Sam Buckberry 2023-07-15
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List the data files for differentiations
dat <- read.csv("differentiation_expts/plot_data/221204_all_diffs.csv")Plotting functions
Plot differentiation results for each genetic background
plot_line <- function(line, fill_cols=c("#009e73", "#eebc4c")){
line_dat <- dat[dat$Line == line, ]
line_dat$Measure <- str_remove(string = line_dat$Measure,
pattern = "Percentage of ")
## Add plot identifier
line_dat$ids <- str_c(line_dat$Experiment, line_dat$Platform,
line_dat$Measure, sep = "_")
ids <- unique(line_dat$ids)
plot_expt <- function(id){
gg <- ggplot(data = line_dat[line_dat$ids == id, ],
aes(x = Group, y = Value,
fill=Group, colour="black")) +
geom_point(size=2, alpha=0.7, shape=21) +
scale_color_manual(values = c("#000000")) +
scale_fill_manual(values = fill_cols) +
stat_summary(fun.data=mean_sdl, fun.args = list(mult=1),
geom="errorbar", color="black", width=0.1,
position = position_nudge(x = 0.25)) +
stat_summary(fun=mean, geom="point", color="black",
position = position_nudge(x = 0.25)) +
ylab("Percentage of cells (%)") + xlab("") +
facet_wrap(Experiment~Platform+Measure,
scales = "free_x", drop = TRUE) +
#scale_y_continuous(breaks = c(10,20,30,40,50,60)) +
theme_bw(base_size = 7, base_line_size = line_mm) +
theme(axis.text.x = element_text(angle = 45, hjust = 1,
colour = 'black'),
plot.background = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_line(size = line_mm,
colour = "grey30",
linetype = "dotted"),
legend.position = "none") +
sams_pub_theme()
return(gg)
}
pl <- lapply(ids, plot_expt)
plot_grid(plotlist = pl, ncol = 4)
}
pdf("differentiation_expts/plots/MSC_all_diff_quant.pdf", width = 4, height = 7)
plot_line("MSC")## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
dev.off()## quartz_off_screen
## 2
pdf("differentiation_expts/plots/NHEK_all_diff_quant.pdf", width = 4, height = 7)
plot_line("NHEK")
dev.off()## quartz_off_screen
## 2
pdf("differentiation_expts/plots/HDF_32F_all_diff_quant.pdf", width = 4, height = 7)
plot_line("HDF", fill_cols = c("#009e73", "#eebc4c", "#a3a3a3"))
dev.off()## quartz_off_screen
## 2
pdf("differentiation_expts/plots/MEL1_all_diff_quant.pdf", width = 4, height = 7)
plot_line("MEL1", fill_cols = c("#a3a3a3", "#009e73", "#eebc4c"))
dev.off()## quartz_off_screen
## 2
plot_line("MSC")
plot_line("NHEK")
plot_line("HDF", fill_cols = c("#009e73", "#eebc4c", "#a3a3a3"))
plot_line("MEL1", fill_cols = c("#a3a3a3", "#009e73", "#eebc4c"))
Plot for each type of differentiation
plot_diff <- function(diff="Butcher_Endoderm",
fill_cols=c("#a3a3a3", "#009e73", "#eebc4c")){
line_dat <- dat[dat$Experiment == diff, ]
line_dat$Measure <- str_remove(string = line_dat$Measure,
pattern = "Percentage of ")
line_dat$Line <- factor(line_dat$Line, levels = c("HDF", "NHEK", "MSC", "MEL1", "H9"))
## Add plot identifier
line_dat$ids <- str_c(line_dat$Experiment, line_dat$Platform, line_dat$Measure, sep = "_")
ids <- unique(line_dat$ids)
gg <- ggplot(data = line_dat, aes(x = Group, y = Value,
fill=Group, colour="black")) +
geom_point(size=2, alpha=0.7, shape=21) +
scale_color_manual(values = c("#000000")) +
scale_fill_manual(values = fill_cols) +
