Validation

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Last updated: 2025-05-14

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Knit directory: prs/

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Artery Aorta Validation

Correlation between PRS & expression PCs

# load prs & pcs
metadata_file <- "analysis/metadata_artery.txt"
metadata <- read.csv(metadata_file, header = T, sep = "\t", stringsAsFactors = T)
metadata$sex <- as.factor(metadata$sex)

traits <- metadata[, 7:43]
pc <- metadata[, 1:5]

# Calculate the correlation between each trait and each PC
correlation_matrix <- cor(traits, pc)
range(correlation_matrix)

[1] -0.2613287  0.2585593

correlation_matrix <- t(correlation_matrix)

# Create the heatmap using corrplot
corrplot(correlation_matrix, method = "color", 
         col = colorRampPalette(c("blue", "white", "red"))(200), # color palette
         addCoef.col = "black", # Add correlation coefficients to the plot
         number.cex = 0.4, # Adjust the font size of the numbers
         tl.col = "black", # text label color
         tl.srt = 90, # rotate text labels
         tl.cex = 0.5, 
         title = "Correlation between Traits and expression PCs", 
         mar = c(0, 0, 1, 0)
)

Past versions of unnamed-chunk-1-1.png

Version	Author	Date
8aa2b83	ElisaChen	2025-05-14

Perform DESeq2 differential expression analysis for each trait

# Load the gene expression data
gene_expr_file <- "data/gene_reads_2017-06-05_v8_artery_aorta.gct"
raw_count_df <- fread(gene_expr_file, header = TRUE, sep = "\t", drop = "id")

# load protein_coding list
protein_coding <- fread("data/protein-coding_gene.txt", sep = "\t")
protein_coding <- protein_coding[, c("symbol", "ensembl_gene_id")]

# keep only protein-coding genes
raw_count_df <- raw_count_df[raw_count_df$Description %in% protein_coding$symbol, ]

id <- raw_count_df$Name
raw_count <- raw_count_df[, -c(1:2)]

# modify GTEx sample names matching names used in PRS data
colnames(raw_count) <- sub("^(GTEX-[^-.]+).*", "\\1", colnames(raw_count))

matching_samples <- intersect(rownames(metadata), colnames(raw_count))
final_count <- raw_count[ , ..matching_samples]

# prefilter: keep only rows that have a count of at least 10
keep_genes <- rowSums(final_count >= 10) > 0
final_count <- final_count[keep_genes, ]
id <- id[keep_genes]
dim(final_count)

# Loop through each trait and run DESeq2
for (trait in colnames(traits)) {
  
  # Standardize PRS for the current trait
  prs_trait <- traits[,trait]
  prs_trait <- scale(prs_trait) # Standardize PRS to mean = 0, sd = 1
    
  # Add the standardized PRS to the metadata for continuous trait
  metadata[,trait] <- prs_trait
  
  # Create the DESeqDataSet for the current trait
  dds <- DESeqDataSetFromMatrix(
    countData = as.matrix(final_count),  # Raw counts
    colData = metadata[, c(1:6, which(colnames(metadata) == trait))],
    design = as.formula(paste("~ PC1 + PC2 + PC3 + PC4 + PC5 + sex +", trait))
  )
  rownames(dds) <- id
  
  # Run DESeq2 analysis
  dds <- DESeq(dds, parallel = TRUE, BPPARAM = MulticoreParam(4))
  
  # Get the results for the current trait
  res <- results(dds)
  
  # Save the results to a file
  write.csv(res, paste0("validation_", trait, "_results_artery_validation.csv"))
  
  # print a summary of the results
  print(paste("Results for trait:", trait))
  print(summary(res))
  
  # plot the MA-plot for the current trait
  png(paste0("ma_plot_", trait, "_artery_validation.png"), width = 800, height = 600)
  plotMA(res, main = paste("Continuous (Artery validation): MA Plot for", trait))
  dev.off()
  
