SFigure 3
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 (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 <- rbind(table_bmp4_cohort1, table_bmp4_cohort2)
# Import Serology Data
table_clindat_healthy <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_Serology_Healthy.xlsx")) %>%
dplyr::select(Study_ID, NTproBNP)
table_clindat_cohort1 <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_ClinicalData_Cohort1.xlsx")) %>%
dplyr::select(Study_ID, NTproBNP, LV_EF)
table_clindat_cohort2 <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_ClinicalData_Cohort2.xlsx")) %>%
dplyr::select(Study_ID, NTproBNP, LV_EF)
# Combine tables
table_clindat_myo <- rbind(table_clindat_cohort1, table_clindat_cohort2)
# 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)
# Combine tables
table_lum_myo <- rbind(table_lum_cohort1, table_lum_cohort2)
# Combine tables
table_all_myo <- table_bmp4_myo %>%
left_join(table_lum_myo, by = "Study_ID") %>%
left_join(table_clindat_myo, 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, m = 5, method = 'pmm', maxit = 5, seed = 1234)
iter imp variable
1 1 NTproBNP LV_EF
1 2 NTproBNP LV_EF
1 3 NTproBNP LV_EF
1 4 NTproBNP LV_EF
1 5 NTproBNP LV_EF
2 1 NTproBNP LV_EF
2 2 NTproBNP LV_EF
2 3 NTproBNP LV_EF
2 4 NTproBNP LV_EF
2 5 NTproBNP LV_EF
3 1 NTproBNP LV_EF
3 2 NTproBNP LV_EF
3 3 NTproBNP LV_EF
3 4 NTproBNP LV_EF
3 5 NTproBNP LV_EF
4 1 NTproBNP LV_EF
4 2 NTproBNP LV_EF
4 3 NTproBNP LV_EF
4 4 NTproBNP LV_EF
4 5 NTproBNP LV_EF
5 1 NTproBNP LV_EF
5 2 NTproBNP LV_EF
5 3 NTproBNP LV_EF
5 4 NTproBNP LV_EF
5 5 NTproBNP LV_EF# 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_all_myo_imp <- complete(imputed_data, 1)
# Combine all data tables
table_all_myo_healthy <- bind_rows(table_all_myo_imp %>% dplyr::select(-LV_EF),
table_all_healthy)BMP4/Gremlin Axis in Healthy, Cohort 1 and Cohort 2
# Prepare data table
table_para <- table_all_myo_healthy %>%
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(limits = c(NA, 10000), breaks = c(1, 10, 100, 1000, 10000)),
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$y.position),
step.increase = 0.02, tip.length = 0) +
scale_y_log10(labels = function(x) format(x, scientific = FALSE, trim = TRUE),
limits = y_lim, breaks = y_brk, expand = expansion(mult = c(0.05, 0))) +
labs(x = NULL, y = biomarker_labels[[param]]) +
theme_classic() +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "none")
plots_list[[param]] <- plot_lum
}
# Combine plots to a panel
panel <- ggarrange(plotlist = plots_list,
ncol = 3, nrow = 1)
print(panel)
Roc Curves BMP4/Gremlin axis
# Combine AM Cohorts
table_all_myo_healthy_01 <- table_all_myo_healthy %>%
mutate(Cohort = ifelse(Cohort == "Healthy", "Healthy", "Myocarditis"))
# Data preparation
table_roc <- table_all_myo_healthy_01 %>%
mutate(outcome = factor(Cohort, levels = c("Healthy", "Myocarditis")),
outcome_binary = ifelse(outcome == "Myocarditis", 1, 0),
r_Grem1_BMP4 = Grem_1 / BMP4,
r_Grem1_Grem2 = Grem_1 / Grem_2)
# Select predictors
param <- c("r_Grem1_Grem2", "r_Grem1_BMP4")
biomarker_labels <- c("Gremlin-1/Gremlin-2", "Gremlin-1/BMP4")
names(biomarker_labels) <- param
# Initialize storage
roc_list <- list()
# Loop through each predictor
for (p in param)
{
roc_obj <- roc(response = table_roc$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)
Correlation Plots
# Rename Datatables
table_corr <- table_all_myo_healthy_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$p.value, 3))),
hjust = 0, vjust = 1, size = 6)
# Store each plot
plots_list[[p]] <- plot
}
# Combine plots to a panel
panel <- ggarrange(plotlist = plots_list,
ncol = 2, nrow = 1,
common.legend = TRUE)
print(panel)
Correlation Plot Hospitalisation
# Read data
table_hosp <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_ClinicalData_Cohort1.xlsx")) %>%
dplyr::select(Study_ID, date_admission, date_discharge)
# Calculate days of hospitalization
table_hosp$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 %>%
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(NTproBNP = list(limits = c(10, 100000), breaks = c(10, 100, 1000, 10000, 100000)),
HGF = list(limits = c(150, 15000), breaks = c(150, 1500, 15000)),
Grem_2 = list(limits = c(100, 100000), breaks = c(100, 300, 1000, 3000, 10000, 30000, 100000)))
# Define axis labels for each biomarker
biomarker_labels <- c("NT-proBNP (ng/l)", "HGF (pg/ml)", "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 <- y_settings[[p]]$limits
y_brk <- y_settings[[p]]$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 = y_lim, breaks = y_brk, expand = expansion(mult = c(0.02, 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(0.02, 0), add = c(0, 0))) +
theme_classic() +
labs(x = "Time of hospitalisation (days)", y = biomarker_labels[[p]]) +
annotate("text", y = max(table_corr_cohort1[[p]], na.rm = TRUE),
x = 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 = 1, vjust = 1, size = 4)
# 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)
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 17:21:46 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