SFigure 4
Anna Joachimbauer
2024-10-02
Last updated: 2026-03-06
Checks: 7 0
Knit directory: Serology-Analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 505a6d7 | anjo1995 | 2026-03-06 | Figure 4-5 |
| html | 505a6d7 | anjo1995 | 2026-03-06 | Figure 4-5 |
| Rmd | 4c1f400 | anjo1995 | 2026-03-06 | Figure 4 |
| html | 4c1f400 | anjo1995 | 2026-03-06 | Figure 4 |
Load Packages
suppressPackageStartupMessages({
# Data handling
library(tidyverse)
library(data.table)
library(magrittr)
library(janitor)
library(here)
library(scales)
library(table1)
library(tableone)
library(flextable)
library(gtsummary)
library(openxlsx)
library(writexl)
library(readxl)
# Visualization
library(ggplot2)
library(ggpubr)
library(ggrepel)
library(ggbeeswarm)
library(ggcorrplot)
library(corrplot)
library(pheatmap)
library(ComplexHeatmap)
library(circlize)
library(RColorBrewer)
library(EnhancedVolcano)
library(plotly)
library(patchwork)
library(cowplot)
library(gridExtra)
library(grid)
library(ggpattern)
# Statistics
library(rstatix)
library(multcomp)
library(car)
library(Hmisc)
library(MASS)
library(MuMIn)
library(broom)
library(glmnet)
library(logistf)
library(drc)
library(caret)
library(mice)
library(pROC)
# Clustering
library(randomForest)
library(factoextra)
library(cluster)
library(Rtsne)
library(umap)
library(dbscan)
library(kernlab)
library(Seurat)
# Others
library(ImmunoLogic)
})Define Filepath
basedir <- here()Read Data (Healthy, Cohort 1, Cohort 2)
# Import BMP4 Data
table_bmp4_healthy <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_BMP4_Healthy.xlsx"))
table_bmp4_cohort1 <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_BMP4_Cohort1.xlsx")) %>%
mutate(Cohort = "Myocarditis")
table_bmp4_cohort2 <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_BMP4_Cohort2.xlsx")) %>%
mutate(Cohort = "Myocarditis")
table_bmp4_acm <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_BMP4_ACM.xlsx")) %>%
mutate (Cohort = "ACM")
# Combine Tables
table_bmp4_myo_acm <- rbind(table_bmp4_cohort1, table_bmp4_cohort2, table_bmp4_acm)
# Import Serology Data
table_clindat_healthy <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_Serology_Healthy.xlsx")) %>%
dplyr::select(Study_ID, Trop_I, NTproBNP, CRP)
table_clindat_cohort1 <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_ClinicalData_Cohort1.xlsx")) %>%
dplyr::select(Study_ID, NTproBNP, LV_EF, Trop_I, CRP)
table_clindat_cohort2 <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_ClinicalData_Cohort2.xlsx")) %>%
dplyr::select(Study_ID, NTproBNP, LV_EF, Trop_I, CRP)
table_clindat_acm <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_Serology_ACM.xlsx")) %>%
dplyr::select(Study_ID, Trop_I, NTproBNP, CRP)
# Add LVEF ACM
table_lvef_acm <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_ClinicalData_ACM.xlsx")) %>%
dplyr::select(Study_ID, LV_EF)
table_clindat_acm_01 <- table_clindat_acm %>%
left_join(table_lvef_acm, by = "Study_ID")
# Combine tables
table_clindat_myo_acm <- rbind(table_clindat_cohort1, table_clindat_cohort2, table_clindat_acm_01)
# Import Luminex Data
table_lum_healthy <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_Lum_Healthy_DL_corr.xlsx")) %>%
dplyr::select(-Cohort)
table_lum_cohort1 <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_Lum_Cohort1_DL_corr.xlsx")) %>%
dplyr::select(-Cohort)
table_lum_cohort2 <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_Lum_Cohort2_DL_corr.xlsx")) %>%
dplyr::select(-Cohort)
table_lum_acm <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_Lum_ACM_DL_corr.xlsx")) %>%
dplyr::select(-Cohort)
# Combine tables
table_lum_myo_acm <- rbind(table_lum_cohort1, table_lum_cohort2, table_lum_acm)
# Combine tables
table_all_myo_acm <- table_bmp4_myo_acm %>%
left_join(table_lum_myo_acm, by = "Study_ID") %>%
left_join(table_clindat_myo_acm, by = "Study_ID")
table_all_healthy <- table_bmp4_healthy %>%
left_join(table_lum_healthy, by = "Study_ID") %>%
left_join(table_clindat_healthy, by = "Study_ID")Multiple Imputation AM Cohort (incl. LVEF)
# Impute missing values
imputed_data <- mice(table_all_myo_acm, m = 5, method = 'pmm', maxit = 5, seed = 1234)
iter imp variable
1 1 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
1 2 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
1 3 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
1 4 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
1 5 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
2 1 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
2 2 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
2 3 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
2 4 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
2 5 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
3 1 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
3 2 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
3 3 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
3 4 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
3 5 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
