Johnson_DOX_24_7
Omar Johnson
2024-07-22
Last updated: 2024-07-23
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Knit directory: DOX_24_Github/
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Load Libraries
Read in Data
Functions
perform_module_comparisons_mutexc_2 <- function(df, module_col, value_col) {
# Ensure the necessary columns exist
if (!(module_col %in% names(df) && value_col %in% names(df))) {
stop("Specified columns do not exist in the dataframe.")
}
# Get a list of all unique modules
modules <- unique(df[[module_col]])
# Initialize an empty list to store combined data frames
combined_df_list <- list()
# Initialize an empty dataframe to store results
results <- data.frame(Module1 = character(),
Module2 = character(),
WilcoxPValue = numeric(),
stringsAsFactors = FALSE)
# Loop through each module
for (module in modules) {
# Data for the current module
current_data <- df %>% filter(!!sym(module_col) == module) %>%
mutate(Group = as.character(module))
# Data for all other modules
other_data <- df %>% filter(!!sym(module_col) != module) %>%
mutate(Group = paste("Not", module, sep=""))
# Combine current module data with other module data
combined_data <- rbind(current_data, other_data)
# Add the combined data to the list
combined_df_list[[module]] <- combined_data
# Perform the Wilcoxon test
test_result <- wilcox.test(current_data[[value_col]], other_data[[value_col]])
# Add the results to the dataframe
results <- rbind(results, data.frame(Module1 = module,
Module2 = "Others",
WilcoxPValue = test_result$p.value))
}
return(list("results" = results, "combined_data" = combined_df_list))
}
perform_module_disease_analysis_genes_3 <- function(toptable, diseaseGenes) {
# Prepare an empty list to collect results
results <- list()
# Ensure 'Modules' and 'hgnc_symbol' columns exist in 'toptable'
if(!"Modules" %in% names(toptable)) {
stop("Column 'Modules' not found in the 'toptable'.")
}
if(!"hgnc_symbol" %in% names(toptable)) {
stop("Column 'hgnc_symbol' not found in the 'toptable'.")
}
# Filter disease genes to include only those that are expressed in toptable
expressedDiseaseGenes <- lapply(diseaseGenes, function(genes) {
intersect(genes, toptable$hgnc_symbol)
})
# Loop through each module
modules <- unique(toptable$Modules)
for (module in modules) {
# Get the genes in the module
moduleGenes <- toptable$hgnc_symbol[toptable$Modules == module]
# Loop through each disease gene set
for (diseaseName in names(expressedDiseaseGenes)) {
# Find the intersecting genes between the module and the expressed disease genes
diseaseModuleIntersect <- intersect(moduleGenes, expressedDiseaseGenes[[diseaseName]])
# Calculate elements for the contingency table
numIntersect = length(diseaseModuleIntersect)
numInModuleNotDisease = length(moduleGenes) - numIntersect
numInDiseaseNotModule = length(expressedDiseaseGenes[[diseaseName]]) - numIntersect
numInNeither = nrow(toptable) - (numIntersect + numInModuleNotDisease + numInDiseaseNotModule)
# Build the contingency table
table <- matrix(c(
numIntersect, # Both in disease list and module
numInModuleNotDisease, # In module but not disease list
numInDiseaseNotModule, # In disease list but not module
numInNeither # In neither list
), nrow = 2, byrow = TRUE)
# Perform chi-squared test and Fisher's exact test with error handling
chiSqTestResult <- tryCatch({
chisq.test(table, correct = TRUE)
}, error = function(e) {
list(p.value = NA)
}, warning = function(w) {
