Last updated: 2026-04-14

Checks: 7 0

Knit directory: dge-analysis/

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Repository version: 27aedd7

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    Modified:   dge-analysis/output/subgroup-ion_channel-analysis/res_ion_channel_vs_unaff.csv
    Modified:   dge-analysis/output/subgroup-ion_channel-analysis/res_ion_channel_vs_unaff_df_genename_05.csv
    Modified:   dge-analysis/output/subgroup-ion_channel-analysis/res_ion_channel_vs_unaff_df_genename_padj05_lfc1.csv
    Modified:   dge-analysis/output/subgroup-ion_channel-analysis/res_ion_channel_vs_unaff_df_genename_padj1.csv
    Modified:   dge-analysis/output/subgroup-ion_channel-analysis/res_ion_channel_vs_unaff_df_genename_padj1_lfc1.csv
    Modified:   dge-analysis/output/subgroup-ion_channel-analysis/res_ion_channel_vs_unaff_siggenes.csv
    Modified:   dge-analysis/output/subgroup-ion_channel-analysis/res_subgroup_vs_unaff_df_genename.csv
    Modified:   dge-analysis/output/subgroup-mito-analysis/counts_vst.csv
    Modified:   dge-analysis/output/subgroup-mito-analysis/counts_vst_limma.csv
    Modified:   dge-analysis/output/subgroup-mito-analysis/res_mito_vs_unaff.csv
    Modified:   dge-analysis/output/subgroup-mito-analysis/res_mito_vs_unaff_df_genename_05.csv
    Modified:   dge-analysis/output/subgroup-mito-analysis/res_mito_vs_unaff_df_genename_padj05_lfc1.csv
    Modified:   dge-analysis/output/subgroup-mito-analysis/res_mito_vs_unaff_df_genename_padj1.csv
    Modified:   dge-analysis/output/subgroup-mito-analysis/res_mito_vs_unaff_df_genename_padj1_lfc1.csv
    Modified:   dge-analysis/output/subgroup-mito-analysis/res_mito_vs_unaff_siggenes.csv
    Modified:   dge-analysis/output/subgroup-mito-analysis/res_subgroup_vs_unaff_df_genename.csv
    Modified:   dge-analysis/output/subgroup-rbc-analysis/counts_vst.csv
    Modified:   dge-analysis/output/subgroup-rbc-analysis/counts_vst_limma.csv
    Modified:   dge-analysis/output/subgroup-rbc-analysis/res_rbc_vs_unaff.csv
    Modified:   dge-analysis/output/subgroup-rbc-analysis/res_rbc_vs_unaff_df_genename_05.csv
    Modified:   dge-analysis/output/subgroup-rbc-analysis/res_rbc_vs_unaff_df_genename_padj05_lfc1.csv
    Modified:   dge-analysis/output/subgroup-rbc-analysis/res_rbc_vs_unaff_df_genename_padj1.csv
    Modified:   dge-analysis/output/subgroup-rbc-analysis/res_rbc_vs_unaff_df_genename_padj1_lfc1.csv
    Modified:   dge-analysis/output/subgroup-rbc-analysis/res_rbc_vs_unaff_siggenes.csv
    Modified:   dge-analysis/output/subgroup-rbc-analysis/res_subgroup_vs_unaff_df_genename.csv
    Modified:   dge-analysis/output/subgroup-rbc-analysis/volcano-plot-rbc.png
    Modified:   dge-analysis/renv.lock

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.

These are the previous versions of the repository in which changes were made to the R Markdown (dge-analysis/analysis/subgroup-analysis.Rmd) and HTML (docs/subgroup-analysis.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 27aedd7 sdhutchins 2026-04-14 Add deconvolution and publication PCA plots.
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Rmd 9f4f1fd GitHub 2025-11-06 Reorganize repository. (#4)
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Overview

The subsetted main analysis tests Affected vs Unaffected in one model. This page repeats that workflow for each genetic subgroup vs Unaffected so subgroup means “samples carrying that label in Subgroup” compared to all Unaffected samples, not subgroup vs subgroup.

We analyze Potassium, HPP, Ion_Channel, CHRN, Mito, and RBC. The statistical model is ~ Batch + group where group is subgroup vs Unaffected. VST stabilizes variance for visualization; removeBatchEffect() removes batch from the VST matrix for heatmaps only; Wald tests and p-values still come from DESeq() on raw counts, same as the main analysis.