4 1 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
4 2 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
4 3 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
4 4 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
4 5 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
5 1 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
5 2 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
5 3 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
5 4 Grem_1 Grem_2 NTproBNP LV_EF Trop_I
5 5 Grem_1 Grem_2 NTproBNP LV_EF Trop_I# Check the imputed data
densityplot(imputed_data, col=c("grey", "blue"), pch = c(1, 20))
| Version | Author | Date |
|---|---|---|
| 4c1f400 | anjo1995 | 2026-03-06 |
# Create a data set with the observed and completed data
table_all_myo_imp <- complete(imputed_data, 1)
# Combine all data tables
table_all_myo_acm_healthy <- bind_rows(table_all_myo_acm %>% dplyr::select(-LV_EF),
table_all_healthy)Routine Blood Parameter between AM and ACM Cohort
# Adjust to assay limits
table_all_myo_acm_healthy <- table_all_myo_acm_healthy %>%
mutate(CRP = if_else(CRP < 1, 1, CRP),
Trop_I = if_else(Trop_I < 10, 10, Trop_I),
NTproBNP = if_else(NTproBNP < 8, 8, NTproBNP))
# Prepare data table
table_para <- table_all_myo_acm_healthy %>%
pivot_longer(cols = where(is.numeric),
names_to = "Parameter",
values_to = "Parameter_val")
# Set Cohort order
table_para$Cohort <- factor(table_para$Cohort,
levels = c("Healthy", "Myocarditis", "ACM"))
# Calculate Stats
# Kruskal–Wallis test
stat.kruskal <- table_para %>%
group_by(Parameter) %>%
kruskal_test(Parameter_val ~ Cohort) %>%
add_significance()
# Dunn post hoc test
stat.dunn <- table_para %>%
group_by(Parameter) %>%
dunn_test(Parameter_val ~ Cohort, p.adjust.method = "BH")
# Calculate max y for positioning p-values
max_y <- table_para %>%
group_by(Parameter) %>%
summarise(max_val = max(Parameter_val, na.rm = TRUE))
# Get min value per parameter (for y limits)
min_y <- table_para %>%
group_by(Parameter) %>%
summarise(min_val = min(Parameter_val, na.rm = TRUE))
# Combine with max_y info for plotting
stat.test <- stat.dunn %>%
left_join(max_y, by = "Parameter") %>%
mutate(y.position = max_val * 1) %>%
group_by(Parameter) %>%
arrange(p.adj)
# Create the loop to create a plot
param <- c("NTproBNP", "Trop_I", "CRP")
# Create x-axis label for loop
biomarker_labels <- c("NT-proBNP (ng/l)", "Troponin I (ng/l)", "CRP (mg/l)")
names(biomarker_labels) <- param
# Create a list for all plots created in the loop
plots_list <- list()
for (param in param)
{
# Filter for the data
temp_data <- table_para %>%
filter(Parameter == param)
temp_stat_test <- stat.test %>%
filter(Parameter == param) %>%
mutate(p_adj_label = ifelse(p.adj < 0.001, "<0.001", sprintf("%.3f", p.adj)))
# define y axis margins
max_y <- max(temp_stat_test$max_val, na.rm = TRUE) * 1.1
max_log10_limit <- 10^(ceiling(log10(max_y)))
min_y_limit <- min_y %>%
filter(Parameter == param) %>%
summarise(min_val = min(min_val, na.rm = TRUE) * 0.9) %>%
pull(min_val)
min_log10_limit <- 10^(floor(log10(min_y_limit)))
y_breaks <- 10^(seq(log10(min_log10_limit), log10(max_log10_limit), by = 1))
# Plot Boxplot
plot <- ggplot(temp_data, aes(x = Cohort, y = Parameter_val)) +
geom_boxplot(aes(fill = Cohort), color = "black", outlier.shape = NA,
width = 0.6, alpha = 0.4) +
geom_point(shape = 21, size = 3, color = "black", aes(fill = Cohort),
position = position_jitter(width = 0.2, height = 0)) +
scale_y_log10(labels = function(x) format(x, scientific = FALSE, trim = TRUE),
limits = c(NA, max_log10_limit),
expand = expansion(mult = c(0.05, 0), add = c(0,0)), breaks = y_breaks) +
scale_fill_manual(values = c("Myocarditis" = "darkred",
"ACM" = "brown2",
"Healthy" = "grey")) +
stat_pvalue_manual(temp_stat_test, label = "p_adj_label",
y.position = log10(temp_stat_test$y.position),
step.increase = 0.01, tip.length = 0) +
labs(x = NULL, y = biomarker_labels[[param]]) +
theme_classic() +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "none")
plots_list[[param]] <- plot
}
# Combine plots to a panel
panel <- ggarrange(plotlist = c(plots_list),
ncol = 3, nrow = 1,
common.legend = TRUE)
print(panel)
| Version | Author | Date |
|---|---|---|
| 505a6d7 | anjo1995 | 2026-03-06 |
LVEF between AM and ACM Cohort
# Set Cohort order
table_imp_lvef <- table_all_myo_acm %>%
mutate(Cohort = factor(Cohort, levels = c("Myocarditis", "ACM")))
# Prepare data table
table_para <- table_imp_lvef %>%
pivot_longer(cols = where(is.numeric),
names_to = "Parameter",
values_to = "Parameter_val")
# Calculate Stats
# Wilcox Test
stat.test <- table_para %>%
group_by(Parameter) %>%
wilcox_test(Parameter_val ~ Cohort) %>%
add_significance() %>%
mutate(p_adj = p.adjust(p, method = "BH"))
# Get max value per parameter (for p-value position)
max_y <- table_para %>%
group_by(Parameter) %>%
summarise(max_val = max(Parameter_val, na.rm = TRUE))
# Get min value per parameter (for y limits)
min_y <- table_para %>%
group_by(Parameter) %>%
