Figure 4
Anna Joachimbauer
2024-10-02
Last updated: 2026-03-06
Checks: 6 1
Knit directory: Serology-Analysis/
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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 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))
# Create a data set with the observed and completed data
table_imp <- complete(imputed_data, 1)Create UMAP and Clustering
# Subset Dataset
table_umap <- table_imp %>%
dplyr::select("Study_ID","IL_2R", "HGF", "CXCL9", "CXCL10", "CCL4",
"Grem_2", "NTproBNP", "LV_EF")
# Label for Study_ID
clindat_label <- table_umap[, "Study_ID"]
# Set Study_ID for rownames
rownames(table_umap) <- table_umap$Study_ID
table_umap_01 <- subset(table_umap, select= -c(Study_ID))
# Logtranformation
table_table_umap_log <- log10(table_umap_01)
# Scale
table_table_umap_log_x <- scale(table_table_umap_log)
# Set seed for reproducibility
set.seed(100)
# Create UMAP transformation
umap_clindat <- umap(table_table_umap_log_x)
# Create UMAP data frame
df_umap_clindat <- data.frame(umap_clindat$layout) %>%
tibble::rownames_to_column() %>%
rename(Study_ID = rowname)
# Spectral Cluster Analysis
# Perform spectral clustering
set.seed(10000)
spectral_result <- specc(as.matrix(df_umap_clindat[, c("X1", "X2")]), centers = 2)
df_umap_clindat$cluster <- as.factor(spectral_result@.Data)
# Plot UMAP with Coloring Cluster Annotation Phenotype colors
plot_umap_clindat <- ggplot(df_umap_clindat,
aes(x = X1, y = X2, fill = cluster)) +
geom_point(shape = 21, size = 4, color = "black", aes(fill = cluster)) +
scale_fill_manual(values = c("2" = "cadetblue4",
"1" = "plum4")) +
labs(title = "Clinical Phenotypes",
x = "UMAP1",
y = "UMAP2") +
theme_classic()
print(plot_umap_clindat) 
# Plot UMAP with Coloring of Cohort
df_umap_clindat_cohort <- merge(df_umap_clindat,
data_frame(Study_ID = table_imp$Study_ID, Cohort = table_imp$Cohort) %>%
mutate( Cohort = case_when(
Cohort == "Immpath" ~ "Cohort1",
Cohort == "TUB_Myocarditis" ~ "Cohort2",TRUE ~ Cohort)),by = "Study_ID")
plot_umap_cohort <- ggplot(df_umap_clindat_cohort,
aes(x = X1, y = X2, fill = Cohort)) +
geom_point(shape = 21, size = 4, color = "black") +
scale_fill_manual(values = c("Cohort1" = "royalblue4",
"Cohort2" = "orange")) +
labs(title = "UMAP: Cohort Distribution",
x = "UMAP1",
y = "UMAP2",
fill = "Cohort") +
theme_classic()
print(plot_umap_cohort) 
Add Cluster Information
table_umap_cluster <- merge( table_umap, df_umap_clindat[, c("Study_ID", "cluster")])
# Relabel Cluster
table_cluster <- table_umap_cluster %>%
mutate(phenotype = case_when(
cluster == "2" ~ "Mild",
cluster == "1" ~ "Severe",
TRUE ~ cluster ))
# Define colors for plots
cluster_colors <- c("Severe" = "plum4",
"Mild" = "cadetblue4")
# Export Table
write.xlsx(table_cluster, file = file.path(basedir,"data", "Table_phenotypes.xlsx"), row.names = FALSE)Importance Score using Random Forest
