Last updated: 2026-03-03

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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 Serology Data
  table_clindat_healthy <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_Serology_Healthy.xlsx")) %>%
                           dplyr::select(Study_ID, Cohort, NTproBNP, Trop_I, CRP)
   
  table_clindat_cohort1 <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_ClinicalData_Cohort1.xlsx")) %>%
                           dplyr::select(Study_ID, NTproBNP, LV_EF, Trop_I, CRP) %>%
                           mutate(Cohort = "Myocarditis")

  table_clindat_cohort2 <- openxlsx::read.xlsx(file.path(basedir, "data", "Table_ClinicalData_Cohort2.xlsx")) %>%
                           dplyr::select(Study_ID, NTproBNP, LV_EF, Trop_I, CRP) %>%
                           mutate(Cohort = "Myocarditis")
  
  table_cohorts <- rbind(table_clindat_cohort1 %>% dplyr::select(Study_ID, Cohort), 
                         table_clindat_cohort2  %>% dplyr::select(Study_ID, Cohort))

Multiple Imputation for Cardiac Function (only available for AM Cohorts)

# Combine data tables
  table_clindat_myo <- rbind(table_clindat_cohort2 %>% dplyr::select(-Cohort), 
                             table_clindat_cohort1 %>% dplyr::select(-Cohort))

# Impute missing values
  imputed_data <- mice(table_clindat_myo, m = 5, method = 'pmm', maxit = 5, seed = 123)


 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
  print(densityplot(imputed_data, col=c("grey", "blue"), pch = c(1, 20)))