Figure 3
Anna Joachimbauer
2024-10-02
Last updated: 2026-03-04
Checks: 6 1
Knit directory: Serology Analysis/
This reproducible R Markdown analysis was created with workflowr (version 1.7.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown is untracked by Git. To know which version of the R
Markdown file created these results, you’ll want to first commit it to
the Git repo. If you’re still working on the analysis, you can ignore
this warning. When you’re finished, you can run
wflow_publish to commit the R Markdown file and build the
HTML.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20260121) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version f44c37b. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .DS_Store
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: analysis/.DS_Store
Ignored: analysis/.RData
Ignored: analysis/.Rhistory
Ignored: output/.DS_Store
Untracked files:
Untracked: analysis/.RData 2
Untracked: analysis/Figure 1.Rmd
Untracked: analysis/Figure 2.Rmd
Untracked: analysis/Figure 3.Rmd
Untracked: analysis/SFigure 1.Rmd
Untracked: analysis/SFigure 2.Rmd
Untracked: analysis/SFigure 3.Rmd
Untracked: analysis/SFigure-3.Rmd
Untracked: output/SFigure2/
Unstaged changes:
Modified: .gitignore
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
There are no past versions. Publish this analysis with
wflow_publish() to start tracking its development.
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"))
table_bmp4_cohort2 <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_BMP4_Cohort2.xlsx"))
# Combine Tables
table_bmp4_myo_healthy <- rbind(table_bmp4_cohort1, table_bmp4_cohort2, table_bmp4_healthy)
# 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, Trop_I, NTproBNP, CRP, LV_EF)
table_clindat_cohort2 <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_ClinicalData_Cohort2.xlsx")) %>%
dplyr::select(Study_ID, Trop_I, NTproBNP, CRP, LV_EF)
# Combine tables
table_clindat_myo_healthy <- rbind(table_clindat_cohort1 %>% dplyr::select(-LV_EF),
table_clindat_cohort2 %>% dplyr::select(-LV_EF),
table_clindat_healthy)
# Import Luminex Data
table_lum_healthy <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_Lum_Healthy_DL_corr.xlsx")) %>%
dplyr::select(-RANTES)
table_lum_cohort1 <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_Lum_Cohort1_DL_corr.xlsx")) %>%
dplyr::select(-RANTES)
table_lum_cohort2 <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_Lum_Cohort2_DL_corr.xlsx")) %>%
dplyr::select(-RANTES)
# Combine tables
table_lum_myo_healthy <- rbind(table_lum_cohort1, table_lum_cohort2, table_lum_healthy) %>%
dplyr::select(-Cohort)
# Combine all data tables
table_all_myo_healthy <- table_lum_myo_healthy %>%
left_join(table_bmp4_myo_healthy, by = "Study_ID") %>%
left_join(table_clindat_myo_healthy, by = "Study_ID")
table_cohorts <- table_all_myo_healthy %>%
dplyr::select(Study_ID, Cohort)Multiple Imputation (all)
# Remove Cohort
table_all_myo_healthy_01 <- table_all_myo_healthy %>%
dplyr::select(-Cohort)
# Impute missing values
imputed_data <- mice(table_all_myo_healthy_01, m = 5, method = 'pmm', maxit = 5, seed = 123)
iter imp variable
1 1 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
1 2 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
1 3 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
1 4 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
1 5 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
2 1 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
2 2 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
2 3 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
2 4 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
2 5 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
3 1 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
3 2 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
3 3 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
3 4 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
3 5 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
4 1 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
4 2 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
4 3 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
4 4 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
4 5 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
5 1 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
5 2 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
5 3 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
5 4 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP
5 5 EOTAXIN BMP4 Grem_1 Trop_I NTproBNP# Check the imputed data
densityplot(imputed_data, col=c("grey", "blue"), pch = c(1, 20))
Warning! The custom
fig.path you set was
ignored by workflowr.
# Create a data set with the observed and completed data
table_imp <- complete(imputed_data, 1)
# Add Cohort
table_all_myo_healthy_imp <- merge (table_imp, table_cohorts, by = "Study_ID")BMP4/Gremlin Axis in Healthy, Cohort 1 and Cohort 2
# Prepare data table
table_para <- table_all_myo_healthy_imp %>%
pivot_longer(cols = where(is.numeric),
names_to = "Parameter",
values_to = "Parameter_val")
# Order table
table_para <- table_para %>%
mutate(Cohort = factor(Cohort, levels = c("Healthy", "Cohort1", "Cohort2")))
# 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) %>%
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(NA, 1000), breaks = c(1, 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",
"Cohort1" = "royalblue4",
"Cohort2" = "orange")) +
stat_pvalue_manual(temp_stat_test, label = "p_adj_label",
y.position = log10(temp_stat_test$y.position),
step.increase = 0.01, tip.length = 0) +
scale_y_log10(labels = function(x) format(x, scientific = FALSE, trim = TRUE),
limits = y_lim, breaks = y_brk, expand = expansion(mult = c(0.05, 0))) +
labs(x = NULL, y = biomarker_labels[[param]]) +
theme_classic() +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "none")
plots_list[[param]] <- plot_lum
}
# Combine plots to a panel
panel <- ggarrange(plotlist = plots_list,
ncol = 3, nrow = 1)
print(panel)
Warning! The custom
fig.path you set was
ignored by workflowr.
