KEGG Analysis
Ha M. Tran
22/08/2021
Last updated: 2023-09-16
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Knit directory:
Mouse_endometrial_transcriptome_2023/1_analysis/
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Modified: 0_data/RDS_objects/dge.rds
Modified: 0_data/RDS_objects/enrichGO.rds
Modified: 0_data/RDS_objects/enrichGO_sig.rds
Modified: 0_data/RDS_objects/fc.rds
Modified: 0_data/RDS_objects/lfc.rds
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Modified: 0_data/RDS_objects/lmTreat_all.rds
Modified: 0_data/RDS_objects/lmTreat_sig.rds
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Deleted: 1_analysis/mmu04151.pv.png
Deleted: 1_analysis/mmu04270.pv.png
Deleted: 1_analysis/mmu04510.pv.png
Deleted: 1_analysis/mmu04640.pv.png
Deleted: 1_analysis/mmu04670.pv.png
Modified: 2_plots/de/pval_1.05.svg
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Modified: 2_plots/ipa/Cell-To-Cell Signaling.svg
Modified: 2_plots/ipa/diseaseAndFunction.svg
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Modified: 3_output/enrichGO_sig.xlsx
Modified: 3_output/enrichKEGG_all.xlsx
Modified: 3_output/enrichKEGG_sig.xlsx
Modified: 3_output/lmTreat_all.xlsx
Modified: 3_output/lmTreat_fc1.5_voom2_all_fdr.xlsx
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 18c6463 | Ha Manh Tran | 2023-09-16 | Added KEGG copyright permission and changed kable |
| Rmd | bddaca3 | tranmanhha135 | 2023-01-23 | fixed kegg table and made minor adjustments |
| html | b44640a | Ha Manh Tran | 2023-01-23 | Build site. |
| Rmd | eb10bef | Ha Manh Tran | 2023-01-22 | workflowr::wflow_publish(here::here("1_analysis/*.Rmd")) |
| html | 8c9178d | tranmanhha135 | 2023-01-21 | added png |
| html | d578f46 | Ha Manh Tran | 2023-01-21 | Build site. |
| Rmd | 159f352 | tranmanhha135 | 2023-01-21 | adding for pathview |
| html | 159f352 | tranmanhha135 | 2023-01-21 | adding for pathview |
| Rmd | 4d51a4e | tranmanhha135 | 2023-01-20 | test png |
| html | 4d51a4e | tranmanhha135 | 2023-01-20 | test png |
| html | 691cf34 | Ha Manh Tran | 2023-01-20 | Build site. |
| Rmd | 7f6bab2 | Ha Manh Tran | 2023-01-20 | workflowr::wflow_publish(here::here("1_analysis/*.Rmd")) |
| Rmd | b6cf190 | tranmanhha135 | 2023-01-19 | quick commit |
| Rmd | 3119fad | tranmanhha135 | 2022-11-05 | build website |
| html | 3119fad | tranmanhha135 | 2022-11-05 | build website |
Data Setup
# working with data
library(dplyr)
library(magrittr)
library(readr)
library(tibble)
library(reshape2)
library(tidyverse)
library(KEGGREST)
# Visualisation:
library(kableExtra)
library(ggplot2)
library(grid)
library(pander)
library(viridis)
library(cowplot)
library(pheatmap)
library(DT)
# Custom ggplot
library(ggplotify)
library(ggpubr)
library(ggbiplot)
library(ggrepel)
# Bioconductor packages:
library(edgeR)
library(limma)
library(Glimma)
library(clusterProfiler)
library(org.Mm.eg.db)
library(enrichplot)
library(pathview)
theme_set(theme_minimal())
pub <- readRDS(here::here("0_data/RDS_objects/pub.rds"))
DT <- readRDS(here::here("0_data/RDS_objects/DT.rds"))Import DGElist Data
DGElist object containing the raw feature count, sample metadata, and gene metadata, created in the Set Up stage.
# load DGElist previously created in the set up
dge <- readRDS(here::here("0_data/RDS_objects/dge.rds"))
fc <- readRDS(here::here("0_data/RDS_objects/fc.rds"))
lfc <- readRDS(here::here("0_data/RDS_objects/lfc.rds"))
lmTreat <- readRDS(here::here("0_data/RDS_objects/lmTreat.rds"))
lmTreat_sig <- readRDS(here::here("0_data/RDS_objects/lmTreat_sig.rds"))KEGG Analysis
KEGG enrichment analysis is performed with the significant DE genes that have absolute FC > 1.5 ( genes from Limma). Top 30 most significant KEGG are displayed. All enriched KEGG pathways are exported.
