Differential Gene Expression Analysis
Last updated: 2025-02-06
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Knit directory: CX5461_Project/
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| Rmd | 3f764ee | sayanpaul01 | 2025-02-06 | Added DGE Analysis with structured chunks |
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📌 Differential Gene Expression Analysis
📌 Load Required Libraries
# Load necessary packages
library(edgeR)Warning: package 'edgeR' was built under R version 4.3.1Warning: package 'limma' was built under R version 4.3.1library(limma)
library(data.table)Warning: package 'data.table' was built under R version 4.3.2library(tidyverse)Warning: package 'tidyverse' was built under R version 4.3.2Warning: package 'ggplot2' was built under R version 4.3.3Warning: package 'tidyr' was built under R version 4.3.3Warning: package 'readr' was built under R version 4.3.3Warning: package 'purrr' was built under R version 4.3.1Warning: package 'dplyr' was built under R version 4.3.2Warning: package 'stringr' was built under R version 4.3.2Warning: package 'lubridate' was built under R version 4.3.1library(ggplot2)
library(dplyr)
library(scales)Warning: package 'scales' was built under R version 4.3.2library(biomaRt)Warning: package 'biomaRt' was built under R version 4.3.2library(Homo.sapiens)Warning: package 'AnnotationDbi' was built under R version 4.3.2Warning: package 'BiocGenerics' was built under R version 4.3.1Warning: package 'Biobase' was built under R version 4.3.1Warning: package 'IRanges' was built under R version 4.3.1Warning: package 'S4Vectors' was built under R version 4.3.1Warning: package 'OrganismDbi' was built under R version 4.3.1Warning: package 'GenomicFeatures' was built under R version 4.3.3Warning: package 'GenomeInfoDb' was built under R version 4.3.3Warning: package 'GenomicRanges' was built under R version 4.3.1📍 Load Data
📌 Normalize and Filter Counts
📌 Prepare DGEList Object
# Subset count matrix based on filtered CPM matrix
x <- counts_matrix[row.names(filcpm_matrix),]
dim(x)[1] 14279 108# Modify Metadata
Metadata_2 <- Metadata
rownames(Metadata_2) <- Metadata_2$Sample_bam
colnames(x) <- Metadata_2$Sample_ID
rownames(Metadata_2) <- Metadata_2$Sample_ID
Metadata_2$Drug_time <- make.names(Metadata_2$Drug_time)
Metadata_2$Ind <- as.character(Metadata_2$Ind)
# ✅ Create DGEList object
dge <- DGEList(counts = x)
dge$samples$group <- factor(Metadata_2$Drug_time)
dge <- calcNormFactors(dge, method = "TMM")
# ✅ Check normalization factors
dge$samples group lib.size norm.factors
CX-5461_0.1_3_17-3 CX.5461_0.1_3 21200035 1.0102693
DOX_0.5_24_17-3 DOX_0.5_24 16563771 1.1076182
DOX_0.5_24_87-1 DOX_0.5_24 21656653 0.9933568
VEH_0.1_24_87-1 VEH_0.1_24 22426842 0.9766783
VEH_0.5_24_87-1 VEH_0.5_24 22162275 0.9924549
CX-5461_0.1_48_87-1 CX.5461_0.1_48 22187620 0.9683497
CX-5461_0.5_48_87-1 CX.5461_0.5_48 20782167 0.9814665