list(p.value = NA)
})
fisherTestResult <- tryCatch({
fisher.test(table)
}, error = function(e) {
list(p.value = NA)
}, warning = function(w) {
list(p.value = NA)
})
# Calculate percent overlap, handle division by zero
percentOverlap <- if (length(moduleGenes) > 0) {
(numIntersect / length(expressedDiseaseGenes[[diseaseName]])) * 100
} else {
0
}
# Convert intersecting genes to a single character string
intersectingGenesStr <- if (numIntersect > 0) {
paste(diseaseModuleIntersect, collapse = ";")
} else {
"" # Use an empty string to indicate no intersection
}
# Append to results list
results[[paste(module, diseaseName, sep = "_")]] <- data.frame(
Modules = module,
Disease = diseaseName,
ChiSqPValue = chiSqTestResult$p.value,
FisherPValue = fisherTestResult$p.value,
PercentOverlap = percentOverlap,
OddsRatio = fisherTestResult$estimate,
IntersectingGenes = intersectingGenesStr
)
}
}
# Combine results into a single data frame
results_df <- do.call(rbind, results)
return(results_df)
}
# Function assignment
perform_fisher_test_FP <- function(vec1, vec2, vec1_name, vec2_name, plot = FALSE) {
# Create labeled factors for vec1 and vec2
vec1_label <- factor(vec1, labels = c(paste0("Not", vec1_name), paste0("Is", vec1_name)))
vec2_label <- factor(vec2, labels = c(paste0("Not", vec2_name), paste0("Is", vec2_name)))
# Create contingency table with labeled factors
table <- table(vec1_label, vec2_label)
# Perform Fisher's exact test
test_result <- fisher.test(table)
p_value <- test_result$p.value
OR <- test_result$estimate
CI <- test_result$conf.int
# Prepare result
result <- list(
ContingencyTable = table,
PValue = p_value,
Odds_ratio = test_result$estimate,
Confidence_Interval = test_result$conf.int
)
# Generate plot if required
if (plot) {
# Convert table to data frame for ggplot
table_df <- as.data.frame(as.table(table))
colnames(table_df) <- c("vec1_label", "vec2_label", "Freq")
# Calculate totals for each vec1_label
totals <- aggregate(Freq ~ vec1_label, data = table_df, sum)
# Merge totals with table_df and calculate percentages
table_df <- merge(table_df, totals, by = "vec1_label", all.x = TRUE)
table_df$Percentage <- with(table_df, Freq.x / Freq.y * 100)
table_df$Group <- table_df$vec2_label
# Stacked bar chart
p <- ggplot(table_df, aes(x = vec1_label, y = Percentage, fill = Group)) +
geom_bar(stat = "identity", position = "stack") + # Adjust position to "stack"
facet_wrap(~ vec1_label) +
theme_minimal() +
labs(x = vec1_name, y = "Percentage", fill = vec2_name, title = paste("")) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
result$Plot <- p
}
return(result)
}
group_by_deciles <- function(x) {
deciles <- cut(x,
breaks = quantile(x, probs = seq(0, 1, by = 0.1), na.rm = TRUE),
include.lowest = TRUE,
labels = paste0("D", 1:10))
return(deciles)
}CVD-Module enrichment
BigGWASsumstat <- read_tsv(file = File_path_1)Rows: 5709 Columns: 15
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (13): riskAllele, pValueAnnotation, riskFrequency, orValue, beta, ci, ma...
dbl (2): pValue, pubmedId
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.BigGWASsumstat$traitName %>% unique() %>% length()[1] 154BigGWASsumstat$riskAllele %>% unique() %>% length()[1] 4280BigGWASsumstat_sep <- separate_rows(BigGWASsumstat, mappedGenes, sep = ",")
BigGWASsumstat[BigGWASsumstat$traitName %in% c("Anthracycline-induced cardiotoxicity in early breast cancer","Anthracycline-induced cardiotoxicity in childhood cancer", "Anthracycline-induced cardiotoxicity in breast cancer"), ]$mappedGenes [1] "BEND4,SHISA3" "MIR548AB,NDUFA4P2" "MPP4"