KEGG pathway images reproduced by permission from Kanehisa Laboratories, September 2023
# chosing the pathways of interest
kegg_id <- c("mmu04670", "mmu04640", "mmu04270", "mmu04151", "mmu04510", "mmu04060")
kegg_pathway <- KEGGREST::keggGet(kegg_id)enrichKEGG <- list()
enrichKEGG_all <- list()
enrichKEGG_sig <- list()
for (i in 1:length(fc)) {
x <- fc[i] %>% as.character()
# find enriched KEGG pathways
enrichKEGG[[x]] <- clusterProfiler::enrichKEGG(
gene = lmTreat_sig[[x]]$entrezid,
keyType = "kegg",
organism = "mmu",
pvalueCutoff = 0.05,
pAdjustMethod = "none"
)
enrichKEGG[[x]] <- enrichKEGG[[x]] %>%
clusterProfiler::setReadable(OrgDb = org.Mm.eg.db, keyType = "ENTREZID")
enrichKEGG_all[[x]] <- enrichKEGG[[x]]@result
# filter the significant and print top 30
enrichKEGG_sig[[x]] <- enrichKEGG_all[[x]] %>%
dplyr::filter(pvalue <= 0.05) %>%
separate(col = BgRatio, sep = "/", into = c("Total", "Universe")) %>%
dplyr::mutate(
logPval = -log(pvalue, 10),
GeneRatio = Count / as.numeric(Total)
) %>%
dplyr::select(c("Description", "GeneRatio", "pvalue", "logPval", "p.adjust", "qvalue", "geneID", "Count"))
# # at the beginnning of a word (after 35 characters), add a newline. shorten the y axis for dot plot
# enrichKEGG_sig[[x]]$Description <- sub(pattern = "(.{1,35})(?:$| )",
# replacement = "\\1\n",
# x = enrichKEGG_sig[[x]]$Description)
#
# # remove the additional newline at the end of the string
# enrichKEGG_sig[[x]]$Description <- sub(pattern = "\n$",
# replacement = "",
# x = enrichKEGG_sig[[x]]$Description)
}FC=1.05
p=1Table
enrichKEGG_sig[[p]] %>%
dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
DT(.,caption = "Significantly enriched KEGG pathways") # kable(caption = "Significantly enriched KEGG pathways") %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# scroll_box(height = "600px")Dot plot
kegg_dot <- list()
upset=list()
for (i in 1:length(fc)) {
x <- fc[i] %>% as.character()
# dot plot, save
kegg_dot[[x]] <- ggplot(enrichKEGG_sig[[x]][1:15, ]) +
geom_point(aes(x = GeneRatio, y = reorder(Description, GeneRatio), colour = logPval, size = Count)) +
scale_color_gradient(low = "dodgerblue3", high = "firebrick3", limits = c(0, NA)) +
scale_size(range = c(1.5,5)) +
ggtitle("KEGG Pathways") +
ylab(label = "") +
xlab(label = "Gene Ratio") +
labs(color = expression("-log"[10] * "Pvalue"), size = "Gene Counts")
ggsave(filename = paste0("kegg_dot_", fc[i], ".svg"), plot = kegg_dot[[x]] + pub, path = here::here("2_plots/kegg/"),
width = 250, height = 130, units = "mm")
upset[[x]] <- upsetplot(x = enrichKEGG[[x]], 10)
ggsave(filename = paste0("upset_kegg_", fc[i], ".svg"), plot = upset[[x]], path = here::here("2_plots/kegg/"),
width = 170, height = 130, units = "mm")
}
kegg_dot[[p]]
FC=1.1
p=p+1Table
enrichKEGG_sig[[p]] %>%
dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
DT(.,caption = "Significantly enriched KEGG pathways") # kable(caption = "Significantly enriched KEGG pathways") %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# scroll_box(height = "600px")Dot plot
kegg_dot[[p]]
FC=1.5
p=p+1Table
enrichKEGG_sig[[p]] %>%
dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
DT(.,caption = "Significantly enriched KEGG pathways") # kable(caption = "Significantly enriched KEGG pathways") %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# scroll_box(height = "600px")Dot plot
