Additional Figures
Ha Tran
24/12/2021
Last updated: 2025-11-27
Checks: 7 0
Knit directory: 5_gd_Tcell/1_analysis/
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Unstaged changes:
Modified: 0_data/RDS_plots/go_combined_dotPlot.rds
Modified: 0_data/RDS_plots/go_combined_parTerm_dotPlot.rds
Modified: 0_data/RDS_plots/go_dotPlot.rds
Modified: 0_data/RDS_plots/go_parTerm_dotPlot.rds
Modified: 0_data/RDS_plots/go_parTerm_scatter.rds
Modified: 0_data/RDS_plots/kegg_dotPlot.rds
Modified: 0_data/RDS_plots/kegg_path_Hmap.rds
Modified: 0_data/RDS_plots/ma_plots.rds
Modified: 0_data/RDS_plots/react_combined_dotPlot.rds
Modified: 0_data/RDS_plots/react_dotPlot.rds
Modified: 0_data/RDS_plots/vol_plots.rds
Modified: 2_plots/1_QC/PC1_PC2.svg
Modified: 2_plots/1_QC/PC1_PC3.svg
Modified: 2_plots/1_QC/PC2_PC3.svg
Modified: 2_plots/2_DE/heat_down_INT vs CONT.svg
Modified: 2_plots/2_DE/heat_down_INT vs SVX_VAS.svg
Modified: 2_plots/2_DE/heat_down_INT vs VAS.svg
Modified: 2_plots/2_DE/heat_down_SVX vs SVX_VAS.svg
Modified: 2_plots/2_DE/heat_down_SVX_VAS vs CONT.svg
Modified: 2_plots/2_DE/heat_down_VAS vs SVX_VAS.svg
Modified: 2_plots/2_DE/heat_up_INT vs CONT.svg
Modified: 2_plots/2_DE/heat_up_INT vs SVX_VAS.svg
Modified: 2_plots/2_DE/heat_up_INT vs VAS.svg
Modified: 2_plots/2_DE/heat_up_SVX vs SVX_VAS.svg
Modified: 2_plots/2_DE/heat_up_SVX_VAS vs CONT.svg
Modified: 2_plots/2_DE/heat_up_VAS vs SVX_VAS.svg
Modified: 2_plots/2_DE/hist_INT vs CONT.svg
Modified: 2_plots/2_DE/hist_INT vs SVX_VAS.svg
Modified: 2_plots/2_DE/hist_INT vs VAS.svg
Modified: 2_plots/2_DE/hist_SVX vs SVX_VAS.svg
Modified: 2_plots/2_DE/hist_SVX_VAS vs CONT.svg
Modified: 2_plots/2_DE/hist_VAS vs SVX_VAS.svg
Modified: 2_plots/3_FA/go/combine_go_dot.svg
Modified: 2_plots/3_FA/go/parTerm_dot_INT vs CONT.svg
Modified: 2_plots/3_FA/go/parTerm_dot_INT vs SVX_VAS.svg
Modified: 2_plots/3_FA/go/parTerm_dot_SVX vs SVX_VAS.svg
Modified: 2_plots/3_FA/go/parTerm_dot_SVX_VAS vs CONT.svg
Modified: 2_plots/3_FA/go/parTerm_dot_VAS vs SVX_VAS.svg
Modified: 2_plots/3_FA/go/semSim_dendrogram_INT vs CONT.svg
Modified: 2_plots/3_FA/go/semSim_dendrogram_INT vs SVX_VAS.svg
Modified: 2_plots/3_FA/go/semSim_dendrogram_SVX vs SVX_VAS.svg
Modified: 2_plots/3_FA/go/semSim_dendrogram_SVX_VAS vs CONT.svg
Modified: 2_plots/3_FA/go/semSim_dendrogram_VAS vs SVX_VAS.svg
Modified: 2_plots/3_FA/go/semSim_scatter_INT vs CONT.svg
Modified: 2_plots/3_FA/go/semSim_scatter_INT vs SVX_VAS.svg
Modified: 2_plots/3_FA/go/semSim_scatter_SVX vs SVX_VAS.svg
Modified: 2_plots/3_FA/go/semSim_scatter_SVX_VAS vs CONT.svg
Modified: 2_plots/3_FA/kegg/combine_kegg_dot.svg
Modified: 2_plots/3_FA/kegg/heat_Neutrophil extracellular trap formation.svg
Modified: 2_plots/3_FA/kegg/heat_PD-L1 expression and PD-1 checkpoint pathway in cancer.svg
Modified: 2_plots/3_FA/kegg/heat_T cell receptor signaling pathway.svg
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Modified: 2_plots/3_FA/kegg/heat_Th17 cell differentiation.svg
Modified: 2_plots/3_FA/kegg/kegg_dot_INT vs CONT.svg
Modified: 2_plots/3_FA/kegg/kegg_dot_INT vs SVX_VAS.svg
Modified: 2_plots/3_FA/kegg/kegg_dot_SVX_VAS vs CONT.svg