# Set phenotype as Factor with safe names
table_cluster$phenotype <- factor(table_cluster$phenotype)
# Remove Study_ID
training_data <- table_cluster %>%
dplyr::select(-Study_ID, -cluster)
# Cross Validation
set.seed(1000)
control <- trainControl(method = "cv", number = 10, classProbs = TRUE)
# Train Random Forest on imputed data
rf_imputed <- train(phenotype ~ .,
data = training_data,
method = "rf",
trControl = control,
importance = TRUE,
num.trees = 500,
maxnodes=4)
# Calculate Accuracy of RF model
cat("Accuracy:", max(rf_imputed$results$Accuracy), "\n")Accuracy: 0.8627273 # Show RF variable importance
var_imp <- varImp(rf_imputed, scale = FALSE)
# Caluclate overall importance
varimp <- varImp(rf_imputed, scale = FALSE)$importance
varimp$Overall <- rowMeans(varimp)
head(varimp[order(-varimp$Overall), ]) Mild Severe Overall
Grem_2 11.594889 11.594889 11.594889
LV_EF 11.107213 11.107213 11.107213
CXCL10 10.932231 10.932231 10.932231
HGF 9.567066 9.567066 9.567066
NTproBNP 8.814286 8.814286 8.814286
CXCL9 7.639063 7.639063 7.639063# Plot Top Prediction Variables
imputed_imp <- varImp(rf_imputed, scale = FALSE)
plot_top_pred <- ggplot(imputed_imp, top = 8) +
geom_bar(stat = "identity", width = 0.1) +
ggtitle("Top Predictors - Cluster phenotype") +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank())
show(plot_top_pred)
Perform Ridge Regression model detect best predictive parameters for severe and mild
set.seed(100)
# Subset Variables
table_ridge <- table_cluster %>%
dplyr::select("Study_ID", "phenotype", "IL_2R", "HGF", "CXCL9", "CXCL10",
"CCL4", "Grem_2", "NTproBNP", "LV_EF")
# Log-transform selected biomarkers
table_ridge <- table_ridge %>%
mutate(across(c(IL_2R, HGF, CXCL9, CXCL10, CCL4, Grem_2, LV_EF, NTproBNP), ~ as.numeric(log2(.))))
# Transform phenotype to binary
table_ridge$phenotype <- factor(table_ridge$phenotype,levels = c("Mild", "Severe"))
table_ridge$phenotype_binary <- ifelse(table_ridge$phenotype == "Severe", 1, 0)
# Prepare X and y
X <- model.matrix(phenotype_binary ~ IL_2R + HGF + CXCL9 + CXCL10 + CCL4 + LV_EF + Grem_2 + NTproBNP, table_ridge)[,-1]
y <- table_ridge$phenotype_binary
# Select optimal lamda
set.seed(1000)
ridge_cv <- cv.glmnet(X, y, family = "binomial", alpha = 0, nfolds = 5)
lambda_min <- ridge_cv$lambda.min
# Fit model
ridge_model <- glmnet(X, y, family = "binomial", alpha = 0, lambda = lambda_min)
# Estimate p values and CI
n_boot <- 100
coef_boot <- matrix(NA, nrow = n_boot, ncol = ncol(X))
colnames(coef_boot) <- colnames(X)
for (i in 1:n_boot)
{
idx <- sample(1:nrow(table_ridge), replace = TRUE)
Xb <- X[idx,]
yb <- y[idx]
model_b <- glmnet(Xb, yb, family = "binomial", alpha = 0, lambda = lambda_min)
coef_boot[i, ] <- as.numeric(coef(model_b)[-1])
}
# Compute mean, 95% CI, and p-value
coef_mean <- apply(coef_boot, 2, mean)