  # volcano plot
  res_tableOE <- as.data.frame(res)
  res_tableOE$gene_name <- raw_count_df$Description[keep_genes]
  res_tableOE <- mutate(res_tableOE, threshold_OE = padj < 0.1)
  res_tableOE <- res_tableOE %>% arrange(padj) %>% mutate(genelabels = "")
  res_tableOE$genelabels[1:10] <- res_tableOE$gene_name[1:10]
  
  volcano_plot <- ggplot(res_tableOE, aes(x = log2FoldChange, y = -log10(padj))) +
    geom_point(aes(colour = threshold_OE)) +
    geom_text_repel(aes(label = genelabels)) +
    ggtitle(label = paste("Continuous: Volcano Plot for", trait), 
            subtitle = "Artery Aorta validation") +
    xlab("log2 fold change") + 
    ylab("-log10 adjusted p-value") +
    theme(legend.position = "none",
          plot.title = element_text(size = rel(1.5), hjust = 0.5),
          axis.title = element_text(size = rel(1.25)))
  
  # Save the volcano plot
  png(paste0("volcano_plot_", trait, "_artery_validation.png"), width = 800, 
      height = 600)
  print(volcano_plot)
  dev.off()
}

metadata_file <- "analysis/metadata_artery_quantile.txt"
metadata <- read.csv(metadata_file, header = T, sep = "\t", stringsAsFactors = T)
metadata$sex <- as.factor(metadata$sex)

traits <- metadata[, 7:43]

# Loop through each trait and run DESeq2
for (trait in colnames(traits)) {
  
  # Create the DESeqDataSet for the current trait
  dds <- DESeqDataSetFromMatrix(
    countData = as.matrix(final_count),  # Raw counts
    colData = metadata[, c(1:6, which(colnames(metadata) == trait))],
    design = as.formula(paste("~ PC1 + PC2 + PC3 + PC4 + PC5 + sex +", trait))
  )
  rownames(dds) <- id
  
  # Run DESeq2 analysis
  dds <- DESeq(dds, parallel = TRUE, BPPARAM = MulticoreParam(4))
  
  # Get the results for the current trait
  res <- results(dds)
  
  # Save the results to a file
  write.csv(res, paste0("validation_", trait, "_quantile_results_artery_validation.csv"))
  
  # print a summary of the results
  print(paste("Results for trait:", trait))
  print(summary(res))
  
  # plot the MA-plot for the current trait
  png(paste0("ma_plot_quantile_", trait, "_artery_validation.png"), width = 800, height = 600)
  plotMA(res, main = paste("Quantile (Artery Validation): MA Plot for", trait))
  dev.off()
  
  # volcano plot
  res_tableOE <- as.data.frame(res)
  res_tableOE$gene_name <- raw_count_df$Description[keep_genes]
  res_tableOE <- mutate(res_tableOE, threshold_OE = padj < 0.1 & 
                          abs(log2FoldChange) >= 0.5)
  res_tableOE <- res_tableOE %>% arrange(padj) %>% mutate(genelabels = "")
  res_tableOE$genelabels[1:10] <- res_tableOE$gene_name[1:10]
  
  volcano_plot <- ggplot(res_tableOE, aes(x = log2FoldChange, y = -log10(padj))) +
    geom_point(aes(colour = threshold_OE)) +
    geom_text_repel(aes(label = genelabels)) +
    ggtitle(paste("Quantile:Volcano Plot for", trait), 
            subtitle = "Artery Aorta validation") +
    xlab("log2 fold change") + 
    ylab("-log10 adjusted p-value") +
    theme(legend.position = "none",
          plot.title = element_text(size = rel(1.5), hjust = 0.5),
          axis.title = element_text(size = rel(1.25)))
  
  # Save the volcano plot
  png(paste0("volcano_plot_quantile_", trait, "_artery_validation.png"), width = 800, height = 600)
  print(volcano_plot)
  dev.off()
}

