SFigure 4
Anna Joachimbauer
2024-10-02
Last updated: 2026-03-06
Checks: 6 1
Knit directory: Serology-Analysis/
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| Rmd | 316f274 | anjo1995 | 2026-03-06 | SFigure5 |
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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 (Cohort 1, Cohort 2)
# Import Phenotypes
table_cluster <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_phenotypes.xlsx"))
# Import BMP4 Data
table_bmp4_cohort1 <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_BMP4_Cohort1.xlsx"))
table_bmp4_cohort2 <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_BMP4_Cohort2.xlsx"))
# Combine Tables
table_bmp4_myo <- rbind(table_bmp4_cohort1, table_bmp4_cohort2) %>%
dplyr::select(-Cohort)
# Import Serology Data
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)
# Combine tables
table_clindat_myo <- rbind(table_clindat_cohort1, table_clindat_cohort2)
# Import Luminex Data
table_lum_cohort1 <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_Lum_Cohort1_DL_corr.xlsx"))
table_lum_cohort2 <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_Lum_Cohort2_DL_corr.xlsx"))
# Combine tables
table_lum_myo <- rbind(table_lum_cohort1, table_lum_cohort2)
# Combine all data tables
table_all_myo <- table_lum_myo %>%
left_join(table_bmp4_myo, by = "Study_ID") %>%
left_join(table_clindat_myo, by = "Study_ID")Multiple Imputation (Myo)
# Impute missing values
imputed_data <- mice(table_all_myo, m = 5, method = 'pmm', maxit = 5, seed = 1234)
iter imp variable
1 1 NTproBNP LV_EF Trop_I
1 2 NTproBNP LV_EF Trop_I
1 3 NTproBNP LV_EF Trop_I
1 4 NTproBNP LV_EF Trop_I
1 5 NTproBNP LV_EF Trop_I
2 1 NTproBNP LV_EF Trop_I
2 2 NTproBNP LV_EF Trop_I
2 3 NTproBNP LV_EF Trop_I
2 4 NTproBNP LV_EF Trop_I
2 5 NTproBNP LV_EF Trop_I
3 1 NTproBNP LV_EF Trop_I
3 2 NTproBNP LV_EF Trop_I
3 3 NTproBNP LV_EF Trop_I
3 4 NTproBNP LV_EF Trop_I
3 5 NTproBNP LV_EF Trop_I
4 1 NTproBNP LV_EF Trop_I
4 2 NTproBNP LV_EF Trop_I
4 3 NTproBNP LV_EF Trop_I
4 4 NTproBNP LV_EF Trop_I
4 5 NTproBNP LV_EF Trop_I
5 1 NTproBNP LV_EF Trop_I
5 2 NTproBNP LV_EF Trop_I
5 3 NTproBNP LV_EF Trop_I
5 4 NTproBNP LV_EF Trop_I
5 5 NTproBNP LV_EF Trop_I# Check the imputed data
densityplot(imputed_data, col=c("grey", "blue"), pch = c(1, 20))
| Version | Author | Date |
|---|---|---|
| 316f274 | anjo1995 | 2026-03-06 |
# Create a data set with the observed and completed data
table_imp <- complete(imputed_data, 1)Abandance Bubble Plot: Selection of parameters
# Subset Myo
table_abund_myo <- table_imp %>%
dplyr::select("Study_ID", "Cohort", "IL_2R", "HGF", "CXCL9", "CXCL10",
"CCL3", "CCL4", "CXCL8", "IL_6",
"Grem_1", "BMP4","Grem_2", "NTproBNP")
# Add Healthy as reference
table_lum_healthy <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_Lum_Healthy_DL_corr.xlsx")) %>%
dplyr::select("Study_ID", "IL_2R", "HGF", "CXCL9", "CXCL10",
"CCL3", "CCL4", "CXCL8", "IL_6")
table_bmp4_healthy <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_BMP4_Healthy.xlsx")) %>%
dplyr::select("Study_ID", "Cohort", "BMP4", "Grem_1", "Grem_2")
table_clindat_healthy <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_Serology_Healthy.xlsx")) %>%
