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Fig4_plots.R
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library(multcomp) #multiple comparison statistics
library(ggplot2) #basic ggplot
library(ggpubr) #provides plots and some statistics
library(ggtext) #allows HTML-tags in text (element_markdown)
library(broom) #simplifies statistical data (tidy())
library(svglite) #for saving images in svg format
setwd("/media/sf_Genomik-data/user_data/Zemlianski Viacheslav/Cut project/Manuscript/Fig4")
theme_set(theme_classic() + theme(text = element_text(family = "Arial")))
################################################################################
#Fig4A, total FA
Fig4A_data <- read.csv(file = "Fig4A_data.csv", header = TRUE) %>% #loading raw data
mutate(sample = paste(genotype, treatment, sep = "_"),
sample = factor(sample, levels = c("WT_YES", "WT_YES_N",
"Dcbf11_YES", "Dcbf11_YES_N")),
genotype = factor(genotype, levels = c("WT", "Dcbf11")),
treatment = factor(treatment, levels = c("YES", "YES_N"))
)
# Fig4A_t_1 <- t.test(x = c(Fig4A_data$amount[Fig4A_data$genotype == "WT" &
# Fig4A_data$treatment == "YES"]),
# y = c(Fig4A_data$amount[Fig4A_data$genotype == "Dcbf11" &
# Fig4A_data$treatment == "YES"]),
# alternative = "greater", var.equal = T)
# Fig4A_t_2 <- t.test(x = c(Fig4A_data$amount[Fig4A_data$genotype == "Dcbf11" &
# Fig4A_data$treatment == "YES-N"]),
# y = c(Fig4A_data$amount[Fig4A_data$genotype == "Dcbf11" &
# Fig4A_data$treatment == "YES"]),
# alternative = "greater", var.equal = T)
# Fig4A_stats <- rbind(tidy(Fig4A_t_1), tidy(Fig4A_t_2)) %>%
# mutate(p.adj = p.adjust(p.value, method = "holm"))
# rm(Fig4A_t_1, Fig4A_t_2)
Fig4A_stats <- aov(amount ~ sample, # statistical test
data = Fig4A_data) %>%
glht(mcp(sample = c("WT_YES - Dcbf11_YES <= 0",
"Dcbf11_YES_N - Dcbf11_YES <= 0"
))) %>%
tidy()
ggplot(Fig4A_data, aes(x = genotype, y = amount, fill = treatment)) + # plot initialization
geom_bar(position = "dodge", stat="summary", fun = "mean", # adding bars
na.rm = T, colour="black", width = 0.5) +
scale_fill_manual(values = c("white", "gray50"), # bars colors and legend labels
labels = c("YES",
expression("YES+NH"[4]*"Cl"))) +
scale_x_discrete(labels = c("WT", expression(italic("\u0394cbf11")))) + # x-labels
scale_y_continuous(expand=c(0, 0), limits=c(0, 40), breaks = seq(0, 35, 5)) + # y-axis settings
ylab("Total FA (\u03BCg/mg DCW)") +
theme(axis.line = element_line(colour = "black", linewidth = 0.75), # axes design
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 9, colour = "black"),
axis.title.y = element_text(size = 9, colour = "black"),
axis.text.y = element_text(size = 9, colour = "black"),
axis.ticks.y = element_line(colour = "black", linewidth = 0.75),
legend.position = "top", # legend design
legend.title = element_blank(),
legend.text = element_text(size = 9, colour = "black"),
legend.text.align = 0,
legend.key.height = unit(0.35, 'cm'),
legend.key.width = unit(0.35, 'cm'),
legend.background = element_blank(),
plot.margin=unit(c(0.2, 0.2, 0.2, 0.2), "cm") # plot margins
) +
geom_point(position=position_dodge(0.5), na.rm=T, show.legend=F, size=2) + # adding individual points
geom_errorbar(data = aggregate(amount~genotype+treatment, # adding errorbars
data = Fig4A_data,
FUN = function(Fig4A_data)
c(AVG = mean(Fig4A_data, na.rm=T),
SD = sd(Fig4A_data, na.rm=T))),