coef_low <- apply(coef_boot, 2, function(x) quantile(x, 0.025))
coef_high <- apply(coef_boot, 2, function(x) quantile(x, 0.975))
p_values <- 2 * pmin(apply(coef_boot, 2, function(x) mean(x > 0)),
apply(coef_boot, 2, function(x) mean(x < 0)))
table_coef <- data.frame(Predictor = colnames(X),
Coefficient = coef_mean,
CI_low = coef_low,
CI_high = coef_high,
p_value = p_values) %>%
mutate(OR = exp(Coefficient),
log_or = log10(OR),
log_conf.low = log10(exp(CI_low)),
log_conf.high = log10(exp(CI_high)),
sig = ifelse(CI_low > 0 | CI_high < 0, "Yes", "No"),
color_dir = ifelse(sig == "No", "No", ifelse(log_or < 0, "Left", "Right")),
Direction = ifelse(OR > 1, "Severe", "Mild"))
# Plot Forest Plot
# Customize x axis scale
custom_trans <- trans_new(name = "custom_or",
transform = function(x) {ifelse(x < 1, 0.2 * log10(x + 1e-6), 0.2 + 0.8 * log10(x))},
inverse = function(x) {ifelse(x < 0.2, 10^(x / 0.2) - 1e-6, 10^((x - 0.2) / 0.8))},
domain = c(0, 4))
plot_forest <- ggplot(table_coef, aes(y = reorder(Predictor, OR), x = OR, fill = color_dir)) +
geom_point(shape = 21, size = 5, color = "black", aes(fill = color_dir)) +
geom_errorbarh(aes(xmin = exp(CI_low), xmax = exp(CI_high)), height = 0.2) +
geom_vline(xintercept = 1, linetype = "dashed", color = "black") +
geom_text(aes(label = paste0(
"OR=", round(OR, 2),
"\np=", format.pval(p_value, digits = 2, eps = .001)),
x = exp(CI_high) * 1.05), size = 3, hjust = 0) +
scale_x_continuous(trans = custom_trans,
labels = scales::label_number(accuracy = 0.1),
breaks = c(0.1, 1, 2, 3),) +
scale_fill_manual(values = c("Left" = "cadetblue4", "Right" = "plum4", "No" = "gray")) +
theme_classic() +
theme(legend.position = "bottom") +
labs(title = "Forest Plot Ridge Regression with Bootstrap CI",
y = "Predictor",
x = "Odds Ratio (95% CI) - per 2x increase in covariante\n
Left: favors Mild phenotype, Right: favors Severe phenotype",
color = "Direction / Significant")
print(plot_forest)
# Summary Table of model
summary_table <- table_coef %>%
mutate(OR = round(OR, 2),
CI = paste0(round(exp(CI_low), 2), " - ", round(exp(CI_high), 2)),
p_value = ifelse(p_value < 0.001, "< 0.001", signif(p_value, 3))) %>%
arrange(desc(OR))%>%
dplyr::select(Predictor, OR, CI, p_value)
# Convert summary to table
table_grob <- gridExtra::tableGrob(summary_table, rows = NULL, theme = gridExtra::ttheme_default(base_size = 10))
# Print table
combined_plot <- patchwork::wrap_elements(table_grob) + plot_layout(widths = c(2, 1))
print(combined_plot)
Correlation Plot
# Subset Data table
table_corr <- table_cluster %>%
dplyr::select("Study_ID", "phenotype", "IL_2R", "HGF", "CXCL9", "CXCL10",
"CCL4", "Grem_2", "NTproBNP", "LV_EF")
# Define colors for plots
cluster_colors <- c("Severe" = "plum4",
"Mild" = "cadetblue4")
# Compute Spearman correlation
cor_test <- cor.test(table_corr$Grem_2, table_corr$LV_EF, method = "spearman")