dplyr::select(Study_ID, NTproBNP)
table_healthy <- table_lum_healthy %>%
left_join(table_bmp4_healthy, by = "Study_ID") %>%
left_join(table_clindat_healthy, by = "Study_ID")
table_abund_all <- bind_rows(table_abund_myo, table_healthy)
# Prepare data table for bubble plot
# Create the loop vector
parameters <- names(which(sapply(table_abund_all, is.numeric) == TRUE))
# Prepare data table
table_para <- table_abund_all %>%
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 each diseased cohort
cohorts_stats <- table_para %>%
filter(Cohort != "Healthy") %>%
group_by(Cohort, Parameter) %>%
summarise(mean_expr_diseased = mean(Parameter_val, na.rm = TRUE)) %>%
left_join(healthy_stats, by = "Parameter") %>%
mutate(log2_fold_change = log2(mean_expr_diseased / mean_expr_healthy))
# Calculate percentage of how many patients have a fold change > 1
diseased_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 = log2_fc >1) %>%
summarise(percentage_fc_1 = mean(fc_1, na.rm = TRUE) * 100)
# Merge stats tables
cohorts_stats <- cohorts_stats %>%
left_join(diseased_percentage_fc_increase,by = c("Cohort", "Parameter"))
# Calculate Mean of both Cohorts
combined_from_cohorts <- cohorts_stats %>%
group_by(Parameter) %>%
summarise(percentage_fc_1_mean = mean(percentage_fc_1, na.rm = TRUE),
log2_fc_mean = mean(log2_fold_change, na.rm = TRUE))
# Order Parameter according to abandance
combined_from_cohorts <- combined_from_cohorts %>%
arrange(desc(percentage_fc_1_mean)) %>%
mutate(Parameter = factor(Parameter, levels = unique(Parameter)))
# Create the Bubble Plot
bubble_plot <- ggplot(combined_from_cohorts, aes(x = Parameter,y = 1,
size = abs(percentage_fc_1_mean), color = log2_fc_mean)) +
geom_point(alpha = 1) +
geom_text(aes(label = round(percentage_fc_1_mean, 1)),
color = "black", size = 3, vjust = -5) +
scale_size(range = c(2, 8)) +
scale_color_gradient2(low = "royalblue", mid = "white", high = "darkred", midpoint = 0) +
labs(title = "Fold Change (Log2) by Parameter",
x = "Parameter", y = "",
size = "Percentage of Patients (FC > 1)", color = "Log2 Fold Change") +
theme_classic() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Display the plot
print(bubble_plot)
| Version | Author | Date |
|---|---|---|
| 316f274 | anjo1995 | 2026-03-06 |
Cardiac dysfunction
# Subset data
table_lvef <- table_imp %>%
dplyr::select("Study_ID", "LV_EF")
# Calculate percentage of low LVEF
percent_lvef_low <- table_lvef %>%
summarise(n_total = n(),
n_low = sum(LV_EF < 52, na.rm = TRUE),
percent_low = (n_low / n_total) * 100 )
# Label low LVEF
table_lvef <- table_imp %>%
mutate(lvef_reduced = case_when(
LV_EF < 52 ~ "Reduced",
TRUE ~ "Normal"))
table_lvef$lvef_reduced <- factor(table_lvef$lvef_reduced, levels = c("Normal", "Reduced"))
# Plot
plot_lvef <- ggplot(table_lvef, aes(x = "", y = LV_EF)) +
geom_boxplot(color = "black", outlier.shape = NA, width = 0.6, alpha = 0.4) +
geom_point(aes(fill = lvef_reduced), shape = 21, size = 3,
color = "black", position = position_jitter(width = 0.2, height = 0)) +
scale_fill_manual(values = c("Normal" = "grey", "Reduced" = "darkred")) +
