Last updated: 2025-05-05

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Rmd cb08ced Estef Vazquez 2025-05-05 Loading deconv

Immune Cell Type Composition Analysis

Loading Libraries and Data

# Load required libraries
library(tidyverse)
library(ggplot2)
library(forcats)
library(ggpubr) 
library(here)


# Load results
cibersort <- readRDS(here("data", "cibersort_res_ulc_lf.rds"))

# Load combined data with ulceration status
ciber_with_groups <- readRDS(here("data", "cibersort_res_ulc.rds"))

# Gather - long format
ciber_gath <- ciber_with_groups %>%
  pivot_longer(
    cols = -c(sample_id, ulceration),  
    names_to = "cell_type",
    values_to = "proportion"
  )

# Order cells by proportion
ciber_gath <- ciber_gath %>% mutate(cell_type = fct_reorder(cell_type, proportion))

# Order by plasma cell proportion within each group
sample_order <- ciber_gath %>%
  filter(cell_type == "Plasma_cells") %>%
  arrange(ulceration, proportion) %>%
  pull(sample_id)

# Set sample_id as factor with the new order
ciber_ordered <- ciber_gath %>%
  mutate(sample_id = factor(sample_id, levels = sample_order))

##Color Palettes

# Color palette for cell types 
immune_palette <- c(
  '#00441B', '#f29175', 'brown', '#B299A7', 'blue', 'lightblue', 'olivedrab', 'orange', 
  '#3F007D', '#8DA0CB', '#CC0066', "#CB181D", '#74a9cf', 'pink', 'deeppink4', 'cadetblue1',
  '#241178', '#66C2A5', "#E78AC3", "#FFD92F", "#CA9E78", "#3F007D"
)

# Define colors
ulceration_colors <- list(
  fill = c("0" = "#730769", "1" = "#E8CC03"), 
  point = c("0" = "#4A044E", "1" = "#938202")  
)

# Set theme
publication_theme <- theme_minimal() +
  theme(
    plot.title = element_text(size = 14, face = "bold", hjust = 0.5, margin = margin(b = 20)),
    axis.title = element_text(size = 12, face = "bold"),
    axis.text = element_text(size = 10, color = "black"),
    legend.position = "top",
    legend.title = element_text(size = 10, face = "bold"),
    legend.text = element_text(size = 9),
    legend.margin = margin(t = 10, b = 10),
    panel.grid = element_blank(),
    plot.margin = margin(10, 20, 20, 10)
  )

Data Preparation

# Transform wide data to long format
prepare_long_format <- function(data) {
  # Check if data already contains cell_type and proportion columns
  if("cell_type" %in% colnames(data) && "proportion" %in% colnames(data)) {
    return(data)  # Already in long format
  }
  
  # Check if ulceration column exists
  if("ulceration" %in% colnames(data)) {
    # Transform wide to long, preserving ulceration
    data %>% pivot_longer(
      cols = -c(sample_id, ulceration),
      names_to = "cell_type",
      values_to = "proportion"
    )
  } else {
    # Transform wide to long without ulceration
    data %>% pivot_longer(
      cols = -sample_id,
      names_to = "cell_type",
      values_to = "proportion"
    )
  }
}

# Order samples by cell type proportion
order_samples <- function(data, order_by_cell = "Plasma_cells", group_by = NULL) {
  # Ensure data is in long format
  data_long <- prepare_long_format(data)
  
  # Filter for specified cell type
  cell_data <- data_long %>% filter(cell_type == order_by_cell)
  
  # Order samples differently based on whether grouping is needed
  if(!is.null(group_by) && group_by %in% colnames(data_long)) {
    # Order within groups
    sample_order <- cell_data %>%
      arrange(!!sym(group_by), proportion) %>%
      pull(sample_id)
  } else {
    # Order overall
    sample_order <- cell_data %>%
      arrange(proportion) %>%
      pull(sample_id)
  }
  
  # Return the original data with ordered sample_id
  data_long %>% mutate(sample_id = factor(sample_id, levels = sample_order))
}

# Function to prepare data for comparison of single cell types
prepare_cell_data <- function(data, cell_column) {
  # Select only required columns
  data %>% 
    select(sample_id, !!sym(cell_column), ulceration) %>%
    gather(key = "cell_type", value = "proportion", -sample_id, -ulceration)
}