We save tables and figures for every subgroup under output/subgroup-{name}-analysis/. This HTML shows only Mito and RBC padj < 0.05 heatmaps and the RBC volcano (supplemental figures in the paper). Objects are redrawn in the document so the site does not depend on fragile image paths.

DGE Analysis Setup

Activate renv and source() the same code/helpers.R as the subsetted analysis so helper names match.

library(tidyverse) # Available via CRAN
library(DESeq2) # Available via Bioconductor
library(RColorBrewer) # Available via CRAN
library(pheatmap) # Available via CRAN
library(genefilter) # Available via Bioconductor
library(limma) # Available via Bioconductor
library(gprofiler2) # Available via CRAN
library(biomaRt) # Available via Bioconductor
library(plotly) # Available via CRAN
library(ggpubr) # Available via CRAN
library(rmarkdown) # Available via CRAN
library(clusterProfiler) # Available via Bioconductor
library(org.Hs.eg.db) # Available via Bioconductor
library(ggrepel) # Available via CRAN
library(ReactomePA) # Available via Bioconductor
library(mygene) # Available via Bioconductor
library(DOSE) # Available via Bioconductor
library(enrichR) # Available via Bioconductor
library(STRINGdb) # Available via Bioconductor
library(EnhancedVolcano)
library(ComplexHeatmap)

Data Import

Counts and metadata match the main analysis. Subgroup can list multiple labels per sample (comma-separated); we split and treat each label as its own subgroup list. The chunk loads everything once; genes_biomart avoids repeated Ensembl lookups inside the loop.

# Load full metadata and counts (no subgroup filter yet)
sample_metadata_full <- read_csv("data/Metadata_2024_11_20.csv")
row.names(sample_metadata_full) <- sample_metadata_full$ID
counts_full <- read_tsv("data/star-salmon/salmon_merged_gene_counts_length_scaled.tsv")

# Load genes of interest
genes_of_interest <- read_csv("data/Prioritized_Genes_From_WGS_2024_11_19.csv") %>%
  pull(Genes) %>%
  unique() %>%
  na.omit()
genes_of_interest <- unique(c(genes_of_interest, "CHI3L1"))

# Process counts: use first column for row names, keep gene info
counts_full <- data.frame(counts_full, row.names = 1, check.names = FALSE)
counts_full$Ensembl_ID <- row.names(counts_full)
drop <- c("Ensembl_ID", "gene_name")
gene_info <- counts_full[, drop]
counts_full <- counts_full[, !(names(counts_full) %in% drop)]

# Derive list of subgroups from metadata (exclude Unaffected)
subgroup_list_raw <- unique(unlist(strsplit(trimws(sample_metadata_full$Subgroup), ",\\s*")))
subgroups_to_analyze <- setdiff(subgroup_list_raw, c("Unaffected", ""))
subgroups_to_analyze <- subgroups_to_analyze[!is.na(subgroups_to_analyze)]

# Subgroups for which we show heatmaps and volcano in the report
heatmap_display_subgroups <- c("Mito", "RBC")
volcano_display_subgroup <- "RBC"

# Retrieve gene annotations once (shared across subgroups)
genes_biomart <- retrieve_gene_info(
  values = gene_info$Ensembl_ID,
  filters = "ensembl_gene_id_version"
)

Annotation colors

ann_colors maps metadata columns to colors so heatmap legends match the publication styling.

study_group_colors <- c(
  "ENE" = "#BFDFBF",
  "IMM" = "#EEBFEE",
  "SOL" = "#FFFF33",
  "STR" = "#BFBFFF",
  "RBC" = "#FFD700",
  "N/A" = "darkgray"
)

group_colors <- c(
  "Potassium" = "#80B1D3",
  "HPP" = "#FB8072",
  "Ion_Channel" = "#FDB462",
  "CHRN" = "#B3DE69",
  "RBC" = "#BC80BD",
  "Mito" = "#CCEBC5",
  "Unaffected" = "gray40"
)

ann_colors <- list(
  Batch = c(B1 = "purple", B2 = "firebrick", B3 = "goldenrod"),
  Affected = c(Affected = "green4", Unaffected = "navy"),
  Study_group = study_group_colors,
  group = group_colors
)

draw_subgroup_heatmap() wraps ComplexHeatmap::pheatmap() so the analysis loop and the report use identical colors and layout.

