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GGPUBR: HOW TO ADD ADJUSTED P-VALUES TO A MULTI-PANEL GGPLOT
2022-07-02 11:50:00 【Xiaoyu 2022】
# Load required R packages
library(ggpubr)
library(rstatix)
# Data preparation
df <- tibble::tribble(
~sample_type, ~expression, ~cancer_type, ~gene,
"cancer", 25.8, "Lung", "Gene1",
"cancer", 25.5, "Liver", "Gene1",
"cancer", 22.4, "Liver", "Gene1",
"cancer", 21.2, "Lung", "Gene1",
"cancer", 24.5, "Liver", "Gene1",
"cancer", 27.3, "Liver", "Gene1",
"cancer", 30.9, "Liver", "Gene1",
"cancer", 17.6, "Breast", "Gene1",
"cancer", 19.7, "Lung", "Gene1",
"cancer", 9.7, "Breast", "Gene1",
"cancer", 15.2, "Breast", "Gene2",
"cancer", 26.4, "Liver", "Gene2",
"cancer", 25.8, "Lung", "Gene2",
"cancer", 9.7, "Breast", "Gene2",
"cancer", 21.2, "Lung", "Gene2",
"cancer", 24.5, "Liver", "Gene2",
"cancer", 14.5, "Breast", "Gene2",
"cancer", 19.7, "Lung", "Gene2",
"cancer", 25.2, "Lung", "Gene2",
"normal", 43.5, "Lung", "Gene1",
"normal", 76.5, "Liver", "Gene1",
"normal", 21.9, "Breast", "Gene1",
"normal", 69.9, "Liver", "Gene1",
"normal", 101.7, "Liver", "Gene1",
"normal", 80.1, "Liver", "Gene1",
"normal", 19.2, "Breast", "Gene1",
"normal", 49.5, "Lung", "Gene1",
"normal", 34.5, "Breast", "Gene1",
"normal", 51.9, "Lung", "Gene1",
"normal", 67.5, "Lung", "Gene2",
"normal", 30, "Breast", "Gene2",
"normal", 76.5, "Liver", "Gene2",
"normal", 88.5, "Liver", "Gene2",
"normal", 69.9, "Liver", "Gene2",
"normal", 49.5, "Lung", "Gene2",
"normal", 80.1, "Liver", "Gene2",
"normal", 79.2, "Liver", "Gene2",
"normal", 12.6, "Breast", "Gene2",
"normal", 97.5, "Liver", "Gene2",
"normal", 64.5, "Liver", "Gene2"
)
# Summary statistics
df %>%
group_by(gene, cancer_type, sample_type) %>%
get_summary_stats(expression, type = "common")
# Statistical test
# group the data by cancer type and gene
# Compare expression values of normal and cancer samples
stat.test <- df %>%
group_by(cancer_type, gene) %>%
t_test(expression ~ sample_type) %>%
adjust_pvalue(method = "bonferroni") %>%
add_significance()
stat.test
# Create boxplot
bxp <- ggboxplot(
df, x = "sample_type", y = "expression",
facet.by = c("gene", "cancer_type")
) +
rotate_x_text(angle = 60)
# Add adjusted p-values
stat.test <- stat.test %>% add_xy_position(x = "sample_type")
bxp + stat_pvalue_manual(stat.test, label = "p.adj")
# Load required R packages
library(ggpubr)
library(rstatix)
# Data preparation
df <- tibble::tribble(
~sample_type, ~expression, ~cancer_type, ~gene,
"cancer", 25.8, "Lung", "Gene1",
"cancer", 25.5, "Liver", "Gene1",
"cancer", 22.4, "Liver", "Gene1",
"cancer", 21.2, "Lung", "Gene1",
"cancer", 24.5, "Liver", "Gene1",
"cancer", 27.3, "Liver", "Gene1",
"cancer", 30.9, "Liver", "Gene1",
"cancer", 17.6, "Breast", "Gene1",
"cancer", 19.7, "Lung", "Gene1",
"cancer", 9.7, "Breast", "Gene1",
"cancer", 15.2, "Breast", "Gene2",
"cancer", 26.4, "Liver", "Gene2",
"cancer", 25.8, "Lung", "Gene2",
"cancer", 9.7, "Breast", "Gene2",
"cancer", 21.2, "Lung", "Gene2",
"cancer", 24.5, "Liver", "Gene2",