stat_summary(fun.data=mean_sdl, fun.args = list(mult=1),
geom="errorbar", color="black", width=0.1,
position = position_nudge(x = 0.35)) +
stat_summary(fun=mean, geom="point", color="black",
position = position_nudge(x = 0.35)) +
ylab("Percentage of cells (%)") + xlab("") +
facet_grid(Platform+Measure~Line,
scales = "free", drop = TRUE) +
scale_y_continuous() +
theme_bw(base_size = 7, base_line_size = line_mm) +
theme(
legend.position = "none",
plot.background = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_line(size = line_mm),
axis.text.x = element_text(angle = 45, hjust = 1, size = 6,
colour = 'black'),
axis.text.y = element_text(size=6, colour='black', angle = 0),
strip.text.y = element_text(size = 6),
text = element_text(size=6),
strip.background = element_blank(),
panel.grid.major.y = element_line(size = line_mm,
colour = "grey30",
linetype = "dotted"),
axis.line.x = element_line(color = 'black', size = line_mm),
axis.line.y = element_line(color = 'black', size = line_mm),
axis.ticks = element_line(color = 'black', size = line_mm)) +
ggtitle(diff)
return(gg)
}
pdf("differentiation_expts/plots/butcher_endoderm_all.pdf", height = 3, width = 3)
plot_diff(diff = "Butcher_Endoderm")
dev.off()## quartz_off_screen
## 2
pdf("differentiation_expts/plots/lung_all.pdf", height = 4, width = 3)
plot_diff(diff = "Lung")
dev.off()## quartz_off_screen
## 2
pdf("differentiation_expts/plots/skeletal_muscle_all.pdf", height = 4, width = 3)
plot_diff(diff = "Skeletal_Muscle")
dev.off()## quartz_off_screen
## 2
pdf("differentiation_expts/plots/neural_all.pdf", height = 4, width = 3)
plot_diff(diff = "Neural")
dev.off()## quartz_off_screen
## 2
pdf("differentiation_expts/plots/nsc_all.pdf", height = 1.95, width = 3)
plot_diff(diff = "NSC")
dev.off()## quartz_off_screen
## 2
plot_diff(diff = "Butcher_Endoderm")
plot_diff(diff = "Lung")
plot_diff(diff = "Skeletal_Muscle")
plot_diff(diff = "Neural")
plot_diff(diff = "NSC")
Calculate differentiation stats
dat$stats_group <- str_c(dat$Line, dat$Experiment, dat$Platform, dat$Measure, sep="_")
dat$origin_layer <- NA
dat$origin_layer[dat$Line == "HDF"] <- "Mesoderm"
dat$origin_layer[dat$Line == "MEL1"] <- "Mesoderm"
dat$origin_layer[dat$Line == "MSC"] <- "Mesoderm"
dat$origin_layer[dat$Line == "NHEK"] <- "Ectoderm"
dat$diff_layer <- NA
dat$diff_layer[dat$Experiment == "Butcher_Endoderm"] <- "Endoderm"
dat$diff_layer[dat$Experiment == "Lung"] <- "Endoderm"
dat$diff_layer[dat$Experiment == "Neural"] <- "Ectoderm"
dat$diff_layer[dat$Experiment == "Skeletal_Muscle"] <- "Mesoderm"
dat$diff_layer[dat$Experiment == "NSC"] <- "Ectoderm"
grps <- unique(dat$stats_group)
grps <- grps[grps != "H9_NSC_FACS_Percentage of NCAM+/FAP-"]
run_t_test <- function(stats_grp){
print(stats_grp)
df <- dat[dat$stats_group == stats_grp, ]
alt <- NA
if (df$origin_layer[1] == df$diff_layer[1]) {
alt <- "less"
} else if (df$origin_layer[1] != df$diff_layer[1]){
alt <- "greater"
}
m1 <- df$Value[df$Group == "Primed-hiPSC"] %>% mean()
m2 <- df$Value[df$Group == "TNT-hiPSC"] %>% mean()
fc <- m2/m1
tt <- t.test(y = df$Value[df$Group == "Primed-hiPSC"],
x = df$Value[df$Group == "TNT-hiPSC"],
#alternative=alt,
alternative="two.sided")
out <- data.frame(Differentiation=df$Experiment[1],