[4] "RPL7" "LINC01982" "PRUNE2,PCA3"
[7] "CDH13" "RARG" "RARG"
[10] "POLRMT,FGF22" # Using gsub to remove '(MTAG)' from traitName
BigGWASsumstat_sep$traitName <- gsub(" \\(MTAG\\)", "", BigGWASsumstat_sep$traitName)
BigGWASsumstat_sep$traitName <- gsub("Anthracycline-induced cardiotoxicity in early breast cancer", "Anthracycline-induced cardiotoxicity", BigGWASsumstat_sep$traitName)
BigGWASsumstat_sep$traitName <- gsub("Anthracycline-induced cardiotoxicity in childhood cancer", "Anthracycline-induced cardiotoxicity", BigGWASsumstat_sep$traitName)
BigGWASsumstat_sep$traitName <- gsub("Anthracycline-induced cardiotoxicity in breast cancer", "Anthracycline-induced cardiotoxicity", BigGWASsumstat_sep$traitName)
BigGWASsumstat_Exp <- BigGWASsumstat_sep[BigGWASsumstat_sep$mappedGenes %in% New_RNA_PRO_DF_3$hgnc_symbol, ]
traits_genes_list <- BigGWASsumstat_Exp %>%
group_by(traitName) %>%
summarise(Genes = list(mappedGenes)) %>%
deframe()
diseaseGenes <- traits_genes_list
# Plot results
results_df <- perform_module_disease_analysis_genes_3(toptable = New_RNA_PRO_DF_3, diseaseGenes =diseaseGenes)
melted_results <- melt(results_df, id.vars = c("Modules", "Disease", "PercentOverlap", "FisherPValue","OddsRatio","IntersectingGenes" ))
# First, create a new column that indicates where to place stars
melted_results$Star <- ifelse(melted_results$FisherPValue < 0.05 & melted_results$OddsRatio > 1, "*", "")
Enriched_diseases <- melted_results[(melted_results$FisherPValue < 0.05) & (melted_results$OddsRatio > 1) & (melted_results$Modules %in% c("green","darkgreen","midnightblue","salmon","lightyellow")), ]$Disease
Disease_lists_GWAS <- melted_results$Disease %>% unique(
)
module_order <- c("green","darkgreen","midnightblue","salmon","lightyellow", "lightgreen","blue", "magenta","darkred", "brown", "yellow", "royalblue", "grey")
melted_results_2 <- melted_results[melted_results$Disease %in% c("Anthracycline-induced cardiotoxicity" ,"Dilated cardiomyopathy", "Atrial fibrillation","Age-related diseases, mortality and associated endophenotypes", "Coronary artery disease or fibrinogen levels (pleiotropy)"), ]
# Factor the Module column in Fulltrait_df
melted_results_2$Modules <- factor(melted_results_2$Modules, levels = module_order)
melted_results_3 <- melted_results_2[melted_results_2$Modules %in% c("green","darkgreen","midnightblue","salmon","lightyellow"), ]
# Vertical
ggplot(melted_results_3, aes(x = Modules, y = Disease, fill = OddsRatio)) +
geom_tile(color = "black") +
scale_fill_gradientn(colors = c("white", "violet", "red"),
values = scales::rescale(c(-10, 1, 10)),
na.value = "grey50", name = "Odds ratio") +
geom_text(aes(label = Star), color = "black", size = 4, na.rm = TRUE) +
labs(x = "", y = "") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
axis.text.y = element_text(hjust = 0.5),
axis.title = element_text(size = 12),
legend.key.size = unit(0.3, 'cm'),
legend.title = element_text(size = 10),
legend.text = element_text(size = 8)
)
melted_results_3_CVD <- melted_results_3Cardiovascular function-Module enrichment
BigGWASsumstat <- read_tsv(file = File_path_2)Rows: 41479 Columns: 15
── Column specification ────────────────────────────────────────────────────────
Delimiter: "\t"
chr (13): riskAllele, pValueAnnotation, riskFrequency, orValue, beta, ci, ma...