DOX_0.1_48_87-1 DOX_0.1_48 25382841 1.0011030
DOX_0.5_48_87-1 DOX_0.5_48 19863531 1.0541528
VEH_0.1_48_87-1 VEH_0.1_48 21473253 1.0138011
VEH_0.5_48_87-1 VEH_0.5_48 20337337 1.0219756
VEH_0.1_24_17-3 VEH_0.1_24 15750538 0.9952313
VEH_0.5_24_17-3 VEH_0.5_24 15638967 1.0367909
CX-5461_0.1_48_17-3 CX.5461_0.1_48 18465522 0.9543636
CX-5461_0.5_48_17-3 CX.5461_0.5_48 16876134 0.9636074
DOX_0.1_48_17-3 DOX_0.1_48 20609601 0.9839708
DOX_0.5_48_17-3 DOX_0.5_48 18829944 1.1876478
VEH_0.1_48_17-3 VEH_0.1_48 24712537 0.9804184
VEH_0.5_48_17-3 VEH_0.5_48 17006648 0.9707617
CX-5461_0.1_3_84-1 CX.5461_0.1_3 17178171 1.0219387
CX-5461_0.5_3_17-3 CX.5461_0.5_3 17694385 1.0103837
CX-5461_0.5_3_84-1 CX.5461_0.5_3 17261114 1.0060170
DOX_0.1_3_84-1 DOX_0.1_3 17352493 0.9911466
DOX_0.5_3_84-1 DOX_0.5_3 17757477 1.0034479
VEH_0.1_3_84-1 VEH_0.1_3 19305213 1.0354377
VEH_0.5_3_84-1 VEH_0.5_3 17297185 1.0285524
CX-5461_0.1_24_84-1 CX.5461_0.1_24 23860498 0.9857321
CX-5461_0.5_24_84-1 CX.5461_0.5_24 19482159 0.9638391
DOX_0.1_24_84-1 DOX_0.1_24 19659504 0.9565317
DOX_0.5_24_84-1 DOX_0.5_24 17875612 1.0212021
VEH_0.1_24_84-1 VEH_0.1_24 16449210 0.9998393
DOX_0.1_3_17-3 DOX_0.1_3 16400599 0.9962197
VEH_0.5_24_84-1 VEH_0.5_24 18783630 0.9616033
CX-5461_0.1_48_84-1 CX.5461_0.1_48 13781326 0.9400278
CX-5461_0.5_48_84-1 CX.5461_0.5_48 20232591 0.9563694
DOX_0.1_48_84-1 DOX_0.1_48 16028063 0.9651474
DOX_0.5_48_84-1 DOX_0.5_48 16269098 1.0652791
VEH_0.1_48_84-1 VEH_0.1_48 15276408 0.9612979
VEH_0.5_48_84-1 VEH_0.5_48 17447290 1.0050272
CX-5461_0.1_3_90-1 CX.5461_0.1_3 15807726 0.9991915
CX-5461_0.5_3_90-1 CX.5461_0.5_3 17946649 0.9956913
DOX_0.1_3_90-1 DOX_0.1_3 20294777 0.9830679
DOX_0.5_3_17-3 DOX_0.5_3 18191217 0.9080939
DOX_0.5_3_90-1 DOX_0.5_3 20699125 0.9833929
VEH_0.1_3_90-1 VEH_0.1_3 20027541 0.9953796
VEH_0.5_3_90-1 VEH_0.5_3 17833214 1.0003025
CX-5461_0.1_24_90-1 CX.5461_0.1_24 15094639 1.0102577
CX-5461_0.5_24_90-1 CX.5461_0.5_24 17758047 1.0114258
DOX_0.1_24_90-1 DOX_0.1_24 16921727 0.9900805
DOX_0.5_24_90-1 DOX_0.5_24 19271264 1.0068767
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CX-5461_0.1_48_90-1 CX.5461_0.1_48 23817456 1.0255425
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CX-5461_0.5_48_90-1 CX.5461_0.5_48 18651093 1.0370010
DOX_0.1_48_90-1 DOX_0.1_48 17767662 1.0233343
DOX_0.5_48_90-1 DOX_0.5_48 20031141 1.1189284
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VEH_0.5_48_90-1 VEH_0.5_48 17657193 1.0550052
CX-5461_0.1_3_75-1 CX.5461_0.1_3 20046785 0.9894881
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DOX_0.5_3_75-1 DOX_0.5_3 15260497 0.9562336
VEH_0.1_3_75-1 VEH_0.1_3 19040007 0.9839769
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VEH_0.5_3_75-1 VEH_0.5_3 21311999 0.9663251
CX-5461_0.1_24_75-1 CX.5461_0.1_24 19256517 1.0184093
CX-5461_0.5_24_75-1 CX.5461_0.5_24 14304255 1.0186309
DOX_0.1_24_75-1 DOX_0.1_24 21875105 1.0386302
DOX_0.5_24_75-1 DOX_0.5_24 17032433 0.9686987
