R/barplot_qpcr.R
In erkutilaslan/biovizR: A Package to Visualize Biological Data Designed for Wet Lab Scientists.
Defines functions
barplot_qpcr
Documented in
barplot_qpcr
#' This function visualizes qPCR results as bar plot.
#'
#' @param qpcr_data Input qPCR data to visualize.
#' @param type Default biorad. Specify model of thermocycler.
#' @param group1 Control group.
#' @param group2 Target group.
#' @param group3 Target group.
#' @param group4 Target group.
#' @param group5 Target group.
#' @param ref1 Reference gene one.
#' @param ref2 Reference gene two.
#' @param goi Gene of interest.
#' @param stat Default TRUE. Enable or disable statistical analysis.
#' @param test Default t-test. Select statistical testing method.
#' @param generate_table Default False. Set to True to create a table with the results everytime run the function.
#' @return A bar plot of qPCR results.
#' @import tidyverse
#' @export
barplot_qpcr <- function(qpcr_data,
type = "biorad",
group1 = "",
group2 = "",
group3 = "",
group4 = "",
group5 = "",
ref1 = "",
ref2 = "",
goi = "",
stat = TRUE,
test = "t-test",
generate_table = FALSE) {
#pvalue to star conversion function
pvalue_star <- function(dat) {
if (dat < 0.001) {
dat <- "***"
} else if (dat < 0.01) {
dat <- "**"
} else if (dat < 0.05) {
dat <- "*"
} else if (dat >= 0.05){
dat <- "ns"
} else {
dat <- NA
}
}
#data import
if (is.character(qpcr_data) == TRUE) {
if (grepl(".csv", as.character(qpcr_data)) == TRUE) {
qpcr_data <- read.csv(qpcr_data)
} else {
qpcr_data <- readxl::read_excel(qpcr_data, sheet = 1)
}
}
#data wrangling
if (type == "biorad") {
if (colnames(qpcr_data[1]) == "X" || colnames(qpcr_data[1]) == "x") {
qpcr_data <- qpcr_data[ , -1]
}
if (stat == TRUE) {
colnames(qpcr_data)[6] <- "Biological.Set.Name"
qpcr_data <- qpcr_data[, c(3, 5, 6, 7)]
} else {
qpcr_data <- qpcr_data[, c(3, 5, 7)]
}
qpcr_data$Sample[qpcr_data$Sample == ""] <- NA
qpcr_data$Target[qpcr_data$Target == "Target"] <- NA
qpcr_data <- na.omit(qpcr_data)
qpcr_data$Cq <- as.numeric(as.character(qpcr_data$Cq))
if (stat == TRUE) {
qpcr_data <- dplyr::group_by(qpcr_data, Target, Sample, Biological.Set.Name)
} else {
qpcr_data <- dplyr::group_by(qpcr_data, Target, Sample)
}
qpcr_data <- dplyr::mutate(qpcr_data, Cq_mean = mean(Cq))
qpcr_data <- dplyr::ungroup(qpcr_data)
if (stat == TRUE) {
qpcr_data <- qpcr_data[ ,-4]
} else {
qpcr_data <- qpcr_data[ ,-3]
}
qpcr_data <- dplyr::distinct(qpcr_data)
#pivot_wider for analysis
qpcr_data <- tidyr::pivot_wider(qpcr_data,
names_from = Target, values_from = Cq_mean)
}
#analysis
#for each sample average the ct values of the 3 ref genes --> ct[ref] = mean( ct[ref1], ct[ref[2], ct[ref3] )
#for each sample, calculate the dct as the difference dct = ct[ref] - ct[goi]
#annotating ref and goi names
colnames(qpcr_data)[match(ref1, colnames(qpcr_data))] <- "ref1"
if (ref2 != "") {
colnames(qpcr_data)[match(ref2, colnames(qpcr_data))] <- "ref2"
}
colnames(qpcr_data)[match(goi, colnames(qpcr_data))] <- "goi"
#avg of refs for multiple refs
if (ref2 != "") {
qpcr_data <- dplyr::mutate(qpcr_data, avg_ref = (ref1 + ref2) / 2)
#dCt calculation for avg of refs
qpcr_data <- dplyr::mutate(qpcr_data, dCt = (goi - avg_ref))
} else {
#dCt calculation for only 1 ref
qpcr_data <- dplyr::mutate(qpcr_data, dCt = (goi - ref1))
}
#avg.dCt of target gene value in control biological replicates
control_ct <- dplyr::filter(qpcr_data, Sample == group1)
qpcr_data <- dplyr::mutate(qpcr_data, avg_control_dCt = mean(control_ct$goi))
#ddCt calculation
qpcr_data <- dplyr::mutate(qpcr_data, ddCt = dCt - avg_control_dCt)
#2^-ddCt calculation
qpcr_data <- dplyr::mutate(qpcr_data, expression = 2^-ddCt)
#mean the expression in biological replicates
control_exp <- dplyr::filter(qpcr_data, Sample == group1)
target_exp1 <- dplyr::filter(qpcr_data, Sample == group2)
if (group3 != "") {
target_exp2 <- dplyr::filter(qpcr_data, Sample == group3)
}
if (group4 != "") {
target_exp3 <- dplyr::filter(qpcr_data, Sample == group4)
}
if (group5 != "") {
target_exp4 <- dplyr::filter(qpcr_data, Sample == group5)
}
control_exp <- dplyr::mutate(control_exp, control_avg_exp = mean(expression))
target_exp1 <- dplyr::mutate(target_exp1, target_avg_exp1 = mean(expression))
if (group3 != "") {
target_exp2 <- dplyr::mutate(target_exp2, target_avg_exp2 = mean(expression))
}
if (group4 != "") {
target_exp3 <- dplyr::mutate(target_exp3, target_avg_exp3 = mean(expression))
}
if (group5 != "") {
target_exp4 <- dplyr::mutate(target_exp4, target_avg_exp4 = mean(expression))
}
qpcr_data <- dplyr::left_join(qpcr_data, control_exp)
qpcr_data <- dplyr::left_join(qpcr_data, target_exp1)
if (group3 != "") {
qpcr_data <- dplyr::left_join(qpcr_data, target_exp2)
}
if (group4 != "") {
qpcr_data <- dplyr::left_join(qpcr_data, target_exp3)
}
if (group5 != "") {
qpcr_data <- dplyr::left_join(qpcr_data, target_exp4)
}
qpcr_data <- dplyr::mutate(qpcr_data, avg_exp = dplyr::coalesce(target_avg_exp1, control_avg_exp))