summarise(min_val = min(Parameter_val, na.rm = TRUE))
# Combine with max_y info for plotting
stat.test <- stat.test %>%
left_join(max_y, by = "Parameter") %>%
left_join(min_y, by = "Parameter") %>%
mutate(y.position = max_val * 1.1)
# Create Plot LVEF
# Filter for the data
temp_data <- table_para %>%
filter(Parameter == "LV_EF")
temp_stat_test <- stat.test %>%
filter(Parameter == "LV_EF") %>%
mutate(p_adj_label = ifelse(p_adj < 0.001, "<0.001", sprintf("%.3f", p_adj)))
plot_lvef <- ggplot(temp_data, aes(x = Cohort, y = Parameter_val)) +
geom_boxplot(aes(fill = Cohort), color = "black", outlier.shape = NA,
width = 0.6, alpha = 0.4) +
geom_point(shape = 21, size = 3, color = "black", aes(fill = Cohort),
position = position_jitter(width = 0.2, height = 0)) +
scale_y_continuous(limits = c(0, 80), expand = c(0, 0)) +
scale_fill_manual(values = c("Myocarditis" = "darkred",
"ACM" = "brown2")) +
stat_pvalue_manual(temp_stat_test, label = "p_adj_label",
y.position = temp_stat_test$y.position,
step.increase = 0.01, tip.length = 0) +
labs(x = NULL, y = "LVEF (%)") +
theme_classic() +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "none")
print(plot_lvef)
Abundance Bubble Plot
# Subset Data
table_lum_myo <- rbind(table_lum_cohort1, table_lum_cohort2) %>%
mutate(Cohort = "Myocarditis")
table_lum <- bind_rows(table_lum_myo,
table_lum_healthy %>% mutate (Cohort = "Healthy"),
table_lum_acm %>% mutate (Cohort = "ACM"))
# Set rownames
rownames(table_lum) <- table_lum$Study_ID
# Prepare data table for bubble plot
# Create the loop vector
parameters <- names(which(sapply(table_lum, is.numeric) == TRUE))
# Prepare data table
table_para <- table_lum %>%
pivot_longer(cols = where(is.numeric),
names_to = "Parameter",
values_to = "Parameter_val")
# Calculate mean expression for the healthy cohort
healthy_stats <- table_para %>%
filter(Cohort == "Healthy") %>%
group_by(Parameter) %>%
summarise(mean_expr_healthy = mean(Parameter_val, na.rm = TRUE))
# Calculate mean expression for AM cohort & calcuate FC
cohorts_stats <- table_para %>%
filter(Cohort != "Healthy") %>%
group_by(Cohort, Parameter) %>%
summarise(mean_expr_myo = mean(Parameter_val, na.rm = TRUE)) %>%
left_join(healthy_stats, by = "Parameter") %>%
mutate(log2_fc = log2(mean_expr_myo / mean_expr_healthy))
# Calculate abundance fold change > 1.5 per parameter in AM cohort
percentage_fc_increase <- table_para %>%
filter(Cohort != "Healthy") %>%
group_by(Cohort, Parameter) %>%
left_join(healthy_stats, by = "Parameter") %>%
mutate(log2_fc = log2(Parameter_val / mean_expr_healthy)) %>%
mutate(fc_1.5 = log2_fc >1.5) %>%
summarise(percentage_fc_1.5 = mean(fc_1.5, na.rm = TRUE) * 100)
# Merge stats tables
cohorts_stats <- cohorts_stats %>%
left_join(percentage_fc_increase,by = c("Cohort", "Parameter"))
# Reorder Cohorts order
cohorts_stats <- cohorts_stats %>%
mutate(Cohort = factor(Cohort, levels = c("ACM", "Myocarditis")))
# Order Parameters
list_parameter_groups <- read.xlsx(paste0(basedir,"/data/Grouping of Lumminex Marker.xlsx"))
cohorts_stats$Parameter <- factor(cohorts_stats$Parameter, levels = list_parameter_groups$Parameter)
# Create the Bubble Plot
bubble_plot <- ggplot(cohorts_stats, aes(x = Parameter, y = Cohort,
size = abs(percentage_fc_1.5),
color = log2_fc)) +
geom_point(alpha = 1) +
scale_size(range = c(2, 8)) +
scale_y_discrete(limits = rev(levels(cohorts_stats$Cohort))) +
scale_color_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0) +
labs(title = "Fold Change (Log2) by Parameter and Cohort",
x = "Parameter", y = "Cohort",
size = "Percentage of Patients (FC > 1.5)", color = "Log2 Fold Change") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
axis.text.y = element_text(size = 12))
# Display the plot
print(bubble_plot)
| Version | Author | Date |
|---|---|---|
| 4c1f400 | anjo1995 | 2026-03-06 |
Data Preparation (ACM & Healthy)
# Subset
table_lum_acm_healthy <- table_lum %>%
filter (Cohort != "Myocarditis")
# Remove Columns with same value in all individuals (Variance = 0)
table_lum_acm_healthy <- table_lum_acm_healthy %>%
select_if(~ !(is.numeric(.) && var(., na.rm = TRUE) == 0))Volcano Plot (ACM & Healthy)
# Set rownames
rownames(table_lum_acm_healthy) <- table_lum_acm_healthy$Study_ID
# Calculate p-values between AM and healthy
# Create the loop.vector (all the parameter columns)
names(which(sapply(table_lum_acm_healthy, is.numeric) == TRUE)) -> parameters
# Create table for stats
table_lum_acm_healthy_stat <- data.table (Parameter = numeric(), group1 = character(),
group2 = character(), n1 = numeric(),
n2 = numeric(), statistic = numeric (),
p = numeric (), p.signif = character(), FDR = numeric())
# Create loop for calculation of stat test by parameter between cohort1 and healthy cohort
for (i in parameters)
{
# Formula to insert parameter in loop
formula = as.formula( paste(i, "Cohort", sep="~") )
stat_parameter <- table_lum_acm_healthy %>%
wilcox_test(formula = formula) %>%
add_significance() %>%
mutate(FDR = p.adjust(p, method = "BH")) %>%
rename(Parameter = .y.)