aes(y = amount[,"AVG"],
ymin = pmax(amount[,"AVG"]-amount[,"SD"], 0),
ymax = amount[,"AVG"]+amount[,"SD"]),
position = position_dodge(0.5), width=0.3,
colour="black", linewidth=0.75
) +
geom_signif(xmin = c(1 - 0.125, 2 - 0.125), # adding statistics
xmax = c(2 - 0.125, 2 + 0.125),
y_position = c(37, 30),
tip_length = 0.07,
annotations = ""
) +
geom_text(data = Fig4A_stats,
aes(x = c(1.5 - 0.125, 2),
y = c(37, 30),
vjust = -0.5,
label = paste0("p=", format(adj.p.value, scientific=T, digits=3)),
fill = NULL),
colour = symnum(Fig4A_stats$adj.p.value,
cutpoints = c(0, 0.05, Inf),
symbols = c("black", "gray")),
size = 9/.pt)
ggsave("Fig4A.png", width = 5, height = 7, units = "cm", dpi = 600) # saving the plot
ggsave("Fig4A.svg", width=5, height=7, units="cm", dpi=600, fix_text_size=F)
################################################################################
#Fig4B, total FA
Fig4B_data <- read.csv(file = "Fig4B_data.csv", header = TRUE) %>% # loading raw data
mutate(sample = paste(genotype, treatment, sep = "_"),
sample = factor(sample, levels = c("WT_YES", "WT_YES_N",
"Dcbf11_YES", "Dcbf11_YES_N")),
genotype = factor(genotype, levels = c("WT", "Dcbf11")),
treatment = factor(treatment, levels = c("YES", "YES_N"))
)
# Fig4B_t_1 <- t.test(x = c(Fig4B_data$amount[Fig4B_data$genotype == "WT" &
# Fig4B_data$treatment == "YES"]),
# y = c(Fig4B_data$amount[Fig4B_data$genotype == "Dcbf11" &
# Fig4B_data$treatment == "YES"]),
# alternative = "two.sided", var.equal = T)
# Fig4B_t_2 <- t.test(x = c(Fig4B_data$amount[Fig4B_data$genotype == "Dcbf11" &
# Fig4B_data$treatment == "YES-N"]),
# y = c(Fig4B_data$amount[Fig4B_data$genotype == "Dcbf11" &
# Fig4B_data$treatment == "YES"]),
# alternative = "two.sided", var.equal = T)
# Fig4B_stats <- rbind(tidy(Fig4B_t_1), tidy(Fig4B_t_2)) %>%
# mutate(p.adj = p.adjust(p.value, method = "holm"))
# rm(Fig4B_t_1, Fig4B_t_2)
Fig4B_stats <- aov(amount ~ sample, # statistical test
data = Fig4B_data) %>%
glht(mcp(sample = c("WT_YES - Dcbf11_YES >= 0",
"Dcbf11_YES_N - Dcbf11_YES >= 0"
))) %>%
tidy()
ggplot(Fig4B_data, aes(x = genotype, y = amount, fill = treatment)) + # plot initialization
geom_bar(position = "dodge", stat="summary", fun = "mean", # adding bars
na.rm = T, colour="black", width = 0.5) +
scale_fill_manual(values = c("white", "gray50"), # bars colors and legend labels
labels = c("YES",
expression("YES+NH"[4]*"Cl"))) +
scale_x_discrete(labels = c("WT", expression(italic("\u0394cbf11")))) + # x-labels
scale_y_continuous(expand=c(0, 0), limits=c(0, 55)) + # y-axis settings
ylab("Saturated FA (% of total)") +
theme(axis.line = element_line(colour = "black", linewidth = 0.75), # axes design
axis.ticks.x = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 9, colour = "black"),
axis.title.y = element_text(size = 9, colour = "black"),
axis.text.y = element_text(size = 9, colour = "black"),
axis.ticks.y = element_line(colour = "black", linewidth = 0.75),
legend.position = "none", # legend design
plot.margin=unit(c(0.2, 0.2, 0.2, 0.2), "cm") # plot margins
) +
geom_point(position=position_dodge(0.5), na.rm=T, show.legend=F, size=2) + # adding individual points
geom_errorbar(data = aggregate(amount~genotype+treatment, # adding errorbars
data = Fig4B_data,
FUN = function(Fig4B_data)
c(AVG = mean(Fig4B_data, na.rm=T),
SD = sd(Fig4B_data, na.rm=T))),
