Figure 2
Anna Joachimbauer
2024-10-02
Last updated: 2026-03-03
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 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_cohort2, table_lum_cohort1, table_lum_healthy)Abundance Bubble Plot
# Set rownames
rownames(table_lum_myo_healthy) <- table_lum_myo_healthy$Study_ID
# Prepare data table for bubble plot
# Create the loop vector
parameters <- names(which(sapply(table_lum_myo_healthy, is.numeric) == TRUE))
# Prepare data table
table_para <- table_lum_myo_healthy %>%
pivot_longer(cols = where(is.numeric),
names_to = "Parameter",
values_to = "Parameter_val")
# Calculate mean expression for the healthy cohort
healthy_stats <- table_para %>%
filter(Cohort == "Healthy") %>%
group_by(Parameter) %>%
summarise(mean_expr_healthy = mean(Parameter_val, na.rm = TRUE))
# Calculate mean expression for AM cohort & calcuate FC
cohorts_stats <- table_para %>%
filter(Cohort != "Healthy") %>%
group_by(Cohort, Parameter) %>%
summarise(mean_expr_myo = mean(Parameter_val, na.rm = TRUE)) %>%
left_join(healthy_stats, by = "Parameter") %>%
mutate(log2_fc = log2(mean_expr_myo / mean_expr_healthy))
# Calculate abundance fold change > 1.5 per parameter in AM cohort
percentage_fc_increase <- table_para %>%
filter(Cohort != "Healthy") %>%
group_by(Cohort, Parameter) %>%
left_join(healthy_stats, by = "Parameter") %>%
mutate(log2_fc = log2(Parameter_val / mean_expr_healthy)) %>%
mutate(fc_1.5 = log2_fc >1.5) %>%
summarise(percentage_fc_1.5 = mean(fc_1.5, na.rm = TRUE) * 100)
# Merge stats tables
cohorts_stats <- cohorts_stats %>%
left_join(percentage_fc_increase,by = c("Cohort", "Parameter"))
# Reorder Cohorts order
cohorts_stats <- cohorts_stats %>%
mutate(Cohort = factor(Cohort, levels = c("Cohort1", "Cohort2")))
# Order Parameters
list_parameter_groups <- read.xlsx(paste0(basedir,"/data/Grouping of Lumminex Marker.xlsx"))
cohorts_stats$Parameter <- factor(cohorts_stats$Parameter, levels = list_parameter_groups$Parameter)
# Create the Bubble Plot
bubble_plot <- ggplot(cohorts_stats, aes(x = Parameter, y = Cohort,
size = abs(percentage_fc_1.5),
color = log2_fc)) +
geom_point(alpha = 1) +
scale_size(range = c(2, 8)) +
scale_y_discrete(limits = rev(levels(cohorts_stats$Cohort))) +
scale_color_gradient2(mid = "white", high = "red", midpoint = 0) +
labs(title = "Fold Change (Log2) by Parameter and Cohort",
x = "Parameter", y = "Cohort",
size = "Percentage of Patients (FC > 1.5)", color = "Log2 Fold Change") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
axis.text.y = element_text(size = 12))
# Display the plot
print(bubble_plot)
Warning! The custom
fig.path you set was
ignored by workflowr.
Combined Volcano Plot (Cohort 1, Cohort 2)
# Set rownames
rownames(table_lum_myo_healthy) <- table_lum_myo_healthy$Study_ID
# Rename Cohorts to AM cohorts
table_lum_myo_healthy$Cohort <- ifelse(table_lum_myo_healthy$Cohort == "Healthy", "Healthy", "Myocarditis")
# Calculate p-values between AM and healthy
# Create the loop.vector (all the parameter columns)
names(which(sapply(table_lum_myo_healthy, is.numeric) == TRUE)) -> parameters
# Create table for stats
table_lum_myo_healthy_stat <- data.table (Parameter = numeric(), group1 = character(),
group2 = character(), n1 = numeric(),
n2 = numeric(), statistic = numeric (),
p = numeric (), p.signif = character(), FDR = numeric())
# Create loop for calculation of stat test by parameter between myocarditis and healthy cohort
for (i in parameters)
{
# Formula to insert parameter in loop
formula = as.formula( paste(i, "Cohort", sep="~") )
stat_parameter <- table_lum_myo_healthy %>%
wilcox_test(formula = formula) %>%
add_significance() %>%
mutate(FDR = p.adjust(p, method = "BH")) %>%
rename(Parameter = .y.)