Run DGE for all subgroups

The loop runs one complete DGE run per subgroup. Full rank means every Batch × group combination needed for ~ Batch + group is estimable; if not (often too few samples in a batch), we skip with a message. Outputs go to output/subgroup-{name}-analysis/.

Only RBC gets a saved volcano PNG. Mito, RBC padj < 0.05 heatmaps, and the RBC volcano ggplot are also held in memory for the sections below.

subgroup_report_heatmaps <- list()
subgroup_report_volcano <- NULL

for (subgroup_name in subgroups_to_analyze) {
  subgroup_lower <- tolower(subgroup_name)
  outpath_var <- paste0("subgroup-", subgroup_lower, "-analysis")
  out_dir <- file.path("output", outpath_var)
  dir.create(out_dir, recursive = TRUE, showWarnings = FALSE)

  # Filter metadata to this subgroup + Unaffected
  sample_metadata <- sample_metadata_full %>%
    mutate(Subgroup_list = strsplit(Subgroup, ",")) %>%
    mutate(in_subgroup = purrr::map_lgl(Subgroup_list, ~ subgroup_name %in% trimws(.))) %>%
    mutate(group = dplyr::case_when(
      in_subgroup ~ subgroup_name,
      Subgroup == "Unaffected" ~ "Unaffected",
      TRUE ~ NA_character_
    )) %>%
    filter(!is.na(group))

  n_sub <- sum(sample_metadata$group == subgroup_name)
  n_unaff <- sum(sample_metadata$group == "Unaffected")
  if (n_sub < 1) {
    message("Skipping ", subgroup_name, ": no samples in subgroup.")
    next
  }

  # Subset counts to these samples and match order
  sample_metadata$ID <- as.character(sample_metadata$ID)
  counts <- counts_full[, colnames(counts_full) %in% sample_metadata$ID, drop = FALSE]
  counts <- counts[, match(sample_metadata$ID, colnames(counts))]
  stopifnot(all(colnames(counts) == sample_metadata$ID))

  # DESeq2
  sample_metadata <- sample_metadata %>%
    mutate(
      Family = factor(Family),
      Affected = factor(Affected),
      Batch = factor(Batch),
      Sex = factor(Sex),
      Ancestry = factor(Ancestry),
      Category = factor(Category),
      group = factor(group)
    )

  # Skip if design matrix is not full rank (e.g. only one batch in this subgroup)
  mm <- model.matrix(~ Batch + group, sample_metadata)
  if (qr(mm)$rank < ncol(mm)) {
    message("Skipping ", subgroup_name, ": model matrix not full rank (e.g. batch and group confounded).")
    next
  }

  dds <- DESeqDataSetFromMatrix(
    countData = round(counts),
    colData = sample_metadata,
    design = ~ Batch + group
  )
  keep <- rowSums(counts(dds)) >= 50
  dds <- dds[keep, ]
  dds <- DESeq(dds)

  # VST and limma batch correction
  vsd <- vst(dds, blind = FALSE)
  counts_vst <- assay(vsd)
  write.csv(counts_vst, file.path(out_dir, "counts_vst.csv"))
  mm <- model.matrix(~ Batch + group, colData(vsd))
  counts_vst_limma <- limma::removeBatchEffect(counts_vst, batch = vsd$Batch, design = mm)
  write.csv(counts_vst_limma, file.path(out_dir, "counts_vst_limma.csv"))
  vsd_limma <- vsd
  assay(vsd_limma) <- counts_vst_limma

  # Results
  res_subgroup_vs_unaff <- results(dds, contrast = c("group", subgroup_name, "Unaffected"))
  res_file_name <- file.path(out_dir, paste0("res_", subgroup_lower, "_vs_unaff.csv"))
  res_subgroup_vs_unaff_df <- process_and_save_results(res_subgroup_vs_unaff, res_file_name)
  res_subgroup_vs_unaff_df <- arrange(res_subgroup_vs_unaff_df, padj)

  res_subgroup_vs_unaff_df_05 <- subset(res_subgroup_vs_unaff_df, padj < 0.05)
  res_subgroup_vs_unaff_df_1 <- subset(res_subgroup_vs_unaff_df, padj < 0.1)
  res_subgroup_vs_unaff_df_padj1_lfc1 <- subset(res_subgroup_vs_unaff_df_1, abs(log2FoldChange) >= 1)
  res_subgroup_vs_unaff_df_padj05_lfc1 <- subset(res_subgroup_vs_unaff_df_05, abs(log2FoldChange) >= 1)