Gene enrichment Analysis

BgRatio: Number of all genes in specific GO term / Number of universal genes

GeneRatio: Number of genes enriched in specific term / Number of input genes

FoldEnrichment: GeneRatio / BgRatio

# continous
dir_path <- "analysis/continuous_artery"
files <- list.files(dir_path, pattern = "differential_expression_.*_results_artery_validation.csv", full.names = TRUE)

for (file in files) {
  trait <- gsub("differential_expression_(.*)_results_artery_validation.csv", "\\1", basename(file))
  trait
  res_tableOE <- read.csv(file, header = T, row.names = 1)
  
  deGenes <- res_tableOE[res_tableOE$padj < 0.1 & 
                           abs(res_tableOE$log2FoldChange) >= 0.5, ]
  deGenes$gene_id <- gsub("\\.\\d+$", "", rownames(deGenes))
  
  gse <- enrichGO(gene = deGenes$gene_id, ont = "BP", 
                OrgDb ="org.Hs.eg.db", keyType = "ENSEMBL")
  
  write.csv(as.data.frame(gse), file = paste0("GO_enrichment_", trait, "_results_artery_validation.csv"))
  
  gse <- as.data.frame(gse)
  gse$GeneRatio_num <- as.numeric(sapply(strsplit(gse$GeneRatio, "/"), 
                                         function(x) x[1])) /
                     as.numeric(sapply(strsplit(gse$GeneRatio, "/"), function(x) x[2]))

  gse$BgRatio_num <- as.numeric(sapply(strsplit(gse$BgRatio, "/"), function(x) x[1])) / 
                     as.numeric(sapply(strsplit(gse$BgRatio, "/"), function(x) x[2]))
  
  gse <- cbind(gse, FoldEnrich = gse$GeneRatio_num/gse$BgRatio_num)
  
  if (nrow(gse) >= 20) {
    enrich_plot <- plotEnrich(gse[1:20,], plot_type = "dot", scale_ratio = 0.5) +
    labs(title = paste("Continuous: Enrichment plot for", trait), 
         subtitle = "Artery Aorta validation") + 
    theme(plot.title = element_text(size = 10)) 
  }else{
    enrich_plot <- plotEnrich(gse, plot_type = "dot", scale_ratio = 0.5) +
    labs(title = paste("Continuous: Enrichment plot for", trait), 
         subtitle = "Artery Aorta validation") + 
    theme(plot.title = element_text(size = 10)) 
  }
  ggsave(paste0("enrichment_plot_", trait, "_artery_validation.png"), plot = enrich_plot, width = 8, height = 6)
}

# quantile
dir_path <- "analysis/quantile_artery"
files <- list.files(dir_path, pattern = "differential_expression_.*_quantile_results_artery_validation.csv",
                    full.names = TRUE)

for (file in files) {
  trait <- gsub("differential_expression_(.*)_quantile_results_artery_validation.csv", "\\1", basename(file))
  trait
  res_tableOE <- read.csv(file, header = T, row.names = 1)
  
  deGenes <- res_tableOE[res_tableOE$padj < 0.1 & 
                           abs(res_tableOE$log2FoldChange) >= 0.5, ]
  deGenes$gene_id <- gsub("\\.\\d+$", "", rownames(deGenes))
  
  gse <- enrichGO(gene = deGenes$gene_id, ont = "BP", 
                OrgDb ="org.Hs.eg.db", keyType = "ENSEMBL")
  
  write.csv(as.data.frame(gse), file = paste0("GO_enrichment_quantile_", trait, "_results_artery_validation.csv"))
  
  gse <- as.data.frame(gse)
  gse$GeneRatio_num <- as.numeric(sapply(strsplit(gse$GeneRatio, "/"), 
                                         function(x) x[1])) /
                     as.numeric(sapply(strsplit(gse$GeneRatio, "/"), function(x) x[2]))

  gse$BgRatio_num <- as.numeric(sapply(strsplit(gse$BgRatio, "/"), function(x) x[1])) / 
                     as.numeric(sapply(strsplit(gse$BgRatio, "/"), function(x) x[2]))
  
  gse <- cbind(gse, FoldEnrich = gse$GeneRatio_num/gse$BgRatio_num)
  
  if (nrow(gse) >= 20) {
    enrich_plot <- plotEnrich(gse[1:20,], plot_type = "dot", scale_ratio = 0.5) +
    labs(title = paste("Quantile: Enrichment plot for", trait), 
         subtitle = "Artery Aorta validation") + 
    theme(plot.title = element_text(size = 10)) 
  }else{
    enrich_plot <- plotEnrich(gse, plot_type = "dot", scale_ratio = 0.5) +
    labs(title = paste("Quantile: Enrichment plot for", trait), 
         subtitle = "Artery Aorta validation") + 
    theme(plot.title = element_text(size = 10)) 
  }
  ggsave(paste0("enrichment_plot_quantile_", trait, "_artery_validation.png"), plot = enrich_plot, width = 8, height = 6)
}