if (group3 != "") {
qpcr_data <- dplyr::mutate(qpcr_data, avg_exp = dplyr::coalesce(target_avg_exp1,
target_avg_exp2,
control_avg_exp))
}
if (group4 != "") {
qpcr_data <- dplyr::mutate(qpcr_data, avg_exp = dplyr::coalesce(target_avg_exp1,
target_avg_exp2,
target_avg_exp3,
control_avg_exp))
}
if (group5 != "") {
qpcr_data <- dplyr::mutate(qpcr_data, avg_exp = dplyr::coalesce(target_avg_exp1,
target_avg_exp2,
target_avg_exp3,
target_avg_exp4,
control_avg_exp))
}
#converting raw expression to percentage for better visualization
if (group3 == "" & group4 == "" & group4 == "") {
qpcr_data <- dplyr::arrange(qpcr_data, match(qpcr_data$Sample, c(group1, group2)))
}
if (group4 == "" & group5 == "") {
qpcr_data <- dplyr::arrange(qpcr_data, match(qpcr_data$Sample, c(group1, group2, group3)))
}
if (group5 == "") {
qpcr_data <- dplyr::arrange(qpcr_data, match(qpcr_data$Sample, c(group1, group2, group3, group4)))
} else {
qpcr_data <- dplyr::arrange(qpcr_data, match(qpcr_data$Sample, c(group1, group2, group3, group4, group5)))
}
qpcr_data <- dplyr::mutate(qpcr_data, percent_exp = qpcr_data$avg_exp/qpcr_data$control_avg_exp[1]*100)
if (stat == TRUE) {
if (test == "t-test" & group3 == "" & group4 == "" & group5 == "") {
#calculating p-value
stats <- t.test(target_exp1$expression, control_exp$expression, alternative = "two.sided")
#converting pvalues to *
pvalue <- pvalue_star(stats$p.value)
#this is the supported df layout for ggpubr::stat_pvalue_manual()
stat1 <- tibble::tribble(~group1, ~group2, ~pvalue,
group1, group2, pvalue)
}
if (test == "t-test" & group3 != "" & group4 == "" & group5 == "") {
qpcr_data <- dplyr::full_join(qpcr_data, control_exp)
qpcr_data <- dplyr::full_join(qpcr_data, target_exp1)
qpcr_data <- dplyr::full_join(qpcr_data, target_exp2)
stats <- pairwise.t.test(qpcr_data$expression, qpcr_data$Sample, p.adjust.method = "fdr")
pvalues <- stats$p.value
pvalues <- pvalues[,group1]
pvalues <- lapply(pvalues, pvalue_star)
pvalue1 <- as.character(pvalues[group2])
pvalue2 <- as.character(pvalues[group3])
stat1 <- tibble::tribble(~group1, ~group2, ~pvalue,
group1, group2, pvalue1)
stat2 <- tibble::tribble(~group1, ~group2, ~pvalue,
group1, group3, pvalue2)
}
if (test == "t-test" & group3 != "" & group4 != "" & group5 == "") {
qpcr_data <- dplyr::full_join(qpcr_data, control_exp)
qpcr_data <- dplyr::full_join(qpcr_data, target_exp1)
qpcr_data <- dplyr::full_join(qpcr_data, target_exp2)
qpcr_data <- dplyr::full_join(qpcr_data, target_exp3)
stats <- pairwise.t.test(qpcr_data$expression, qpcr_data$Sample, p.adjust.method = "fdr")
pvalues <- stats$p.value
pvalues <- pvalues[,group1]
pvalues <- lapply(pvalues, pvalue_star)
pvalue1 <- as.character(pvalues[group2])
pvalue2 <- as.character(pvalues[group3])
pvalue3 <- as.character(pvalues[group4])
stat1 <- tibble::tribble(~group1, ~group2, ~pvalue,
group1, group2, pvalue1)
stat2 <- tibble::tribble(~group1, ~group2, ~pvalue,
group1, group3, pvalue2)
stat3 <- tibble::tribble(~group1, ~group2, ~pvalue,
group1, group4, pvalue3)
}
if (test == "t-test" & group3 != "" & group4 != "" & group5 != "") {
qpcr_data <- dplyr::full_join(qpcr_data, control_exp)
qpcr_data <- dplyr::full_join(qpcr_data, target_exp1)
qpcr_data <- dplyr::full_join(qpcr_data, target_exp2)
qpcr_data <- dplyr::full_join(qpcr_data, target_exp3)
qpcr_data <- dplyr::full_join(qpcr_data, target_exp4)
stats <- pairwise.t.test(qpcr_data$expression, qpcr_data$Sample, p.adjust.method = "fdr")
pvalues <- stats$p.value
pvalues <- pvalues[,group1]
pvalues <- lapply(pvalues, pvalue_star)
pvalue1 <- as.character(pvalues[group2])
pvalue2 <- as.character(pvalues[group3])
pvalue3 <- as.character(pvalues[group4])
pvalue4 <- as.character(pvalues[group5])
stat1 <- tibble::tribble(~group1, ~group2, ~pvalue,
group1, group2, pvalue1)
stat2 <- tibble::tribble(~group1, ~group2, ~pvalue,
group1, group3, pvalue2)
stat3 <- tibble::tribble(~group1, ~group2, ~pvalue,
group1, group4, pvalue3)
stat4 <- tibble::tribble(~group1, ~group2, ~pvalue,
group1, group5, pvalue4)
}
#anova test here
if (test == "anova") {
#first data wrangling for anova function
#anova test
stats <- aov()
stats_anova <- TukeyHSD(stats)
}
}
#calculating sd
target_sd1 <- sd(target_exp1$expression)
control_sd <- sd(control_exp$expression)
if (group3 != "") {
target_sd2 <- sd(target_exp2$expression)
}
if (group4 != "") {
target_sd3 <- sd(target_exp3$expression)
}
if (group5 != "") {
target_sd4 <- sd(target_exp4$expression)
}
#distinct only 1 column
qpcr_data <- qpcr_data[!duplicated(qpcr_data$percent_exp), ]
#converting raw sd into percentage
qpcr_data <- tibble::column_to_rownames(qpcr_data, var = "Sample")
percent_sd1 <- control_sd/qpcr_data[group1, "avg_exp"]*100
percent_sd2 <- target_sd1/qpcr_data[group2, "avg_exp"]*100
if (group3 == "" & group4 == "" & group5 == "") {
percent_sd <- c(percent_sd1, percent_sd2)
}
if (group3 != "") {
percent_sd3 <- target_sd2/qpcr_data[group3, "avg_exp"]*100
percent_sd <- c(percent_sd1, percent_sd2, percent_sd3)