Modified: 2_plots/3_FA/kegg/kegg_dot_VAS vs SVX_VAS.svg
Modified: 2_plots/3_FA/kegg/kegg_upset_INT vs CONT.svg
Modified: 2_plots/3_FA/kegg/kegg_upset_INT vs SVX_VAS.svg
Modified: 2_plots/3_FA/kegg/kegg_upset_SVX_VAS vs CONT.svg
Modified: 2_plots/3_FA/kegg/kegg_upset_VAS vs SVX_VAS.svg
Modified: 2_plots/3_FA/reactome/combine_react_dot.svg
Modified: 2_plots/3_FA/reactome/react_dot_INT vs CONT.svg
Modified: 2_plots/3_FA/reactome/react_dot_INT vs SVX_VAS.svg
Modified: 2_plots/3_FA/reactome/react_dot_INT vs VAS.svg
Modified: 2_plots/3_FA/reactome/react_dot_SVX vs SVX_VAS.svg
Modified: 2_plots/3_FA/reactome/react_dot_SVX_VAS vs CONT.svg
Modified: 2_plots/3_FA/reactome/react_dot_VAS vs SVX_VAS.svg
Modified: 2_plots/3_FA/reactome/react_upset_INT vs CONT.svg
Modified: 2_plots/3_FA/reactome/react_upset_INT vs SVX_VAS.svg
Modified: 2_plots/3_FA/reactome/react_upset_INT vs VAS.svg
Modified: 2_plots/3_FA/reactome/react_upset_SVX vs SVX_VAS.svg
Modified: 2_plots/3_FA/reactome/react_upset_SVX_VAS vs CONT.svg
Modified: 2_plots/3_FA/reactome/react_upset_VAS vs SVX_VAS.svg
Modified: 2_plots/4_paper/subset_degs.svg
Modified: 2_plots/combine_ipa_dot.svg
Modified: 2_plots/dnf_plot.svg
Modified: 2_plots/intVsvxVAS.svg
Modified: 2_plots/upstream_hmap.svg
Modified: 3_output/DEGs.xlsx
Modified: 3_output/Gene Ontology.xlsx
Modified: 3_output/KEGG.xlsx
Modified: 3_output/Reactome.xlsx
Modified: 3_output/deg_all_new.xlsx
Modified: 3_output/deg_sig_new.xlsx
Modified: 3_output/eigenvalues.xlsx
Modified: 3_output/enrichKEGG_sig.xlsx
Modified: 3_output/reactome_all_new.xlsx
Modified: 3_output/reactome_sig_new.xlsx
Modified: 3_output/semSim_GO_sig.xlsx
Modified: README.html
Modified: README.md
Modified: figure/dot2-1.png
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Modified: figure/dot5-1.png
Modified: figure/upset2-1.png
Modified: figure/upset3-1.png
Modified: figure/upset4-1.png
Modified: figure/upset5-1.png
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | d519e7f | Ha Tran | 2024-12-03 | Build site. |
| Rmd | 9fc0156 | Ha Tran | 2024-12-03 | workflowr::wflow_publish(here::here("1_analysis/*.Rmd")) |
| html | 5dce909 | Ha Tran | 2024-11-07 | Build site. |
| Rmd | 6b2e610 | Ha Tran | 2024-11-07 | workflowr::wflow_publish(here::here("1_analysis/*.Rmd")) |
| html | d71eeb4 | Ha Tran | 2024-10-16 | Build site. |
| html | ae93fcc | tranmanhha135 | 2024-02-02 | Build site. |
| Rmd | fbcdd69 | tranmanhha135 | 2024-02-02 | workflowr::wflow_publish(here::here("1_analysis/*.Rmd")) |
| Rmd | ccb39d7 | tranmanhha135 | 2022-10-22 | safety commit |
| Rmd | 2c24612 | tranmanhha135 | 2022-10-13 | build website |
| html | 2c24612 | tranmanhha135 | 2022-10-13 | build website |
| Rmd | 866017e | tranmanhha135 | 2022-10-13 | minor changes |
| Rmd | 324032b | tranmanhha135 | 2022-10-11 | resize images |
| Rmd | 43675d3 | tranmanhha135 | 2022-10-06 | Add IPA data |
| html | 43675d3 | tranmanhha135 | 2022-10-06 | Add IPA data |
| html | 11a5cf4 | tranmanhha135 | 2022-10-03 | build wedsite |
| Rmd | 192d010 | tranmanhha135 | 2022-09-20 | functional enrichment with new dataset |
| Rmd | 0df047f | tranmanhha135 | 2022-09-08 | minor changes to build and publish |