  # Annotated results with gene names
  res_subgroup_vs_unaff_df_genename <- res_subgroup_vs_unaff_df
  res_subgroup_vs_unaff_df_genename$Ensembl_ID <- row.names(res_subgroup_vs_unaff_df)
  res_subgroup_vs_unaff_df_genename <- merge(
    res_subgroup_vs_unaff_df_genename,
    gene_info,
    by = "Ensembl_ID",
    all.x = TRUE
  )
  res_subgroup_vs_unaff_df_genename <- res_subgroup_vs_unaff_df_genename[
    , c("gene_name", "Ensembl_ID", setdiff(names(res_subgroup_vs_unaff_df_genename), c("gene_name", "Ensembl_ID")))
  ]
  res_subgroup_vs_unaff_df_genename <- res_subgroup_vs_unaff_df_genename[
    order(res_subgroup_vs_unaff_df_genename$padj),
  ]
  write.csv(
    res_subgroup_vs_unaff_df_genename,
    file.path(out_dir, "res_subgroup_vs_unaff_df_genename.csv"),
    row.names = FALSE
  )

  # Filtered result CSVs
  process_and_save_filtered_results(
    res_subgroup_vs_unaff_df_genename,
    padj_threshold = 0.05, lfc_threshold = NULL,
    outpath = outpath_var,
    filename = paste0("res_", subgroup_lower, "_vs_unaff_df_genename_05.csv")
  )
  process_and_save_filtered_results(
    res_subgroup_vs_unaff_df_genename,
    padj_threshold = 0.05, lfc_threshold = 1,
    outpath = outpath_var,
    filename = paste0("res_", subgroup_lower, "_vs_unaff_df_genename_padj05_lfc1.csv")
  )
  process_and_save_filtered_results(
    res_subgroup_vs_unaff_df_genename,
    padj_threshold = 0.1, lfc_threshold = NULL,
    outpath = outpath_var,
    filename = paste0("res_", subgroup_lower, "_vs_unaff_df_genename_padj1.csv")
  )
  process_and_save_filtered_results(
    res_subgroup_vs_unaff_df_genename,
    padj_threshold = 0.1, lfc_threshold = 1,
    outpath = outpath_var,
    filename = paste0("res_", subgroup_lower, "_vs_unaff_df_genename_padj1_lfc1.csv")
  )

  # Heatmaps: build matrix and save PNG for every subgroup
  res_subgroup_vs_unaff_df <- res_subgroup_vs_unaff_df[order(res_subgroup_vs_unaff_df$padj), ]
  ensembl_to_gene <- setNames(gene_info$gene_name, gene_info$Ensembl_ID)

  # Heatmap padj < 0.1, |log2FC| >= 1
  topgenes_byensemblid <- rownames(res_subgroup_vs_unaff_df_padj1_lfc1)
  if (length(topgenes_byensemblid) > 0) {
    topgenes_mat <- assay(vsd_limma)[topgenes_byensemblid, , drop = FALSE]
    if (nrow(topgenes_mat) == 1) {
      topgenes_mat <- topgenes_mat - mean(topgenes_mat)
    } else {
      topgenes_mat <- topgenes_mat - rowMeans(topgenes_mat)
    }
    rownames(topgenes_mat) <- ensembl_to_gene[rownames(topgenes_mat)]
    topgenes_mat <- topgenes_mat[order(res_subgroup_vs_unaff_df$padj[topgenes_byensemblid]), , drop = FALSE]
    annotation_df <- as.data.frame(colData(vsd_limma)[, c("group", "Affected"), drop = FALSE])
    png(file.path(out_dir, paste0(subgroup_name, "_padj1_heatmap.png")), width = 14, height = 10, units = "in", res = 1200)
    draw_subgroup_heatmap(topgenes_mat, annotation_df)
    dev.off()
  }