Result

continuous_dir <- "analysis/continuous_artery"
quantile_dir <- "analysis/quantile_artery"

# Get the list of differential expression results and GO enrichment results
de_files_continuous <- list.files(continuous_dir, pattern = "differential_expression_.*_results_artery_validation.csv", 
                                  full.names = TRUE)
go_files_continuous <- list.files(continuous_dir, pattern = "GO_enrichment_.*_results_artery_validation.csv", 
                                  full.names = TRUE)

de_files_quantile <- list.files(quantile_dir, pattern = "differential_expression_.*_quantile_results_artery_validation.csv", 
                                full.names = TRUE)
go_files_quantile <- list.files(quantile_dir, pattern = "GO_enrichment_quantile_.*_results_artery_validation.csv", 
                                full.names = TRUE)

# Initialize a data frame to store the results
results_df_continuous <- data.frame(trait = character(),
                                    num_significant_de = integer(),
                                    upregulated = integer(),
                                    downregulated = integer(),
                                    num_enriched_go = integer(),
                                    stringsAsFactors = FALSE)

results_df_quantile <- data.frame(trait = character(),
                                  num_significant_de = integer(),
                                  upregulated = integer(),
                                  downregulated = integer(),
                                  num_enriched_go = integer(),
                                  stringsAsFactors = FALSE)

# Function to extract significant DE genes and GO enriched pathways
extract_results <- function(de_file, go_file, analysis_type) {
  # Load the differential expression results
  res <- read.csv(de_file, header = T, row.names = 1)
  
  # Filter for significant DE genes (padj < 0.1)
  significant_de <- res[!is.na(res$padj) & res$padj < 0.1, ]
  
  # Count number of upregulated and downregulated genes
  upregulated <- sum(significant_de$log2FoldChange > 0)
  downregulated <- sum(significant_de$log2FoldChange < 0)
  
  # Load the GO enrichment results
  go_res <- read.csv(go_file, header = T, row.names = 1)
  
  # Count the number of enriched GO pathways (adjusted p-value < 0.05)
  enriched_go <- sum(go_res$p.adjust < 0.05)
  
  if (analysis_type == "continuous"){
    # Extract trait name from the file name
    trait_name <- gsub("differential_expression_|_results.csv", "", basename(de_file))
    
    # Create a row for this trait and add it to the results data frame
    results_df_continuous <<- rbind(results_df_continuous, 
                                    data.frame(trait = trait_name,
                                               num_significant_de = nrow(significant_de),
                                               upregulated = upregulated,
                                              downregulated = downregulated,
                                              num_enriched_go = enriched_go))
  }else if (analysis_type == "quantile") {
    # Extract trait name from the file name
    trait_name <- gsub("differential_expression_|_quantile_results.csv", "", basename(de_file))
    
    # Create a row for this trait and add it to the results data frame
    results_df_quantile <<- rbind(
      results_df_quantile,
      data.frame(
        trait = trait_name,
        num_significant_de = nrow(significant_de),
        upregulated = upregulated,
        downregulated = downregulated,
        num_enriched_go = enriched_go
      )
    )
  }
}

# Loop through the continuous files and extract results
for (i in seq_along(de_files_continuous)) {
  extract_results(de_files_continuous[i], go_files_continuous[i], "continuous")
}