aes(y = amount[,"AVG"],
ymin = pmax(amount[,"AVG"]-amount[,"SD"], 0),
ymax = amount[,"AVG"]+amount[,"SD"]),
position = position_dodge(0.5), width=0.3,
colour="black", linewidth=0.75
) +
geom_signif(xmin = c(1 - 0.125, 2 - 0.125), # adding statistics
xmax = c(2 - 0.125, 2 + 0.125),
y_position = c(43, 49),
tip_length = 0.04,
annotations = ""
) +
geom_text(data = Fig4B_stats,
aes(x = c(1.5 - 0.125, 2),
y = c(43, 49),
vjust = -0.5,
label = paste0("p=", format(adj.p.value, scientific=T, digits=3)),
fill = NULL),
colour = symnum(Fig4B_stats$adj.p.value,
cutpoints = c(0, 0.05, Inf),
symbols = c("black", "gray")),
size = 9/.pt)
ggsave("Fig4B.png", width = 5, height = 6, units = "cm", dpi = 600) # saving the plot
ggsave("Fig4B.svg", width=5, height=6, units="cm", dpi=600, fix_text_size=F)
################################################################################
# Fig4C, individual FA content
Fig4C_data <- read.csv(file = "Fig4C_data.csv", header = TRUE) %>% # loading raw data
mutate(sample = paste(genotype, treatment, sep = "_"),
sample = factor(sample, levels = c("WT_YES", "WT_YES-N",
"Dcbf11_YES", "Dcbf11_YES-N")),
genotype = factor(genotype, levels = c("WT", "Dcbf11")),
treatment = factor(treatment, levels = c("YES", "YES-N")),
FAx = as.integer(as.factor(FA))
)
Fig4C_stats <- compare_means(amount ~ genotype, # statistical tests
group.by = "FA",
data = Fig4C_data[Fig4C_data$treatment == "YES",],
method = "t.test",
alternative = "two.sided",
var.equal = T,
p.adjust.method = "holm"
)
Fig4C_plot_base <- ggplot(data = Fig4C_data, # basic plot settings for Fig4C
aes(x = FAx, fill = sample, y = amount)) + # plot initialization
geom_bar(position="dodge", stat="summary", fun = "mean",
na.rm = T, colour="black", width = 0.8) +
scale_fill_manual(
values = c("red", "pink", "blue", "lightblue"), # bar colors
labels = c("WT <span style='color:#ffffff'>_____</span> YES", # legend labels
"WT <span style='color:#ffffff'>_____</span> YES+NH<sub>4</sub>Cl",
"<i>\u0394cbf11</i> <span stile='color:#ffffff'></span> YES",
"<i>\u0394cbf11</i> <span stile='color:#ffffff'></span> YES+NH<sub>4</sub>Cl"
)) +
ylab("FA content (% of total)") + # y-axis label
scale_x_continuous(breaks = seq(1, 10, 0.5), expand=c(0.02, 0.02),
labels = c("C12:0", "", "C14:0", "", "C16:0", "",
"C16:1", "", "C18:0", "", "C18:1", "",
"C20:0", "", "C22:0", "", "C24:0", "",
"C26:0")) + # removing background
theme(legend.text = element_markdown(size = 9),
axis.line = element_line(colour = "black", linewidth = 0.75), # axes design
axis.ticks.x = element_line(colour = c(rep(c(NA, "black"), 9), NA),
linewidth = 0.75),
# axis.ticks.length.x = unit(0.4, "cm"),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 8, colour = "black"),
axis.title.y = element_text(size = 9, colour = "black"),
axis.text.y = element_text(size = 9, colour = "black"),
axis.ticks.y = element_line(colour = "black", linewidth = 0.75),
plot.margin=unit(c(0, 0.2, 0.5, 0.2), "cm") # plot margins
) +
geom_point(position=position_dodge(0.8), na.rm=T, # adding individual points
show.legend=F, size=0.5, alpha = 0.3) +
geom_errorbar(data = aggregate(amount~FAx+sample, # adding errorbars
data = Fig4C_data,
FUN = function(Fig4C_data)
c(AVG = mean(Fig4C_data, na.rm=T),
SD = sd(Fig4C_data, na.rm=T))),
aes(y = amount[,"AVG"],
ymin = pmax(amount[,"AVG"]-amount[,"SD"], 0),