# Combine currently calculated stats with previous calculations
table_lum_acm_healthy_stat <- rbind(table_lum_acm_healthy_stat, stat_parameter)
}
# Calculate FC between AM and healthy
# Calculate Mean of parameters per group
table_lum_acm_healthy_log2 <- table_lum_acm_healthy %>%
group_by(Cohort) %>%
summarise(across(FGF_2:CXCL8, ~ mean(.x, na.rm = TRUE)))
# Transverse Data Table and set rownames
table_lum_acm_healthy_log2_t <- table_lum_acm_healthy_log2 %>%
t %>%
as.data.frame() %>%
row_to_names(1)
# Convert whole data table to numeric
table_lum_acm_healthy_log2_t <- mutate_all(table_lum_acm_healthy_log2_t,
function(x) as.numeric(as.character(x)))
# Calculate FC between healthy and cohort1
table_lum_acm_healthy_log2_t$log2_fc <- log2(table_lum_acm_healthy_log2_t$ACM /
table_lum_acm_healthy_log2_t$Healthy)
# Add p-values to data table
# Extract only Parameters and p-values
stat.test_p <- table_lum_acm_healthy_stat %>%
dplyr::select(Parameter, FDR)
# Merge data tables (log2, FC, p val)
table_lum_acm_healthy_log2_t$Parameter <- rownames(table_lum_acm_healthy_log2_t)
table_lum_acm_healthy_volcano <- merge(table_lum_acm_healthy_log2_t, stat.test_p, by = "Parameter")
# Create Volcano Plot
# Mark parameters if FC > 1.5 = upregulated
table_lum_acm_healthy_volcano$diff_expression <- ifelse (table_lum_acm_healthy_volcano$log2_fc > 1.5,
"up", "no")
# Add new column to relabel parameters FC of > 1.5
table_lum_acm_healthy_volcano$label_para <- ifelse (table_lum_acm_healthy_volcano$log2_fc > 1.5,
table_lum_acm_healthy_volcano$Parameter, "")
plot_volcano <- EnhancedVolcano(table_lum_acm_healthy_volcano,
lab = table_lum_acm_healthy_volcano$label_para,
x = 'log2_fc', y = 'FDR',
FCcutoff = 1.5, pointSize = 3.5, labSize = 4, colAlpha = 0.7,
boxedLabels = TRUE, col = c("grey", "grey", "grey", "brown2"),
xlab = bquote(~Log[2]~ "fold change"), ylab = bquote("FDR-adjusted p-value"),
xlim = c(0, 4), ylim = c(0, 21), pCutoff = 0.05,
title = NULL, subtitle = NULL, border = 'full',
drawConnectors = TRUE,widthConnectors = 0.5, colConnectors = "brown2") +
theme_classic() +
theme(panel.grid = element_blank(),
panel.border = element_rect(colour = "black", fill = NA,),
legend.position = "none")
print( plot_volcano)
| Version | Author | Date |
|---|---|---|
| 4c1f400 | anjo1995 | 2026-03-06 |
Heatmap Selected Parameters
# Select preserved cytokines
table_lum <- table_lum %>%
dplyr::select(Study_ID, Cohort, HGF, IL_2R, CXCL9, CXCL10, CXCL8, IL_6, CCL3, CCL4)
# Name Rownames with study ID and remove Study ID and Cohort column
table_lum_01 <- subset(table_lum, select = -c(Study_ID, Cohort))
# Convert Data Table to Data Frame
table_lum_01 <- as.data.frame(table_lum_01)
# Transpose Data Table
table_lum_t <- t(table_lum_01)
## Column Annotations
# Create list with Study_ID & Cohort
list_cohort_group <- subset(table_lum, select= c(Study_ID, Cohort))
rownames(list_cohort_group) <- list_cohort_group$Study_ID
list_cohort_group <- subset(list_cohort_group, select= -c(Study_ID))
## Row Annotations
# Create list with Parameters
list_parameter_groups <- read.xlsx(paste0(basedir,"/data/Grouping of Lumminex Marker.xlsx"))
# Create list with Columns of data table
list_parameter_datatable <- as.data.frame(rownames(table_lum_t))
colnames(list_parameter_datatable) <- "Parameter"
# Merge to match the order of parameters
list_parameter_groups_01 <- merge(list_parameter_groups, list_parameter_datatable, by.y = "Parameter")