plot <- ggplot(table_corr, aes(y = LV_EF, x = Grem_2, 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(300, 1000, 3000, 10000, 35000),
limits = c(300,35000)) +
scale_y_continuous(limits = c(0,75),
breaks = c(25, 50, 75),
expand = c(0, 0)) +
theme_classic() +
labs(y = "LVEF (%)", x = "Gremlin-2 (pg/ml)") +
annotate("text",
y = min(table_corr$LV_EF, na.rm = TRUE),
x = max(table_corr$Grem_2, na.rm = TRUE),
label = paste0("r = ", round(cor_test$estimate, 2),
"\np = ", ifelse(cor_test$p.value < 0.001, "< 0.001",
signif(cor_test$p.value, 3))),
hjust = 1,vjust = 1, size = 5) +
scale_color_manual(values = cluster_colors) +
coord_cartesian(clip = "off")
print(plot)
Roc Curve
# Data preparation
table_roc <- table_cluster %>%
dplyr::select(Study_ID, phenotype, CXCL10, Grem_2, LV_EF) %>%
mutate(phenotype = factor(phenotype, levels = c("Mild", "Severe")),
outcome_binary = ifelse(phenotype == "Severe", 1, 0))
# Select predictors
param <- c("CXCL10", "Grem_2", "LV_EF")
biomarker_labels <- c(CXCL10 = "CXCL10", Grem_2 = "Gremlin-2", LV_EF = "LVEF", Combined = "Combined model")
# Initialize storage
roc_list <- list()
# Create ROC Curve for each parameter
for (p in param)
{
roc_obj <- roc(response = table_roc$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 comined model
# 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) 1.729e+00 1.522e+00 1.136 0.255979
CXCL10 3.989e-02 9.423e-03 4.233 2.30e-05 ***
Grem_2 1.830e-04 5.502e-05 3.325 0.000883 ***
LV_EF -1.442e-01 3.665e-02 -3.935 8.31e-05 ***
---
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: 51.087 on 99 degrees of freedom
AIC: 59.087
Number of Fisher Scoring iterations: 8 # Predicted probabilities (prob of being Severe)
predictions <- predict(model, type = "response")
# Compute ROC curve for combined model
roc_combined <- roc(response = table_roc$outcome_binary,
predictor = predictions,
levels = c(0, 1),
direction = "<",
ci = TRUE)
roc_list[["Combined"]] <- roc_combined
# 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", "lightblue4", "darkgrey", "plum4"),
labels = legend_text) +
theme_classic() +
theme(panel.border = element_rect(color = "black", fill = NA, linewidth = 1),
legend.position = "bottom",
legend.direction = "vertical") +
labs(x = "1 - Specificity",
y = "Sensitivity",
color = "Legend") +
coord_equal()
print(roc_plot)
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 proxy_0.4-27
[7] digest_0.6.39 png_0.1-8 shape_1.4.6.1
[10] git2r_0.36.2 alabama_2023.1.0 deldir_2.0-4
[13] httpcode_0.3.0 parallelly_1.45.1 fontLiberation_0.1.0
[16] reshape2_1.4.5 httpuv_1.6.16 foreach_1.5.2
[19] BiocGenerics_0.52.0 withr_3.0.2 xfun_0.54
[22] crul_1.4.2 emmeans_1.10.4 systemfonts_1.3.1
[25] ragg_1.5.0 zoo_1.8-14 GlobalOptions_0.1.3
[28] gtools_3.9.5 pbapply_1.7-4 Formula_1.2-5