# Loop through the quantile files and extract results
for (i in seq_along(de_files_quantile)) {
  extract_results(de_files_quantile[i], go_files_quantile[i], "quantile")
}

colnames(results_df_continuous) <- c("Trait",
                          "Significant differential expressed genes", 
                          "Up", "Down", "Significant GO enriched pathways")
colnames(results_df_quantile) <- c("Trait",
                          "Significant differential expressed genes", 
                          "Up", "Down", "Significant GO enriched pathways")
knitr::kable(results_df_continuous, caption = "Continuous")

Continuous

Trait	Significant differential expressed genes	Up	Down	Significant GO enriched pathways
Basophil.count_results_artery_validation.csv	284	254	30	141
Basophil.percentage.of.white.cells_results_artery_validation.csv	349	273	76	109
Celiac_GCST90014442_results_artery_validation.csv	418	346	72	171
Celiac_GCST90468120_results_artery_validation.csv	103	16	87	111
Eosinophil.count_results_artery_validation.csv	251	186	65	121
Eosinophil.percentage.of.white.cells_results_artery_validation.csv	139	12	127	69
Hematocrit_results_artery_validation.csv	184	38	146	78
Hemoglobin.concentration_results_artery_validation.csv	217	77	140	88
High.light.scatter.reticulocyte.count_results_artery_validation.csv	67	56	11	2
High.light.scatter.reticulocyte.percentage.of.red.cells_results_artery_validation.csv	93	80	13	22
IBD_GCST90013901_results_artery_validation.csv	373	107	266	149
IBD_GCST90013951_results_artery_validation.csv	479	135	344	151
Immature.fraction.of.reticulocytes_results_artery_validation.csv	269	159	110	101
LUPUS_GCST003156_results_artery_validation.csv	132	44	88	7
LUPUS_GCST011096_results_artery_validation.csv	131	52	79	32
Lymphocyte.count_results_artery_validation.csv	197	89	108	55
Lymphocyte.percentage.of.white.cells_results_artery_validation.csv	200	17	183	116
Mean.corpuscular.hemoglobin_results_artery_validation.csv	293	103	190	77
Mean.corpuscular.hemoglobin.concentration_results_artery_validation.csv	1057	172	885	144
Mean.corpuscular.volume_results_artery_validation.csv	205	140	65	77
Mean.platelet.volume_results_artery_validation.csv	155	126	29	63
Mean.reticulocyte.volume_results_artery_validation.csv	115	77	38	76
Mean.sphered.corpuscular.volume_results_artery_validation.csv	73	29	44	38
Monocyte.count_results_artery_validation.csv	206	185	21	130
Monocyte.percentage.of.white.cells_results_artery_validation.csv	77	53	24	19
Neutrophil.count_results_artery_validation.csv	436	307	129	183
Neutrophil.percentage.of.white.cells_results_artery_validation.csv	466	313	153	220
Platelet.count_results_artery_validation.csv	139	68	71	137
Platelet.crit_results_artery_validation.csv	310	169	141	229
Platelet.distribution.width_results_artery_validation.csv	251	180	71	73
Red.blood.cell.count_results_artery_validation.csv	162	25	137	65
Red.cell.distribution.width_results_artery_validation.csv	242	93	149	74
Reticulocyte.count_results_artery_validation.csv	33	18	15	42
Reticulocyte.fraction.of.red.cells_results_artery_validation.csv	47	33	14	52
T1D_GCST90000529_results_artery_validation.csv	123	100	23	74
T1D_GCST90014023_results_artery_validation.csv	447	282	165	140
White.blood.cell.count_results_artery_validation.csv	481	412	69	183

knitr::kable(results_df_quantile, caption = "Quantile (Stratify trait into top 25% and remaining)")

Quantile (Stratify trait into top 25% and remaining)