dbl (2): pValue, pubmedId
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.BigGWASsumstat_sep <- separate_rows(BigGWASsumstat, mappedGenes, sep = ",")
GWAS_Traits_to_remove <- BigGWASsumstat_sep[BigGWASsumstat_sep$mappedGenes == "-", ]$traitName %>% unique()
BigGWASsumstat_Exp <- BigGWASsumstat_sep[BigGWASsumstat_sep$mappedGenes %in% New_RNA_PRO_DF_3$hgnc_symbol, ]
traits_genes_list <- BigGWASsumstat_Exp %>%
group_by(traitName) %>%
summarise(Genes = list(mappedGenes)) %>%
deframe()
diseaseGenes <- traits_genes_list
# Plot results
results_df <- perform_module_disease_analysis_genes_3(toptable = New_RNA_PRO_DF_3, diseaseGenes =diseaseGenes)
melted_results <- melt(results_df, id.vars = c("Modules", "Disease", "PercentOverlap", "FisherPValue","OddsRatio", "IntersectingGenes" ))
# First, create a new column that indicates where to place stars
melted_results$Star <- ifelse(melted_results$FisherPValue < 0.05 & melted_results$OddsRatio > 1, "*", "")
Enriched_diseases <- melted_results[(melted_results$FisherPValue < 0.05) & (melted_results$OddsRatio > 1) & (melted_results$Modules %in% c("green","darkgreen","midnightblue","salmon","lightyellow")), ]$Disease
Disease_lists_GWAS <- melted_results$Disease %>% unique(
)
melted_results_2 <- melted_results
melted_results_2 <- melted_results_2[melted_results_2$Disease %in% Enriched_diseases, ]
module_order <- c("green","darkgreen","midnightblue","salmon","lightyellow", "lightgreen","blue", "magenta","darkred", "brown", "yellow", "royalblue", "grey")
# Factor the Module column in Fulltrait_df
melted_results_2$Modules <- factor(melted_results_2$Modules, levels = module_order)
melted_results_3 <- melted_results_2[melted_results_2$Modules %in% c("green","darkgreen","midnightblue","salmon","lightyellow"), ]
melted_results_3_Traits_removed <- melted_results_3[!melted_results_3$Disease %in% GWAS_Traits_to_remove, ]
melted_results_4_Traits_removed <- melted_results_3_Traits_removed %>%
mutate(gene_count = sapply(strsplit(IntersectingGenes, ";"), length)) %>%
filter(gene_count >= 2)
GWAS_clin_traits <- melted_results_4_Traits_removed$Disease %>% unique()
melted_results_3_Traits_removed_plot <- melted_results_3_Traits_removed[melted_results_3_Traits_removed$Disease %in%GWAS_clin_traits, ]
ggplot(melted_results_3_Traits_removed_plot, aes(x = Modules, y = Disease, fill = OddsRatio)) +
geom_tile(color = "black") +
scale_fill_gradientn(colors = c("white", "violet", "red"),
values = scales::rescale(c(-10, 1, 10)),
na.value = "grey50", name = "Odds ratio") +
geom_text(aes(label = Star), color = "black", size = 4, na.rm = TRUE) +
labs(x = "", y = "") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
axis.text.y = element_text(hjust = 0.5),
axis.title = element_text(size = 12),
legend.key.size = unit(0.3, 'cm'),
legend.title = element_text(size = 10),
legend.text = element_text(size = 8)
)
#
Unique_clintrait_genes <-
strsplit(melted_results_3_Traits_removed_plot$IntersectingGenes, ";") %>% unlist() %>% unique()Fig-7-B
# Combining CVD diseases and traits together
melted_results_3_Traits_removed_plot$Disease %>% unique() [1] "Age-related disease endophenotypes"
[2] "Age-related diseases, mortality and associated endophenotypes"
[3] "Alkenylphosphatidylcholine (P-17:0/20:4) (b) levels"
[4] "Alkenylphosphatidylcholine (P-18:0/20:4) levels"
[5] "Alkenylphosphatidylcholine (P-18:0/22:5) levels"
[6] "Alkenylphosphatidylcholine (P-36:3) levels"
[7] "Alkenylphosphatidylcholine (P-38:5) (a) levels"
[8] "Alkenylphosphatidylcholine (P-38:5) (b) levels"
[9] "Alkenylphosphatidylethanolamine (P-18:1/20:3) (a) levels"