VEH_0.1_24_75-1 VEH_0.1_24 19796422 1.0295019
VEH_0.5_24_75-1 VEH_0.5_24 21524523 1.0376940
CX-5461_0.1_48_75-1 CX.5461_0.1_48 20955873 0.9752971
CX-5461_0.5_48_75-1 CX.5461_0.5_48 19924867 0.9768371
DOX_0.1_48_75-1 DOX_0.1_48 17698471 1.0369925
CX-5461_0.1_24_17-3 CX.5461_0.1_24 18239213 0.9808618
DOX_0.5_48_75-1 DOX_0.5_48 15422887 1.0296304
VEH_0.1_48_75-1 VEH_0.1_48 21014387 1.0100812
VEH_0.5_48_75-1 VEH_0.5_48 21346438 1.0015670
CX-5461_0.1_3_78-1 CX.5461_0.1_3 20824890 0.9686975
CX-5461_0.5_3_78-1 CX.5461_0.5_3 19425405 1.0100551
DOX_0.1_3_78-1 DOX_0.1_3 18950843 0.9933529
DOX_0.5_3_78-1 DOX_0.5_3 21654404 0.9808038
VEH_0.1_3_78-1 VEH_0.1_3 20054872 0.9802655
VEH_0.5_3_78-1 VEH_0.5_3 17661233 1.0220629
CX-5461_0.1_24_78-1 CX.5461_0.1_24 16754941 0.9785599
CX-5461_0.5_24_17-3 CX.5461_0.5_24 16014473 1.0010257
CX-5461_0.5_24_78-1 CX.5461_0.5_24 16077548 0.9819166
DOX_0.1_24_78-1 DOX_0.1_24 27842910 0.9788383
DOX_0.5_24_78-1 DOX_0.5_24 6326261 1.0007627
VEH_0.1_24_78-1 VEH_0.1_24 18032196 0.9767098
VEH_0.5_24_78-1 VEH_0.5_24 18727398 0.9931695
CX-5461_0.1_48_78-1 CX.5461_0.1_48 21292146 1.0036183
CX-5461_0.5_48_78-1 CX.5461_0.5_48 18392707 0.9988905
DOX_0.1_48_78-1 DOX_0.1_48 18735925 1.0433629
DOX_0.5_48_78-1 DOX_0.5_48 16079343 1.1063695
VEH_0.1_48_78-1 VEH_0.1_48 20500742 1.0149206
DOX_0.1_24_17-3 DOX_0.1_24 23900805 1.0243090
VEH_0.5_48_78-1 VEH_0.5_48 18433692 1.0396358
CX-5461_0.1_3_87-1 CX.5461_0.1_3 19664754 0.9573708
CX-5461_0.5_3_87-1 CX.5461_0.5_3 21451819 1.0192062
DOX_0.1_3_87-1 DOX_0.1_3 20624889 0.9539861
DOX_0.5_3_87-1 DOX_0.5_3 22838835 0.9919424
VEH_0.1_3_87-1 VEH_0.1_3 17616767 0.9841881
VEH_0.5_3_87-1 VEH_0.5_3 22148086 1.0203028
CX-5461_0.1_24_87-1 CX.5461_0.1_24 30093416 0.9439250
CX-5461_0.5_24_87-1 CX.5461_0.5_24 22093483 0.9589794
DOX_0.1_24_87-1 DOX_0.1_24 21415578 0.9549937📌 Create Design Matrix
# ✅ Create Design Matrix
design <- model.matrix(~ 0 + Metadata_2$Drug_time)
colnames(design) <- gsub("Metadata_2\\$Drug_time", "", colnames(design))
design CX.5461_0.1_24 CX.5461_0.1_3 CX.5461_0.1_48 CX.5461_0.5_24 CX.5461_0.5_3
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94 0 0 0 0 0 0 0
95 0 0 0 0 0 0 0
96 1 0 0 0 0 0 0
97 0 0 0 1 0 0 0
98 0 0 0 0 0 0 0
99 0 0 0 0 0 0 1
100 0 0 0 0 0 0 0
101 0 0 0 0 0 0 0
102 0 0 0 0 0 0 0
103 0 0 0 0 0 0 0
104 0 0 1 0 0 0 0
105 0 0 0 0 0 1 0
106 0 0 0 0 0 0 0
107 0 0 0 0 0 0 0
108 0 0 0 0 0 0 0
attr(,"assign")
[1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
attr(,"contrasts")
attr(,"contrasts")$`Metadata_2$Drug_time`
[1] "contr.treatment"📌 Voom Transformation and Linear Modeling
# ✅ Duplicate Correlation for Individual Effect
corfit <- duplicateCorrelation(object = dge$counts, design = design, block = Metadata_2$Ind)
# ✅ Voom Transformation
v <- voom(dge, design, block = Metadata_2$Ind, correlation = corfit$consensus.correlation, plot = TRUE)
| Version | Author | Date |
|---|---|---|