ymax = amount[,"AVG"]+amount[,"SD"]),
position = position_dodge(0.8), width=0.3,
colour="black", linewidth=0.5)
Fig4C_subset1 = c("C16:0", "C18:0", "C18:1") # 1st subset of FA
Fig4C_plot_1 <- Fig4C_plot_base + # top plot subpanel
scale_y_continuous(expand=c(0, 0), limits=c(-20, 87)) + # y-axis scale
coord_cartesian(ylim = c(0, 87), expand = T, clip = "on") +
labs(tag = "\u2193ZOOM\u2193", textsize = 9) + # adding zoom pointer
theme(legend.position = c(0.8, 0.5),
legend.title = element_blank(),
legend.key.height = unit(0.35, 'cm'),
legend.key.width = unit(0.35, 'cm'),
plot.tag.position = c(0.53, -0.04), # zoom pointer setings
plot.tag = element_text(size = 9)
) +
geom_signif(xmin = which(Fig4C_stats$FA %in% Fig4C_subset1) - 0.3, # adding statistics
xmax = which(Fig4C_stats$FA %in% Fig4C_subset1) + 0.1,
y_position = c(21, 21, 80),
tip_length = 0.01,
annotation = "") +
geom_text(data = Fig4C_stats[Fig4C_stats$FA %in% Fig4C_subset1,],
aes(x = which(Fig4C_stats$FA %in% Fig4C_subset1),
y = c(21, 21, 80),
vjust = c(0, 0, -0.5),
hjust = c(-0.1, -0.1, 0.6),
label = paste0("p=", format(p.adj, scientific=T, digits=2)),
fill = NULL),
colour = symnum(Fig4C_stats$p.adj[Fig4C_stats$FA
%in% Fig4C_subset1],
cutpoints = c(0, 0.05, Inf),
symbols = c("black", "gray")),
size = 9/.pt, angle = c(90, 90, 0))
Fig4C_subset2 <- c("C12:0", "C14:0", "C16:1", "C24:0") # 2nd subset of FA
Fig4C_plot_2 <- Fig4C_plot_base + # middle plot subpanel
scale_y_continuous(expand=c(0, 0)) + # y-axis settings
coord_cartesian(ylim = c(0, 4), clip = "on") +
labs(tag = "\u2193ZOOM\u2193", textsize = 9) + # adding zoom pointer
theme(legend.position = "none", # removing legend
plot.tag.position = c(0.53, -0.04), # zoom pointer settings
plot.tag = element_text(size = 9)) +
geom_signif(xmin = which(Fig4C_stats$FA %in% Fig4C_subset2) - 0.3, # adding statistics
xmax = which(Fig4C_stats$FA %in% Fig4C_subset2) + 0.1,
y_position = c(1.7, 1.1, 1.9, 2.4),
tip_length = 0.0005,
annotation = "") +
geom_text(data = Fig4C_stats[Fig4C_stats$FA %in% Fig4C_subset2,],
aes(x = which(Fig4C_stats$FA %in% Fig4C_subset2),
y = c(1.7, 1.1, 1.9, 2.4),
vjust = 0,
hjust = -0.1,
label = paste0("p=", format(p.adj, scientific=T, digits=2)),
fill = NULL),
colour = symnum(Fig4C_stats$p.adj[Fig4C_stats$FA %in%
Fig4C_subset2],
cutpoints = c(0, 0.05, Inf),
symbols = c("black", "gray")),
size = 9/.pt, angle = 90)
Fig4C_subset3 <- c("C20:0", "C22:0", "C26:0") # 3rd subset of FA
Fig4C_plot_3 <- Fig4C_plot_base + # bottom plot subpanel
scale_y_continuous(expand=c(0, 0)) + # y-axis settings
coord_cartesian(ylim = c(0, 0.65), clip = "on") +
theme(legend.position = "none") + # removing legend
geom_signif(xmin = which(Fig4C_stats$FA %in% Fig4C_subset3) - 0.3, # adding statistics
xmax = which(Fig4C_stats$FA %in% Fig4C_subset3) + 0.1,
y_position = c(0.4, 0.2, 0.2),
tip_length = 0.0001,
annotation = "") +
geom_text(data = Fig4C_stats[Fig4C_stats$FA %in% Fig4C_subset3,],
aes(x = which(Fig4C_stats$FA %in% Fig4C_subset3),
y = c(0.4, 0.2, 0.2),
vjust = 0,
hjust = -0.1,
label = paste0("p=", format(p.adj, scientific=T, digits=2)),
fill = NULL),
colour = symnum(Fig4C_stats$p.adj[Fig4C_stats$FA %in%
Fig4C_subset3],
cutpoints = c(0, 0.05, Inf),
symbols = c("black", "gray")),
size = 9/.pt, angle = c(90, 90, 90))
ggarrange(Fig4C_plot_1, Fig4C_plot_2, Fig4C_plot_3, ncol = 1, align = "v") # merging subpanels