Plotting

# Function to create grouped stacked barplot by ulceration status
plot_grouped_barplot <- function(data, title = "Immune Cell Composition by Ulceration Status") {
  # Ensure data is in long format and ordered
  if(!("ulceration" %in% colnames(data))) {
    stop("Data must contain 'ulceration' column for grouped barplot")
  }
  
  ggplot(data, aes(x = sample_id, y = proportion, fill = cell_type)) +
    geom_col(position = "fill", width = 0.8) +
    scale_fill_manual(values = immune_palette) +
    scale_y_continuous(labels = scales::percent, breaks = seq(0, 1, 0.2)) +
    facet_grid(~ ulceration, scales = "free_x", space = "free_x",
               labeller = labeller(ulceration = c("0" = "Non-ulcerated", "1" = "Ulcerated"))) +
    labs(
      title = title,
      x = "Samples",
      y = "Estimated Cell Proportion (CIBERSORTx)",
      fill = "Immune Cell Type"
    ) +
    theme_minimal() +
    theme(
      plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
      plot.subtitle = element_text(size = 12, hjust = 0.5),
      axis.text.x = element_text(angle = 90, hjust = 1, size = 8),
      axis.text.y = element_text(size = 10),
      axis.title = element_text(size = 12, face = "bold"),
      legend.title = element_text(size = 10, face = "bold"),
      legend.text = element_text(size = 9),
      panel.grid.major.x = element_blank(),
      panel.grid.minor = element_blank(),
      strip.text = element_text(size = 12, face = "bold"),  # Group label formatting
      strip.background = element_rect(fill = "white"),    
      panel.spacing = unit(1, "lines")                      # Space between groups
    )
}


# Comparative boxplot for cell types
plot_cell_boxplot <- function(
  data, 
  cell_name,
  y_max = NULL,     
  y_increment = NULL 
) {
  if(is.null(y_max)) {
    y_max <- ceiling(max(data$proportion) * 1.2 * 100) / 100
  }
  
  if(is.null(y_increment)) {
    y_increment <- y_max / 5
  }
  stat_pos <- y_max * 0.8
  
  ggplot(data, aes(x = cell_type, y = proportion, fill = ulceration)) +
    # Boxplot layer
    geom_boxplot(
      outlier.shape = NA,
      width = 0.5,
      alpha = 0.8
    ) +
    # Points layer with jitter
    geom_point(
      aes(color = ulceration),
      size = 2,
      alpha = 0.6,
      position = position_jitterdodge(
        jitter.width = 0.15,
        dodge.width = 0.5,
        seed = 123
      )
    ) +
    # Colors
    scale_fill_manual(
      values = ulceration_colors$fill,
      name = "Ulceration Status",
      labels = c("0" = "Non-ulcerated", "1" = "Ulcerated")
    ) +
    scale_color_manual(
      values = ulceration_colors$point,
      guide = "none"
    ) +
    # Y-axis 
    scale_y_continuous(
      limits = c(0, y_max),
      breaks = seq(0, y_max, by = y_increment),
      labels = scales::number_format(accuracy = 0.01),
      expand = expansion(mult = c(0.05, 0.1))
    ) +
    # Statistical test
    stat_compare_means(
      aes(group = ulceration),
      label.y = stat_pos,
      size = 4,
      label = "p.format",
      label.x.npc = "center"
    ) +
    # Labels
    labs(
      title = paste0(cell_name, " in Acral Melanoma"),
      y = "Cell Proportion (CIBERSORTx)",
      caption = "Statistical test: Wilcoxon rank-sum test"
    ) +
    # Apply theme
    publication_theme +
    theme(axis.text.x = element_text(angle = 0, hjust = 0.5))
}

Analysis and Visualization

Immune Cell Composition by Ulceration Status

# Create grouped barplot
plot_grouped_barplot(
  ciber_ordered,
  title = "Immune Cell Composition by Ulceration Status in Acral Melanoma"
)

Analysis of Immune Cell Types by Ulceration Status

Plasma Cells

plasma_data <- prepare_cell_data(ciber_with_groups, "Plasma_cells")

plot_cell_boxplot(
  plasma_data,
  "Plasma Cells",
  y_max = 1,
  y_increment = 0.2
)

Eosinophils

eosinophils_data <- prepare_cell_data(ciber_with_groups, "Eosinophils")

plot_cell_boxplot(
  eosinophils_data,
  "Eosinophils",
  y_max = 0.2,  
  y_increment = 0.02
)