Roc Curves BMP4/Gremlin axis
# Combine AM Cohorts
table_all_myo_healthy_imp_01 <- table_all_myo_healthy_imp %>%
mutate(Cohort = ifelse(Cohort == "Healthy", "Healthy", "Myocarditis"))
# Data preparation
table_roc <- table_all_myo_healthy_imp_01 %>%
mutate(outcome = factor(Cohort, levels = c("Healthy", "Myocarditis")),
outcome_binary = ifelse(outcome == "Myocarditis", 1, 0),
r_Grem1_BMP4 = Grem_1 / BMP4,
r_Grem1_Grem2 = Grem_1 / Grem_2)
# Select predictors
param <- c("r_Grem1_Grem2", "r_Grem1_BMP4")
biomarker_labels <- c("Gremlin-1/Gremlin-2", "Gremlin-1/BMP4")
names(biomarker_labels) <- param
# Initialize storage
roc_list <- list()
# Loop through each predictor
for (p in param)
{
roc_obj <- roc(response = table_roc$outcome_binary,
predictor = table_roc[[p]],
levels = c(0, 1),
direction = "auto",
ci = TRUE,
legacy.axes = TRUE)
roc_list[[p]] <- roc_obj
}
# AUC & CI values
auc_vals <- sapply(roc_list[1:2], function(x) as.numeric(auc(x)))
auc_ci <- lapply(roc_list[1:2], function(x) ci.auc(x))
# Create Legend
legend_text <- mapply(function(pred, auc, ci)
{
paste0(pred," (AUC = ", round(auc, 2),", 95% CI: ", round(ci[1], 2), "–", round(ci[3], 2), ")")
},
biomarker_labels[param], 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("red", "darkred"),
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)
Warning! The custom
fig.path you set was
ignored by workflowr.
Correlation Plots
# Rename Datatables
table_corr <- table_all_myo_healthy_imp_01
# Define biomarkers
biomarkers <- c("CXCL10", "CXCL9")
x_settings <- list(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)))
# Define axis labels for each biomarker
biomarker_labels <- c("CXCL10 (pg/ml)", "CXCL9 (pg/ml)")
names(biomarker_labels) <- biomarkers
# Fixed variable
fixed_param <- "Grem_2"
# Create list for plots
plots_list <- list()
# Loop Correlation Plot
for (p in biomarkers)
{
# Compute Spearman correlation
cor_test <- cor.test(table_corr[[fixed_param]], table_corr[[p]], method = "spearman")
# Axis settings
x_lim <- x_settings[[p]]$limits
x_brk <- x_settings[[p]]$breaks
# Scatter plot with regression line
plot <- ggplot(table_corr, aes_string(x = table_corr[[p]], y = fixed_param)) +
geom_point(shape = 21, size = 3, color = "black", aes(fill = Cohort),
position = position_jitter(width = 0.05, height = 0)) +
geom_smooth(method = "lm", se = TRUE, color = "black") +
scale_fill_manual(values = c("Myocarditis" = "darkred", "Healthy" = "grey")) +
scale_x_log10(labels = function(x) format(x, scientific = FALSE, trim = TRUE),
limits = x_lim, breaks = x_brk, expand = expansion(mult = c(0.05, 0))) +
scale_y_log10(labels = function(x) format(x, scientific = FALSE, trim = TRUE),
limits = c(NA, 100000), breaks = c(100, 300, 1000, 3000, 10000, 30000, 100000),
expand = expansion(mult = c(0.05, 0), add = c(0,0))) +
theme_classic() +
labs(x = biomarker_labels[[p]], y = "Gremlin-2 (pg/ml)") +
annotate("text", x = min(table_corr[[p]], na.rm = TRUE),
y = max(table_corr[[fixed_param]], na.rm = TRUE),
label = paste0("r = ", round(cor_test$estimate, 2),
"\np = ", ifelse(cor_test$p.value < 0.001, "< 0.001",signif(cor_test$p.value, 3))),
hjust = 0, vjust = 1, size = 6)
# Store each plot
plots_list[[p]] <- plot
}
# Combine plots to a panel
panel <- ggarrange(plotlist = plots_list,
ncol = 2, nrow = 1,
common.legend = TRUE)
print(panel)
Warning! The custom
fig.path you set was
ignored by workflowr.