| html | 0df047f | tranmanhha135 | 2022-09-08 | minor changes to build and publish |
| Rmd | 889dfb5 | Ha Tran | 2022-01-01 | changed colour scheme, minor cosmetic changes |
| html | 889dfb5 | Ha Tran | 2022-01-01 | changed colour scheme, minor cosmetic changes |
| html | 54e0166 | Ha Manh Tran | 2022-01-01 | Build site. |
| html | 32454d5 | Ha Manh Tran | 2022-01-01 | Build site. |
| Rmd | c667dd0 | Ha Manh Tran | 2022-01-01 | workflowr::wflow_publish(files = here::here(c("1_analysis/index.Rmd", |
# load DGElist previously created in the set up
# dge <- readRDS(here::here("0_data/RDS_objects/dge.rds"))
lm <- readRDS(here::here("0_data/RDS_objects/lm.rds"))
lm_all <- readRDS(here::here("0_data/RDS_objects/lm_all.rds"))
lm_sig <- readRDS(here::here("0_data/RDS_objects/lm_sig.rds"))
# Comp <- readRDS(here::here("0_data/RDS_objects/comp.rds"))
# to increase the knitting speed. change to T to save all plots
savePlots <- T
export <- T# Theme
bossTheme <- readRDS(here::here("0_data/functions/bossTheme.rds"))
bossTheme_bar <- readRDS(here::here("0_data/functions/bossTheme_bar.rds"))
groupColour <- readRDS(here::here("0_data/functions/groupColour.rds"))
groupColour_dark <- readRDS(here::here("0_data/functions/groupColour_dark.rds"))
expressionCol <- readRDS(here::here("0_data/functions/expressionCol.rds"))
expressionCol_dark <- readRDS(here::here("0_data/functions/expressionCol_dark.rds"))
compColour <- readRDS(here::here("0_data/functions/compColour.rds"))
DT <- readRDS(here::here("0_data/functions/DT.rds"))
# Plotting
convert_to_superscript <- readRDS(here::here("0_data/functions/convert_to_superscript.rds"))
exponent <- readRDS(here::here("0_data/functions/exponent.rds"))
format_y_axis <- readRDS(here::here("0_data/functions/format_y_axis.rds"))
firstCap <- function(x) {
substr(x, 1, 1) <- toupper(substr(x, 1, 1))
x
}IPA analysis
Regulated Pathways
read_excel_allsheets <- function(filename, tibble = FALSE) {
sheets <- readxl::excel_sheets(filename)
x <- lapply(sheets, function(X) readxl::read_excel(filename, sheet = X))
if(!tibble) x <- lapply(x, as.data.frame)
names(x) <- sheets
x
}
pathways <- read_excel_allsheets(here::here("0_data/raw_data/IPA_pathways.xlsx"))
pathways <- lapply(pathways, function(x) {
colnames(x) <- c("name", "logPval", "pval", "ratio", "zScore", "molecules")
x <- x %>% dplyr::mutate(pval = 10^-logPval, .after = logPval)
x$name <- x$name %>% firstCap() %>% str_wrap(width = 45)
return(x)
})Venn diagram
Dot plot
# combine all df in list into one df
library(stringi)
ipa_dot_all <- as.data.frame(do.call(rbind, pathways)) %>%
rownames_to_column("group")
ipa_dot_all$group <- gsub(pattern = "\\..*", "", ipa_dot_all$group) %>% as.factor()
# remove the " vs SVX_VAS" from group name
ipa_dot_all$group <- gsub(pattern = " vs SVX_VAS", "", ipa_dot_all$group) %>% as.factor()
# clean group names and change to factor
ipa_dot_all$group <- factor(ipa_dot_all$group,levels = c("INT", "SVX", "VAS"))
ipa_dot_all <- ipa_dot_all %>% dplyr::mutate(count = stri_count(molecules,fixed = ",") + 1,
state = case_when(zScore < 0 ~ "Decreased",
zScore > 0 ~ "Increased",
TRUE ~ "NA"))
ipa_dot_all$name <- ipa_dot_all$name %>% str_wrap(28)