Trait	Significant differential expressed genes	Up	Down	Significant GO enriched pathways
Basophil.count_quantile_results_artery_validation.csv	325	292	33	135
Basophil.percentage.of.white.cells_quantile_results_artery_validation.csv	218	186	32	125
Celiac_GCST90014442_quantile_results_artery_validation.csv	392	332	60	135
Celiac_GCST90468120_quantile_results_artery_validation.csv	72	26	46	103
Eosinophil.count_quantile_results_artery_validation.csv	249	209	40	133
Eosinophil.percentage.of.white.cells_quantile_results_artery_validation.csv	32	6	26	64
Hematocrit_quantile_results_artery_validation.csv	127	63	64	159
Hemoglobin.concentration_quantile_results_artery_validation.csv	122	84	38	153
High.light.scatter.reticulocyte.count_quantile_results_artery_validation.csv	60	17	43	134
High.light.scatter.reticulocyte.percentage.of.red.cells_quantile_results_artery_validation.csv	134	105	29	55
IBD_GCST90013901_quantile_results_artery_validation.csv	159	115	44	139
IBD_GCST90013951_quantile_results_artery_validation.csv	226	108	118	139
Immature.fraction.of.reticulocytes_quantile_results_artery_validation.csv	140	101	39	111
LUPUS_GCST003156_quantile_results_artery_validation.csv	143	57	86	78
LUPUS_GCST011096_quantile_results_artery_validation.csv	163	68	95	114
Lymphocyte.count_quantile_results_artery_validation.csv	278	100	178	89
Lymphocyte.percentage.of.white.cells_quantile_results_artery_validation.csv	127	22	105	123
Mean.corpuscular.hemoglobin_quantile_results_artery_validation.csv	319	143	176	119
Mean.corpuscular.hemoglobin.concentration_quantile_results_artery_validation.csv	793	133	660	471
Mean.corpuscular.volume_quantile_results_artery_validation.csv	269	166	103	144
Mean.platelet.volume_quantile_results_artery_validation.csv	193	164	29	19
Mean.reticulocyte.volume_quantile_results_artery_validation.csv	167	49	118	48
Mean.sphered.corpuscular.volume_quantile_results_artery_validation.csv	188	21	167	86
Monocyte.count_quantile_results_artery_validation.csv	575	359	216	108
Monocyte.percentage.of.white.cells_quantile_results_artery_validation.csv	169	109	60	145
Neutrophil.count_quantile_results_artery_validation.csv	339	247	92	363
Neutrophil.percentage.of.white.cells_quantile_results_artery_validation.csv	551	373	178	268
Platelet.count_quantile_results_artery_validation.csv	152	129	23	124
Platelet.crit_quantile_results_artery_validation.csv	310	275	35	153
Platelet.distribution.width_quantile_results_artery_validation.csv	354	309	45	181
Red.blood.cell.count_quantile_results_artery_validation.csv	118	26	92	29
Red.cell.distribution.width_quantile_results_artery_validation.csv	172	75	97	150
Reticulocyte.count_quantile_results_artery_validation.csv	61	26	35	99
Reticulocyte.fraction.of.red.cells_quantile_results_artery_validation.csv	39	15	24	73
T1D_GCST90000529_quantile_results_artery_validation.csv	131	95	36	45
T1D_GCST90014023_quantile_results_artery_validation.csv	379	266	113	170
White.blood.cell.count_quantile_results_artery_validation.csv	284	231	53	260

Session information

sessionInfo()

R version 4.2.2 (2022-10-31)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
 [1] clusterProfiler_4.6.2       enrichplot_1.18.4          
 [3] org.Hs.eg.db_3.16.0         AnnotationDbi_1.60.2       
 [5] genekitr_1.2.8              ggrepel_0.9.6              
 [7] BiocParallel_1.32.6         DESeq2_1.38.3              
 [9] SummarizedExperiment_1.28.0 Biobase_2.58.0             
[11] MatrixGenerics_1.10.0       matrixStats_1.2.0          
[13] GenomicRanges_1.50.2        GenomeInfoDb_1.34.9        
[15] IRanges_2.32.0              S4Vectors_0.36.2           
[17] BiocGenerics_0.44.0         corrplot_0.95              
[19] ggplot2_3.5.1               dplyr_1.1.4                
[21] data.table_1.16.4           workflowr_1.7.1            