# Define the predefined order for Group
group_order <- c("Cytokines", "Chemokines", "Tissue Cytokines")
# Order list based on predefined order
selected_markers <- c("HGF","IL_2R", "CXCL9", "CXCL10", "CXCL8", "IL_6", "CCL3", "CCL4")
# Filter and order
list_parameter_groups_02 <- list_parameter_groups_01 %>%
filter(Parameter %in% selected_markers) %>%
mutate(Group = factor(Group, levels = group_order),
Parameter = factor(Parameter, levels = selected_markers)) %>%
arrange(Parameter)
# Extract ordered parameter vector
list_parameter_groups_03 <- as.character(list_parameter_groups_02$Parameter)
# Reorder Data Table based on the new ordered list
table_lum_t_ordered <- as.data.frame(table_lum_t)
# Convert it back to matrix
table_lum_t_ordered <- as.matrix(table_lum_t_ordered)
# Log Transform
table_lum_t_ordered <- log2(table_lum_t_ordered)
# Scale
table_lum_t_ordered <- t(apply(table_lum_t_ordered, 1, scale))
# Create Cohort Label Colors of Annotations in heatmap
ann_colors <- list(Cohort = c("Myocarditis" = "darkred", "Healthy" = "grey", "ACM" = "brown2"),
Group = c("Cytokines" = "darkseagreen", "Chemokines" = "lightblue", "Tissue Cytokines" = "mediumpurple3"))
# Column Annotation
col_anno <- HeatmapAnnotation(df = list_cohort_group, col = ann_colors)
# Row Annotation
# Prepare row annotations as a data frame
row_anno <- rowAnnotation(Group = list_parameter_groups_02$Group,
col = list(Group = c("Cytokines" = "darkseagreen",
"Chemokines" = "lightblue",
"Tissue Cytokines" = "mediumpurple3")))
# Create Heatmap
heatmap_lum_sel <- Heatmap(table_lum_t_ordered,
name = "Expression",
col = circlize::colorRamp2(c(-5, 0, 5), c("blue", "white", "red")),
cluster_rows = TRUE,
cluster_columns = TRUE,
clustering_method_columns = "ward.D",
show_column_names = TRUE,
column_names_gp = gpar(fontsize = 8),
row_names_gp = gpar(fontsize = 8),
width = unit(15, "cm"), height = unit(5, "cm"),
top_annotation = col_anno,
right_annotation = row_anno)
# Draw Heatmap
show(heatmap_lum_sel)
| Version | Author | Date |
|---|---|---|
| 4c1f400 | anjo1995 | 2026-03-06 |
BMP4/Gremlin axis
# Subset
table_bmp4 <- table_all_myo_acm_healthy %>%
dplyr::select(Study_ID, Cohort, BMP4, Grem_1, Grem_2)
# Prepare data table
table_para <- table_bmp4 %>%
pivot_longer(cols = where(is.numeric),
names_to = "Parameter",
values_to = "Parameter_val")
# Set Cohort order
table_para$Cohort <- factor(table_para$Cohort,
levels = c("Healthy", "Myocarditis", "ACM"))
# Calculate Stats
# Kruskal–Wallis test
stat.kruskal <- table_para %>%
group_by(Parameter) %>%
kruskal_test(Parameter_val ~ Cohort) %>%
add_significance()
# Dunn post hoc test
stat.dunn <- table_para %>%
group_by(Parameter) %>%
dunn_test(Parameter_val ~ Cohort, p.adjust.method = "BH")
# Calculate max y for positioning p-values
max_y <- table_para %>%
group_by(Parameter) %>%
summarise(max_val = max(Parameter_val, na.rm = TRUE))
# Get min value per parameter (for y limits)
min_y <- table_para %>%
group_by(Parameter) %>%
summarise(min_val = min(Parameter_val, na.rm = TRUE))
# Combine with max_y info for plotting
stat.test <- stat.dunn %>%
left_join(max_y, by = "Parameter") %>%
mutate(y.position = max_val * 1) %>%
group_by(Parameter) %>%
arrange(p.adj)
# Create the loop to create a plot
param <- c("BMP4", "Grem_1","Grem_2")
y_settings <- list(BMP4 = list(limits = c(NA, 1000), breaks = c(10, 100, 1000)),
Grem_1 = list(limits = c(NA, 100000), breaks = c(10, 100, 1000, 10000, 100000)),
Grem_2 = list(limits = c(NA, 100000), breaks = c(300, 1000, 3000, 10000, 30000, 100000)))