[10] "Alkenylphosphatidylethanolamine (P-18:1/20:3) (b) levels"
[11] "Alkenylphosphatidylethanolamine (P-20:0/18:2) levels"
[12] "Alkylphosphatidylcholine (O-38:5) levels"
[13] "Ascending aorta distensibility (MTAG)"
[14] "Asymmetrical dimethylarginine levels"
[15] "B-type natriuretic peptide to N-terminal pro B-type natriuretic peptide ratio"
[16] "Ceramide (d19:1/20:0) levels"
[17] "Ceramide (d19:1/26:0) levels"
[18] "Frontal QRS-T angle"
[19] "Heart rate response to beta blockers (atenolol monotherapy)"
[20] "Heart rate response to recovery post exercise"
[21] "Left ventricular global circumferential strain"
[22] "Left ventricular mass to end-diastolic volume ratio"
[23] "Myocardial fractal dimension (slice 2)"
[24] "QT interval (drug interaction)"
[25] "Radial peak diastolic strain rate"
[26] "Right ventricular ejection fraction"
[27] "Total Phosphatidylcholine levels"
[28] "Total Trihexosylcermide levels" melted_results_3_Traits_removed_plot_clean <- melted_results_3_Traits_removed_plot[!melted_results_3_Traits_removed_plot$Disease %in% c("Age-related disease endophenotypes", "Age-related diseases, mortality and associated endophenotypes"), ]
melted_results_3_Traits_removed_plot_clean$Disease %>% unique() [1] "Alkenylphosphatidylcholine (P-17:0/20:4) (b) levels"
[2] "Alkenylphosphatidylcholine (P-18:0/20:4) levels"
[3] "Alkenylphosphatidylcholine (P-18:0/22:5) levels"
[4] "Alkenylphosphatidylcholine (P-36:3) levels"
[5] "Alkenylphosphatidylcholine (P-38:5) (a) levels"
[6] "Alkenylphosphatidylcholine (P-38:5) (b) levels"
[7] "Alkenylphosphatidylethanolamine (P-18:1/20:3) (a) levels"
[8] "Alkenylphosphatidylethanolamine (P-18:1/20:3) (b) levels"
[9] "Alkenylphosphatidylethanolamine (P-20:0/18:2) levels"
[10] "Alkylphosphatidylcholine (O-38:5) levels"
[11] "Ascending aorta distensibility (MTAG)"
[12] "Asymmetrical dimethylarginine levels"
[13] "B-type natriuretic peptide to N-terminal pro B-type natriuretic peptide ratio"
[14] "Ceramide (d19:1/20:0) levels"
[15] "Ceramide (d19:1/26:0) levels"
[16] "Frontal QRS-T angle"
[17] "Heart rate response to beta blockers (atenolol monotherapy)"
[18] "Heart rate response to recovery post exercise"
[19] "Left ventricular global circumferential strain"
[20] "Left ventricular mass to end-diastolic volume ratio"
[21] "Myocardial fractal dimension (slice 2)"
[22] "QT interval (drug interaction)"
[23] "Radial peak diastolic strain rate"
[24] "Right ventricular ejection fraction"
[25] "Total Phosphatidylcholine levels"
[26] "Total Trihexosylcermide levels" melted_results_3_Traits_removed_plot %>% dim()[1] 140 9melted_results_3_Traits_removed_plot_clean %>% dim()[1] 130 9melted_results_3_Traits_removed_plot_clean$Type <- c("trait")
melted_results_3_CVD$Type <- c("disease")
TotalGWAS_DF <- rbind(melted_results_3_CVD, melted_results_3_Traits_removed_plot_clean)
TotalGWAS_DF$Type <- factor(TotalGWAS_DF$Type, levels = c("trait","disease" ) )
ggplot(TotalGWAS_DF, aes(x = Modules, y = Disease, fill = OddsRatio)) +
geom_tile(color = "black") +
scale_fill_gradientn(colors = c("white", "violet", "red"),
values = scales::rescale(c(-10, 1, 10)),
na.value = "grey50", name = "Odds ratio") +
geom_text(aes(label = Star), color = "black", size = 4, na.rm = TRUE) +
labs(x = "", y = "") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
axis.text.y = element_text(hjust = 0.5),
axis.title = element_text(size = 12),
legend.key.size = unit(0.3, 'cm'),
legend.title = element_text(size = 10),
legend.text = element_text(size = 8)
)
# Add Type column to each data frame
melted_results_3_Traits_removed_plot_clean$Type <- "trait"