kegg_dot[[p]]
Pathway specific heatmaps
FC=1.05
p=1Leukocyte transendothelial migration
q=1Heatmap
# create df with normalised read counts with an additional entrezid column for binding
logCPM <- cpm(dge, prior.count = 3, log = TRUE)
logCPM <- logCPM[,1:7]
logCPM <- cbind(logCPM, dge$genes$entrezid)
rownames(logCPM) <- dge$genes$gene_name
colnames(logCPM) <- c("Control 1", "Control 2", "Control 4", "Intact 1", "Intact 2", "Intact 3", "Intact 4", "entrezid")
### full pathway method
# complete_pathway <- kegg_pathway[[1]]$GENE %>% as.data.frame()
# complete_pathway <- focal_adhesion[seq(1, nrow(focal_adhesion), 2),]
# match_complete_pathway <- logCPM[,"entrezid"] %in% complete_pathway
# df for heatmap annotation of sample group
anno <- as.factor(dge$samples$group) %>% as.data.frame()
anno <- anno[1:7,] %>% as.data.frame()
colnames(anno) <- "Sample Groups"
anno$`Sample Groups` <- gsub("CONT", "Control", anno$`Sample Groups`)
anno$`Sample Groups` <- gsub("INT", "Intact", anno$`Sample Groups`)
rownames(anno) <- colnames(logCPM[, 1:7])
# setting colour of sample group annotation
# original sample colours
# anno_colours <- c("#66C2A5", "#FC8D62")
# new sample colours
anno_colours <- c("#f8766d", "#a3a500")
names(anno_colours) <- c("Control", "Intact")matrix <- list()
display_matrix <- list()
kegg_heat=list()
my_palette <- colorRampPalette(c(
rgb(32,121,226, maxColorValue = 255),
# rgb(144,203,180, maxColorValue = 255),
rgb(254,248,239, maxColorValue = 255),
# rgb(251,192,52, maxColorValue = 255),
rgb(226,46,45, maxColorValue = 255)))(n = 201)
for (i in 1:length(fc)) {
x <- fc[i] %>% as.character()
for (j in 1:length(kegg_id)) {
y <- kegg_pathway[[j]]$PATHWAY_MAP
partial <- enrichKEGG_all[[x]][, c("ID", "geneID")]
partial <- partial[kegg_id[j], "geneID"] %>% as.data.frame()
partial <- separate_rows(partial, ., sep = "/")
colnames(partial) <- "entrezid"
# heatmap matrix
match <- rownames(logCPM) %in% partial$entrezid
matrix[[x]][[y]] <- logCPM[match, c("Control 1", "Control 2", "Control 4", "Intact 1", "Intact 2", "Intact 3", "Intact 4")] %>% as.data.frame()
# changing the colname to numeric for some reason, cant remember
matrix[[x]][[y]][, c("Control 1", "Control 2", "Control 4", "Intact 1", "Intact 2", "Intact 3", "Intact 4")] <- as.numeric(as.character(unlist(matrix[[x]][[y]][, c("Control 1", "Control 2", "Control 4", "Intact 1", "Intact 2", "Intact 3", "Intact 4")])))
# display matrix
match2 <- lmTreat_sig[[x]][, "gene_name"] %in% partial$entrezid
display_matrix[[x]][[y]] <- lmTreat_sig[[x]][match2, c("gene_name", "logFC", "P.Value", "adj.P.Val", "description")] %>%
as.data.frame()
colnames(display_matrix[[x]][[y]]) <- c("Gene Name", "logFC", "P Value", "Adjusted P Value", "Description")
## Heatmap
kegg_heat[[x]][[y]] <- pheatmap(
mat = matrix[[x]][[y]],
### Publish
show_colnames = T,
main = paste0(y, "\n"),
legend = F,
annotation_legend = F,
fontsize = 10,
fontsize_col = 11,
fontsize_number = 7,
fontsize_row = 10,
treeheight_row = 25,
treeheight_col = 10,
cluster_cols = T,
clustering_distance_rows = "euclidean",
legend_breaks = c(seq(-3, 11, by = .5), 1.4),
legend_labels = c(seq(-3, 11, by = .5), "Z-Score"),
angle_col = 90,
cutree_cols = 2,
cutree_rows = 2,
color = my_palette,
scale = "row",
border_color = NA,
annotation_col = anno,
annotation_colors = list("Sample Groups" = anno_colours),