# Define axis labels for each biomarker
biomarker_labels <- c("BMP4 (pg/ml)", "Gremlin-1 (pg/ml)", "Gremlin-2 (pg/ml)")
names(biomarker_labels) <- param
# Create a list for all plots created in the loop
plots_list <- list()
for (param in param)
{
# Filter for the data
temp_data <- table_para %>%
filter(Parameter == param )
temp_stat_test <- stat.test %>%
filter(Parameter == param ) %>%
mutate(p_adj_label = ifelse(p.adj < 0.001, "<0.001", sprintf("%.3f", p.adj)))
# Axis settings
y_lim <- y_settings[[param ]]$limits
y_brk <- y_settings[[param ]]$breaks
plot_lum <- ggplot(temp_data, aes(x = Cohort, y = Parameter_val)) +
geom_boxplot(aes(fill = Cohort), color = "black", outlier.shape = NA,
width = 0.6, alpha = 0.4) +
geom_point(shape = 21, size = 3, color = "black", aes(fill = Cohort),
position = position_jitter(width = 0.2, height = 0)) +
scale_fill_manual(values = c("Healthy" = "grey",
"Myocarditis" = "darkred",
"ACM" = "brown2")) +
stat_pvalue_manual(temp_stat_test, label = "p_adj_label",
y.position = log10(temp_stat_test$y.position),
step.increase = 0.02, tip.length = 0) +
scale_y_log10(labels = function(x) format(x, scientific = FALSE, trim = TRUE),
limits = y_lim, breaks = y_brk, expand = expansion(mult = c(0.05, 0))) +
labs(x = NULL, y = biomarker_labels[[param]]) +
theme_classic() +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "none")
plots_list[[param]] <- plot_lum
}
# Combine plots to a panel
panel <- ggarrange(plotlist = plots_list,
ncol = 3, nrow = 1)
print(panel)
session info
sessionInfo()R version 4.4.3 (2025-02-28)
Platform: aarch64-apple-darwin20
Running under: macOS 26.3
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Zurich
tzcode source: internal
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ImmunoLogic_0.0.0.9000 Seurat_5.3.1 SeuratObject_5.2.0
[4] sp_2.2-0 kernlab_0.9-33 dbscan_1.2-0
[7] umap_0.2.10.0 Rtsne_0.17 cluster_2.1.8
[10] factoextra_1.0.7 randomForest_4.7-1.2 pROC_1.19.0.1
[13] mice_3.18.0 caret_7.0-1 lattice_0.22-6
[16] drc_3.0-1 logistf_1.26.1 glmnet_4.1-10
[19] Matrix_1.7-2 broom_1.0.11 MuMIn_1.48.11
[22] Hmisc_5.2-3 car_3.1-3 carData_3.0-5
[25] multcomp_1.4-28 TH.data_1.1-4 MASS_7.3-65
[28] survival_3.8-3 mvtnorm_1.3-3 rstatix_0.7.3
[31] ggpattern_1.2.1 gridExtra_2.3 cowplot_1.2.0
[34] patchwork_1.3.2 plotly_4.11.0 EnhancedVolcano_1.24.0
[37] RColorBrewer_1.1-3 circlize_0.4.16 ComplexHeatmap_2.22.0
[40] pheatmap_1.0.12 corrplot_0.95 ggcorrplot_0.1.4.1
[43] ggbeeswarm_0.7.2 ggrepel_0.9.6 ggpubr_0.6.2
[46] readxl_1.4.5 writexl_1.5.0 openxlsx_4.2.5.2
[49] gtsummary_2.4.0 flextable_0.9.6 tableone_0.13.2
[52] table1_1.4.3 scales_1.4.0 here_1.0.2
[55] janitor_2.2.0 magrittr_2.0.4 data.table_1.17.8
[58] lubridate_1.9.4 forcats_1.0.1 stringr_1.6.0
[61] dplyr_1.1.4 purrr_1.2.0 readr_2.1.6
[64] tidyr_1.3.1 tibble_3.3.0 ggplot2_4.0.1
[67] tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] IRanges_2.40.1 nnet_7.3-20 goftest_1.2-3
[4] vctrs_0.6.5 spatstat.random_3.4-3 digest_0.6.39
[7] png_0.1-8 shape_1.4.6.1 git2r_0.36.2
[10] alabama_2023.1.0 deldir_2.0-4 httpcode_0.3.0
[13] parallelly_1.45.1 fontLiberation_0.1.0 reshape2_1.4.5
[16] httpuv_1.6.16 foreach_1.5.2 BiocGenerics_0.52.0
[19] withr_3.0.2 xfun_0.54 crul_1.4.2
[22] emmeans_1.10.4 systemfonts_1.3.1 ragg_1.5.0
[25] zoo_1.8-14 GlobalOptions_0.1.3 gtools_3.9.5
[28] pbapply_1.7-4 Formula_1.2-5 promises_1.5.0
[31] otel_0.2.0 httr_1.4.7 globals_0.18.0
[34] fitdistrplus_1.2-4 rstudioapi_0.17.1 pan_1.9