# Combine currently calculated stats with previous calculations
table_lum_myo_healthy_stat <- rbind(table_lum_myo_healthy_stat, stat_parameter)
}
# Calculate FC between AM and healthy
# Calculate Mean of parameters per group
table_lum_myo_healthy_log2 <- table_lum_myo_healthy %>%
group_by(Cohort) %>%
summarise(across(FGF_2:CXCL8, ~ mean(.x, na.rm = TRUE)))
# Transverse Data Table and set rownames
table_lum_myo_healthy_log2_t <- table_lum_myo_healthy_log2 %>%
t %>%
as.data.frame() %>%
row_to_names(1)
# Convert whole data table to numeric
table_lum_myo_healthy_log2_t <- mutate_all(table_lum_myo_healthy_log2_t,
function(x) as.numeric(as.character(x)))
# Calculate FC between healthy and AM Cohort
table_lum_myo_healthy_log2_t$log2_fc <- log2(table_lum_myo_healthy_log2_t$Myocarditis /
table_lum_myo_healthy_log2_t$Healthy)
# Add p-values to data table
# Extract only Parameters and p-values
stat.test_p <- table_lum_myo_healthy_stat %>%
dplyr::select(Parameter, FDR)
# Merge data tables (log2, FC, p val)
table_lum_myo_healthy_log2_t$Parameter <- rownames(table_lum_myo_healthy_log2_t)
table_lum_myo_healthy_volcano <- merge(table_lum_myo_healthy_log2_t, stat.test_p, by = "Parameter")
# Create Volcano Plot
# Mark parameters if FC > 1.5 = upregulated
table_lum_myo_healthy_volcano$diff_expression <- ifelse (table_lum_myo_healthy_volcano$log2_fc > 1.5,
"up", "no")
# Add new column to relabel parameters FC of > 1.5
table_lum_myo_healthy_volcano$label_para <- ifelse (table_lum_myo_healthy_volcano$log2_fc > 1.5,
table_lum_myo_healthy_volcano$Parameter, "")
plot_volcano <- EnhancedVolcano(table_lum_myo_healthy_volcano,
lab = table_lum_myo_healthy_volcano$label_para,
x = 'log2_fc', y = 'FDR',
FCcutoff = 1.5, pointSize = 3.5, labSize = 4, colAlpha = 0.7,
boxedLabels = TRUE, col = c("grey", "grey", "grey", "darkred"),
xlab = bquote(~Log[2]~ "fold change"), ylab = bquote("FDR-adjusted p-value"),
xlim = c(0, 3.5), ylim = c(0, 21), pCutoff = 0.05,
title = NULL, subtitle = NULL, border = 'full',
drawConnectors = TRUE,widthConnectors = 0.5, colConnectors = "darkred") +
theme_classic() +
theme(panel.grid = element_blank(),
panel.border = element_rect(colour = "black", fill = NA,),
legend.position = "none")
print( plot_volcano)
Warning! The custom
fig.path you set was
ignored by workflowr.