Group = stats_grp, Line=df$Line[1],
Method=df$Platform[1],
Measure=df$Measure[1],
Lab=df$Lab[1],
Origin=df$origin_layer[1],
Differentiation=df$diff_layer[1],
n_primed=length(df$Value[df$Group == "Primed-hiPSC"]),
n_tnt=length(df$Value[df$Group == "TNT-hiPSC"]),
primed_mean=round(m1, digits = 1),
tnt_mean=round(m2, digits = 1),
#t_test=str_c("TNT ", alt),
t_statistic=round(tt$statistic,digits = 1),
log_fc = round(log2(fc),digits = 1),
p=format(tt$p.value, scientific = TRUE, digits = 2))
return(out)
}
tt_dat <- lapply(grps, run_t_test) %>% do.call(rbind, .)## [1] "HDF_Butcher_Endoderm_Immunofluorescence_Percentage of SOX17+"
## [1] "HDF_Butcher_Endoderm_Immunofluorescence_Percentage of FOXA2+"
## [1] "MSC_Butcher_Endoderm_Immunofluorescence_Percentage of SOX17+"
## [1] "MSC_Butcher_Endoderm_Immunofluorescence_Percentage of FOXA2+"
## [1] "NHEK_Butcher_Endoderm_Immunofluorescence_Percentage of SOX17+"
## [1] "NHEK_Butcher_Endoderm_Immunofluorescence_Percentage of FOXA2+"
## [1] "HDF_Lung_FACS_Percentage of CD47+/EPCAM+"
## [1] "MSC_Lung_FACS_Percentage of CD47+/EPCAM+"
## [1] "NHEK_Lung_FACS_Percentage of CD47+/EPCAM+"
## [1] "HDF_Lung_Immunofluorescence_Percentage of TTF1+"
## [1] "HDF_Lung_Immunofluorescence_Percentage of GATA6+"
## [1] "MSC_Lung_Immunofluorescence_Percentage of TTF1+"
## [1] "MSC_Lung_Immunofluorescence_Percentage of GATA6+"
## [1] "NHEK_Lung_Immunofluorescence_Percentage of TTF1+"
## [1] "NHEK_Lung_Immunofluorescence_Percentage of GATA6+"
## [1] "HDF_Neural_FACS_Percentage of CD56+/CD57+"
## [1] "MSC_Neural_FACS_Percentage of CD56+/CD57+"
## [1] "NHEK_Neural_FACS_Percentage of CD56+/CD57+"
## [1] "HDF_Neural_Immunofluorescence_Percentage of SOX1+"
## [1] "HDF_Neural_Immunofluorescence_Percentage of PAX6+"
## [1] "MSC_Neural_Immunofluorescence_Percentage of SOX1+"
## [1] "MSC_Neural_Immunofluorescence_Percentage of PAX6+"
## [1] "NHEK_Neural_Immunofluorescence_Percentage of SOX1+"
## [1] "NHEK_Neural_Immunofluorescence_Percentage of PAX6+"
## [1] "HDF_Skeletal_Muscle_FACS_Percentage of CD146+/CD56+"
## [1] "MSC_Skeletal_Muscle_FACS_Percentage of CD146+/CD56+"
## [1] "NHEK_Skeletal_Muscle_FACS_Percentage of CD146+/CD56+"
## [1] "HDF_Skeletal_Muscle_Immunofluorescence_Percentage of PAX7+"
## [1] "HDF_Skeletal_Muscle_Immunofluorescence_Percentage of PAX3+"
## [1] "MSC_Skeletal_Muscle_Immunofluorescence_Percentage of PAX7+"
## [1] "MSC_Skeletal_Muscle_Immunofluorescence_Percentage of PAX3+"
## [1] "NHEK_Skeletal_Muscle_Immunofluorescence_Percentage of PAX7+"
## [1] "NHEK_Skeletal_Muscle_Immunofluorescence_Percentage of PAX3+"
## [1] "MEL1_Neural_FACS_Percentage of CD56+/CD57+"
## [1] "MEL1_Skeletal_Muscle_FACS_Percentage of CD146+/CD56+"
## [1] "MEL1_Butcher_Endoderm_Immunofluorescence_Percentage of SOX17+"
## [1] "MEL1_Butcher_Endoderm_Immunofluorescence_Percentage of FOXA2+"
## [1] "MEL1_Lung_Immunofluorescence_Percentage of TTF1+"
## [1] "MEL1_Lung_Immunofluorescence_Percentage of GATA6+"
## [1] "MEL1_Neural_Immunofluorescence_Percentage of SOX1+"
## [1] "MEL1_Neural_Immunofluorescence_Percentage of PAX6+"
## [1] "MEL1_Skeletal_Muscle_Immunofluorescence_Percentage of PAX7+"
## [1] "MEL1_Skeletal_Muscle_Immunofluorescence_Percentage of PAX3+"
## [1] "HDF_NSC_FACS_Percentage of NCAM+/FAP-"
## [1] "NHEK_NSC_FACS_Percentage of NCAM+/FAP-"
## [1] "MSC_NSC_FACS_Percentage of NCAM+/FAP-"
## [1] "MEL1_NSC_FACS_Percentage of NCAM+/FAP-"
tt_clean <- tt_dat[ ,-c(2,6:8)]