[31] promises_1.5.0 otel_0.2.0 httr_1.4.7
[34] globals_0.18.0 fitdistrplus_1.2-4 rstudioapi_0.17.1
[37] pan_1.9 miniUI_0.1.2 generics_0.1.4
[40] base64enc_0.1-3 curl_7.0.0 S4Vectors_0.44.0
[43] mitools_2.4 polyclip_1.10-7 quadprog_1.5-8
[46] xtable_1.8-4 doParallel_1.0.17 evaluate_1.0.5
[49] hms_1.1.4 irlba_2.3.5.1 colorspace_2.1-2
[52] polynom_1.4-1 ROCR_1.0-11 reticulate_1.44.1
[55] spatstat.data_3.1-9 lmtest_0.9-40 snakecase_0.11.1
[58] later_1.4.4 spatstat.geom_3.6-1 future.apply_1.20.0
[61] scattermore_1.2 survey_4.4-2 matrixStats_1.5.0
[64] RcppAnnoy_0.0.22 class_7.3-23 pillar_1.11.1
[67] nlme_3.1-167 iterators_1.0.14 compiler_4.4.3
[70] RSpectra_0.16-2 stringi_1.8.7 gower_1.0.2
[73] jomo_2.7-6 tensor_1.5.1 minqa_1.2.8
[76] plyr_1.8.9 crayon_1.5.3 abind_1.4-8
[79] orthopolynom_1.0-6.1 sandwich_3.1-1 whisker_0.4.1
[82] codetools_0.2-20 textshaping_1.0.4 basefun_1.2-4
[85] recipes_1.3.1 openssl_2.3.4 bslib_0.9.0
[88] e1071_1.7-14 GetoptLong_1.0.5 mime_0.13
[91] splines_4.4.3 Rcpp_1.1.0 fastDummies_1.7.5
[94] coneproj_1.20 variables_1.1-2 cellranger_1.1.0
[97] knitr_1.50 clue_0.3-66 lme4_1.1-38
[100] fs_1.6.6 listenv_0.10.0 checkmate_2.3.3
[103] Rdpack_2.6.4 ggsignif_0.6.4 estimability_1.5.1
[106] tzdb_0.5.0 pkgconfig_2.0.3 tools_4.4.3
[109] cachem_1.1.0 rbibutils_2.4 numDeriv_2016.8-1.1
[112] viridisLite_0.4.2 DBI_1.2.3 fastmap_1.2.0
[115] rmarkdown_2.30 ica_1.0-3 tram_1.2-4
[118] sass_0.4.10 officer_0.6.6 coda_0.19-4.1
[121] dotCall64_1.2 RANN_2.6.2 rpart_4.1.24
[124] farver_2.1.2 reformulas_0.4.2 mgcv_1.9-1
[127] yaml_2.3.11 workflowr_1.7.2 foreign_0.8-88
[130] cli_3.6.5 stats4_4.4.3 lifecycle_1.0.4
[133] uwot_0.2.4 askpass_1.2.1 lava_1.8.0
[136] backports_1.5.0 mlt_1.6-6 timechange_0.3.0
[139] gtable_0.3.6 rjson_0.2.23 ggridges_0.5.7
[142] progressr_0.18.0 parallel_4.4.3 jsonlite_2.0.0
[145] RcppHNSW_0.6.0 mitml_0.4-5 qrng_0.0-10
[148] spatstat.utils_3.2-0 zip_2.3.1 jquerylib_0.1.4
[151] spatstat.univar_3.1-5 timeDate_4051.111 lazyeval_0.2.2
[154] shiny_1.12.0 htmltools_0.5.9 sctransform_0.4.2
[157] glue_1.8.0 gfonts_0.2.0 BB_2019.10-1
[160] spam_2.11-1 gdtools_0.3.7 rprojroot_2.1.1
[163] boot_1.3-31 igraph_2.2.1 R6_2.6.1
[166] labeling_0.4.3 ipred_0.9-15 nloptr_2.2.1
[169] tidyselect_1.2.1 vipor_0.4.7 plotrix_3.8-4
[172] htmlTable_2.4.3 operator.tools_1.6.3 xml2_1.5.1
[175] fontBitstreamVera_0.1.1 future_1.68.0 ModelMetrics_1.2.2.2
[178] KernSmooth_2.23-26 S7_0.2.1 fontquiver_0.2.1
[181] htmlwidgets_1.6.4 rlang_1.1.6 spatstat.sparse_3.1-0
[184] spatstat.explore_3.6-0 uuid_1.2-1 formula.tools_1.7.1
[187] hardhat_1.4.1 beeswarm_0.4.0 prodlim_2023.08.28 date()[1] "Fri Mar 6 19:19:35 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 proxy_0.4-27
[7] digest_0.6.39 png_0.1-8 shape_1.4.6.1
[10] git2r_0.36.2 alabama_2023.1.0 deldir_2.0-4
[13] httpcode_0.3.0 parallelly_1.45.1 fontLiberation_0.1.0