  # Heatmap padj < 0.05, |log2FC| >= 1
  topgenes_byensemblid_05 <- rownames(res_subgroup_vs_unaff_df_padj05_lfc1)
  if (length(topgenes_byensemblid_05) > 0) {
    topgenes_mat_05 <- assay(vsd_limma)[topgenes_byensemblid_05, , drop = FALSE]
    if (nrow(topgenes_mat_05) == 1) {
      topgenes_mat_05 <- topgenes_mat_05 - mean(topgenes_mat_05)
    } else {
      topgenes_mat_05 <- topgenes_mat_05 - rowMeans(topgenes_mat_05)
    }
    rownames(topgenes_mat_05) <- ensembl_to_gene[rownames(topgenes_mat_05)]
    topgenes_mat_05 <- topgenes_mat_05[order(res_subgroup_vs_unaff_df$padj[topgenes_byensemblid_05]), , drop = FALSE]
    df_ann <- as.data.frame(colData(vsd_limma)[, "group", drop = FALSE])
    if (subgroup_name %in% heatmap_display_subgroups) {
      # Store the heatmap inputs so the report can redraw the same figure inline.
      subgroup_report_heatmaps[[subgroup_name]] <- list(
        mat = topgenes_mat_05,
        annotation_col = df_ann
      )
    }
    png(file.path(out_dir, paste0(subgroup_name, "_significant_padj05_heatmap.png")), width = 10, height = 8, units = "in", res = 300)
    draw_subgroup_heatmap(topgenes_mat_05, df_ann)
    dev.off()
  }

  # Volcano: save only for RBC so it is not overwritten
  if (subgroup_name == volcano_display_subgroup) {
    output_volcano_file <- file.path(out_dir, paste0("volcano-plot-", subgroup_lower, ".png"))
    subgroup_report_volcano <- generate_volcano_plot(
      res_data = res_subgroup_vs_unaff_df_genename,
      gene_labels = "gene_name",
      x_col = "log2FoldChange",
      y_col = "padj",
      select_genes = "",
      xlab_text = bquote(~ Log[2] ~ "fold change"),
      ylab_text = bquote(~ -Log[10] ~ "padj"),
      p_cutoff = 0.05,
      fc_cutoff = 1.0,
      xlim_range = c(-25, 25),
      ylim_range = c(0, 7),
      output_file = output_volcano_file,
      print_plot = FALSE
    )
  }

  # MA-plot significant genes CSV
  maplot <- generate_ma_plot(
    data = res_subgroup_vs_unaff_df,
    genenames = as.vector(res_subgroup_vs_unaff_df_genename$gene_name),
    main_title = paste0(subgroup_name, " vs Unaffected MA Plot"),
    top_genes = 30
  )
  significant_data <- maplot$data %>%
    filter(grepl("Up|Down", sig)) %>%
    mutate(direction = ifelse(grepl("Up", sig), "Up", "Down")) %>%
    dplyr::select(-sig)
  write.csv(
    significant_data,
    file.path(out_dir, paste0("res_", subgroup_lower, "_vs_unaff_siggenes.csv")),
    row.names = FALSE
  )

  # Save RData for this subgroup (for optional reload)
  save(
    dds, vsd, vsd_limma,
    res_subgroup_vs_unaff_df, res_subgroup_vs_unaff_df_genename,
    res_subgroup_vs_unaff_df_padj05_lfc1, res_subgroup_vs_unaff_df_padj1_lfc1,
    file = file.path(out_dir, paste0(subgroup_name, "-subgroup-analysis.RData"))
  )

  message("Processed: ", subgroup_name, " (n = ", n_sub, " vs Unaffected n = ", n_unaff, ")")
}
Coefficients not estimable: batch1 batch2 
Coefficients not estimable: batch1 batch2 
Coefficients not estimable: batch1 batch2 
Coefficients not estimable: batch1 
Coefficients not estimable: batch1 batch2

Heatmaps (Mito and RBC only)

Every subgroup gets padj < 0.1 and padj < 0.05 heatmaps on disk. Here, Mito and RBC padj < 0.05 panels reuse the batch-corrected VST matrix from the loop, row-mean centered so color shows deviation from each gene’s average across displayed samples. Columns are annotated with group (subgroup vs Unaffected).