scale_y_continuous(limits = c(0, 80), expand = c(0, 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 = "right") +
annotate("text",x = 1, y = 75, size = 5,fontface = "bold", label = paste0(round(percent_lvef_low$percent_low, 0),
"% of AM patients\nLVEF <52%"))
print(plot_lvef)
| Version | Author | Date |
|---|---|---|
| 316f274 | anjo1995 | 2026-03-06 |
Comparison Parameters in immuno-clinical phenotypes
# Define colors for plots
cluster_colors <- c("Severe" = "plum4",
"Mild" = "cadetblue4")
# Prepare data table
table_para <- table_cluster %>%
pivot_longer(cols = where(is.numeric),
names_to = "Parameter",
values_to = "Parameter_val")
# Put outcome as ordered factor
table_para$phenotype <- factor(table_para$phenotype,
levels = c("Mild", "Severe"))
# Calculate Stats
# Wilcox Test
stat.test <- table_para %>%
group_by(Parameter) %>%
wilcox_test(Parameter_val ~ phenotype) %>%
add_significance() %>%
mutate(p_adj = p.adjust(p, method = "BH"))
# Get max value per parameter (for p-value position)
max_y <- table_para %>%
group_by(Parameter) %>%
summarise(max_val = max(Parameter_val, na.rm = TRUE))
# Get min value per parameter (for y limits)
min_y <- table_para %>%
group_by(Parameter) %>%
summarise(min_val = min(Parameter_val, na.rm = TRUE))
# Combine with max_y info for plotting
stat.test <- stat.test %>%
left_join(max_y, by = "Parameter") %>%
left_join(min_y, by = "Parameter") %>%
mutate(y.position = max_val)
# Define biomarkers
biomarkers <- c("NTproBNP", "CXCL10", "CXCL9",
"IL_2R", "CCL4", "HGF", "Grem_2")
y_settings <- list(NTproBNP = list(limits = c(NA, 100000), breaks = c(10, 100, 1000, 10000, 100000)),
CXCL10 = list(limits = c(NA, 3000), breaks = c(30, 100, 300, 1000, 3000)),
CXCL9 = list(limits = c(NA, 10000), breaks = c(100, 300, 1000, 3000, 10000)),
IL_2R = list(limits = c(NA, 30000), breaks = c(30, 100, 300, 1000, 3000, 10000, 30000)),
CCL4 = list(limits = c(NA, 30000), breaks = c(30, 100, 300, 1000, 3000, 10000, 30000)),
HGF = list(limits = c(NA, 30000), breaks = c(30, 100, 300, 1000, 3000, 10000, 30000)),
Grem_2 = list(limits = c(NA, 100000), breaks = c(300, 1000, 3000, 10000, 30000, 100000)))
# Define axis labels for each biomarker
biomarker_labels <- c("NT-proBNP (ng/l)", "CXCL10 (pg/ml)", "CXCL9 (pg/ml)",
"IL-2R (pg/ml)", "CCL4 (pg/ml)", "HGF (pg/ml)", "Gremlin-2 (pg/ml)")
names(biomarker_labels) <- biomarkers
# Create a plot list
plots_list <- list()
for (param in biomarkers)
{
# Filter for the data
temp_data <- table_para %>%
filter(Parameter == param)
temp_stat_test <- stat.test %>%
filter(Parameter == param) %>%
mutate(p_adj_label = ifelse(p_adj < 0.001, "<0.001", sprintf("%.3f", p_adj)))
# Axis settings
y_lim <- y_settings[[param]]$limits
y_brk <- y_settings[[param]]$breaks
plot <- ggplot(temp_data, aes(x = phenotype, y = Parameter_val)) +
geom_boxplot(aes(fill = phenotype), color = "black", outlier.shape = NA,
width = 0.6, alpha = 0.4) +
geom_point(shape = 21, size = 3, color = "black", aes(fill = phenotype),
position = position_jitter(width = 0.2, height = 0)) +
scale_fill_manual(values = cluster_colors) +
scale_y_log10(labels = function(x) format(x, scientific = FALSE, trim = TRUE),
limits = y_lim, breaks = y_brk, expand = expansion(mult = c(0.05, 0))) +