Heatmap Selected Parameters (Healthy, Cohort 1, Cohort 2)
# Name Rownames with study ID and remove Study ID and Cohort column
table_lum_myo_healthy_01 <- subset(table_lum_myo_healthy, select = -c(Study_ID, Cohort))
# Convert Data Table to Data Frame
table_lum_myo_healthy_01 <- as.data.frame(table_lum_myo_healthy_01)
# Select preserved cytokines
table_lum_myo_healthy_01 <- table_lum_myo_healthy_01 %>%
dplyr::select(HGF, IL_2R, CXCL9, CXCL10, CXCL8, IL_6, CCL3, CCL4)
# Transpose Data Table
table_lum_myo_healthy_01_t <- t(table_lum_myo_healthy_01)
## Column Annotations
# Create list with Study_ID & Cohort
list_cohort_group <- subset(table_lum_myo_healthy, select= c(Study_ID, Cohort))
rownames(list_cohort_group) <- list_cohort_group$Study_ID
list_cohort_group <- subset(list_cohort_group, select= -c(Study_ID))
## Row Annotations
# Create list with Parameters
list_parameter_groups <- read.xlsx(paste0(basedir,"/data/Grouping of Lumminex Marker.xlsx"))
# Create list with Columns of data table
list_parameter_datatable <- as.data.frame(rownames(table_lum_myo_healthy_01_t))
colnames(list_parameter_datatable) <- "Parameter"
# Merge to match the order of parameters
list_parameter_groups_01 <- merge(list_parameter_groups, list_parameter_datatable, by.y = "Parameter")
# Define the predefined order for Group
group_order <- c("Cytokines", "Chemokines", "Tissue Cytokines")
# Order list based on predefined order
selected_markers <- c("HGF","IL_2R", "CXCL9", "CXCL10", "CXCL8", "IL_6", "CCL3", "CCL4")
# Filter and order
list_parameter_groups_02 <- list_parameter_groups_01 %>%
filter(Parameter %in% selected_markers) %>%
mutate(Group = factor(Group, levels = group_order),
Parameter = factor(Parameter, levels = selected_markers)) %>%
arrange(Parameter)
# Extract ordered parameter vector
list_parameter_groups_03 <- as.character(list_parameter_groups_02$Parameter)
# Reorder Data Table based on the new ordered list
table_lum_myo_healthy_01_t_ordered <- as.data.frame(table_lum_myo_healthy_01_t)
# Convert it back to matrix
table_lum_myo_healthy_01_t_ordered <- as.matrix(table_lum_myo_healthy_01_t_ordered)
# Log Transform
table_lum_myo_healthy_01_t_ordered <- log2(table_lum_myo_healthy_01_t_ordered)
# Scale
table_lum_myo_healthy_01_t_ordered <- t(apply(table_lum_myo_healthy_01_t_ordered, 1, scale))
# Create Cohort Label Colors of Annotations in heatmap
ann_colors <- list(Cohort = c("Myocarditis" = "darkred", "Healthy" = "grey"),
Group = c("Cytokines" = "darkseagreen", "Chemokines" = "lightblue", "Tissue Cytokines" = "mediumpurple3"))
# Column Annotation
col_anno <- HeatmapAnnotation(df = list_cohort_group, col = ann_colors)
# Row Annotation
# Prepare row annotations as a data frame
row_anno <- rowAnnotation(Group = list_parameter_groups_02$Group,
col = list(Group = c("Cytokines" = "darkseagreen",
"Chemokines" = "lightblue",
"Tissue Cytokines" = "mediumpurple3")))
# Create Heatmap
heatmap_lum_sel <- Heatmap(table_lum_myo_healthy_01_t_ordered,
name = "Expression",
col = circlize::colorRamp2(c(-5, 0, 5), c("blue", "white", "red")),
cluster_rows = TRUE,
cluster_columns = TRUE,
clustering_method_columns = "ward.D",
show_column_names = TRUE,
column_names_gp = gpar(fontsize = 8),
row_names_gp = gpar(fontsize = 8),
width = unit(15, "cm"), height = unit(5, "cm"),
top_annotation = col_anno,
right_annotation = row_anno)
# Draw Heatmap
show(heatmap_lum_sel)
Warning! The custom
fig.path you set was
ignored by workflowr.