Mito vs Unaffected

Genes pass padj < 0.05 and |log2FC| ≥ 1 for Mito vs Unaffected in this run.

RBC vs Unaffected

Same cutoffs for RBC vs Unaffected.

Volcano plot (RBC only)

The volcano plot shows log2 fold change (RBC vs Unaffected) on the x-axis and −log10 FDR (padj) on the y-axis. Points above the horizontal line pass padj < 0.05, and vertical lines mark |log2FC| = 1. The figure is the ggplot from the loop; a copy is still written to output/subgroup-rbc-analysis/volcano-plot-rbc.png.

Output summary

For each subgroup, the following were written under output/subgroup-{name}-analysis/ for reproducibility and sharing:

  • counts_vst.csv, counts_vst_limma.csv
  • res_{name}_vs_unaff.csv, res_subgroup_vs_unaff_df_genename.csv
  • Filtered result CSVs (padj 0.05 / 0.1, with and without LFC threshold)
  • {Subgroup}_padj1_heatmap.png, {Subgroup}_significant_padj05_heatmap.png
  • res_{name}_vs_unaff_siggenes.csv (MA-plot significant genes)
  • For RBC only: volcano-plot-rbc.png
  • {Subgroup}-subgroup-analysis.RData (DESeq2 and result objects for reproducibility)

sessionInfo()
R version 4.5.1 (2025-06-13)
Platform: aarch64-apple-darwin20
Running under: macOS Tahoe 26.3

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1

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: America/Chicago
tzcode source: internal

attached base packages:
[1] grid      stats4    stats     graphics  grDevices datasets  utils    
[8] methods   base     