tt_clean$Measure <- str_remove(string = tt_clean$Measure, pattern = "Percentage of ")
tt_clean$Method[tt_clean$Method == "Immunofluorescence"] <- "IF"
tt_clean <- tt_clean[order(tt_clean$Differentiation, tt_clean$Line, tt_clean$Method, tt_clean$Measure), ]
options(scipen = 999)
write.csv(x = tt_clean, file = "differentiation_expts/stats_tests_summary_table.csv", quote = FALSE)Run stats for hESC vs TNT, and hESC vs Primed for MEL1 line
mel1_dat <- dat[dat$Line == "MEL1", ]
stats_grp <- "MEL1_Neural_FACS_Percentage of CD56+/CD57+"
run_t_test_hesc <- function(stats_grp){
print(stats_grp)
df <- mel1_dat[mel1_dat$stats_group == stats_grp, ]
alt <- NA
m1 <- df$Value[df$Group == "Primed-hiPSC"] %>% mean()
m2 <- df$Value[df$Group == "TNT-hiPSC"] %>% mean()
m3 <- df$Value[df$Group == "hESC"] %>% mean()
fc_tnt <- m2/m3
fc_primed <- m1/m3
tt_primed <- t.test(y = df$Value[df$Group == "hESC"],
x = df$Value[df$Group == "Primed-hiPSC"],
#alternative=alt,
alternative="two.sided")
tt_tnt <- t.test(y = df$Value[df$Group == "hESC"],
x = df$Value[df$Group == "TNT-hiPSC"],
#alternative=alt,
alternative="two.sided")
out <- data.frame(Differentiation=df$Experiment[1],
Group = stats_grp, Line=df$Line[1],
Method=df$Platform[1],
Measure=df$Measure[1],
Lab=df$Lab[1],
Origin=df$origin_layer[1],
Differentiation=df$diff_layer[1],
n_primed=length(df$Value[df$Group == "Primed-hiPSC"]),
n_tnt=length(df$Value[df$Group == "TNT-hiPSC"]),
n_hesc=length(df$Value[df$Group == "hESC"]),
primed_mean=round(m1, digits = 1),
tnt_mean=round(m2, digits = 1),
hesc_mean=round(m3, digits = 1),
#t_test=str_c("TNT ", alt),
t_statistic_primed=round(tt_primed$statistic,digits = 1),
t_statistic_tnt=round(tt_tnt$statistic,digits = 1),
log_fc_primed = round(log2(fc_primed),digits = 1),
log_fc_tnt = round(log2(fc_tnt),digits = 1),
p_primed=format(tt_primed$p.value, scientific = TRUE, digits = 2),
p_tnt=format(tt_tnt$p.value, scientific = TRUE, digits = 2),
significant_primed = (tt_primed$p.value < 0.05),
significant_tnt= (tt_tnt$p.value < 0.05),
primed_direction=ifelse(test = (m1 < m3), yes = "Less", no = "Greater"),
tnt_direction=ifelse(test = (m2 < m3), yes = "Less", no = "Greater"))
return(out)
}
mel1_stats_groups <- unique(mel1_dat$stats_group)
tt_dat_mel1 <- lapply(mel1_stats_groups, run_t_test_hesc) %>% do.call(rbind, .)## [1] "MEL1_Neural_FACS_Percentage of CD56+/CD57+"
## [1] "MEL1_Skeletal_Muscle_FACS_Percentage of CD146+/CD56+"
## [1] "MEL1_Butcher_Endoderm_Immunofluorescence_Percentage of SOX17+"
## [1] "MEL1_Butcher_Endoderm_Immunofluorescence_Percentage of FOXA2+"
## [1] "MEL1_Lung_Immunofluorescence_Percentage of TTF1+"
## [1] "MEL1_Lung_Immunofluorescence_Percentage of GATA6+"
## [1] "MEL1_Neural_Immunofluorescence_Percentage of SOX1+"
## [1] "MEL1_Neural_Immunofluorescence_Percentage of PAX6+"
## [1] "MEL1_Skeletal_Muscle_Immunofluorescence_Percentage of PAX7+"
## [1] "MEL1_Skeletal_Muscle_Immunofluorescence_Percentage of PAX3+"
## [1] "MEL1_NSC_FACS_Percentage of NCAM+/FAP-"
tt_dat_mel1$Measure <- str_remove(string = tt_dat_mel1$Measure, pattern = "Percentage of ")
