>%
ungroup())
#Levene test for equal variances
leveneTest(brdupos~injection, data = neutotal %>%
filter(X > 0, Delay == "48hours") %>%
group_by(injection, Label, Delay, Date) %>%
summarize(brdupos = mean(BrdU)*100) %>%
ungroup())
# Extract the residuals
aov_residuals <- residuals(object = atab)
# Run Shapiro-Wilk test for normality
shapiro.test(x = aov_residuals)
#Wilcox test for non-parametric comparisons
wilcox_test <- neutotal %>%
filter(X > 0, Delay == "48hours") %>%
group_by(injection, Label, Delay, Date) %>%
summarize(brdupos = mean(BrdU)*100) %>%
ungroup() %>%
pairwise_wilcox_test(brdupos~injection, ref.group = "0d1d")
wilcox_test(data = neutotal, injection~brdupos)
wilcox_test(data = neutotal, brdupos~injection)
neutotal
wilcox_test(data = neutotal, injection ~ BrdU)
wilcox_test(data = neutotal, BrdU ~ injection)
neutotal
atab <- aov(brdupos~injection, data = neutotal %>%
filter(X > 0, Delay == "48hours") %>%
group_by(injection, Label, Delay, Date) %>%
summarize(brdupos = mean(BrdU)*100) %>%
ungroup())
#Levene test for equal variances
leveneTest(brdupos~injection, data = neutotal %>%
filter(X > 0, Delay == "48hours") %>%
group_by(injection, Label, Delay, Date) %>%
summarize(brdupos = mean(BrdU)*100) %>%
ungroup())
# Extract the residuals
aov_residuals <- residuals(object = atab)
# Run Shapiro-Wilk test for normality
shapiro.test(x = aov_residuals)
#Wilcox test for non-parametric comparisons
wilcox_test <- neutotal %>%
filter(X > 0, Delay == "48hours") %>%
group_by(injection, Label, Delay, Date) %>%
summarize(brdupos = mean(BrdU)*100) %>%
ungroup() %>%
pairwise_wilcox_test(brdupos~injection, ref.group = "0d1d")
neutotal
wilcox_test(data = neutotal, BrdU ~ injection)
neutotal
#Wilcox test for non-parametric comparisons
wilcox_test <- neutotal %>%
filter(X > 0, Delay == "48hours") %>%
group_by(injection, Label, Delay, Date) %>%
summarize(brdupos = mean(BrdU)*100) %>%
ungroup() %>%
pairwise_wilcox_test(brdupos~injection, ref.group = "0d1d")
wilcox_test
neutotal
# stats for Figure 5E
neutotal
neu3 <- neutotal %>%
filter(X > 0) %>%
group_by(injection, Label, Delay, Date) %>%
group_by(injection)
neu3
neu3 <- neutotal %>%
filter(X > 0) %>%
group_by(Label, Age)
neu3
neu3 <- neutotal %>%
filter(X > 0) %>%
group_by(Label, Age)
# stats for Figure 5E
neutotal
neu3 <- neutotal %>%
filter(X > 0) %>%
group_by(Label, Age)
neu3
neu3 <- neutotal %>%
filter(X > 0) %>%
group_by(Label, Age)
neu3
# for revisions
neu3 <- neutotal %>%
filter(X > 0, Delay == "48hours") %>%
group_by(injection, Label, Delay, Date) %>%
summarize(brdupos = mean(BrdU)*100) %>%
group_by(injection)
neu3
neu3 <- neutotal %>%
filter(X > 0) %>%
group_by(injection, Label)
neu3
neu3 <- neutotal %>%
filter(X > 0) %>%
group_by(injection)
neu3
# stats for Figure 5E
neu3 <- neutotal %>%
filter(X > 0) %>%
group_by(injection, Label) %>%
summarize(brdupos = mean(BrdU)*100) %>%
group_by(injection)
neu3
# stats for Figure 5E
neu3 <- neutotal %>%
filter(X > 0) %>%
group_by(injection, Label) %>%
group_by(injection)
neu3
# stats for Figure 5E
neu3 <- neutotal %>%
filter(X > 0) %>%
group_by(injection, Label) %>%
summarize(brdupos = mean(BrdU)*100) %>%
group_by(injection)
neu3
# stats for Figure 5E
neu3 <- neutotal %>%
filter(X > 0) %>%
group_by(injection, Label) %>%
summarize(N = n()) %>%
group_by(injection)
neu3
wilcox_test <- neutotal %>%
filter(X > 0) %>%
group_by(injection, Label) %>%
summarize(N = n()) %>%
ungroup() %>%
pairwise_wilcox_test(N~injection, ref.group = "0d1d")
wilcox_test
wilcox_test <- neutotal %>%
filter(X > 0) %>%
group_by(injection, Label) %>%
summarize(N = n()) %>%
ungroup() %>%
pairwise_wilcox_test(N~injection)
wilcox_test
wilcox_test <- neutotal %>%
filter(X > 0) %>%
group_by(Age, Label) %>%
summarize(N = n()) %>%
ungroup() %>%
pairwise_wilcox_test(N~injection)
wilcox_test <- neutotal %>%
filter(X > 0) %>%
group_by(Age, Label) %>%
summarize(N = n()) %>%
ungroup() %>%
pairwise_wilcox_test(N~injection)
neutotal
wilcox_test <- neutotal %>%
filter(X > 0) %>%
group_by(Age, Label) %>%
summarize(N = n()) %>%
ungroup() %>%
pairwise_wilcox_test(N~Age)
wilcox_test