other attached packages:
 [1] ComplexHeatmap_2.24.1       EnhancedVolcano_1.26.0     
 [3] STRINGdb_2.20.0             enrichR_3.4                
 [5] DOSE_4.2.0                  mygene_1.44.0              
 [7] txdbmaker_1.4.2             GenomicFeatures_1.60.0     
 [9] ReactomePA_1.52.0           ggrepel_0.9.6              
[11] org.Hs.eg.db_3.21.0         AnnotationDbi_1.70.0       
[13] clusterProfiler_4.16.0      rmarkdown_2.29             
[15] ggpubr_0.6.1                plotly_4.11.0              
[17] biomaRt_2.64.0              gprofiler2_0.2.3           
[19] limma_3.64.1                genefilter_1.90.0          
[21] pheatmap_1.0.13             RColorBrewer_1.1-3         
[23] DESeq2_1.48.1               SummarizedExperiment_1.38.1
[25] Biobase_2.68.0              MatrixGenerics_1.20.0      
[27] matrixStats_1.5.0           GenomicRanges_1.60.0       
[29] GenomeInfoDb_1.44.0         IRanges_2.42.0             
[31] S4Vectors_0.46.0            BiocGenerics_0.54.0        
[33] generics_0.1.4              lubridate_1.9.4            
[35] forcats_1.0.0               stringr_1.5.1              
[37] dplyr_1.1.4                 purrr_1.1.0                
[39] readr_2.1.5                 tidyr_1.3.1                
[41] tibble_3.3.0                ggplot2_3.5.2              
[43] tidyverse_2.0.0             workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] fs_1.6.6                 bitops_1.0-9             enrichplot_1.28.4       
  [4] doParallel_1.0.17        httr_1.4.7               tools_4.5.1             
  [7] backports_1.5.0          R6_2.6.1                 lazyeval_0.2.2          
 [10] GetoptLong_1.0.5         withr_3.0.2              graphite_1.54.0         
 [13] prettyunits_1.2.0        gridExtra_2.3            textshaping_1.0.1       
 [16] cli_3.6.5                sass_0.4.10              systemfonts_1.2.3       
 [19] Rsamtools_2.24.0         yulab.utils_0.2.0        gson_0.1.0              
 [22] foreign_0.8-90           R.utils_2.13.0           WriteXLS_6.8.0          
 [25] plotrix_3.8-4            rstudioapi_0.17.1        RSQLite_2.4.2           
 [28] shape_1.4.6.1            gridGraphics_0.5-1       BiocIO_1.18.0           
 [31] vroom_1.6.5              gtools_3.9.5             car_3.1-3               
 [34] GO.db_3.21.0             Matrix_1.7-3             abind_1.4-8             
 [37] R.methodsS3_1.8.2        lifecycle_1.0.4          whisker_0.4.1           
 [40] yaml_2.3.10              carData_3.0-5            gplots_3.2.0            
 [43] qvalue_2.40.0            SparseArray_1.8.0        BiocFileCache_2.16.0    
 [46] blob_1.2.4               promises_1.3.3           crayon_1.5.3            
 [49] ggtangle_0.0.7           lattice_0.22-7           cowplot_1.2.0           
 [52] annotate_1.86.1          KEGGREST_1.48.1          pillar_1.11.0           
 [55] knitr_1.50               fgsea_1.34.2             rjson_0.2.23            
 [58] codetools_0.2-20         fastmatch_1.1-6          glue_1.8.0              
 [61] getPass_0.2-4            ggfun_0.2.0              data.table_1.17.8       
 [64] vctrs_0.6.5              png_0.1-8                treeio_1.32.0           
 [67] gtable_0.3.6             gsubfn_0.7               cachem_1.1.0            
 [70] xfun_0.52                S4Arrays_1.8.1           tidygraph_1.3.1         
 [73] survival_3.8-3           iterators_1.0.14         statmod_1.5.0           
 [76] nlme_3.1-168             ggtree_3.16.3            bit64_4.6.0-1           
 [79] progress_1.2.3           filelock_1.0.3           rprojroot_2.1.0         
 [82] bslib_0.9.0              KernSmooth_2.23-26       rpart_4.1.24            
 [85] colorspace_2.1-1         DBI_1.2.3                Hmisc_5.2-3             
 [88] nnet_7.3-20              tidyselect_1.2.1         processx_3.8.6          
 [91] chron_2.3-62             bit_4.6.0                compiler_4.5.1          
 [94] curl_6.4.0               git2r_0.36.2             httr2_1.2.0             
 [97] graph_1.86.0             htmlTable_2.4.3          xml2_1.3.8              
[100] DelayedArray_0.34.1      rtracklayer_1.68.0       caTools_1.18.3          
[103] checkmate_2.3.2          scales_1.4.0             callr_3.7.6             
[106] rappdirs_0.3.3           digest_0.6.37            XVector_0.48.0          
[109] htmltools_0.5.8.1        pkgconfig_2.0.3          base64enc_0.1-3         
[112] dbplyr_2.5.0             fastmap_1.2.0            GlobalOptions_0.1.2     
[115] rlang_1.1.6              htmlwidgets_1.6.4        UCSC.utils_1.4.0        
[118] farver_2.1.2             jquerylib_0.1.4          jsonlite_2.0.0          
[121] BiocParallel_1.42.1      GOSemSim_2.34.0          R.oo_1.27.1             
[124] RCurl_1.98-1.17          magrittr_2.0.3           Formula_1.2-5           
[127] GenomeInfoDbData_1.2.14  ggplotify_0.1.2          patchwork_1.3.1         
[130] Rcpp_1.1.0               proto_1.0.0              ape_5.8-1               
[133] viridis_0.6.5            sqldf_0.4-11             stringi_1.8.7           
[136] ggraph_2.2.1             MASS_7.3-65              plyr_1.8.9              
[139] parallel_4.5.1           Biostrings_2.76.0        graphlayouts_1.2.2      
[142] splines_4.5.1            hash_2.2.6.3             circlize_0.4.16         
[145] hms_1.1.3                locfit_1.5-9.12          ps_1.9.1                
[148] igraph_2.1.4             ggsignif_0.6.4           reshape2_1.4.4          
[151] XML_3.99-0.18            evaluate_1.0.4           renv_1.1.4              
[154] BiocManager_1.30.26      foreach_1.5.2            tzdb_0.5.0              
[157] tweenr_2.0.3             httpuv_1.6.16            polyclip_1.10-7         
[160] clue_0.3-66              ggforce_0.5.0            broom_1.0.8             
[163] xtable_1.8-4             restfulr_0.0.16          reactome.db_1.92.0      
[166] tidytree_0.4.6           rstatix_0.7.2            later_1.4.2             
[169] ragg_1.4.0               viridisLite_0.4.2        aplot_0.2.8             
[172] memoise_2.0.1            GenomicAlignments_1.44.0 cluster_2.1.8.1         
[175] timechange_0.3.0