annotation_names_col = F,
annotation = T,
silent = T,
labels_row = as.expression(lapply(rownames(matrix[[x]][[y]]), function(a) bquote(italic(.(a)))))
) %>% as.ggplot()
# save
ggsave(filename = paste0("heat_", x, "_", y, ".svg"),
plot = kegg_heat[[x]][[y]],
path = here::here("2_plots/kegg/"),
width = 166,
height = 250,
units = "mm")}
}kegg_heat[[p]][[q]]
Table
display_matrix[[p]][[q]] %>%
dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
DT(.,caption = "DE genes") # kable() %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# scroll_box(height = "600px")Pathview
# adjusting the kegg id to suit the parameters of the pathview funtion
adj.keggID <- gsub("mmu", "", kegg_id)
for (i in 1:length(fc)) {
x <- fc[i] %>% as.character()
# extract the logFC from the DE gene list
pathview_table <- dplyr::select(.data = lmTreat_sig[[x]], c("logFC")) %>% as.matrix()
# run pathview with Ensembl ID instead of entrezID
pathview <- pathview(
gene.data = pathview_table[, 1],
gene.idtype = "ENSEMBL",
pathway.id = adj.keggID,
species = "mmu",
out.suffix = "pv",
kegg.dir = here::here("2_plots/kegg/"),
kegg.native = T
)
# move the result file to the plot directory
file.rename(
from = paste0("mmu", adj.keggID, ".pv.png"),
to = here::here(paste0("docs/figure/kegg.Rmd/pv_", x, "_", kegg_id, ".png"))
)
}[1] "Note: 163 of 1629 unique input IDs unmapped."[1] "Note: 148 of 1447 unique input IDs unmapped."[1] "Note: 60 of 388 unique input IDs unmapped."
Pathview
Hematopoietic cell lineage
q=q+1Heatmap
kegg_heat[[p]][[q]]
Tables
display_matrix[[p]][[q]] %>%
dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
DT(.,caption = "DE genes") # kable() %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# scroll_box(height = "600px")Pathview
Pathview
Vascular smooth muscle contraction
q=q+1Heatmap
kegg_heat[[p]][[q]]
Tables
display_matrix[[p]][[q]] %>%
dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
DT(.,caption = "DE genes") # kable() %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# scroll_box(height = "600px")Pathview
Pathview
PI3K-Akt signaling pathway
q=q+1Heatmap
kegg_heat[[p]][[q]]
Tables
display_matrix[[p]][[q]] %>%
dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
DT(.,caption = "DE genes") # kable() %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# scroll_box(height = "600px")Pathview
Pathview
Focal adhesion
q=q+1Heatmap
kegg_heat[[p]][[q]]
Tables
display_matrix[[p]][[q]] %>%
dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
DT(.,caption = "DE genes") # kable() %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# scroll_box(height = "600px")Pathview
Pathview
Cytokine-cytokine receptor interaction
q=q+1Heatmap
kegg_heat[[p]][[q]]
Tables
display_matrix[[p]][[q]] %>%
dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
DT(.,caption = "DE genes") # kable() %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# scroll_box(height = "600px")Pathview
Pathview
FC=1.1
p=p+1Leukocyte transendothelial migration
q=1Heatmap
kegg_heat[[p]][[q]]
Tables
display_matrix[[p]][[q]] %>%
dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
DT(.,caption = "DE genes") # kable() %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# scroll_box(height = "600px")Pathview
Pathview
Hematopoietic cell lineage
q=q+1Heatmap
kegg_heat[[p]][[q]]
Tables
display_matrix[[p]][[q]] %>%
dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