tt_dat_mel1$Method[tt_dat_mel1$Method == "Immunofluorescence"] <- "IF"
options(scipen = 999)
wb_ed_fig10_all <- openxlsx::createWorkbook()
openxlsx::addWorksheet(wb_ed_fig10_all, sheetName = "wb_ed_fig10_all")
openxlsx::writeData(wb = wb_ed_fig10_all, sheet = "wb_ed_fig10_all",
x = tt_dat_mel1)
openxlsx::saveWorkbook(wb = wb_ed_fig10_all,
file = "ED_Figure_10_source_data.xlsx", overwrite = TRUE)library(gt)
colnames(tt_clean)[5:11] <- c("Primed(n)", "TNT(n)", "Primed(%)", "TNT(%)",
"t-stat", "Log2 FC", "P-value")
tt_clean$Significance <- gtools::stars.pval(p.value = as.numeric(tt_clean$`P-value`))
tt_clean$Differentiation[tt_clean$Differentiation == "Butcher_Endoderm"] <- "Endoderm differentiation"
tt_clean$Differentiation[tt_clean$Differentiation == "Lung"] <- "Lung epithelial differentiation"
tt_clean$Differentiation[tt_clean$Differentiation == "Neural"] <- "Cortical neuron differentiation"
tt_clean$Differentiation[tt_clean$Differentiation == "NSC"] <- "Neural stem cell differentiation"
tt_clean$Differentiation[tt_clean$Differentiation == "Skeletal_Muscle"] <- "Skeletal muscle differentiation"
tt_clean$Line[tt_clean$Line == "MEL1"] <- "MEL1 2° Fib"
tt_clean$Line[tt_clean$Line == "HDF"] <- "HDF (32F)"
b <- colnames(tt_clean)
D <- matrix(rep(b, times=length(unique(tt_clean$Differentiation))),
nrow = length(unique(tt_clean$Differentiation)),
byrow = TRUE) %>% as.data.frame() %>%
janitor::row_to_names(1,remove_row = F)
D$Differentiation <- unique(tt_clean$Differentiation)
tt_join <- rbind(D, tt_clean)
tab <- tt_join %>% gt(groupname_col ="Differentiation") %>%
tab_options(column_labels.hidden = F) %>%
tab_style(style = list(cell_fill(color = "Grey")),
locations = cells_body(rows = Differentiation == "Differentiation")) %>%
tab_header(title = "Quantification of Primed-hiPSC and TNT-hiPSC differentiation efficiency") %>%
tab_options(heading.background.color = "#EFFBFC",
table.font.size = 8,
data_row.padding = 1,
stub.border.style = "dashed",
stub.border.color = "#989898",stub.border.width = "1px",
summary_row.border.color = "#989898",
table.width = "75%",
grand_summary_row.background.color = "Navy",
#column_labels.background.color = "black",
table.font.color = "black",
row_group.border.bottom.color = "black",
row_group.font.weight = "bold",
row_group.border.bottom.width = 1,
row_group.padding = 5,
row_group.background.color = "grey",
footnotes.font.size = 8,
stub.font.weight = "bold") %>%
tab_options(column_labels.hidden = F) %>%
tab_style(style = list(cell_fill(color = "Grey")),
locations = cells_body(rows = Differentiation == "Differentiation")) %>%
tab_footnote(footnote = "For Method column, IF = Immunofluorescence, FACS = Fluoroescent Activated Cell Sorting") %>%
tab_footnote(footnote = "All statistical comparisons performed using two-sided t-test") %>%
tab_footnote(footnote = "Significance: <0.0001 '***', <0.001 '**', <0.01 '*', <0.05 '.'") %>%
tab_footnote(footnote = "Primed(n) and TNT(n) represent the number of samples in each group for statistical testing")
gtsave(data = tab, filename = "differentiation-stats-table.pdf", path = "differentiation_expts/")