}
if (group4 != "") {
percent_sd4 <- target_sd3/qpcr_data[group4, "avg_exp"]*100
percent_sd <- c(percent_sd1, percent_sd2, percent_sd3, percent_sd4)
}
if (group5 != "") {
percent_sd5 <- target_sd4/qpcr_data[group5, "avg_exp"]*100
percent_sd <- c(percent_sd1, percent_sd2, percent_sd3, percent_sd4, percent_sd5)
}
percent_exp1 <- qpcr_data[group1, "percent_exp"]
percent_exp2 <- qpcr_data[group2, "percent_exp"]
if (group3 == "" & group4 == "" & group5 == "") {
percent_exp <- c(percent_exp1, percent_exp2)
}
if (group3 != "") {
percent_exp3 <- qpcr_data[group3, "percent_exp"]
percent_exp <- c(percent_exp1, percent_exp2, percent_exp3)
}
if (group4 != "") {
percent_exp4 <- qpcr_data[group4, "percent_exp"]
percent_exp <- c(percent_exp1, percent_exp2, percent_exp3, percent_exp4)
}
if (group5 != "") {
percent_exp5 <- qpcr_data[group5, "percent_exp"]
percent_exp <- c(percent_exp1, percent_exp2, percent_exp3, percent_exp4, percent_exp5)
}
qpcr_data <- tibble::rownames_to_column(qpcr_data, var = "Sample")
#removing na to only visualize sample of interest
qpcr_data <- qpcr_data[!is.na(qpcr_data$percent_exp), ]
if (generate_table == TRUE) {
#generating results as a table
samples <- as.character(qpcr_data$Sample)
expressions <- qpcr_data$percent_exp
sds <- percent_sd
stats <- c(stat1$pvalue, stat1$pvalue)
targets <- c(goi, goi)
results_table <- data.frame(samples, expressions, sds, stats, targets)
write.table(results_table, quote = FALSE, sep = ",", file = paste0(goi, "_table.csv"), row.names = FALSE)
}
#visualization
if (group3 == "" && group4 == "" && group5 == "") {
if (stat == TRUE) {
final_plot <- ggpubr::ggbarplot(qpcr_data,
x = "Sample",
y = "percent_exp",
fill = "808080",
xlab = "Sample",
ylab = "Relative mRNA level",
title = goi,
size = 1,
sort.by.groups = FALSE,
ggtheme = ggpubr::theme_pubr(base_size = 18)) +
ggplot2::theme(axis.line = ggplot2::element_line(size = 1)) +
ggplot2::geom_errorbar(size = 1, ggplot2::aes(x = group2,
ymin = percent_exp2 - percent_sd2,
ymax = percent_exp2 + percent_sd2,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1, ggplot2::aes(x = group1,
ymin = percent_exp1 - percent_sd1,
ymax = percent_exp1 + percent_sd1,
width = 0.1)) +
ggpubr::stat_pvalue_manual(stat1, label = "pvalue",
y.position = max(percent_exp) + max(percent_sd) + 10,
bracket.size = 1, label.size = 8)
} else {
final_plot <- ggpubr::ggbarplot(qpcr_data,
x = "Sample",
y = "percent_exp",
fill = "808080",
xlab = "Sample",
ylab = "Relative mRNA level",
size = 1,
title = goi,
palette = "npg",
lab.size = 5,
lab.vjust = 0.5,
lab.hjust = 1.2,
sort.by.groups = FALSE,
ggtheme = ggpubr::theme_pubr(base_size = 18)) +
ggplot2::theme(axis.line = ggplot2::element_line(size = 1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group2,
ymin = percent_exp2 - percent_sd2,
ymax = percent_exp2 + percent_sd2,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group1,
ymin = percent_exp1 - percent_sd1,
ymax = percent_exp1 + percent_sd1,
width = 0.1))
}
} else if (group4 == "" && group5 == "") {
if (stat == TRUE) {
final_plot <- ggpubr::ggbarplot(qpcr_data,
x = "Sample",
y = "percent_exp",
fill = "808080",
xlab = "Sample",
ylab = "Relative mRNA level",
size = 1,
title = goi,
palette = "npg",
lab.size = 5,
lab.vjust = 0.5,
lab.hjust = 1.2,
sort.by.groups = FALSE,
ggtheme = ggpubr::theme_pubr(base_size = 18)) +
ggplot2::theme(axis.line = ggplot2::element_line(size = 1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group2,
ymin = percent_exp2 - percent_sd2,
ymax = percent_exp2 + percent_sd2,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group3,
ymin = percent_exp3 - percent_sd3,
ymax = percent_exp3 + percent_sd3,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group1,
ymin = percent_exp1 - percent_sd1,
ymax = percent_exp1 + percent_sd1,
width = 0.1)) +
ggpubr::stat_pvalue_manual(stat1, label = "pvalue",
y.position = max(percent_exp) + max(percent_sd) + 10,
bracket.size = 1, label.size = 8)
ggpubr::stat_pvalue_manual(stat2, label = "pvalue",
y.position = max(percent_exp) + max(percent_sd) + 20,
bracket.size = 1, label.size = 8)
} else {
final_plot <- ggpubr::ggbarplot(qpcr_data,
x = "Sample",
y = "percent_exp",
fill = "808080",
xlab = "Sample",
ylab = "Relative mRNA level",
size = 1,
title = goi,
palette = "npg",
lab.size = 5,
lab.vjust = 0.5,
lab.hjust = 1.2,
sort.by.groups = FALSE,
ggtheme = ggpubr::theme_pubr(base_size = 18)) +
ggplot2::theme(axis.line = ggplot2::element_line(size = 1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group2,
ymin = percent_exp2 - percent_sd2,
ymax = percent_exp2 + percent_sd2,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group3,
ymin = percent_exp3 - percent_sd3,
ymax = percent_exp3 + percent_sd3,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group1,
ymin = percent_exp1 - percent_sd1,
ymax = percent_exp1 + percent_sd1,
width = 0.1))
}
} else if (group5 == "") {
if (stat == TRUE) {
final_plot <- ggpubr::ggbarplot(qpcr_data,