Correlation Plot Hospitalisation
# Read data
table_hosp <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_ClinicalData_Cohort1.xlsx")) %>%
dplyr::select(Study_ID, date_admission, date_discharge)
# Calculate days of hospitalization
table_hosp$hosp_time <- (as.Date(table_hosp$date_discharge, format = "%Y-%m-%d") -
as.Date(table_hosp$date_admission, format = "%Y-%m-%d"))
table_hosp <- table_hosp %>%
mutate(hosp_time = as.numeric(hosp_time)) %>%
dplyr::select(-date_discharge, -date_admission)
# Extract Cohort1
table_all_cohort1 <- table_all_myo_healthy_imp %>%
filter (Cohort == "Cohort1")
# Merge data
table_corr_cohort1 <- table_all_cohort1 %>%
left_join(table_hosp, by = "Study_ID")
# Define biomarkers
biomarkers <- c("NTproBNP", "HGF", "Grem_2")
y_settings <- list(HGF = list(limits = c(NA, 15000), breaks = c(150, 1500, 15000)),
NTproBNP = 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("HGF (pg/ml)", "NT-proBNP (ng/l)", "Gremlin-2 (pg/ml)")
names(biomarker_labels) <- biomarkers
# Fixed variable
fixed_param <- "hosp_time"
# Create list for plots
plots_list <- list()
# Loop Correlation Plot
for (p in biomarkers)
{
# Compute Spearman correlation
cor_test <- cor.test(table_corr_cohort1[[fixed_param]], table_corr_cohort1[[p]], method = "spearman")
# Axis settings
y_lim <- x_settings[[p]]$limits
y_brk <- x_settings[[p]]$breaks
# Scatter plot with regression line
plot <- ggplot(table_corr_cohort1, aes_string(x = fixed_param, y = table_corr_cohort1[[p]])) +
geom_point(shape = 21, size = 3, color = "black", aes(fill = Cohort),
position = position_jitter(width = 0.05, height = 0)) +
geom_smooth(method = "lm", se = TRUE, color = "black") +
scale_fill_manual(values = c("Cohort1" = "royalblue4")) +
scale_y_log10(labels = function(x) format(x, scientific = FALSE, trim = TRUE),
limits = x_lim, breaks = x_brk, expand = expansion(mult = c(0.05, 0))) +
scale_x_log10(labels = function(x) format(x, scientific = FALSE, trim = TRUE),
limits = c(NA, 30), breaks = c(3, 10, 30),
expand = expansion(mult = c(0.05, 0), add = c(0, 0))) +
theme_classic() +
labs(x = "Time of hospitalisation (days)", y = biomarker_labels[[p]]) +
annotate("text", x = min(table_corr_cohort1[[p]], na.rm = TRUE),
y = max(table_corr_cohort1[[fixed_param]], 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 = 0, vjust = 1, size = 6)
# Store each plot
plots_list[[p]] <- plot
}
# Combine plots to a panel
panel <- ggarrange(plotlist = plots_list,
ncol = 3, nrow = 1,
common.legend = TRUE)
print(panel)
Warning! The custom
fig.path you set was
ignored by workflowr.
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 codetools_0.2-20 textshaping_1.0.4
[82] basefun_1.2-4 recipes_1.3.1 openssl_2.3.4
[85] bslib_0.9.0 GetoptLong_1.0.5 mime_0.13
[88] splines_4.4.3 Rcpp_1.1.0 fastDummies_1.7.5
[91] coneproj_1.20 variables_1.1-2 cellranger_1.1.0
[94] knitr_1.50 clue_0.3-66 lme4_1.1-38
[97] fs_1.6.6 listenv_0.10.0 checkmate_2.3.3
[100] Rdpack_2.6.4 ggsignif_0.6.4 estimability_1.5.1
[103] tzdb_0.5.0 pkgconfig_2.0.3 tools_4.4.3
[106] cachem_1.1.0 rbibutils_2.4 numDeriv_2016.8-1.1
[109] viridisLite_0.4.2 DBI_1.2.3 fastmap_1.2.0