ipa_dot_all$name <- factor(ipa_dot_all$name, levels = unique(ipa_dot_all$name[order(ipa_dot_all$zScore, decreasing = F)]))
path_dot1 <- ggplot(ipa_dot_all) +
geom_point(aes(x = group, y = name, colour = zScore, size = count, shape = state)) +
scale_color_gradientn(colors = rev(c("#FB8072","#FDB462","#fffdab","#8DD3C7","#80B1D3")),
values = scales::rescale(c(min(ipa_dot_all$zScore), max(ipa_dot_all$zScore))),
limit = c(-4,4),
breaks = scales::pretty_breaks(n = 5)) +
scale_size(range = c(5,12),limits = c(min(ipa_dot_all$count), max(ipa_dot_all$count))) +
scale_shape_manual(values = c("\u25BC","\u25B2", "\u25CF")) +
labs(x = "", y = "", color = "Z-scores", size = "# Molecules", shape = "")+
bossTheme(base_size = 14,legend = "none") +
guides(size = guide_legend(order = 1),
shape = guide_legend(override.aes = list(size = 3),order = 2))
path_dot1
ggsave(filename = paste0("combine_ipa_dot.svg"), plot = path_dot1, path = here::here("2_plots/"),
width = 11, height = 13, units = "cm")Upstream Regulators
library(stringr)
upstream <- read_excel_allsheets(here::here("0_data/raw_data/IPA_upstreamRegs.xlsx"))
upstream <- lapply(upstream, function(x) {
colnames(x) <- c("name", "exprLogRatio", "type", "activationState", "activationScore", "pval", "molecules", "network")
x <- x %>% dplyr::mutate(logPval = -log10(pval), .after = pval)
x$name <- x$name %>% firstCap() %>% str_wrap(width = 45)
return(x)
})
# interesting_type <- c("cytokine", "group", "transmembrane receptor", "transcription regulator")
intersect_upstream <- intersect(upstream[[1]]$name,upstream[[2]]$name) %>% intersect(., upstream[[3]]$name)
# interesting_upstream <- c("beta-estradiol", "prednisolone", "PRL", "Tgf beta", "TGFB1", "FGF7", "BMP2", "FGF2", "VEGFA", "HIF1A")
heatMatrix_up <- lapply(upstream, function(x) {
x <- x[!str_detect(x$type, "chemical"),]
x <- bind_rows(
x %>%
dplyr::filter(activationScore > 0) %>%
arrange(desc(logPval)) %>%
head(18))
x <- x %>% remove_rownames()
mat <- x %>% dplyr::select(c("name", "activationScore", "type"))
return(mat)
})
heatMatrix_down <- lapply(upstream, function(x) {
x <- x[!str_detect(x$type, "chemical"),]
x <- bind_rows(
x %>%
dplyr::filter(activationScore < 0) %>%
arrange(desc(logPval)) %>%
head(18))
x <- x %>% remove_rownames()
mat <- x %>% dplyr::select(c("name", "activationScore", "type"))
return(mat)
})
heatMatrix_up <- merge(heatMatrix_up[[1]],heatMatrix_up[[2]],by= "name", all=T) %>% merge(.,heatMatrix_up[[3]],by= "name", all=T)
colnames(heatMatrix_up) <- c("name","INT", "type", "SVX", "type2", "VAS", "type3")
heatMatrix_up <- heatMatrix_up %>%
dplyr::mutate(combinedType = coalesce(type, type2, type3)) %>%
# dplyr::mutate(combinedType = factor(combinedType, levels = interesting_type)) %>%
arrange(combinedType) %>% dplyr::select(c("name","INT", "SVX", "VAS", "combinedType"))
heatMatrix_up$name <- gsub("MiR-30c-5p \\(and other miRNAs w/seed GUAAACA\\)", "MiR-30c-5p", heatMatrix_up$name)
lookup <- tibble(gene_name = lm_all[[1]]$gene_name, gene_name_CAP = str_to_upper(lm_all[[1]]$gene_name))
replacement_vector <- setNames(lookup$gene_name, lookup$gene_name_CAP)