x = "Sample",
y = "percent_exp",
fill = "808080",
xlab = "Sample",
ylab = "Relative mRNA level",
title = goi,
size = 1,
palette = "npg",
lab.size = 5,
lab.vjust = 0.5,
lab.hjust = 1.2,
sort.by.groups = FALSE,
ggtheme = ggpubr::theme_pubr(base_size = 18)) +
ggplot2::theme(axis.line = ggplot2::element_line(size = 1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group2,
ymin = percent_exp2 - percent_sd2,
ymax = percent_exp2 + percent_sd2,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group3,
ymin = percent_exp3 - percent_sd3,
ymax = percent_exp3 + percent_sd3,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group4,
ymin = percent_exp4 - percent_sd4,
ymax = percent_exp4 + percent_sd4,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group1,
ymin = percent_exp1 - percent_sd1,
ymax = percent_exp1 + percent_sd1,
width = 0.1)) +
ggpubr::stat_pvalue_manual(stat1, label = "pvalue",
y.position = max(percent_exp) + max(percent_sd) + 10,
bracket.size = 1, label.size = 8) +
ggpubr::stat_pvalue_manual(stat2, label = "pvalue",
y.position = max(percent_exp) + max(percent_sd) + 20,
bracket.size = 1, label.size = 8) +
ggpubr::stat_pvalue_manual(stat3, label = "pvalue",
y.position = max(percent_exp) + max(percent_sd) + 30,
bracket.size = 1, label.size = 8)
} else {
final_plot <- ggpubr::ggbarplot(qpcr_data,
x = "Sample",
y = "percent_exp",
fill = "808080",
xlab = "Sample",
ylab = "Relative mRNA level",
title = goi,
size = 1,
palette = "npg",
lab.size = 5,
lab.vjust = 0.5,
lab.hjust = 1.2,
sort.by.groups = FALSE,
ggtheme = ggpubr::theme_pubr(base_size = 18)) +
ggplot2::theme(axis.line = ggplot2::element_line(size = 1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group2,
ymin = percent_exp2 - percent_sd2,
ymax = percent_exp2 + percent_sd2,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group3,
ymin = percent_exp3 - percent_sd3,
ymax = percent_exp3 + percent_sd3,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group4,
ymin = percent_exp4 - percent_sd4,
ymax = percent_exp4 + percent_sd4,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group1,
ymin = percent_exp1 - percent_sd1,
ymax = percent_exp1 + percent_sd1,
width = 0.1))
}
} else {
if (stat == TRUE) {
final_plot <- ggpubr::ggbarplot(qpcr_data,
x = "Sample",
y = "percent_exp",
fill = "808080",
xlab = "Sample",
ylab = "Relative mRNA level",
size = 1,
title = goi,
palette = "npg",
lab.size = 5,
lab.vjust = 0.5,
lab.hjust = 1.2,
sort.by.groups = FALSE,
ggtheme = ggpubr::theme_pubr(base_size = 18)) +
ggplot2::theme(axis.line = ggplot2::element_line(size = 1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group2,
ymin = percent_exp2 - percent_sd2,
ymax = percent_exp2 + percent_sd2,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group3,
ymin = percent_exp3 - percent_sd3,
ymax = percent_exp3 + percent_sd3,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group4,
ymin = percent_exp4 - percent_sd4,
ymax = percent_exp4 + percent_sd4,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group5,
ymin = percent_exp5 - percent_sd5,
ymax = percent_exp5 + percent_sd5,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group1,
ymin = percent_exp1 - percent_sd1,
ymax = percent_exp1 + percent_sd1,
width = 0.1)) +
ggpubr::stat_pvalue_manual(stat1, label = "pvalue",
y.position = max(percent_exp) + max(percent_sd) + 10,
bracket.size = 1, label.size = 8) +
ggpubr::stat_pvalue_manual(stat2, label = "pvalue",
y.position = max(percent_exp) + max(percent_sd) + 20,
bracket.size = 1, label.size = 8) +
ggpubr::stat_pvalue_manual(stat3, label = "pvalue",
y.position = max(percent_exp) + max(percent_sd) + 30,
bracket.size = 1, label.size = 8) +
ggpubr::stat_pvalue_manual(stat4, label = "pvalue",
y.position = max(percent_exp) + max(percent_sd) + 40,
bracket.size = 1, label.size = 8)
} else {
final_plot <- ggpubr::ggbarplot(qpcr_data,
x = "Sample",
y = "percent_exp",
fill = "808080",
xlab = "Sample",
ylab = "Relative mRNA level",
size = 1,
title = goi,
palette = "npg",
lab.size = 5,
lab.vjust = 0.5,
lab.hjust = 1.2,
sort.by.groups = FALSE,
ggtheme = ggpubr::theme_pubr(base_size = 18)) +
ggplot2::theme(axis.line = ggplot2::element_line(size = 1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group2,
ymin = percent_exp2 - percent_sd2,
ymax = percent_exp2 + percent_sd2,
width = 0.1)) +
gplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group3,
ymin = percent_exp3 - percent_sd3,
ymax = percent_exp3 + percent_sd3,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group4,
ymin = percent_exp4 - percent_sd4,
ymax = percent_exp4 + percent_sd4,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group5,
ymin = percent_exp5 - percent_sd5,
ymax = percent_exp5 + percent_sd5,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group1,
ymin = percent_exp1 - percent_sd1,
ymax = percent_exp1 + percent_sd1,
width = 0.1))
}
}
plot(final_plot)
return(final_plot)
}
erkutilaslan/biovizR documentation built on June 18, 2022, 6:22 p.m.