[112] rmarkdown_2.30 ica_1.0-3 tram_1.2-4
[115] sass_0.4.10 officer_0.6.6 coda_0.19-4.1
[118] dotCall64_1.2 RANN_2.6.2 rpart_4.1.24
[121] farver_2.1.2 reformulas_0.4.2 mgcv_1.9-1
[124] yaml_2.3.11 workflowr_1.7.2 foreign_0.8-88
[127] cli_3.6.5 stats4_4.4.3 lifecycle_1.0.4
[130] uwot_0.2.4 askpass_1.2.1 lava_1.8.0
[133] backports_1.5.0 mlt_1.6-6 timechange_0.3.0
[136] gtable_0.3.6 rjson_0.2.23 ggridges_0.5.7
[139] progressr_0.18.0 parallel_4.4.3 jsonlite_2.0.0
[142] RcppHNSW_0.6.0 mitml_0.4-5 qrng_0.0-10
[145] spatstat.utils_3.2-0 zip_2.3.1 jquerylib_0.1.4
[148] spatstat.univar_3.1-5 timeDate_4051.111 lazyeval_0.2.2
[151] shiny_1.12.0 htmltools_0.5.9 sctransform_0.4.2
[154] glue_1.8.0 gfonts_0.2.0 BB_2019.10-1
[157] spam_2.11-1 gdtools_0.3.7 rprojroot_2.1.1
[160] boot_1.3-31 igraph_2.2.1 R6_2.6.1
[163] labeling_0.4.3 ipred_0.9-15 nloptr_2.2.1
[166] tidyselect_1.2.1 vipor_0.4.7 plotrix_3.8-4
[169] htmlTable_2.4.3 operator.tools_1.6.3 xml2_1.5.1
[172] fontBitstreamVera_0.1.1 future_1.68.0 ModelMetrics_1.2.2.2
[175] KernSmooth_2.23-26 S7_0.2.1 fontquiver_0.2.1
[178] htmlwidgets_1.6.4 rlang_1.1.6 spatstat.sparse_3.1-0
[181] spatstat.explore_3.6-0 uuid_1.2-1 formula.tools_1.7.1
[184] hardhat_1.4.1 beeswarm_0.4.0 prodlim_2023.08.28 date()[1] "Wed Mar 4 15:27:10 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 codetools_0.2-20 textshaping_1.0.4
[82] basefun_1.2-4 recipes_1.3.1 openssl_2.3.4
[85] bslib_0.9.0 GetoptLong_1.0.5 mime_0.13
[88] splines_4.4.3 Rcpp_1.1.0 fastDummies_1.7.5
[91] coneproj_1.20 variables_1.1-2 cellranger_1.1.0
[94] knitr_1.50 clue_0.3-66 lme4_1.1-38
[97] fs_1.6.6 listenv_0.10.0 checkmate_2.3.3
[100] Rdpack_2.6.4 ggsignif_0.6.4 estimability_1.5.1
[103] tzdb_0.5.0 pkgconfig_2.0.3 tools_4.4.3
[106] cachem_1.1.0 rbibutils_2.4 numDeriv_2016.8-1.1
[109] viridisLite_0.4.2 DBI_1.2.3 fastmap_1.2.0
[112] rmarkdown_2.30 ica_1.0-3 tram_1.2-4
[115] sass_0.4.10 officer_0.6.6 coda_0.19-4.1
[118] dotCall64_1.2 RANN_2.6.2 rpart_4.1.24
[121] farver_2.1.2 reformulas_0.4.2 mgcv_1.9-1
[124] yaml_2.3.11 workflowr_1.7.2 foreign_0.8-88
[127] cli_3.6.5 stats4_4.4.3 lifecycle_1.0.4
[130] uwot_0.2.4 askpass_1.2.1 lava_1.8.0
[133] backports_1.5.0 mlt_1.6-6 timechange_0.3.0
[136] gtable_0.3.6 rjson_0.2.23 ggridges_0.5.7
[139] progressr_0.18.0 parallel_4.4.3 jsonlite_2.0.0
[142] RcppHNSW_0.6.0 mitml_0.4-5 qrng_0.0-10
[145] spatstat.utils_3.2-0 zip_2.3.1 jquerylib_0.1.4
[148] spatstat.univar_3.1-5 timeDate_4051.111 lazyeval_0.2.2
[151] shiny_1.12.0 htmltools_0.5.9 sctransform_0.4.2
[154] glue_1.8.0 gfonts_0.2.0 BB_2019.10-1
[157] spam_2.11-1 gdtools_0.3.7 rprojroot_2.1.1
[160] boot_1.3-31 igraph_2.2.1 R6_2.6.1
[163] labeling_0.4.3 ipred_0.9-15 nloptr_2.2.1
[166] tidyselect_1.2.1 vipor_0.4.7 plotrix_3.8-4
[169] htmlTable_2.4.3 operator.tools_1.6.3 xml2_1.5.1
[172] fontBitstreamVera_0.1.1 future_1.68.0 ModelMetrics_1.2.2.2
[175] KernSmooth_2.23-26 S7_0.2.1 fontquiver_0.2.1
[178] htmlwidgets_1.6.4 rlang_1.1.6 spatstat.sparse_3.1-0
[181] spatstat.explore_3.6-0 uuid_1.2-1 formula.tools_1.7.1
[184] hardhat_1.4.1 beeswarm_0.4.0 prodlim_2023.08.28