replacement_vector <- replacement_vector[!is.na(names(replacement_vector)) & names(replacement_vector) != ""]
# heatMatrix_up <- heatMatrix_up[[1]]
heatMatrix_up$correctName <- map_chr(heatMatrix_up$name, ~ str_replace_all(., replacement_vector))
heatMatrix_up$correctName <- str_replace_all(heatMatrix_up$correctName, ",", "/")
heatMatrix_up <- heatMatrix_up %>% filter(!is.na(INT))
# saveRDS(heatMatrix, here::here("0_data/rds_objects/heatMatrix.rds"))
# heatMatrix <- readRDS(here::here("0_data/rds_objects/heatMatrix.rds"))
heatMatrix_up_anno <- heatMatrix_up[,c(1,6)]
heatMatrix_up_anno <- heatMatrix_up_anno %>% left_join(lm_all[[2]][,c("gene_name","adj.P.Val")],by = join_by(correctName == gene_name)) %>% dplyr::rename(INT = adj.P.Val)
heatMatrix_up_anno <- heatMatrix_up_anno %>% left_join(lm_all[[3]][,c("gene_name","adj.P.Val")],by = join_by(correctName == gene_name)) %>% dplyr::rename(SVX = adj.P.Val)
heatMatrix_up_anno <- heatMatrix_up_anno %>% left_join(lm_all[[4]][,c("gene_name","adj.P.Val")],by = join_by(correctName == gene_name)) %>% dplyr::rename(VAS = adj.P.Val)
heatMatrix_up_anno <- heatMatrix_up_anno %>%
dplyr::mutate(across(where(is.numeric), ~ as.character(signif(., 3)))) %>%
dplyr::mutate(across(c(INT, SVX, VAS), ~ case_when(
as.numeric(.) < 0.0001 ~ "****",
as.numeric(.) < 0.001 ~ "***",
as.numeric(.) < 0.01 ~ "**",
as.numeric(.) < 0.05 ~ "*",
TRUE ~ as.character(.)
)))
heatMatrix_down <- merge(heatMatrix_down[[1]],heatMatrix_down[[2]],by= "name", all=T) %>% merge(.,heatMatrix_down[[3]],by= "name", all=T)
colnames(heatMatrix_down) <- c("name","INT", "type", "SVX", "type2", "VAS", "type3")
heatMatrix_down <- heatMatrix_down %>%
dplyr::mutate(combinedType = coalesce(type, type2, type3)) %>%
# dplyr::mutate(combinedType = factor(combinedType, levels = interesting_type)) %>%
arrange(combinedType) %>% dplyr::select(c("name","INT", "SVX", "VAS", "combinedType"))
heatMatrix_down$name <- gsub("MiR-30c-5p \\(and other miRNAs w/seed GUAAACA\\)", "MiR-30c-5p", heatMatrix_down$name)
# heatMatrix_down <- heatMatrix_down[[1]]
heatMatrix_down$correctName <- map_chr(heatMatrix_down$name, ~ str_replace_all(., replacement_vector))
heatMatrix_down$correctName <- str_replace_all(heatMatrix_down$correctName, ",", "/")
heatMatrix_down <- heatMatrix_down %>% filter(!is.na(INT))
heatMatrix_down_anno <- heatMatrix_down[,c(1,6)]
heatMatrix_down_anno <- heatMatrix_down_anno %>% left_join(lm_all[[2]][,c("gene_name","adj.P.Val")],by = join_by(correctName == gene_name)) %>% dplyr::rename(INT = adj.P.Val)
heatMatrix_down_anno <- heatMatrix_down_anno %>% left_join(lm_all[[3]][,c("gene_name","adj.P.Val")],by = join_by(correctName == gene_name)) %>% dplyr::rename(SVX = adj.P.Val)
heatMatrix_down_anno <- heatMatrix_down_anno %>% left_join(lm_all[[4]][,c("gene_name","adj.P.Val")],by = join_by(correctName == gene_name)) %>% dplyr::rename(VAS = adj.P.Val)
heatMatrix_down_anno <- heatMatrix_down_anno %>%
dplyr::mutate(across(where(is.numeric), ~ as.character(signif(., 3)))) %>%
dplyr::mutate(across(c(INT, SVX, VAS), ~ case_when(
as.numeric(.) < 0.0001 ~ "****",
as.numeric(.) < 0.001 ~ "***",
as.numeric(.) < 0.01 ~ "**",
as.numeric(.) < 0.05 ~ "*",
TRUE ~ as.character(.)