R Package Documentation
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Defines functions barplot_qpcr
Documented in barplot_qpcr
#' This function visualizes qPCR results as bar plot.
#'
#' @param qpcr_data Input qPCR data to visualize.
#' @param type Default biorad. Specify model of thermocycler.
#' @param group1 Control group.
#' @param group2 Target group.
#' @param group3 Target group.
#' @param group4 Target group.
#' @param group5 Target group.
#' @param ref1 Reference gene one.
#' @param ref2 Reference gene two.
#' @param goi Gene of interest.
#' @param stat Default TRUE. Enable or disable statistical analysis.
#' @param test Default t-test. Select statistical testing method.
#' @param generate_table Default False. Set to True to create a table with the results everytime run the function.
#' @return A bar plot of qPCR results.
#' @import tidyverse
#' @export
barplot_qpcr <- function(qpcr_data,
type = "biorad",
group1 = "",
group2 = "",
group3 = "",
group4 = "",
group5 = "",
ref1 = "",
ref2 = "",
goi = "",
stat = TRUE,
test = "t-test",
generate_table = FALSE) {
#pvalue to star conversion function
pvalue_star <- function(dat) {
if (dat < 0.001) {
dat <- "***"
} else if (dat < 0.01) {
dat <- "**"
} else if (dat < 0.05) {
dat <- "*"
} else if (dat >= 0.05){
dat <- "ns"
} else {
dat <- NA
}
}
#data import
if (is.character(qpcr_data) == TRUE) {
if (grepl(".csv", as.character(qpcr_data)) == TRUE) {
qpcr_data <- read.csv(qpcr_data)
} else {
qpcr_data <- readxl::read_excel(qpcr_data, sheet = 1)
}
}
#data wrangling
if (type == "biorad") {
if (colnames(qpcr_data[1]) == "X" || colnames(qpcr_data[1]) == "x") {
qpcr_data <- qpcr_data[ , -1]
}
if (stat == TRUE) {
colnames(qpcr_data)[6] <- "Biological.Set.Name"
qpcr_data <- qpcr_data[, c(3, 5, 6, 7)]
} else {
qpcr_data <- qpcr_data[, c(3, 5, 7)]
}
qpcr_data$Sample[qpcr_data$Sample == ""] <- NA
qpcr_data$Target[qpcr_data$Target == "Target"] <- NA
qpcr_data <- na.omit(qpcr_data)
qpcr_data$Cq <- as.numeric(as.character(qpcr_data$Cq))
if (stat == TRUE) {
qpcr_data <- dplyr::group_by(qpcr_data, Target, Sample, Biological.Set.Name)
} else {
qpcr_data <- dplyr::group_by(qpcr_data, Target, Sample)
}
qpcr_data <- dplyr::mutate(qpcr_data, Cq_mean = mean(Cq))
qpcr_data <- dplyr::ungroup(qpcr_data)
if (stat == TRUE) {
qpcr_data <- qpcr_data[ ,-4]
} else {
qpcr_data <- qpcr_data[ ,-3]
}
qpcr_data <- dplyr::distinct(qpcr_data)
#pivot_wider for analysis
qpcr_data <- tidyr::pivot_wider(qpcr_data,
names_from = Target, values_from = Cq_mean)
}
#analysis
#for each sample average the ct values of the 3 ref genes --> ct[ref] = mean( ct[ref1], ct[ref[2], ct[ref3] )
#for each sample, calculate the dct as the difference dct = ct[ref] - ct[goi]
#annotating ref and goi names
colnames(qpcr_data)[match(ref1, colnames(qpcr_data))] <- "ref1"
if (ref2 != "") {
colnames(qpcr_data)[match(ref2, colnames(qpcr_data))] <- "ref2"
}
colnames(qpcr_data)[match(goi, colnames(qpcr_data))] <- "goi"
#avg of refs for multiple refs
if (ref2 != "") {
qpcr_data <- dplyr::mutate(qpcr_data, avg_ref = (ref1 + ref2) / 2)
#dCt calculation for avg of refs
qpcr_data <- dplyr::mutate(qpcr_data, dCt = (goi - avg_ref))
} else {
#dCt calculation for only 1 ref
qpcr_data <- dplyr::mutate(qpcr_data, dCt = (goi - ref1))
}
#avg.dCt of target gene value in control biological replicates
control_ct <- dplyr::filter(qpcr_data, Sample == group1)
qpcr_data <- dplyr::mutate(qpcr_data, avg_control_dCt = mean(control_ct$goi))
#ddCt calculation
qpcr_data <- dplyr::mutate(qpcr_data, ddCt = dCt - avg_control_dCt)
#2^-ddCt calculation
qpcr_data <- dplyr::mutate(qpcr_data, expression = 2^-ddCt)
#mean the expression in biological replicates
control_exp <- dplyr::filter(qpcr_data, Sample == group1)
target_exp1 <- dplyr::filter(qpcr_data, Sample == group2)
if (group3 != "") {
target_exp2 <- dplyr::filter(qpcr_data, Sample == group3)
}
if (group4 != "") {
target_exp3 <- dplyr::filter(qpcr_data, Sample == group4)
}
if (group5 != "") {
target_exp4 <- dplyr::filter(qpcr_data, Sample == group5)
}
control_exp <- dplyr::mutate(control_exp, control_avg_exp = mean(expression))
target_exp1 <- dplyr::mutate(target_exp1, target_avg_exp1 = mean(expression))
if (group3 != "") {
target_exp2 <- dplyr::mutate(target_exp2, target_avg_exp2 = mean(expression))
}
if (group4 != "") {
target_exp3 <- dplyr::mutate(target_exp3, target_avg_exp3 = mean(expression))
}
if (group5 != "") {
target_exp4 <- dplyr::mutate(target_exp4, target_avg_exp4 = mean(expression))
}
qpcr_data <- dplyr::left_join(qpcr_data, control_exp)
qpcr_data <- dplyr::left_join(qpcr_data, target_exp1)
if (group3 != "") {
qpcr_data <- dplyr::left_join(qpcr_data, target_exp2)
}
if (group4 != "") {
qpcr_data <- dplyr::left_join(qpcr_data, target_exp3)
}
if (group5 != "") {
qpcr_data <- dplyr::left_join(qpcr_data, target_exp4)
}
qpcr_data <- dplyr::mutate(qpcr_data, avg_exp = dplyr::coalesce(target_avg_exp1, control_avg_exp))
if (group3 != "") {