"cancer", 14.5, "Breast", "Gene2",
"cancer", 19.7, "Lung", "Gene2",
"cancer", 25.2, "Lung", "Gene2",
"normal", 43.5, "Lung", "Gene1",
"normal", 76.5, "Liver", "Gene1",
"normal", 21.9, "Breast", "Gene1",
"normal", 69.9, "Liver", "Gene1",
"normal", 101.7, "Liver", "Gene1",
"normal", 80.1, "Liver", "Gene1",
"normal", 19.2, "Breast", "Gene1",
"normal", 49.5, "Lung", "Gene1",
"normal", 34.5, "Breast", "Gene1",
"normal", 51.9, "Lung", "Gene1",
"normal", 67.5, "Lung", "Gene2",
"normal", 30, "Breast", "Gene2",
"normal", 76.5, "Liver", "Gene2",
"normal", 88.5, "Liver", "Gene2",
"normal", 69.9, "Liver", "Gene2",
"normal", 49.5, "Lung", "Gene2",
"normal", 80.1, "Liver", "Gene2",
"normal", 79.2, "Liver", "Gene2",
"normal", 12.6, "Breast", "Gene2",
"normal", 97.5, "Liver", "Gene2",
"normal", 64.5, "Liver", "Gene2"
)
# Summary statistics
df %>%
group_by(gene, cancer_type, sample_type) %>%
get_summary_stats(expression, type = "common")
# Statistical test
# group the data by cancer type and gene
# Compare expression values of normal and cancer samples
stat.test <- df %>%
group_by(cancer_type, gene) %>%
t_test(expression ~ sample_type) %>%
adjust_pvalue(method = "bonferroni") %>%
add_significance()
stat.test
# Create boxplot
bxp <- ggboxplot(
df, x = "sample_type", y = "expression",
facet.by = c("gene", "cancer_type")
) +
rotate_x_text(angle = 60)
# Add adjusted p-values
stat.test <- stat.test %>% add_xy_position(x = "sample_type")
bxp + stat_pvalue_manual(stat.test, label = "p.adj.signif")
# Load required R packages
library(ggpubr)
library(rstatix)
# Data preparation
df <- tibble::tribble(
~sample_type, ~expression, ~cancer_type, ~gene,
"cancer", 25.8, "Lung", "Gene1",
"cancer", 25.5, "Liver", "Gene1",
"cancer", 22.4, "Liver", "Gene1",
"cancer", 21.2, "Lung", "Gene1",
"cancer", 24.5, "Liver", "Gene1",
"cancer", 27.3, "Liver", "Gene1",
"cancer", 30.9, "Liver", "Gene1",
"cancer", 17.6, "Breast", "Gene1",
"cancer", 19.7, "Lung", "Gene1",
"cancer", 9.7, "Breast", "Gene1",
"cancer", 15.2, "Breast", "Gene2",
"cancer", 26.4, "Liver", "Gene2",
"cancer", 25.8, "Lung", "Gene2",
"cancer", 9.7, "Breast", "Gene2",
"cancer", 21.2, "Lung", "Gene2",
"cancer", 24.5, "Liver", "Gene2",
"cancer", 14.5, "Breast", "Gene2",
"cancer", 19.7, "Lung", "Gene2",
"cancer", 25.2, "Lung", "Gene2",
"normal", 43.5, "Lung", "Gene1",
"normal", 76.5, "Liver", "Gene1",
"normal", 21.9, "Breast", "Gene1",
"normal", 69.9, "Liver", "Gene1",
"normal", 101.7, "Liver", "Gene1",
"normal", 80.1, "Liver", "Gene1",
"normal", 19.2, "Breast", "Gene1",
"normal", 49.5, "Lung", "Gene1",
"normal", 34.5, "Breast", "Gene1",
"normal", 51.9, "Lung", "Gene1",
"normal", 67.5, "Lung", "Gene2",
"normal", 30, "Breast", "Gene2",
"normal", 76.5, "Liver", "Gene2",
"normal", 88.5, "Liver", "Gene2",
"normal", 69.9, "Liver", "Gene2",
"normal", 49.5, "Lung", "Gene2",
"normal", 80.1, "Liver", "Gene2",
"normal", 79.2, "Liver", "Gene2",
"normal", 12.6, "Breast", "Gene2",
"normal", 97.5, "Liver", "Gene2",
"normal", 64.5, "Liver", "Gene2"
)
# Summary statistics
df %>%