)))
# df for heatmap annotation of sample group
anno <- dplyr::select(.data = rbind(heatMatrix_up, heatMatrix_down), c("name","combinedType"))
anno$combinedType <- str_to_title(anno$combinedType)
# anno <- anno[1:46,1:2] %>% column_to_rownames("name")
colnames(anno) <- c("name","Molecule Type")
anno$`Molecule Type` <- gsub("Transmembrane Receptor", "Transmembrane\nreceptor", anno$`Molecule Type`)
anno$`Molecule Type` <- gsub("Transcription Regulator", "Transcription\nregulator", anno$`Molecule Type`)
anno$`Molecule Type` <- gsub("Ligand-Dependent Nuclear Receptor", "Ligand-dependent\nnuclear receptor", anno$`Molecule Type`)
anno$`Molecule Type` <- gsub("G-Protein Coupled Receptor", "GPCRs", anno$`Molecule Type`)
anno$`Molecule Type` <- gsub("Mature Microrna", "Mature miRNA", anno$`Molecule Type`)
anno$`Molecule Type` <- gsub("Microrna", "miRNA", anno$`Molecule Type`)
anno$`Molecule Type` <- factor(anno$`Molecule Type`)
# colorRampPalette(rev(c("#FB8072","#FDB462","#ffffd5","#8DD3C7","#80B1D3")))(300)
anno_colours <- colorRampPalette(brewer.pal(9, "Set1"))(length(levels(anno$`Molecule Type`)))
names(anno_colours) <- levels(anno$`Molecule Type`)
mat1 <- heatMatrix_up[,c(1,2,3,4)] %>% column_to_rownames("name") %>% as.matrix()
mat1[is.na(mat1)] <- 0
mat1_anno <- heatMatrix_up_anno[,c(1,3,4,5)] %>% column_to_rownames("name") %>% as.matrix()
mat1_anno[is.na(mat1_anno)] <- ""
mat2 <- heatMatrix_down[,1:4] %>% remove_rownames() %>% column_to_rownames("name") %>% as.matrix()
mat2[is.na(mat2)] <- 0
mat2_anno <- heatMatrix_down_anno[,c(1,3,4,5)] %>% remove_rownames() %>% column_to_rownames("name") %>% as.matrix()
mat2_anno[is.na(mat2_anno)] <- ""
breaks <-seq(-3.5,3.5, by = 1)
#
# showtext_auto(enable = T)
hmap1 <- pheatmap(
# MAIN
mat = mat1,
display_numbers = mat1_anno,
color = colorRampPalette(rev(c("#FB8072","#FDB462","grey95","#8DD3C7","#80B1D3")))(length(breaks)),
cellwidth = 35,
# cellheight = 40,
scale = "none",
# Col
cluster_cols = F,
border_color = "white",
angle_col = "0",
# gaps_row = c(23,30,38),
# Row
cluster_rows = F,
# Labs
show_colnames = T,
show_rownames = T,
legend = F,
breaks = breaks,
heatmap_legend_param = list(title = "Z-score", legend_width = unit(7, "cm")),
# Annotation
annotation_legend = F,
# legend_labels = T,
annotation_row = anno[anno$name %in% rownames(mat1),1:2] %>% remove_rownames() %>% column_to_rownames("name"),
annotation_colors = list("Molecule Type" = anno_colours),
annotation_names_row = F,
# Fonts
fontfamily = "Arial",
fontsize = 12,
fontsize_col = 12,
fontsize_number = 8,
fontsize_row = 10
) %>% as.ggplot()
hmap2 <- pheatmap(
# MAIN
mat = mat2,
display_numbers = mat2_anno,
color = colorRampPalette(rev(c("#FB8072","#FDB462","grey95","#8DD3C7","#80B1D3")))(length(breaks)),
cellwidth = 35,
# cellheight = 40,
scale = "none",
# Col
cluster_cols = F,
border_color = "white",
angle_col = "0",
# Row
cluster_rows = F,
# Labs
show_colnames = T,
show_rownames = T,
legend = T,
breaks = breaks,
# heatmap_legend_param = list(title = "Z-score", legend_width = unit(7, "cm")),
heatmap_legend_param = list(title = "Z-score",
direction= "vertical",
merge_legend = T,
legend_direction = "vertical",
legend_height = unit(4, "cm")),
# Annotation
annotation_legend = T,
# legend_labels = T,
annotation_row = anno[anno$name %in% rownames(mat2),1:2] %>% remove_rownames() %>% column_to_rownames("name"),
annotation_colors = list("Molecule Type" = anno_colours),
annotation_names_row = F,
# Fonts
fontfamily = "Arial",
fontsize = 12,
fontsize_col = 12,
fontsize_number = 8,
fontsize_row = 10
) %>% as.ggplot()