stat_pvalue_manual(temp_stat_test, label = "p_adj_label",
y.position = log10(temp_stat_test$y.position),
step.increase = 0.1,
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
}
# 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 = phenotype, y = Parameter_val)) +
geom_boxplot(aes(fill = phenotype), color = "black", outlier.shape = NA,
width = 0.6, alpha = 0.4) +
geom_point(shape = 21, size = 3, color = "black", aes(fill = phenotype),
position = position_jitter(width = 0.2, height = 0)) +
scale_fill_manual(values = cluster_colors) +
scale_y_continuous(limits = c(0, 80), expand = c(0, 0)) +
stat_pvalue_manual(temp_stat_test, label = "p_adj_label",
y.position = temp_stat_test$y.position,
step.increase = 0.1,
tip.length = 0) +
labs(x = NULL, y = "LVEF (%)") +
theme_classic() +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "none")
# Combine plots to a panel
panel <- ggarrange(plotlist = c(plot_lvef, plots_list),
ncol = 4, nrow = 2, common.legend = TRUE)
print(panel)
| Version | Author | Date |
|---|---|---|
| 316f274 | anjo1995 | 2026-03-06 |
Correlation Plot
# Define cluster colors
cluster_colors <- c("Severe" = "plum4",
"Mild" = "cadetblue4",
"Healthy" = "lightgrey")
# Healthy patients
table_healthy <- table_healthy %>%
dplyr::select(Study_ID, CXCL10, Grem_2, NTproBNP) %>%
mutate(phenotype = "Healthy")
# Clustered patients
table_cluster_01 <- table_cluster %>%
dplyr::select(Study_ID, phenotype) %>%
left_join(table_imp %>% dplyr::select(Study_ID, CXCL10, Grem_2, NTproBNP),by = "Study_ID")
# Combine
table_corr <- rbind(table_cluster_01, table_healthy)
# Compute Spearman correlation
cor_test <- cor.test(table_corr$Grem_2, table_corr$NTproBNP, method = "spearman")
plot <- ggplot(table_corr, aes(y = Grem_2, x = NTproBNP, color = phenotype)) +
geom_point(aes(fill = phenotype, size = CXCL10),
shape = 21, color = "black", alpha = 0.8) +
scale_size_continuous(name = "CXCL10 (pg/ml)",
range = c(2, 15),
breaks = c(35, 50, 100),
labels = c("35", "50", "100"),
limits = c(35, 600)) +
geom_smooth(method = "lm", se = TRUE, color = "black") +
scale_fill_manual(values = cluster_colors) +
scale_x_log10(labels = function(y) format(y, scientific = FALSE, trim = TRUE),
breaks = c(10, 100, 1000, 10000, 100000),
limits = c(10, 100000)) +
scale_y_log10(labels = function(x) format(x, scientific = FALSE, trim = TRUE),
limits = c(NA, 30000), breaks = c(300, 1000, 3000, 10000, 30000),
expand = expansion(mult = c(0.05, 0), add = c(0,0))) +
theme_classic() +
labs(y = "Gremlin-2 (pg/ml)", x = "NT-proBNP (ng/l)") +
annotate("text",
y = min(table_corr$Grem_2, na.rm = TRUE)*1.4,
x = max(table_corr$NTproBNP, na.rm = TRUE)*0.8,
label = paste0("r = ", round(cor_test$estimate, 2),
"\np = ", ifelse(cor_test$p.value < 0.001, "< 0.001",
signif(cor_test$p.value, 3))),
hjust = 0,vjust = 1, size = 4) +
scale_color_manual(values = cluster_colors)
print(plot)
| Version | Author | Date |
|---|---|---|
| 316f274 | anjo1995 | 2026-03-06 |
Roc Curve
# Data preparation
table_roc <- table_cluster %>%
dplyr::select(Study_ID, phenotype, CXCL9, IL_2R, CCL4, HGF, NTproBNP) %>%
mutate(phenotype = factor(phenotype, levels = c("Mild", "Severe")),