Data Preparation Correlation (Healthy, Cohort 1, Cohort 2)
# Import routine blood parameters
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)
table_clindat_cohort2 <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_ClinicalData_Cohort2.xlsx")) %>%
dplyr::select(Study_ID, Trop_I, NTproBNP, CRP)
# Combine
table_clindat <- bind_rows(table_clindat_cohort1, table_clindat_healthy, table_clindat_cohort2)
# Merge all data tables
table_all_data <- table_lum_myo_healthy %>%
left_join(table_clindat, by = "Study_ID")Correlation Plots NTproBNP & Lum (Healthy, Cohort 1, Cohort 2)
# Subset dataset
table_corr <- subset(table_all_data, select = c("Study_ID", "Cohort",
"NTproBNP", "IL_2R", "CXCL9", "HGF"))
# Fixed variable
fixed_param <- "NTproBNP"
# Correlation plot IL_2R
cor_test <- cor.test(table_corr[[fixed_param]], table_corr[["IL_2R"]], method = "spearman")
plot_corr_1 <- ggplot(table_corr, aes_string(x = "IL_2R", 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_y_log10(labels = function(x) format(x, scientific = FALSE, trim = TRUE),
limits = c(NA, 100000),
expand = expansion(mult = c(0.05, 0), add = c(0,0))) +
scale_x_log10(labels = function(x) format(x, scientific = FALSE, trim = TRUE),
limits = c(NA, 30000), breaks = c(300, 3000, 30000),
expand = expansion(mult = c(0.05, 0), add = c(0,0))) +
theme_classic() +
labs( y = paste0(fixed_param, " (pg/ml)"), x = "IL-2R (pg/ml)") +
annotate("text", y = min(table_corr[[fixed_param]], na.rm = TRUE),
x = max(table_corr[["IL_2R"]], 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 = 0, size = 5)
# Correlation plot CXCL9
cor_test <- cor.test(table_corr[[fixed_param]], table_corr[["CXCL9"]], method = "spearman")
plot_corr_2 <- ggplot(table_corr, aes_string(x = "CXCL9", 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_y_log10(labels = function(x) format(x, scientific = FALSE, trim = TRUE),
limits = c(NA, 100000),
expand = expansion(mult = c(0.05, 0), add = c(0,0))) +
scale_x_log10(labels = function(x) format(x, scientific = FALSE, trim = TRUE),
limits = c(NA, 10000), breaks = c(100, 300, 1000, 3000, 10000),
expand = expansion(mult = c(0.05, 0), add = c(0,0))) +
theme_classic() +
labs( y = paste0(fixed_param, " (pg/ml)"), x = "CXCL9 (pg/ml)") +
annotate("text", y = min(table_corr[[fixed_param]], na.rm = TRUE),
x = max(table_corr[["CXCL9"]], na.rm = TRUE),
label = paste0("r = ", round(cor_test$estimate, 2),
"\np = ", ifelse(cor_test$p.value < 0.001, "< 0.001", signif(cor_test$p.value, 3))),
hjust = 1, vjust = 0, size = 5)
# Correlation plot HGF
cor_test <- cor.test(table_corr[[fixed_param]], table_corr[["HGF"]], method = "spearman")
plot_corr_3 <- ggplot(table_corr, aes_string(x = "HGF", y = fixed_param)) +
geom_point(shape = 21, size = 3, color = "black", aes(fill = Cohort),
position = position_jitter(width = 0.05, height = 0)) +
geom_smooth(method = "lm", se = TRUE, color = "black") +
scale_fill_manual(values = c("Myocarditis" = "darkred",
"Healthy" = "grey")) +
scale_y_log10(labels = function(x) format(x, scientific = FALSE, trim = TRUE),
limits = c(NA, 100000),
expand = expansion(mult = c(0.05, 0), add = c(0,0))) +
scale_x_log10(labels = function(x) format(x, scientific = FALSE, trim = TRUE),
limits = c(NA, 15000), breaks = c(150, 1500, 15000),
expand = expansion(mult = c(0.08, 0), add = c(0,0))) +
theme_classic() +
labs( y = paste0(fixed_param, " (pg/ml)"), x = "HGF (pg/ml)") +
annotate("text", y = min(table_corr[[fixed_param]], na.rm = TRUE),
x = max(table_corr[["HGF"]], na.rm = TRUE),
label = paste0("r = ", round(cor_test$estimate, 2),
"\np = ", ifelse(cor_test$p.value < 0.001, "< 0.001", signif(cor_test$p.value, 3))),
hjust = 1, vjust = 0, size = 5)
# Combine plots
panel <- ggarrange(plotlist = c(plot_corr_1, plot_corr_2, plot_corr_3),
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] "Tue Mar 3 18:31:09 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