| d6850ef | sayanpaul01 | 2025-02-06 |
# ✅ Fit Linear Model
fit <- lmFit(v, design, block = Metadata_2$Ind, correlation = corfit$consensus.correlation)📌 Create Contrast Matrix
contrast_matrix <- makeContrasts(
CX_0.1_3vsVEH_0.1_3 = CX.5461_0.1_3 - VEH_0.1_3,
CX_0.1_24vsVEH_0.1_24 = CX.5461_0.1_24 - VEH_0.1_24,
CX_0.1_48vsVEH_0.1_48 = CX.5461_0.1_48 - VEH_0.1_48,
CX_0.5_3vsVEH_0.5_3 = CX.5461_0.5_3 - VEH_0.5_3,
CX_0.5_24vsVEH_0.5_24 = CX.5461_0.5_24 - VEH_0.5_24,
CX_0.5_48vsVEH_0.5_48 = CX.5461_0.5_48 - VEH_0.5_48,
DOX_0.1_3vsVEH_0.1_3 = DOX_0.1_3 - VEH_0.1_3,
DOX_0.1_24vsVEH_0.1_24 = DOX_0.1_24 - VEH_0.1_24,
DOX_0.1_48vsVEH_0.1_48 = DOX_0.1_48 - VEH_0.1_48,
DOX_0.5_3vsVEH_0.5_3 = DOX_0.5_3 - VEH_0.5_3,
DOX_0.5_24vsVEH_0.5_24 = DOX_0.5_24 - VEH_0.5_24,
DOX_0.5_48vsVEH_0.5_48 = DOX_0.5_48 - VEH_0.5_48,
levels = design
)📌 Fit Model and Generate Results
# ✅ Apply Contrasts
fit2 <- contrasts.fit(fit, contrast_matrix)
fit2 <- eBayes(fit2)📌 Mean-Variance Trend Plot
# Plot Mean-Variance Trend
plotSA(fit2, main = "Final model: Mean-Variance trend")
| Version | Author | Date |
|---|---|---|
| 1ab310a | sayanpaul01 | 2025-02-06 |
📌Summary of Results
# ✅ Summary of Results
results_summary <- decideTests(fit2, adjust.method = "BH", p.value = 0.05)
summary(results_summary) CX_0.1_3vsVEH_0.1_3 CX_0.1_24vsVEH_0.1_24 CX_0.1_48vsVEH_0.1_48
Down 0 204 294
NotSig 14278 14074 13961
Up 1 1 24
CX_0.5_3vsVEH_0.5_3 CX_0.5_24vsVEH_0.5_24 CX_0.5_48vsVEH_0.5_48
Down 1 259 337
NotSig 14277 14001 13909
Up 1 19 33
DOX_0.1_3vsVEH_0.1_3 DOX_0.1_24vsVEH_0.1_24 DOX_0.1_48vsVEH_0.1_48
Down 1 2528 1696
NotSig 14277 9215 10818
Up 1 2536 1765
DOX_0.5_3vsVEH_0.5_3 DOX_0.5_24vsVEH_0.5_24 DOX_0.5_48vsVEH_0.5_48
Down 476 5067 5177
NotSig 13746 4451 4151
Up 57 4761 4951📌 Extract Differentially Expressed Genes
# Generate Top Table for Specific Comparisons
Toptable_CX_0.1_3 <- topTable(fit = fit2, coef = "CX_0.1_3vsVEH_0.1_3", number = nrow(x), adjust.method = "BH", p.value = 1, sort.by = "none")
Toptable_CX_0.1_24 <- topTable(fit = fit2, coef = "CX_0.1_24vsVEH_0.1_24", number = nrow(x), adjust.method = "BH", p.value = 1, sort.by = "none")
Toptable_CX_0.1_48 <- topTable(fit = fit2, coef = "CX_0.1_48vsVEH_0.1_48", number = nrow(x), adjust.method = "BH", p.value = 1, sort.by = "none")
Toptable_CX_0.5_3 <- topTable(fit = fit2, coef = "CX_0.5_3vsVEH_0.5_3", number = nrow(x), adjust.method = "BH", p.value = 1, sort.by = "none")
Toptable_CX_0.5_24 <- topTable(fit = fit2, coef = "CX_0.5_24vsVEH_0.5_24", number = nrow(x), adjust.method = "BH", p.value = 1, sort.by = "none")
Toptable_CX_0.5_48 <- topTable(fit = fit2, coef = "CX_0.5_48vsVEH_0.5_48", number = nrow(x), adjust.method = "BH", p.value = 1, sort.by = "none")
Toptable_DOX_0.1_3 <- topTable(fit = fit2, coef = "DOX_0.1_3vsVEH_0.1_3", number = nrow(x), adjust.method = "BH", p.value = 1, sort.by = "none")
Toptable_DOX_0.1_24 <- topTable(fit = fit2, coef = "DOX_0.1_24vsVEH_0.1_24", number = nrow(x), adjust.method = "BH", p.value = 1, sort.by = "none")