DT(.,caption = "DE genes") # kable() %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# scroll_box(height = "600px")Pathview
Pathview
Vascular smooth muscle contraction
q=q+1Heatmap
kegg_heat[[p]][[q]]
Tables
display_matrix[[p]][[q]] %>%
dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
DT(.,caption = "DE genes") # kable() %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# scroll_box(height = "600px")Pathview
Pathview
PI3K-Akt signaling pathway
q=q+1Heatmap
kegg_heat[[p]][[q]]
Tables
display_matrix[[p]][[q]] %>%
dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
DT(.,caption = "DE genes") # kable() %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# scroll_box(height = "600px")Pathview
Pathview
Focal adhesion
q=q+1Heatmap
kegg_heat[[p]][[q]]
Tables
display_matrix[[p]][[q]] %>%
dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
DT(.,caption = "DE genes") # kable() %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# scroll_box(height = "600px")Pathview
Pathview
Cytokine-cytokine receptor interaction
q=q+1Heatmap
kegg_heat[[p]][[q]]
Tables
display_matrix[[p]][[q]] %>%
dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
DT(.,caption = "DE genes") # kable() %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# scroll_box(height = "600px")Pathview
Pathview
FC=1.5
p=p+1Leukocyte transendothelial migration
q=1Heatmap
kegg_heat[[p]][[q]]
Tables
display_matrix[[p]][[q]] %>%
dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
DT(.,caption = "DE genes") # kable() %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# scroll_box(height = "600px")Pathview
Pathview
Hematopoietic cell lineage
q=q+1Heatmap
kegg_heat[[p]][[q]]
Tables
display_matrix[[p]][[q]] %>%
dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
DT(.,caption = "DE genes") # kable() %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# scroll_box(height = "600px")Pathview
Pathview
Vascular smooth muscle contraction
q=q+1Heatmap
kegg_heat[[p]][[q]]
Tables
display_matrix[[p]][[q]] %>%
dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
DT(.,caption = "DE genes") # kable() %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# scroll_box(height = "600px")Pathview
Pathview
PI3K-Akt signaling pathway
q=q+1Heatmap
kegg_heat[[p]][[q]]
Tables
display_matrix[[p]][[q]] %>%
dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
DT(.,caption = "DE genes") # kable() %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# scroll_box(height = "600px")Pathview
Pathview
Focal adhesion
q=q+1Heatmap
kegg_heat[[p]][[q]]
Tables
display_matrix[[p]][[q]] %>%
dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
DT(.,caption = "DE genes") # kable() %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# scroll_box(height = "600px")Pathview
Pathview
Cytokine-cytokine receptor interaction
q=q+1Heatmap
kegg_heat[[p]][[q]]
Tables
display_matrix[[p]][[q]] %>%
dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>%
DT(.,caption = "DE genes") # kable() %>%
# kable_styling(bootstrap_options = c("striped", "hover")) %>%
# scroll_box(height = "600px")Pathview
Pathview
Export Data
# save to csv
writexl::write_xlsx(x = enrichKEGG_all, here::here("3_output/enrichKEGG_all.xlsx"))
writexl::write_xlsx(x = enrichKEGG_sig, here::here("3_output/enrichKEGG_sig.xlsx"))
sessionInfo()R version 4.3.1 (2023-06-16 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)