# png(filename = here::here("2_plots/3_FA/ipa/legend.png"),res = 900,width = 8.267,height = 10.63,units = "in")
# draw(hmap2, merge_legend = T, heatmap_legend_side = "right",
# annotation_legend_side = "right")
# dev.off()
hmap_combined <- hmap1 + plot_spacer() + hmap2 +
plot_layout(widths = c(4,-1.2,4.5))
hmap_combined
ggsave(filename = "upstream_hmap.svg", plot = hmap_combined, path = here::here("2_plots/"),
width = 18, height = 11, units = "cm")Disease and Function
disease_function <- read_excel_allsheets(here::here("0_data/raw_data/IPA_disease_function.xlsx"))
disease_function <- lapply(disease_function, function(x) {
colnames(x) <- c("Categories", "name", "pval", "activationState", "activationScore", "molecules", "numMolecules")
x <- x %>% separate(col = Categories, sep = ",", into = c("Category 1", "Category 2", "Category 3",
"Category 4", "Category 5", "Category 6",
"Category 7", "Category 8", "Category 9",
"Category 10", "Category 11"),remove = F,fill = "right")
x <- x %>% dplyr::mutate(logPval = -log10(pval), .after = pval)
# x$name <- x$name %>% firstCap() %>% str_wrap(width = 45)
return(x)
})
library(stringr)
dnf_terms <- disease_function[[1]] %>% slice(.,1:50) %>% arrange(desc(logPval)) %>% separate_rows(data = ., molecules, sep = ",")
dnf_terms$`Category 1` <- dnf_terms$`Category 1` %>% str_to_upper()
dnf_terms$name <- dnf_terms$name %>% firstCap() %>% str_wrap(width = 45)
# Load libraries
library(ggraph)
library(igraph)
library(tidyverse)
library(RColorBrewer)
# library(dplyr)
# Step 1: Define edges to represent the hierarchy
edges_t <- dnf_terms %>%
arrange(`Category 1`, name) %>%
select(Category_1 = `Category 1`, name) %>%
distinct() %>%
dplyr::rename(.,from = Category_1, to = name)
t <- data.frame(from = "origin", to = unique(edges_t$from))
edges_t <- rbind(t, edges_t)
connect_t <- dnf_terms %>%
arrange(`Category 1`, name) %>%
select(name, molecules) %>%
# separate_rows(molecules, sep = ",") %>%
group_by(molecules) %>%
filter(n() > 1) %>% # Retain only molecules appearing in multiple "name" entries
reframe(connections = combn(name, 2, simplify = FALSE)) %>%
unnest_wider(connections, names_sep = "_") %>%
dplyr::rename(from = connections_1, to = connections_2)
connect_t$value <- runif(nrow(connect_t)) ###
# Condense connect_t by removing 'molecules', counting unique connections, and removing reciprocal pairs
connect_t <- connect_t %>%
# select(-molecules) %>% # Remove the 'molecules' column
mutate(pair = map2_chr(from, to, ~paste(sort(c(.x, .y)), collapse = "-"))) %>% # Create unique identifier for pairs
group_by(pair) %>% # Group by the unique 'from'-'to' pairs
summarise(from = first(from), # Retain the original 'from' and 'to' values
to = first(to),
value = n(), .groups = "drop") %>% # Count occurrences to represent the number of molecules between pairs
dplyr::select(-pair) # Remove the 'pair' column after processing
# Step 3: Create a vertices data frame
vertices_t <- tibble(
name = unique(c(edges_t$from, edges_t$to)),
# group = if_else(name %in% edges_t$from, NA_character_, edges_t$from[match(name, edges_t$to)]),
)
# Let's add a column with the group of each name. It will be useful later to color points
vertices_t$group <- edges_t$from[ match( vertices_t$name, edges_t$to ) ]
# Add 'value' after ensuring vertices_t is fully initialized
vertices_t$value <- dnf_terms$numMolecules[ match( vertices_t$name, dnf_terms$name) ]
vertices_t$state <- dnf_terms$activationState[ match( vertices_t$name, dnf_terms$name) ]
# Calculate angle and position for labels
vertices_t$id <- NA
myleaves <- which(is.na(match(vertices_t$name, edges_t$from)))
nleaves <- length(myleaves)
vertices_t$id[myleaves] <- seq(1:nleaves)
vertices_t$angle <- 90 - 360 * (vertices_t$id / nleaves)