qpcr_data <- dplyr::mutate(qpcr_data, avg_exp = dplyr::coalesce(target_avg_exp1,
target_avg_exp2,
control_avg_exp))
}
if (group4 != "") {
qpcr_data <- dplyr::mutate(qpcr_data, avg_exp = dplyr::coalesce(target_avg_exp1,
target_avg_exp2,
target_avg_exp3,
control_avg_exp))
}
if (group5 != "") {
qpcr_data <- dplyr::mutate(qpcr_data, avg_exp = dplyr::coalesce(target_avg_exp1,
target_avg_exp2,
target_avg_exp3,
target_avg_exp4,
control_avg_exp))
}
#converting raw expression to percentage for better visualization
if (group3 == "" & group4 == "" & group4 == "") {
qpcr_data <- dplyr::arrange(qpcr_data, match(qpcr_data$Sample, c(group1, group2)))
}
if (group4 == "" & group5 == "") {
qpcr_data <- dplyr::arrange(qpcr_data, match(qpcr_data$Sample, c(group1, group2, group3)))
}
if (group5 == "") {
qpcr_data <- dplyr::arrange(qpcr_data, match(qpcr_data$Sample, c(group1, group2, group3, group4)))
} else {
qpcr_data <- dplyr::arrange(qpcr_data, match(qpcr_data$Sample, c(group1, group2, group3, group4, group5)))
}
qpcr_data <- dplyr::mutate(qpcr_data, percent_exp = qpcr_data$avg_exp/qpcr_data$control_avg_exp[1]*100)
if (stat == TRUE) {
if (test == "t-test" & group3 == "" & group4 == "" & group5 == "") {
#calculating p-value
stats <- t.test(target_exp1$expression, control_exp$expression, alternative = "two.sided")
#converting pvalues to *
pvalue <- pvalue_star(stats$p.value)
#this is the supported df layout for ggpubr::stat_pvalue_manual()
stat1 <- tibble::tribble(~group1, ~group2, ~pvalue,
group1, group2, pvalue)
}
if (test == "t-test" & group3 != "" & group4 == "" & group5 == "") {
qpcr_data <- dplyr::full_join(qpcr_data, control_exp)
qpcr_data <- dplyr::full_join(qpcr_data, target_exp1)
qpcr_data <- dplyr::full_join(qpcr_data, target_exp2)
stats <- pairwise.t.test(qpcr_data$expression, qpcr_data$Sample, p.adjust.method = "fdr")
pvalues <- stats$p.value
pvalues <- pvalues[,group1]
pvalues <- lapply(pvalues, pvalue_star)
pvalue1 <- as.character(pvalues[group2])
pvalue2 <- as.character(pvalues[group3])
stat1 <- tibble::tribble(~group1, ~group2, ~pvalue,
group1, group2, pvalue1)
stat2 <- tibble::tribble(~group1, ~group2, ~pvalue,
group1, group3, pvalue2)
}
if (test == "t-test" & group3 != "" & group4 != "" & group5 == "") {
qpcr_data <- dplyr::full_join(qpcr_data, control_exp)
qpcr_data <- dplyr::full_join(qpcr_data, target_exp1)
qpcr_data <- dplyr::full_join(qpcr_data, target_exp2)
qpcr_data <- dplyr::full_join(qpcr_data, target_exp3)
stats <- pairwise.t.test(qpcr_data$expression, qpcr_data$Sample, p.adjust.method = "fdr")
pvalues <- stats$p.value
pvalues <- pvalues[,group1]
pvalues <- lapply(pvalues, pvalue_star)
pvalue1 <- as.character(pvalues[group2])
pvalue2 <- as.character(pvalues[group3])
pvalue3 <- as.character(pvalues[group4])
stat1 <- tibble::tribble(~group1, ~group2, ~pvalue,
group1, group2, pvalue1)
stat2 <- tibble::tribble(~group1, ~group2, ~pvalue,
group1, group3, pvalue2)
stat3 <- tibble::tribble(~group1, ~group2, ~pvalue,
group1, group4, pvalue3)
}
if (test == "t-test" & group3 != "" & group4 != "" & group5 != "") {
qpcr_data <- dplyr::full_join(qpcr_data, control_exp)
qpcr_data <- dplyr::full_join(qpcr_data, target_exp1)
qpcr_data <- dplyr::full_join(qpcr_data, target_exp2)
qpcr_data <- dplyr::full_join(qpcr_data, target_exp3)
qpcr_data <- dplyr::full_join(qpcr_data, target_exp4)
stats <- pairwise.t.test(qpcr_data$expression, qpcr_data$Sample, p.adjust.method = "fdr")
pvalues <- stats$p.value
pvalues <- pvalues[,group1]
pvalues <- lapply(pvalues, pvalue_star)
pvalue1 <- as.character(pvalues[group2])
pvalue2 <- as.character(pvalues[group3])
pvalue3 <- as.character(pvalues[group4])
pvalue4 <- as.character(pvalues[group5])
stat1 <- tibble::tribble(~group1, ~group2, ~pvalue,
group1, group2, pvalue1)
stat2 <- tibble::tribble(~group1, ~group2, ~pvalue,
group1, group3, pvalue2)
stat3 <- tibble::tribble(~group1, ~group2, ~pvalue,
group1, group4, pvalue3)
stat4 <- tibble::tribble(~group1, ~group2, ~pvalue,
group1, group5, pvalue4)
}
#anova test here
if (test == "anova") {
#first data wrangling for anova function
#anova test
stats <- aov()
stats_anova <- TukeyHSD(stats)
}
}
#calculating sd
target_sd1 <- sd(target_exp1$expression)
control_sd <- sd(control_exp$expression)
if (group3 != "") {
target_sd2 <- sd(target_exp2$expression)
}
if (group4 != "") {
target_sd3 <- sd(target_exp3$expression)
}
if (group5 != "") {
target_sd4 <- sd(target_exp4$expression)
}
#distinct only 1 column
qpcr_data <- qpcr_data[!duplicated(qpcr_data$percent_exp), ]
#converting raw sd into percentage
qpcr_data <- tibble::column_to_rownames(qpcr_data, var = "Sample")
percent_sd1 <- control_sd/qpcr_data[group1, "avg_exp"]*100
percent_sd2 <- target_sd1/qpcr_data[group2, "avg_exp"]*100
if (group3 == "" & group4 == "" & group5 == "") {
percent_sd <- c(percent_sd1, percent_sd2)
}
if (group3 != "") {
percent_sd3 <- target_sd2/qpcr_data[group3, "avg_exp"]*100
percent_sd <- c(percent_sd1, percent_sd2, percent_sd3)