Matrix products: default
locale:
[1] LC_COLLATE=English_Australia.utf8 LC_CTYPE=English_Australia.utf8
[3] LC_MONETARY=English_Australia.utf8 LC_NUMERIC=C
[5] LC_TIME=English_Australia.utf8
time zone: Australia/Adelaide
tzcode source: internal
attached base packages:
[1] stats4 grid stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] pathview_1.40.0 enrichplot_1.20.1 org.Mm.eg.db_3.17.0
[4] AnnotationDbi_1.62.2 IRanges_2.34.1 S4Vectors_0.38.1
[7] Biobase_2.60.0 BiocGenerics_0.46.0 clusterProfiler_4.8.3
[10] Glimma_2.10.0 edgeR_3.42.4 limma_3.56.2
[13] ggrepel_0.9.3 ggbiplot_0.55 scales_1.2.1
[16] plyr_1.8.8 ggpubr_0.6.0 ggplotify_0.1.2
[19] DT_0.29 pheatmap_1.0.12 cowplot_1.1.1
[22] viridis_0.6.4 viridisLite_0.4.2 pander_0.6.5
[25] kableExtra_1.3.4 KEGGREST_1.40.0 lubridate_1.9.2
[28] forcats_1.0.0 stringr_1.5.0 purrr_1.0.1
[31] tidyr_1.3.0 ggplot2_3.4.3 tidyverse_2.0.0
[34] reshape2_1.4.4 tibble_3.2.1 readr_2.1.4
[37] magrittr_2.0.3 dplyr_1.1.2
loaded via a namespace (and not attached):
[1] splines_4.3.1 later_1.3.1
[3] bitops_1.0-7 polyclip_1.10-4
[5] graph_1.78.0 XML_3.99-0.14
[7] lifecycle_1.0.3 rstatix_0.7.2
[9] rprojroot_2.0.3 lattice_0.21-8
[11] MASS_7.3-60 crosstalk_1.2.0
[13] backports_1.4.1 sass_0.4.7
[15] rmarkdown_2.24 jquerylib_0.1.4
[17] yaml_2.3.7 httpuv_1.6.11
[19] DBI_1.1.3 RColorBrewer_1.1-3
[21] abind_1.4-5 zlibbioc_1.46.0
[23] rvest_1.0.3 GenomicRanges_1.52.0
[25] ggraph_2.1.0 RCurl_1.98-1.12
[27] yulab.utils_0.0.9 tweenr_2.0.2
[29] git2r_0.32.0 GenomeInfoDbData_1.2.10
[31] tidytree_0.4.5 svglite_2.1.1
[33] codetools_0.2-19 DelayedArray_0.26.7
[35] DOSE_3.26.1 xml2_1.3.5
[37] ggforce_0.4.1 tidyselect_1.2.0
[39] aplot_0.2.1 farver_2.1.1
[41] matrixStats_1.0.0 webshot_0.5.5
[43] jsonlite_1.8.7 ellipsis_0.3.2
[45] tidygraph_1.2.3 systemfonts_1.0.4
[47] tools_4.3.1 ragg_1.2.5
[49] treeio_1.24.3 Rcpp_1.0.11
[51] glue_1.6.2 gridExtra_2.3
[53] here_1.0.1 xfun_0.39
[55] DESeq2_1.40.2 qvalue_2.32.0
[57] MatrixGenerics_1.12.3 GenomeInfoDb_1.36.3
[59] withr_2.5.0 fastmap_1.1.1
[61] fansi_1.0.4 digest_0.6.33
[63] timechange_0.2.0 R6_2.5.1
[65] gridGraphics_0.5-1 textshaping_0.3.6
[67] colorspace_2.1-0 GO.db_3.17.0
[69] RSQLite_2.3.1 utf8_1.2.3
[71] generics_0.1.3 data.table_1.14.8
[73] graphlayouts_1.0.0 httr_1.4.7
[75] htmlwidgets_1.6.2 S4Arrays_1.0.6
[77] scatterpie_0.2.1 whisker_0.4.1
[79] pkgconfig_2.0.3 gtable_0.3.4
[81] blob_1.2.4 workflowr_1.7.1
[83] XVector_0.40.0 shadowtext_0.1.2
[85] htmltools_0.5.5 carData_3.0-5
[87] fgsea_1.26.0 ggupset_0.3.0
[89] png_0.1-8 ggfun_0.1.3
[91] knitr_1.44 rstudioapi_0.15.0
[93] tzdb_0.4.0 curl_5.0.2
[95] nlme_3.1-163 org.Hs.eg.db_3.17.0
[97] cachem_1.0.8 parallel_4.3.1
[99] HDO.db_0.99.1 pillar_1.9.0
[101] vctrs_0.6.3 promises_1.2.0.1
[103] car_3.1-2 Rgraphviz_2.44.0
[105] KEGGgraph_1.60.0 evaluate_0.21
[107] cli_3.6.1 locfit_1.5-9.8
[109] compiler_4.3.1 rlang_1.1.1
[111] crayon_1.5.2 ggsignif_0.6.4
[113] labeling_0.4.3 fs_1.6.3
[115] writexl_1.4.2 stringi_1.7.12
[117] BiocParallel_1.34.2 munsell_0.5.0
[119] Biostrings_2.68.1 lazyeval_0.2.2
[121] GOSemSim_2.26.1 Matrix_1.6-1
[123] hms_1.1.3 patchwork_1.1.3
[125] bit64_4.0.5 SummarizedExperiment_1.30.2
[127] igraph_1.5.1 broom_1.0.5
[129] memoise_2.0.1 bslib_0.5.1
[131] ggtree_3.8.2 fastmatch_1.1-4
[133] bit_4.0.5 downloader_0.4
[135] gson_0.1.0 ape_5.7-1