ggsave("Fig4C.png", width = 11, height = 17, units = "cm", dpi = 600) # saving the plot
ggsave("Fig4C.svg", width=11, height=17, units="cm", dpi=600, fix_text_size=F)
################################################################################
# Fig4E, neutral lipid content
Fig4E_data <- read.csv(file = "Fig4E_data.csv", header = TRUE) %>% # loading raw data
mutate(sample = paste(genotype, treatment, sep = "_"),
sample = factor(sample, levels = c("WT_YES", "WT_YES_N",
"Dcbf11_YES", "Dcbf11_YES_N")),
genotype = factor(genotype, levels = c("WT", "Dcbf11")),
treatment = factor(treatment, levels = c("YES", "YES_N"))
)
Fig4E_stats <- compare_means(amount ~ genotype, # statistical tests
group.by = "lipid",
data = Fig4E_data[Fig4E_data$treatment == "YES",],
method = "t.test",
alternative = "two.sided",
var.equal = T,
p.adjust.method = "holm"
)
ggplot(data = Fig4E_data, # basic plot settings for Fig4C
aes(x = lipid, fill = sample, y = amount)) + # plot initialization
geom_bar(position="dodge", stat="summary", fun = "mean",
na.rm = T, colour="black", width = 0.8) +
scale_fill_manual(
values = c("red", "pink", "blue", "lightblue"), # bar colors
labels = c("WT <span style='color:#ffffff'>_____</span> YES", # legend labels
"WT <span style='color:#ffffff'>_____</span> YES+NH<sub>4</sub>Cl",
"<i>\u0394cbf11</i> <span stile='color:#ffffff'></span> YES",
"<i>\u0394cbf11</i> <span stile='color:#ffffff'></span> YES+NH<sub>4</sub>Cl"
)) +
scale_y_continuous(expand=c(0, 0), limits=c(0, 50000)) + # y-axis settings
ylab("Neutral lipids content (AU)") + # y-axis label
scale_x_discrete(labels = c("SE-1", "SE-2", "SQ")) + # removing background
theme(legend.text = element_markdown(size = 9),
axis.line = element_line(colour = "black", linewidth = 0.75), # axes design
axis.ticks.x = element_line(colour = "black", linewidth = 0.75),
# axis.ticks.length.x = unit(0.4, "cm"),
axis.title.x = element_blank(),
axis.text.x = element_text(size = 8, colour = "black"),
axis.title.y = element_text(size = 9, colour = "black"),
axis.text.y = element_text(size = 9, colour = "black"),
axis.ticks.y = element_line(colour = "black", linewidth = 0.75),
legend.position = "none",
plot.margin=unit(c(0.2, 0.2, 0.2, 0.2), "cm") # plot margins
) +
geom_point(position=position_dodge(0.8), na.rm=T, # adding individual points
show.legend=F, size=2, alpha = 1) +
geom_errorbar(data = aggregate(amount~lipid+sample, # adding errorbars
data = Fig4E_data,
FUN = function(Fig4E_data)
c(AVG = mean(Fig4E_data, na.rm=T),
SD = sd(Fig4E_data, na.rm=T))),
aes(y = amount[,"AVG"],
ymin = pmax(amount[,"AVG"]-amount[,"SD"], 0),
ymax = amount[,"AVG"]+amount[,"SD"]),
position = position_dodge(0.8), width=0.3,
colour="black", linewidth=0.5
) +
geom_signif(xmin = c(1 - 0.3, 2 - 0.3, 3 - 0.3),
xmax = c(1 + 0.1, 2 + 0.1, 3 + 0.1),
y_position = c(44000, 39000, 20000),
tip_length = 0.03,
annotation = ""
) +
geom_text(data = Fig4E_stats,
aes(x = c(1 - 0.1, 2 - 0.1, 3 - 0.1),
y = c(44000, 39000, 20000),
vjust = -0.5,
label = paste0("p=", format(p.adj, scientific=T, digits=2)),
fill = NULL),
colour = symnum(Fig4E_stats$p.adj,
cutpoints = c(0, 0.05, Inf),
symbols = c("black", "gray")),
size = 9/.pt)
ggsave("Fig4E.png", width = 9, height = 6, units = "cm", dpi = 600) # saving the plot
ggsave("Fig4E.svg", width=9, height=6, units="cm", dpi=600, fix_text_size=F)