outcome_binary = ifelse(phenotype == "Severe", 1, 0))
# Select predictors
param <- c("CXCL9", "IL_2R", "CCL4", "HGF", "NTproBNP")
biomarker_labels <- c(CXCL9 = "CXCL9", IL_2R = "IL_2R", CCL4 = "CCL4", HGF = "HGF", NTproBNP = "NT-proBNP", Full = "Full model")
# Initialize storage
roc_list <- list()
# Create ROC Curve for each parameter
for (p in param)
{
roc_obj <- roc(response = table_roc$outcome_binary,
predictor = table_roc[[p]],
levels = c(0, 1),
direction = "auto",
ci = TRUE,
legacy.axes = TRUE)
roc_list[[p]] <- roc_obj
}
# Create ROC Curve for full model
table_roc <- table_cluster %>%
dplyr::select("Study_ID", "phenotype", "IL_2R", "HGF", "CXCL9", "CXCL10",
"CCL4", "Grem_2", "NTproBNP", "LV_EF") %>%
mutate(phenotype = factor(phenotype, levels = c("Mild", "Severe")),
outcome_binary = ifelse(phenotype == "Severe", 1, 0))
param = c("IL_2R", "HGF", "CXCL9", "CXCL10", "CCL4", "Grem_2", "NTproBNP", "LV_EF")
# Create formula dynamically
formula_str <- paste("outcome_binary ~", paste(param, collapse = " + "))
model <- glm(as.formula(formula_str),
data = table_roc,
family = binomial)
summary(model)
Call:
glm(formula = as.formula(formula_str), family = binomial, data = table_roc)
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.720e+00 2.987e+00 -1.245 0.21301
IL_2R 6.959e-04 3.514e-04 1.980 0.04766 *
HGF 4.556e-04 3.376e-04 1.350 0.17715
CXCL9 2.433e-03 3.088e-03 0.788 0.43085
CXCL10 3.433e-02 1.206e-02 2.848 0.00440 **
CCL4 2.627e-04 5.739e-04 0.458 0.64712
Grem_2 2.500e-04 7.730e-05 3.234 0.00122 **
NTproBNP 1.384e-04 9.303e-05 1.488 0.13680
LV_EF -1.183e-01 4.878e-02 -2.426 0.01528 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 142.701 on 102 degrees of freedom
Residual deviance: 35.948 on 94 degrees of freedom
AIC: 53.948
Number of Fisher Scoring iterations: 9 # Predicted probabilities (prob of being Severe)
predictions <- predict(model, type = "response")
# Compute ROC curve for combined model
roc_full <- roc(response = table_roc$outcome_binary,
predictor = predictions,
levels = c(0, 1),
direction = "<",
ci = TRUE)
roc_list[["Full"]] <- roc_full
# AUC & CI values
auc_vals <- sapply(roc_list, function(x) as.numeric(auc(x)))
auc_ci <- lapply(roc_list, function(x) ci.auc(x))
# Create Legend
legend_text <- mapply(function(name, auc, ci) {
paste0(biomarker_labels[name],
" (AUC = ", round(auc,2),
", 95% CI: ", round(ci[1],2),
"–", round(ci[3],2), ")")},
names(roc_list), auc_vals,auc_ci)
# Combine ROC curves into ggplot
roc_plot <- ggroc(roc_list, legacy.axes = TRUE) +
geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "grey") +
scale_color_manual(values = c("lightblue", "deepskyblue3", "navyblue", "lightblue4", "grey", "plum4"),
labels = legend_text) +
theme_classic() +
theme(panel.border = element_rect(color = "black", fill = NA, linewidth = 1),
legend.position = "right",
legend.direction = "vertical") +
labs(x = "1 - Specificity",
y = "Sensitivity",
color = "Legend") +
coord_equal()
print(roc_plot)
| Version | Author | Date |
|---|---|---|
| 316f274 | anjo1995 | 2026-03-06 |
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 19:36:25 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