loaded via a namespace (and not attached):
  [1] shadowtext_0.1.4       fastmatch_1.1-6        plyr_1.8.9            
  [4] igraph_1.5.1           lazyeval_0.2.2         splines_4.2.2         
  [7] usethis_3.1.0          urltools_1.7.3         digest_0.6.37         
 [10] yulab.utils_0.2.0      htmltools_0.5.8.1      GOSemSim_2.24.0       
 [13] viridis_0.6.5          GO.db_3.16.0           magrittr_2.0.3        
 [16] memoise_2.0.1          remotes_2.5.0          openxlsx_4.2.5.2      
 [19] Biostrings_2.66.0      annotate_1.76.0        graphlayouts_1.0.1    
 [22] prettyunits_1.2.0      colorspace_2.1-1       blob_1.2.4            
 [25] xfun_0.50              callr_3.7.6            crayon_1.5.3          
 [28] RCurl_1.98-1.16        jsonlite_1.8.9         scatterpie_0.2.4      
 [31] ape_5.7-1              glue_1.8.0             polyclip_1.10-7       
 [34] gtable_0.3.6           zlibbioc_1.44.0        XVector_0.38.0        
 [37] DelayedArray_0.24.0    pkgbuild_1.4.6         scales_1.3.0          
 [40] DOSE_3.24.2            DBI_1.2.3              miniUI_0.1.1.1        
 [43] Rcpp_1.0.14            viridisLite_0.4.2      xtable_1.8-4          
 [46] progress_1.2.3         gridGraphics_0.5-1     tidytree_0.4.6        
 [49] bit_4.5.0.1            europepmc_0.4.3        profvis_0.4.0         
 [52] htmlwidgets_1.6.4      httr_1.4.7             fgsea_1.24.0          
 [55] RColorBrewer_1.1-3     ellipsis_0.3.2         urlchecker_1.0.1      
 [58] pkgconfig_2.0.3        XML_3.99-0.18          farver_2.1.2          
 [61] sass_0.4.9             locfit_1.5-9.8         ggplotify_0.1.2       
 [64] tidyselect_1.2.1       rlang_1.1.5            reshape2_1.4.4        
 [67] later_1.4.1            munsell_0.5.1          tools_4.2.2           
 [70] cachem_1.1.0           downloader_0.4         cli_3.6.3             
 [73] generics_0.1.3         RSQLite_2.3.9          gson_0.1.0            
 [76] devtools_2.4.5         evaluate_1.0.3         stringr_1.5.1         
 [79] fastmap_1.2.0          yaml_2.3.10            ggtree_3.6.2          
 [82] processx_3.8.5         knitr_1.49             bit64_4.6.0-1         
 [85] fs_1.6.5               tidygraph_1.3.0        zip_2.3.2             
 [88] purrr_1.0.2            KEGGREST_1.38.0        ggraph_2.1.0          
 [91] nlme_3.1-160           mime_0.12              whisker_0.4.1         
 [94] aplot_0.2.4            ggvenn_0.1.10          xml2_1.3.6            
 [97] compiler_4.2.2         rstudioapi_0.17.1      png_0.1-8             
[100] treeio_1.22.0          tibble_3.2.1           tweenr_2.0.3          
[103] geneplotter_1.76.0     bslib_0.9.0            stringi_1.8.4         
[106] ps_1.8.1               lattice_0.22-6         Matrix_1.5-1          
[109] vctrs_0.6.5            pillar_1.10.1          lifecycle_1.0.4       
[112] triebeard_0.4.1        jquerylib_0.1.4        cowplot_1.1.3         
[115] bitops_1.0-9           httpuv_1.6.15          patchwork_1.3.0       
[118] qvalue_2.30.0          R6_2.5.1               promises_1.3.2        
[121] gridExtra_2.3          sessioninfo_1.2.2      codetools_0.2-20      
[124] pkgload_1.4.0          MASS_7.3-58.1          rprojroot_2.0.4       
[127] withr_3.0.2            GenomeInfoDbData_1.2.9 parallel_4.2.2        
[130] hms_1.1.3              grid_4.2.2             ggfun_0.1.8           
[133] tidyr_1.3.1            HDO.db_0.99.1          rmarkdown_2.29        
[136] git2r_0.33.0           getPass_0.2-4          ggforce_0.4.1         
[139] shiny_1.10.0           geneset_0.2.7