melted_results_3_CVD$Type <- "disease"
# Combine the data frames
TotalGWAS_DF <- rbind(melted_results_3_CVD, melted_results_3_Traits_removed_plot_clean)
# Factor the Type column
TotalGWAS_DF$Type <- factor(TotalGWAS_DF$Type, levels = c("trait", "disease"))
# Factor and order the Disease column based on Type
TotalGWAS_DF <- TotalGWAS_DF %>%
mutate(Disease = factor(Disease, levels = rev(unique(Disease[order(Type)]))))
# Plot the data
ggplot(TotalGWAS_DF, aes(x = Modules, y = Disease, fill = OddsRatio)) +
geom_tile(color = "black") +
scale_fill_gradientn(colors = c("white", "violet", "red"),
values = scales::rescale(c(-10, 1, 10)),
na.value = "grey50", name = "Odds ratio") +
geom_text(aes(label = Star), color = "black", size = 4, na.rm = TRUE) +
labs(x = "", y = "") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),
axis.text.y = element_text(hjust = 0.5),
axis.title = element_text(size = 12),
legend.key.size = unit(0.3, 'cm'),
legend.title = element_text(size = 10),
legend.text = element_text(size = 8)
)
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur/Monterey 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] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] scales_1.2.1
[2] ReactomePA_1.40.0
[3] impute_1.70.0
[4] WGCNA_1.72-1
[5] fastcluster_1.2.3
[6] dynamicTreeCut_1.63-1
[7] BioNERO_1.4.2
[8] reshape2_1.4.4
[9] ggridges_0.5.4
[10] biomaRt_2.52.0
[11] ggvenn_0.1.10
[12] UpSetR_1.4.0
[13] DOSE_3.22.1
[14] variancePartition_1.26.0
[15] clusterProfiler_4.4.4
[16] pheatmap_1.0.12
[17] qvalue_2.28.0
[18] Homo.sapiens_1.3.1
[19] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[20] org.Hs.eg.db_3.15.0
[21] GO.db_3.15.0
[22] OrganismDbi_1.38.1
[23] GenomicFeatures_1.48.4
[24] AnnotationDbi_1.58.0
[25] cluster_2.1.4
[26] ggfortify_0.4.16
[27] lubridate_1.9.2
[28] forcats_1.0.0
[29] stringr_1.5.0
[30] dplyr_1.1.2
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[36] tidyverse_2.0.0
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[38] RUVSeq_1.30.0
[39] edgeR_3.38.4
[40] limma_3.52.4
[41] EDASeq_2.30.0
[42] ShortRead_1.54.0
[43] GenomicAlignments_1.32.1
[44] SummarizedExperiment_1.26.1
[45] MatrixGenerics_1.8.1
[46] matrixStats_1.0.0
[47] Rsamtools_2.12.0
[48] GenomicRanges_1.48.0
[49] Biostrings_2.64.1
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[51] XVector_0.36.0
[52] IRanges_2.30.1
[53] S4Vectors_0.34.0
[54] BiocParallel_1.30.4
[55] Biobase_2.56.0
[56] BiocGenerics_0.42.0
[57] workflowr_1.7.1
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[28] viridis_0.6.4 xfun_0.40 hms_1.1.3
[31] jquerylib_0.1.4 evaluate_0.21 promises_1.2.1
[34] fansi_1.0.4 restfulr_0.0.15 progress_1.2.2
[37] caTools_1.18.2 dbplyr_2.3.3 htmlwidgets_1.6.2
[40] igraph_1.5.1 DBI_1.1.3 ggnewscale_0.4.9
[43] backports_1.4.1 annotate_1.74.0 aod_1.3.2
[46] deldir_1.0-9 vctrs_0.6.3 abind_1.4-5
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[67] downloader_0.4 filelock_1.0.2 BiocFileCache_2.4.0
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[136] networkD3_0.4 Rcpp_1.0.11 broom_1.0.5
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[148] statmod_1.5.0 yulab.utils_0.0.8 RBGL_1.72.0
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[160] R6_2.5.1 Hmisc_5.1-0 pillar_1.9.0
[163] htmltools_0.5.6 glue_1.6.2 fastmap_1.1.1
[166] minqa_1.2.5 codetools_0.2-19 fgsea_1.22.0
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[178] survival_3.5-7 statnet.common_4.9.0 rmarkdown_2.24
[181] munsell_0.5.0 GetoptLong_1.0.5 DO.db_2.9
[184] GenomeInfoDbData_1.2.8 iterators_1.0.14 gtable_0.3.4
[187] rbibutils_2.2.15