}
if (group4 != "") {
percent_sd4 <- target_sd3/qpcr_data[group4, "avg_exp"]*100
percent_sd <- c(percent_sd1, percent_sd2, percent_sd3, percent_sd4)
}
if (group5 != "") {
percent_sd5 <- target_sd4/qpcr_data[group5, "avg_exp"]*100
percent_sd <- c(percent_sd1, percent_sd2, percent_sd3, percent_sd4, percent_sd5)
}
percent_exp1 <- qpcr_data[group1, "percent_exp"]
percent_exp2 <- qpcr_data[group2, "percent_exp"]
if (group3 == "" & group4 == "" & group5 == "") {
percent_exp <- c(percent_exp1, percent_exp2)
}
if (group3 != "") {
percent_exp3 <- qpcr_data[group3, "percent_exp"]
percent_exp <- c(percent_exp1, percent_exp2, percent_exp3)
}
if (group4 != "") {
percent_exp4 <- qpcr_data[group4, "percent_exp"]
percent_exp <- c(percent_exp1, percent_exp2, percent_exp3, percent_exp4)
}
if (group5 != "") {
percent_exp5 <- qpcr_data[group5, "percent_exp"]
percent_exp <- c(percent_exp1, percent_exp2, percent_exp3, percent_exp4, percent_exp5)
}
qpcr_data <- tibble::rownames_to_column(qpcr_data, var = "Sample")
#removing na to only visualize sample of interest
qpcr_data <- qpcr_data[!is.na(qpcr_data$percent_exp), ]
if (generate_table == TRUE) {
#generating results as a table
samples <- as.character(qpcr_data$Sample)
expressions <- qpcr_data$percent_exp
sds <- percent_sd
stats <- c(stat1$pvalue, stat1$pvalue)
targets <- c(goi, goi)
results_table <- data.frame(samples, expressions, sds, stats, targets)
write.table(results_table, quote = FALSE, sep = ",", file = paste0(goi, "_table.csv"), row.names = FALSE)
}
#visualization
if (group3 == "" && group4 == "" && group5 == "") {
if (stat == TRUE) {
final_plot <- ggpubr::ggbarplot(qpcr_data,
x = "Sample",
y = "percent_exp",
fill = "808080",
xlab = "Sample",
ylab = "Relative mRNA level",
title = goi,
size = 1,
sort.by.groups = FALSE,
ggtheme = ggpubr::theme_pubr(base_size = 18)) +
ggplot2::theme(axis.line = ggplot2::element_line(size = 1)) +
ggplot2::geom_errorbar(size = 1, ggplot2::aes(x = group2,
ymin = percent_exp2 - percent_sd2,
ymax = percent_exp2 + percent_sd2,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1, ggplot2::aes(x = group1,
ymin = percent_exp1 - percent_sd1,
ymax = percent_exp1 + percent_sd1,
width = 0.1)) +
ggpubr::stat_pvalue_manual(stat1, label = "pvalue",
y.position = max(percent_exp) + max(percent_sd) + 10,
bracket.size = 1, label.size = 8)
} else {
final_plot <- ggpubr::ggbarplot(qpcr_data,
x = "Sample",
y = "percent_exp",
fill = "808080",
xlab = "Sample",
ylab = "Relative mRNA level",
size = 1,
title = goi,
palette = "npg",
lab.size = 5,
lab.vjust = 0.5,
lab.hjust = 1.2,
sort.by.groups = FALSE,
ggtheme = ggpubr::theme_pubr(base_size = 18)) +
ggplot2::theme(axis.line = ggplot2::element_line(size = 1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group2,
ymin = percent_exp2 - percent_sd2,
ymax = percent_exp2 + percent_sd2,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group1,
ymin = percent_exp1 - percent_sd1,
ymax = percent_exp1 + percent_sd1,
width = 0.1))
}
} else if (group4 == "" && group5 == "") {
if (stat == TRUE) {
final_plot <- ggpubr::ggbarplot(qpcr_data,
x = "Sample",
y = "percent_exp",
fill = "808080",
xlab = "Sample",
ylab = "Relative mRNA level",
size = 1,
title = goi,
palette = "npg",
lab.size = 5,
lab.vjust = 0.5,
lab.hjust = 1.2,
sort.by.groups = FALSE,
ggtheme = ggpubr::theme_pubr(base_size = 18)) +
ggplot2::theme(axis.line = ggplot2::element_line(size = 1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group2,
ymin = percent_exp2 - percent_sd2,
ymax = percent_exp2 + percent_sd2,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group3,
ymin = percent_exp3 - percent_sd3,
ymax = percent_exp3 + percent_sd3,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group1,
ymin = percent_exp1 - percent_sd1,
ymax = percent_exp1 + percent_sd1,
width = 0.1)) +
ggpubr::stat_pvalue_manual(stat1, label = "pvalue",
y.position = max(percent_exp) + max(percent_sd) + 10,
bracket.size = 1, label.size = 8)
ggpubr::stat_pvalue_manual(stat2, label = "pvalue",
y.position = max(percent_exp) + max(percent_sd) + 20,
bracket.size = 1, label.size = 8)
} else {
final_plot <- ggpubr::ggbarplot(qpcr_data,
x = "Sample",
y = "percent_exp",
fill = "808080",
xlab = "Sample",
ylab = "Relative mRNA level",
size = 1,
title = goi,
palette = "npg",
lab.size = 5,
lab.vjust = 0.5,
lab.hjust = 1.2,
sort.by.groups = FALSE,
ggtheme = ggpubr::theme_pubr(base_size = 18)) +
ggplot2::theme(axis.line = ggplot2::element_line(size = 1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group2,
ymin = percent_exp2 - percent_sd2,
ymax = percent_exp2 + percent_sd2,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group3,
ymin = percent_exp3 - percent_sd3,
ymax = percent_exp3 + percent_sd3,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group1,
ymin = percent_exp1 - percent_sd1,
ymax = percent_exp1 + percent_sd1,
width = 0.1))
}
} else if (group5 == "") {
if (stat == TRUE) {
final_plot <- ggpubr::ggbarplot(qpcr_data,