Macrophages M0

macrophages_m0_data <- prepare_cell_data(ciber_with_groups, "Macrophages_M0")

plot_cell_boxplot(
  macrophages_m0_data,
  "Macrophages M0",
  y_max = 1,  
  y_increment = 0.2
)

Session Information

sessionInfo()
R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=es_MX.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=es_MX.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=es_MX.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=es_MX.UTF-8 LC_IDENTIFICATION=C       

time zone: America/Mexico_City
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] here_1.0.1      ggpubr_0.6.0    lubridate_1.9.4 forcats_1.0.0  
 [5] stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2     readr_2.1.5    
 [9] tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0
[13] workflowr_1.7.1

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      xfun_0.49         bslib_0.8.0       processx_3.8.4   
 [5] rstatix_0.7.2     callr_3.7.6       tzdb_0.4.0        vctrs_0.6.5      
 [9] tools_4.4.0       ps_1.8.1          generics_0.1.3    pkgconfig_2.0.3  
[13] lifecycle_1.0.4   compiler_4.4.0    farver_2.1.2      git2r_0.33.0     
[17] munsell_0.5.1     getPass_0.2-4     carData_3.0-5     httpuv_1.6.15    
[21] htmltools_0.5.8.1 sass_0.4.9        yaml_2.3.10       Formula_1.2-5    
[25] later_1.4.1       pillar_1.10.0     car_3.1-3         jquerylib_0.1.4  
[29] whisker_0.4.1     cachem_1.1.0      abind_1.4-5       tidyselect_1.2.1 
[33] digest_0.6.37     stringi_1.8.4     rprojroot_2.0.4   fastmap_1.2.0    
[37] grid_4.4.0        colorspace_2.1-1  cli_3.6.3         magrittr_2.0.3   
[41] broom_1.0.7       withr_3.0.2       scales_1.3.0      promises_1.3.2   
[45] backports_1.5.0   timechange_0.3.0  rmarkdown_2.29    httr_1.4.7       
[49] ggsignif_0.6.4    hms_1.1.3         evaluate_1.0.1    knitr_1.49       
[53] rlang_1.1.4       Rcpp_1.0.13-1     glue_1.8.0        rstudioapi_0.17.1
[57] jsonlite_1.8.9    R6_2.5.1          fs_1.6.5         

sessionInfo()
R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=es_MX.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=es_MX.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=es_MX.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=es_MX.UTF-8 LC_IDENTIFICATION=C       

time zone: America/Mexico_City
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] here_1.0.1      ggpubr_0.6.0    lubridate_1.9.4 forcats_1.0.0  
 [5] stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2     readr_2.1.5    
 [9] tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0
[13] workflowr_1.7.1

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      xfun_0.49         bslib_0.8.0       processx_3.8.4   
 [5] rstatix_0.7.2     callr_3.7.6       tzdb_0.4.0        vctrs_0.6.5      
 [9] tools_4.4.0       ps_1.8.1          generics_0.1.3    pkgconfig_2.0.3  
[13] lifecycle_1.0.4   compiler_4.4.0    farver_2.1.2      git2r_0.33.0     
[17] munsell_0.5.1     getPass_0.2-4     carData_3.0-5     httpuv_1.6.15    
[21] htmltools_0.5.8.1 sass_0.4.9        yaml_2.3.10       Formula_1.2-5    
[25] later_1.4.1       pillar_1.10.0     car_3.1-3         jquerylib_0.1.4  
[29] whisker_0.4.1     cachem_1.1.0      abind_1.4-5       tidyselect_1.2.1 
[33] digest_0.6.37     stringi_1.8.4     rprojroot_2.0.4   fastmap_1.2.0    
[37] grid_4.4.0        colorspace_2.1-1  cli_3.6.3         magrittr_2.0.3   
[41] broom_1.0.7       withr_3.0.2       scales_1.3.0      promises_1.3.2   
[45] backports_1.5.0   timechange_0.3.0  rmarkdown_2.29    httr_1.4.7       
[49] ggsignif_0.6.4    hms_1.1.3         evaluate_1.0.1    knitr_1.49       
[53] rlang_1.1.4       Rcpp_1.0.13-1     glue_1.8.0        rstudioapi_0.17.1
[57] jsonlite_1.8.9    R6_2.5.1          fs_1.6.5