[16] reshape2_1.4.5 httpuv_1.6.16 foreach_1.5.2
[19] BiocGenerics_0.52.0 withr_3.0.2 xfun_0.54
[22] crul_1.4.2 emmeans_1.10.4 systemfonts_1.3.1
[25] ragg_1.5.0 zoo_1.8-14 GlobalOptions_0.1.3
[28] gtools_3.9.5 pbapply_1.7-4 Formula_1.2-5
[31] promises_1.5.0 otel_0.2.0 httr_1.4.7
[34] globals_0.18.0 fitdistrplus_1.2-4 rstudioapi_0.17.1
[37] pan_1.9 miniUI_0.1.2 generics_0.1.4
[40] base64enc_0.1-3 curl_7.0.0 S4Vectors_0.44.0
[43] mitools_2.4 polyclip_1.10-7 quadprog_1.5-8
[46] xtable_1.8-4 doParallel_1.0.17 evaluate_1.0.5
[49] hms_1.1.4 irlba_2.3.5.1 colorspace_2.1-2
[52] polynom_1.4-1 ROCR_1.0-11 reticulate_1.44.1
[55] spatstat.data_3.1-9 lmtest_0.9-40 snakecase_0.11.1
[58] later_1.4.4 spatstat.geom_3.6-1 future.apply_1.20.0
[61] scattermore_1.2 survey_4.4-2 matrixStats_1.5.0
[64] RcppAnnoy_0.0.22 class_7.3-23 pillar_1.11.1
[67] nlme_3.1-167 iterators_1.0.14 compiler_4.4.3
[70] RSpectra_0.16-2 stringi_1.8.7 gower_1.0.2
[73] jomo_2.7-6 tensor_1.5.1 minqa_1.2.8
[76] plyr_1.8.9 crayon_1.5.3 abind_1.4-8
[79] orthopolynom_1.0-6.1 sandwich_3.1-1 whisker_0.4.1
[82] codetools_0.2-20 textshaping_1.0.4 basefun_1.2-4
[85] recipes_1.3.1 openssl_2.3.4 bslib_0.9.0
[88] e1071_1.7-14 GetoptLong_1.0.5 mime_0.13
[91] splines_4.4.3 Rcpp_1.1.0 fastDummies_1.7.5
[94] coneproj_1.20 variables_1.1-2 cellranger_1.1.0
[97] knitr_1.50 clue_0.3-66 lme4_1.1-38
[100] fs_1.6.6 listenv_0.10.0 checkmate_2.3.3
[103] Rdpack_2.6.4 ggsignif_0.6.4 estimability_1.5.1
[106] tzdb_0.5.0 pkgconfig_2.0.3 tools_4.4.3
[109] cachem_1.1.0 rbibutils_2.4 numDeriv_2016.8-1.1
[112] viridisLite_0.4.2 DBI_1.2.3 fastmap_1.2.0
[115] rmarkdown_2.30 ica_1.0-3 tram_1.2-4
[118] sass_0.4.10 officer_0.6.6 coda_0.19-4.1
[121] dotCall64_1.2 RANN_2.6.2 rpart_4.1.24
[124] farver_2.1.2 reformulas_0.4.2 mgcv_1.9-1
[127] yaml_2.3.11 workflowr_1.7.2 foreign_0.8-88
[130] cli_3.6.5 stats4_4.4.3 lifecycle_1.0.4
[133] uwot_0.2.4 askpass_1.2.1 lava_1.8.0
[136] backports_1.5.0 mlt_1.6-6 timechange_0.3.0
[139] gtable_0.3.6 rjson_0.2.23 ggridges_0.5.7
[142] progressr_0.18.0 parallel_4.4.3 jsonlite_2.0.0
[145] RcppHNSW_0.6.0 mitml_0.4-5 qrng_0.0-10
[148] spatstat.utils_3.2-0 zip_2.3.1 jquerylib_0.1.4
[151] spatstat.univar_3.1-5 timeDate_4051.111 lazyeval_0.2.2
[154] shiny_1.12.0 htmltools_0.5.9 sctransform_0.4.2
[157] glue_1.8.0 gfonts_0.2.0 BB_2019.10-1
[160] spam_2.11-1 gdtools_0.3.7 rprojroot_2.1.1
[163] boot_1.3-31 igraph_2.2.1 R6_2.6.1
[166] labeling_0.4.3 ipred_0.9-15 nloptr_2.2.1
[169] tidyselect_1.2.1 vipor_0.4.7 plotrix_3.8-4
[172] htmlTable_2.4.3 operator.tools_1.6.3 xml2_1.5.1
[175] fontBitstreamVera_0.1.1 future_1.68.0 ModelMetrics_1.2.2.2
[178] KernSmooth_2.23-26 S7_0.2.1 fontquiver_0.2.1
[181] htmlwidgets_1.6.4 rlang_1.1.6 spatstat.sparse_3.1-0
[184] spatstat.explore_3.6-0 uuid_1.2-1 formula.tools_1.7.1
[187] hardhat_1.4.1 beeswarm_0.4.0 prodlim_2023.08.28