[37] miniUI_0.1.2 generics_0.1.4 base64enc_0.1-3
[40] curl_7.0.0 S4Vectors_0.44.0 mitools_2.4
[43] polyclip_1.10-7 quadprog_1.5-8 xtable_1.8-4
[46] doParallel_1.0.17 evaluate_1.0.5 hms_1.1.4
[49] irlba_2.3.5.1 colorspace_2.1-2 polynom_1.4-1
[52] ROCR_1.0-11 reticulate_1.44.1 spatstat.data_3.1-9
[55] lmtest_0.9-40 snakecase_0.11.1 later_1.4.4
[58] spatstat.geom_3.6-1 future.apply_1.20.0 scattermore_1.2
[61] survey_4.4-2 matrixStats_1.5.0 RcppAnnoy_0.0.22
[64] class_7.3-23 pillar_1.11.1 nlme_3.1-167
[67] iterators_1.0.14 compiler_4.4.3 RSpectra_0.16-2
[70] stringi_1.8.7 gower_1.0.2 jomo_2.7-6
[73] tensor_1.5.1 minqa_1.2.8 plyr_1.8.9
[76] crayon_1.5.3 abind_1.4-8 orthopolynom_1.0-6.1
[79] sandwich_3.1-1 whisker_0.4.1 codetools_0.2-20
[82] textshaping_1.0.4 basefun_1.2-4 recipes_1.3.1
[85] openssl_2.3.4 bslib_0.9.0 GetoptLong_1.0.5
[88] mime_0.13 splines_4.4.3 Rcpp_1.1.0
[91] fastDummies_1.7.5 coneproj_1.20 variables_1.1-2
[94] cellranger_1.1.0 knitr_1.50 clue_0.3-66
[97] lme4_1.1-38 fs_1.6.6 listenv_0.10.0
[100] checkmate_2.3.3 Rdpack_2.6.4 ggsignif_0.6.4
[103] estimability_1.5.1 tzdb_0.5.0 pkgconfig_2.0.3
[106] tools_4.4.3 cachem_1.1.0 rbibutils_2.4
[109] numDeriv_2016.8-1.1 viridisLite_0.4.2 DBI_1.2.3
[112] fastmap_1.2.0 rmarkdown_2.30 ica_1.0-3
[115] tram_1.2-4 sass_0.4.10 officer_0.6.6
[118] coda_0.19-4.1 dotCall64_1.2 RANN_2.6.2
[121] rpart_4.1.24 farver_2.1.2 reformulas_0.4.2
[124] mgcv_1.9-1 yaml_2.3.11 workflowr_1.7.2
[127] foreign_0.8-88 cli_3.6.5 stats4_4.4.3
[130] lifecycle_1.0.4 uwot_0.2.4 askpass_1.2.1
[133] lava_1.8.0 backports_1.5.0 mlt_1.6-6
[136] timechange_0.3.0 gtable_0.3.6 rjson_0.2.23
[139] ggridges_0.5.7 progressr_0.18.0 parallel_4.4.3
[142] jsonlite_2.0.0 RcppHNSW_0.6.0 mitml_0.4-5
[145] qrng_0.0-10 spatstat.utils_3.2-0 zip_2.3.1
[148] jquerylib_0.1.4 spatstat.univar_3.1-5 timeDate_4051.111
[151] lazyeval_0.2.2 shiny_1.12.0 htmltools_0.5.9
[154] sctransform_0.4.2 glue_1.8.0 gfonts_0.2.0
[157] BB_2019.10-1 spam_2.11-1 gdtools_0.3.7
[160] rprojroot_2.1.1 boot_1.3-31 igraph_2.2.1
[163] R6_2.6.1 labeling_0.4.3 ipred_0.9-15
[166] nloptr_2.2.1 tidyselect_1.2.1 vipor_0.4.7
[169] plotrix_3.8-4 htmlTable_2.4.3 operator.tools_1.6.3
[172] xml2_1.5.1 fontBitstreamVera_0.1.1 future_1.68.0
[175] ModelMetrics_1.2.2.2 KernSmooth_2.23-26 S7_0.2.1
[178] fontquiver_0.2.1 htmlwidgets_1.6.4 rlang_1.1.6
[181] spatstat.sparse_3.1-0 spatstat.explore_3.6-0 uuid_1.2-1
[184] formula.tools_1.7.1 hardhat_1.4.1 beeswarm_0.4.0
[187] prodlim_2023.08.28 date()[1] "Fri Mar 6 19:41:08 2026"
sessionInfo()R version 4.4.3 (2025-02-28)
Platform: aarch64-apple-darwin20
Running under: macOS 26.3
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Zurich
tzcode source: internal
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ImmunoLogic_0.0.0.9000 Seurat_5.3.1 SeuratObject_5.2.0
[4] sp_2.2-0 kernlab_0.9-33 dbscan_1.2-0
[7] umap_0.2.10.0 Rtsne_0.17 cluster_2.1.8
[10] factoextra_1.0.7 randomForest_4.7-1.2 pROC_1.19.0.1
[13] mice_3.18.0 caret_7.0-1 lattice_0.22-6
[16] drc_3.0-1 logistf_1.26.1 glmnet_4.1-10
[19] Matrix_1.7-2 broom_1.0.11 MuMIn_1.48.11
[22] Hmisc_5.2-3 car_3.1-3 carData_3.0-5
[25] multcomp_1.4-28 TH.data_1.1-4 MASS_7.3-65
[28] survival_3.8-3 mvtnorm_1.3-3 rstatix_0.7.3
[31] ggpattern_1.2.1 gridExtra_2.3 cowplot_1.2.0
[34] patchwork_1.3.2 plotly_4.11.0 EnhancedVolcano_1.24.0
[37] RColorBrewer_1.1-3 circlize_0.4.16 ComplexHeatmap_2.22.0
[40] pheatmap_1.0.12 corrplot_0.95 ggcorrplot_0.1.4.1
[43] ggbeeswarm_0.7.2 ggrepel_0.9.6 ggpubr_0.6.2