x = "Sample",
y = "percent_exp",
fill = "808080",
xlab = "Sample",
ylab = "Relative mRNA level",
title = goi,
size = 1,
palette = "npg",
lab.size = 5,
lab.vjust = 0.5,
lab.hjust = 1.2,
sort.by.groups = FALSE,
ggtheme = ggpubr::theme_pubr(base_size = 18)) +
ggplot2::theme(axis.line = ggplot2::element_line(size = 1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group2,
ymin = percent_exp2 - percent_sd2,
ymax = percent_exp2 + percent_sd2,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group3,
ymin = percent_exp3 - percent_sd3,
ymax = percent_exp3 + percent_sd3,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group4,
ymin = percent_exp4 - percent_sd4,
ymax = percent_exp4 + percent_sd4,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group1,
ymin = percent_exp1 - percent_sd1,
ymax = percent_exp1 + percent_sd1,
width = 0.1)) +
ggpubr::stat_pvalue_manual(stat1, label = "pvalue",
y.position = max(percent_exp) + max(percent_sd) + 10,
bracket.size = 1, label.size = 8) +
ggpubr::stat_pvalue_manual(stat2, label = "pvalue",
y.position = max(percent_exp) + max(percent_sd) + 20,
bracket.size = 1, label.size = 8) +
ggpubr::stat_pvalue_manual(stat3, label = "pvalue",
y.position = max(percent_exp) + max(percent_sd) + 30,
bracket.size = 1, label.size = 8)
} else {
final_plot <- ggpubr::ggbarplot(qpcr_data,
x = "Sample",
y = "percent_exp",
fill = "808080",
xlab = "Sample",
ylab = "Relative mRNA level",
title = goi,
size = 1,
palette = "npg",
lab.size = 5,
lab.vjust = 0.5,
lab.hjust = 1.2,
sort.by.groups = FALSE,
ggtheme = ggpubr::theme_pubr(base_size = 18)) +
ggplot2::theme(axis.line = ggplot2::element_line(size = 1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group2,
ymin = percent_exp2 - percent_sd2,
ymax = percent_exp2 + percent_sd2,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group3,
ymin = percent_exp3 - percent_sd3,
ymax = percent_exp3 + percent_sd3,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group4,
ymin = percent_exp4 - percent_sd4,
ymax = percent_exp4 + percent_sd4,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group1,
ymin = percent_exp1 - percent_sd1,
ymax = percent_exp1 + percent_sd1,
width = 0.1))
}
} else {
if (stat == TRUE) {
final_plot <- ggpubr::ggbarplot(qpcr_data,
x = "Sample",
y = "percent_exp",
fill = "808080",
xlab = "Sample",
ylab = "Relative mRNA level",
size = 1,
title = goi,
palette = "npg",
lab.size = 5,
lab.vjust = 0.5,
lab.hjust = 1.2,
sort.by.groups = FALSE,
ggtheme = ggpubr::theme_pubr(base_size = 18)) +
ggplot2::theme(axis.line = ggplot2::element_line(size = 1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group2,
ymin = percent_exp2 - percent_sd2,
ymax = percent_exp2 + percent_sd2,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group3,
ymin = percent_exp3 - percent_sd3,
ymax = percent_exp3 + percent_sd3,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group4,
ymin = percent_exp4 - percent_sd4,
ymax = percent_exp4 + percent_sd4,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group5,
ymin = percent_exp5 - percent_sd5,
ymax = percent_exp5 + percent_sd5,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group1,
ymin = percent_exp1 - percent_sd1,
ymax = percent_exp1 + percent_sd1,
width = 0.1)) +
ggpubr::stat_pvalue_manual(stat1, label = "pvalue",
y.position = max(percent_exp) + max(percent_sd) + 10,
bracket.size = 1, label.size = 8) +
ggpubr::stat_pvalue_manual(stat2, label = "pvalue",
y.position = max(percent_exp) + max(percent_sd) + 20,
bracket.size = 1, label.size = 8) +
ggpubr::stat_pvalue_manual(stat3, label = "pvalue",
y.position = max(percent_exp) + max(percent_sd) + 30,
bracket.size = 1, label.size = 8) +
ggpubr::stat_pvalue_manual(stat4, label = "pvalue",
y.position = max(percent_exp) + max(percent_sd) + 40,
bracket.size = 1, label.size = 8)
} else {
final_plot <- ggpubr::ggbarplot(qpcr_data,
x = "Sample",
y = "percent_exp",
fill = "808080",
xlab = "Sample",
ylab = "Relative mRNA level",
size = 1,
title = goi,
palette = "npg",
lab.size = 5,
lab.vjust = 0.5,
lab.hjust = 1.2,
sort.by.groups = FALSE,
ggtheme = ggpubr::theme_pubr(base_size = 18)) +
ggplot2::theme(axis.line = ggplot2::element_line(size = 1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group2,
ymin = percent_exp2 - percent_sd2,
ymax = percent_exp2 + percent_sd2,
width = 0.1)) +
gplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group3,
ymin = percent_exp3 - percent_sd3,
ymax = percent_exp3 + percent_sd3,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group4,
ymin = percent_exp4 - percent_sd4,
ymax = percent_exp4 + percent_sd4,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group5,
ymin = percent_exp5 - percent_sd5,
ymax = percent_exp5 + percent_sd5,
width = 0.1)) +
ggplot2::geom_errorbar(size = 1,
ggplot2::aes(x = group1,
ymin = percent_exp1 - percent_sd1,
ymax = percent_exp1 + percent_sd1,
width = 0.1))
}
}
plot(final_plot)
return(final_plot)
}
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