Toptable_DOX_0.1_48 <- topTable(fit = fit2, coef = "DOX_0.1_48vsVEH_0.1_48", number = nrow(x), adjust.method = "BH", p.value = 1, sort.by = "none")
Toptable_DOX_0.5_3 <- topTable(fit = fit2, coef = "DOX_0.5_3vsVEH_0.5_3", number = nrow(x), adjust.method = "BH", p.value = 1, sort.by = "none")
Toptable_DOX_0.5_24 <- topTable(fit = fit2, coef = "DOX_0.5_24vsVEH_0.5_24", number = nrow(x), adjust.method = "BH", p.value = 1, sort.by = "none")
Toptable_DOX_0.5_48 <- topTable(fit = fit2, coef = "DOX_0.5_48vsVEH_0.5_48", number = nrow(x), adjust.method = "BH", p.value = 1, sort.by = "none")📌 Volcano Plots for Differential Expression Analysis
# Define a function to generate volcano plots
generate_volcano_plot <- function(toptable, title) {
# Add Significance Labels
toptable$Significance <- "Not Significant"
toptable$Significance[toptable$logFC > 0 & toptable$adj.P.Val < 0.05] <- "Upregulated"
toptable$Significance[toptable$logFC < 0 & toptable$adj.P.Val < 0.05] <- "Downregulated"
# Generate Volcano Plot
ggplot(toptable, aes(x = logFC, y = -log10(P.Value), color = Significance)) +
geom_point(alpha = 0.4, size = 2) +
scale_color_manual(values = c("Downregulated" = "red", "Upregulated" = "blue", "Not Significant" = "gray")) +
xlim(-5, 5) +
labs(title = title, x = "log2 Fold Change", y = "-log10 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))) +
theme_bw()
}
# Generate volcano plots for all comparisons
volcano_plots <- list(
"CX_0.1_3" = generate_volcano_plot(Toptable_CX_0.1_3, "Volcano Plot CX_0.1_3 (adj P-val<0.05)"),
"CX_0.1_24" = generate_volcano_plot(Toptable_CX_0.1_24, "Volcano Plot CX_0.1_24 (adj P-val<0.05)"),
"CX_0.1_48" = generate_volcano_plot(Toptable_CX_0.1_48, "Volcano Plot CX_0.1_48 (adj P-val<0.05)"),
"CX_0.5_3" = generate_volcano_plot(Toptable_CX_0.5_3, "Volcano Plot CX_0.5_3 (adj P-val<0.05)"),
"CX_0.5_24" = generate_volcano_plot(Toptable_CX_0.5_24, "Volcano Plot CX_0.5_24 (adj P-val<0.05)"),
"CX_0.5_48" = generate_volcano_plot(Toptable_CX_0.5_48, "Volcano Plot CX_0.5_48 (adj P-val<0.05)"),
"DOX_0.1_3" = generate_volcano_plot(Toptable_DOX_0.1_3, "Volcano Plot DOX_0.1_3 (adj P-val<0.05)"),
"DOX_0.1_24" = generate_volcano_plot(Toptable_DOX_0.1_24, "Volcano Plot DOX_0.1_24 (adj P-val<0.05)"),
"DOX_0.1_48" = generate_volcano_plot(Toptable_DOX_0.1_48, "Volcano Plot DOX_0.1_48 (adj P-val<0.05)"),
"DOX_0.5_3" = generate_volcano_plot(Toptable_DOX_0.5_3, "Volcano Plot DOX_0.5_3 (adj P-val<0.05)"),
"DOX_0.5_24" = generate_volcano_plot(Toptable_DOX_0.5_24, "Volcano Plot DOX_0.5_24 (adj P-val<0.05)"),
"DOX_0.5_48" = generate_volcano_plot(Toptable_DOX_0.5_48, "Volcano Plot DOX_0.5_48 (adj P-val<0.05)")
)
# Display each volcano plot
for (plot_name in names(volcano_plots)) {
print(volcano_plots[[plot_name]])
}
Warning: Removed 3 rows containing missing values or values outside the scale range
(`geom_point()`).
Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).
Warning: Removed 34 rows containing missing values or values outside the scale range
(`geom_point()`).
Warning: Removed 28 rows containing missing values or values outside the scale range
(`geom_point()`).
Warning: Removed 24 rows containing missing values or values outside the scale range
(`geom_point()`).
sessionInfo()R version 4.3.0 (2023-04-21 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 22631)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] Homo.sapiens_1.3.1
[2] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[3] org.Hs.eg.db_3.18.0
[4] GO.db_3.18.0
[5] OrganismDbi_1.44.0
[6] GenomicFeatures_1.54.4
[7] GenomicRanges_1.54.1
[8] GenomeInfoDb_1.38.8
[9] AnnotationDbi_1.64.1
[10] IRanges_2.36.0
[11] S4Vectors_0.40.1
[12] Biobase_2.62.0
[13] BiocGenerics_0.48.1
[14] biomaRt_2.58.2
[15] scales_1.3.0
[16] lubridate_1.9.3
[17] forcats_1.0.0
[18] stringr_1.5.1
[19] dplyr_1.1.4
[20] purrr_1.0.2
[21] readr_2.1.5
[22] tidyr_1.3.1
[23] tibble_3.2.1
[24] ggplot2_3.5.1
[25] tidyverse_2.0.0
[26] data.table_1.14.10
[27] edgeR_4.0.1
[28] limma_3.58.1
[29] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] DBI_1.2.3 bitops_1.0-7
[3] RBGL_1.78.0 rlang_1.1.3
[5] magrittr_2.0.3 git2r_0.35.0
[7] matrixStats_1.4.1 compiler_4.3.0
[9] RSQLite_2.3.3 getPass_0.2-4
[11] png_0.1-8 callr_3.7.6
[13] vctrs_0.6.5 pkgconfig_2.0.3
[15] crayon_1.5.3 fastmap_1.1.1
[17] dbplyr_2.5.0 XVector_0.42.0
[19] labeling_0.4.3 Rsamtools_2.18.0
[21] promises_1.3.0 rmarkdown_2.29
[23] tzdb_0.4.0 graph_1.80.0
[25] ps_1.8.1 bit_4.0.5
[27] xfun_0.50 zlibbioc_1.48.0
[29] cachem_1.0.8 jsonlite_1.8.9
[31] progress_1.2.3 blob_1.2.4
[33] later_1.3.2 DelayedArray_0.28.0
[35] BiocParallel_1.36.0 parallel_4.3.0
[37] prettyunits_1.2.0 R6_2.5.1
[39] bslib_0.8.0 stringi_1.8.3
[41] rtracklayer_1.62.0 jquerylib_0.1.4
[43] SummarizedExperiment_1.32.0 Rcpp_1.0.12
[45] knitr_1.49 Matrix_1.6-1.1
[47] httpuv_1.6.15 timechange_0.3.0
[49] tidyselect_1.2.1 abind_1.4-8
[51] rstudioapi_0.17.1 yaml_2.3.10
[53] codetools_0.2-20 curl_6.0.1
[55] processx_3.8.5 lattice_0.22-5
[57] withr_3.0.2 KEGGREST_1.42.0
[59] evaluate_1.0.3 BiocFileCache_2.10.2
[61] xml2_1.3.6 Biostrings_2.70.1
[63] BiocManager_1.30.25 pillar_1.10.1
[65] filelock_1.0.3 MatrixGenerics_1.14.0
[67] whisker_0.4.1 generics_0.1.3
[69] rprojroot_2.0.4 RCurl_1.98-1.13
[71] hms_1.1.3 munsell_0.5.1
[73] glue_1.7.0 tools_4.3.0
[75] BiocIO_1.12.0 locfit_1.5-9.8
[77] GenomicAlignments_1.38.2 fs_1.6.3
[79] XML_3.99-0.17 grid_4.3.0
[81] colorspace_2.1-0 GenomeInfoDbData_1.2.11
[83] restfulr_0.0.15 cli_3.6.1
[85] rappdirs_0.3.3 S4Arrays_1.2.1
[87] gtable_0.3.6 sass_0.4.9
[89] digest_0.6.34 SparseArray_1.2.4
[91] farver_2.1.2 rjson_0.2.23
[93] memoise_2.0.1 htmltools_0.5.8.1
[95] lifecycle_1.0.4 httr_1.4.7
[97] statmod_1.5.0 bit64_4.0.5