[46] readxl_1.4.5 writexl_1.5.0 openxlsx_4.2.5.2
[49] gtsummary_2.4.0 flextable_0.9.6 tableone_0.13.2
[52] table1_1.4.3 scales_1.4.0 here_1.0.2
[55] janitor_2.2.0 magrittr_2.0.4 data.table_1.17.8
[58] lubridate_1.9.4 forcats_1.0.1 stringr_1.6.0
[61] dplyr_1.1.4 purrr_1.2.0 readr_2.1.6
[64] tidyr_1.3.1 tibble_3.3.0 ggplot2_4.0.1
[67] tidyverse_2.0.0
loaded via a namespace (and not attached):
[1] IRanges_2.40.1 nnet_7.3-20 goftest_1.2-3
[4] vctrs_0.6.5 spatstat.random_3.4-3 digest_0.6.39
[7] png_0.1-8 shape_1.4.6.1 git2r_0.36.2
[10] alabama_2023.1.0 deldir_2.0-4 httpcode_0.3.0
[13] parallelly_1.45.1 fontLiberation_0.1.0 reshape2_1.4.5
[16] httpuv_1.6.16 foreach_1.5.2 BiocGenerics_0.52.0
[19] withr_3.0.2 xfun_0.54 crul_1.4.2
[22] emmeans_1.10.4 systemfonts_1.3.1 ragg_1.5.0
[25] zoo_1.8-14 GlobalOptions_0.1.3 gtools_3.9.5
[28] pbapply_1.7-4 Formula_1.2-5 promises_1.5.0
[31] otel_0.2.0 httr_1.4.7 globals_0.18.0
[34] fitdistrplus_1.2-4 rstudioapi_0.17.1 pan_1.9
[37] miniUI_0.1.2 generics_0.1.4 base64enc_0.1-3
[40] curl_7.0.0 S4Vectors_0.44.0 mitools_2.4
[43] polyclip_1.10-7 quadprog_1.5-8 xtable_1.8-4
[46] doParallel_1.0.17 evaluate_1.0.5 hms_1.1.4
[49] irlba_2.3.5.1 colorspace_2.1-2 polynom_1.4-1
[52] ROCR_1.0-11 reticulate_1.44.1 spatstat.data_3.1-9
[55] lmtest_0.9-40 snakecase_0.11.1 later_1.4.4
[58] spatstat.geom_3.6-1 future.apply_1.20.0 scattermore_1.2
[61] survey_4.4-2 matrixStats_1.5.0 RcppAnnoy_0.0.22
[64] class_7.3-23 pillar_1.11.1 nlme_3.1-167
[67] iterators_1.0.14 compiler_4.4.3 RSpectra_0.16-2
[70] stringi_1.8.7 gower_1.0.2 jomo_2.7-6
[73] tensor_1.5.1 minqa_1.2.8 plyr_1.8.9
[76] crayon_1.5.3 abind_1.4-8 orthopolynom_1.0-6.1
[79] sandwich_3.1-1 whisker_0.4.1 codetools_0.2-20
[82] textshaping_1.0.4 basefun_1.2-4 recipes_1.3.1
[85] openssl_2.3.4 bslib_0.9.0 GetoptLong_1.0.5
[88] mime_0.13 splines_4.4.3 Rcpp_1.1.0
[91] fastDummies_1.7.5 coneproj_1.20 variables_1.1-2
[94] cellranger_1.1.0 knitr_1.50 clue_0.3-66
[97] lme4_1.1-38 fs_1.6.6 listenv_0.10.0
[100] checkmate_2.3.3 Rdpack_2.6.4 ggsignif_0.6.4
[103] estimability_1.5.1 tzdb_0.5.0 pkgconfig_2.0.3
[106] tools_4.4.3 cachem_1.1.0 rbibutils_2.4
[109] numDeriv_2016.8-1.1 viridisLite_0.4.2 DBI_1.2.3
[112] fastmap_1.2.0 rmarkdown_2.30 ica_1.0-3
[115] tram_1.2-4 sass_0.4.10 officer_0.6.6
[118] coda_0.19-4.1 dotCall64_1.2 RANN_2.6.2
[121] rpart_4.1.24 farver_2.1.2 reformulas_0.4.2
[124] mgcv_1.9-1 yaml_2.3.11 workflowr_1.7.2
[127] foreign_0.8-88 cli_3.6.5 stats4_4.4.3
[130] lifecycle_1.0.4 uwot_0.2.4 askpass_1.2.1
[133] lava_1.8.0 backports_1.5.0 mlt_1.6-6
[136] timechange_0.3.0 gtable_0.3.6 rjson_0.2.23
[139] ggridges_0.5.7 progressr_0.18.0 parallel_4.4.3
[142] jsonlite_2.0.0 RcppHNSW_0.6.0 mitml_0.4-5
[145] qrng_0.0-10 spatstat.utils_3.2-0 zip_2.3.1
[148] jquerylib_0.1.4 spatstat.univar_3.1-5 timeDate_4051.111
[151] lazyeval_0.2.2 shiny_1.12.0 htmltools_0.5.9
[154] sctransform_0.4.2 glue_1.8.0 gfonts_0.2.0
[157] BB_2019.10-1 spam_2.11-1 gdtools_0.3.7
[160] rprojroot_2.1.1 boot_1.3-31 igraph_2.2.1
[163] R6_2.6.1 labeling_0.4.3 ipred_0.9-15
[166] nloptr_2.2.1 tidyselect_1.2.1 vipor_0.4.7
[169] plotrix_3.8-4 htmlTable_2.4.3 operator.tools_1.6.3
[172] xml2_1.5.1 fontBitstreamVera_0.1.1 future_1.68.0
[175] ModelMetrics_1.2.2.2 KernSmooth_2.23-26 S7_0.2.1
[178] fontquiver_0.2.1 htmlwidgets_1.6.4 rlang_1.1.6
[181] spatstat.sparse_3.1-0 spatstat.explore_3.6-0 uuid_1.2-1
[184] formula.tools_1.7.1 hardhat_1.4.1 beeswarm_0.4.0
[187] prodlim_2023.08.28