group_by(gene, cancer_type, sample_type) %>%
get_summary_stats(expression, type = "common")
# Statistical test
# group the data by cancer type and gene
# Compare expression values of normal and cancer samples
stat.test <- df %>%
group_by(cancer_type, gene) %>%
t_test(expression ~ sample_type) %>%
adjust_pvalue(method = "bonferroni") %>%
add_significance()
stat.test
# Create boxplot
bxp <- ggboxplot(
df, x = "sample_type", y = "expression",
facet.by = c("gene", "cancer_type")
) +
rotate_x_text(angle = 60)
# Add adjusted p-values
stat.test <- stat.test %>% add_xy_position(x = "sample_type")
# Hide ns and change the bracket tip length
bxp + stat_pvalue_manual(
stat.test, label = "p.adj.signif",
hide.ns = TRUE, tip.length = 0
)
# Load required R packages
library(ggpubr)
library(rstatix)
# Data preparation
df <- tibble::tribble(
~sample_type, ~expression, ~cancer_type, ~gene,
"cancer", 25.8, "Lung", "Gene1",
"cancer", 25.5, "Liver", "Gene1",
"cancer", 22.4, "Liver", "Gene1",
"cancer", 21.2, "Lung", "Gene1",
"cancer", 24.5, "Liver", "Gene1",
"cancer", 27.3, "Liver", "Gene1",
"cancer", 30.9, "Liver", "Gene1",
"cancer", 17.6, "Breast", "Gene1",
"cancer", 19.7, "Lung", "Gene1",
"cancer", 9.7, "Breast", "Gene1",
"cancer", 15.2, "Breast", "Gene2",
"cancer", 26.4, "Liver", "Gene2",
"cancer", 25.8, "Lung", "Gene2",
"cancer", 9.7, "Breast", "Gene2",
"cancer", 21.2, "Lung", "Gene2",
"cancer", 24.5, "Liver", "Gene2",
"cancer", 14.5, "Breast", "Gene2",
"cancer", 19.7, "Lung", "Gene2",
"cancer", 25.2, "Lung", "Gene2",
"normal", 43.5, "Lung", "Gene1",
"normal", 76.5, "Liver", "Gene1",
"normal", 21.9, "Breast", "Gene1",
"normal", 69.9, "Liver", "Gene1",
"normal", 101.7, "Liver", "Gene1",
"normal", 80.1, "Liver", "Gene1",
"normal", 19.2, "Breast", "Gene1",
"normal", 49.5, "Lung", "Gene1",
"normal", 34.5, "Breast", "Gene1",
"normal", 51.9, "Lung", "Gene1",
"normal", 67.5, "Lung", "Gene2",
"normal", 30, "Breast", "Gene2",
"normal", 76.5, "Liver", "Gene2",
"normal", 88.5, "Liver", "Gene2",
"normal", 69.9, "Liver", "Gene2",
"normal", 49.5, "Lung", "Gene2",
"normal", 80.1, "Liver", "Gene2",
"normal", 79.2, "Liver", "Gene2",
"normal", 12.6, "Breast", "Gene2",
"normal", 97.5, "Liver", "Gene2",
"normal", 64.5, "Liver", "Gene2"
)
# Summary statistics
df %>%
group_by(gene, cancer_type, sample_type) %>%
get_summary_stats(expression, type = "common")
# Statistical test
# group the data by cancer type and gene
# Compare expression values of normal and cancer samples
stat.test <- df %>%
group_by(cancer_type, gene) %>%
t_test(expression ~ sample_type) %>%
adjust_pvalue(method = "bonferroni") %>%
add_significance()
stat.test
# Create boxplot
bxp <- ggboxplot(
df, x = "sample_type", y = "expression",
facet.by = c("gene", "cancer_type")
) +
rotate_x_text(angle = 60)
# Add adjusted p-values
stat.test <- stat.test %>% add_xy_position(x = "sample_type")
# Show p-values and significance levels
bxp + stat_pvalue_manual(
stat.test, label = "{p.adj}{p.adj.signif}",
hide.ns = TRUE, tip.length = 0
)
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