# Calculate label alignment and angle adjustment
vertices_t$hjust <- ifelse(vertices_t$angle < -90, 1, 0)
vertices_t$angle <- ifelse(vertices_t$angle < -90, vertices_t$angle + 180, vertices_t$angle)
# Step 4: Create the graph
mygraph_t <- graph_from_data_frame(edges_t, vertices = vertices_t)
# Match connections to vertex IDs
from_t <- match(connect_t$from, vertices_t$name)
to_t <- match(connect_t$to, vertices_t$name)
# Step 5: Create the plot
dnf_plot <- ggraph(mygraph_t, layout = 'dendrogram', circular = T) +
geom_conn_bundle(data = get_con(from = from_t, to = to_t),aes(colour=..index..), edge_alpha = 0.5,edge_width = 0.5, tension = 1) +
scale_edge_colour_distiller(palette = "Greys") +
# Adjust label positions and colors for better separation
geom_node_text(aes(x = x*1.3, y = y*1.3, label = name, angle = angle, hjust = hjust, colour = group), size = 4, alpha = 1,show.legend = F) +
# Add node points for clarity
geom_node_point(aes(filter = leaf, x = x*1.1, y=y*1.1, colour=group, size=value, alpha=0.2)) +
scale_colour_manual(values = colorRampPalette(brewer.pal(10, "Paired"))(13)) +
# scale_shape_manual(values = c(15, 16),) +
scale_size(range = c(5,18),limits = c(min(dnf_terms$numMolecules), max(dnf_terms$numMolecules)) ) +
theme_void() +
theme(
legend.position = "none",
plot.margin = unit(c(0, 0, 0, 0), "cm")
) +
expand_limits(x = c(-3, 3), y = c(-3, 3))
ggsave(filename = "dnf_plot.svg", plot = dnf_plot,path = here::here("2_plots/"),width = 40, height = 40, units = "cm")
# wrap_plots(list(combine_ipaPath, dnf_plot))
#
dnf_plot
| Version | Author | Date |
|---|---|---|
| 5dce909 | Ha Tran | 2024-11-07 |
INT vs CONT
Network plot
DE genes regulated by predicted upstream
regulators and pathways
Alluvial plot
Proportion of DE genes regulated by predicted
upstream regulators & functional terms
Extra figures
Supp Fig 1
patch <- readRDS(here::here("0_data/functions/patch.rds"))
enrichGO_sig <- readRDS(here::here("0_data/RDS_objects/enrichGO_sig_new.rds"))
collatedPath <- read_excel_allsheets(here::here("0_data/raw_data/Immune-related enriched terms GO Kegg reactome KF_15102024.xlsx"))
collatedPath <- collatedPath$`Collated - all databases` %>%
dplyr::filter(if_any(c(`Signigifcant in comparison`, Also, `Also 2`), ~ . == "INT vs SVX/VAS")) %>% dplyr::mutate(`Pathway description` = `Pathway description`%>% firstCap() %>% str_wrap(width = 45))
go <- collatedPath %>% filter(str_detect(Database,"GO")) %>%
dplyr::filter(`Pathway description` %in% enrichGO_sig$`INT vs SVX_VAS`$Description)
# combine all df in list into one df
go_dot_all <- as.data.frame(do.call(rbind, enrichGO_sig[c(2,3,4)])) %>%
rownames_to_column("group") %>%
dplyr::filter(Description %in% go$`Pathway description`)
# clean group names and change to factor
go_dot_all$group <- gsub(pattern = "\\..*", "", go_dot_all$group) %>% as.factor()
# go_subset <- lapply(enrichGO_sig, function(x) { x %>% filter(Description %in% go_dot_all$Description)})
# factor the descriptions
top <- as.data.frame(do.call(rbind, lapply(enrichGO_sig[c(2, 3, 4)], function(x) {
x %>% dplyr::filter(Description %in% go_dot_all$Description) %>%
arrange(p.adjust) %>%
dplyr::slice(1:40) %>%
dplyr::select(3)
}))) %>% rownames_to_column("group")
top <- melt(top, "group")
terms <- top$value %>% as.factor() %>% levels()
go_dot_all <- go_dot_all[go_dot_all$Description %in% terms,]
go_dot_all$group <- gsub(pattern = " vs SVX_VAS", "", go_dot_all$group) %>% as.factor()
go_dot_all$group <- factor(go_dot_all$group,levels = c("INT", "SVX", "VAS"))
go_dot_all$Description <- go_dot_all$Description %>% str_wrap(38)
combine_go_1 <- ggplot(go_dot_all[1:25,]) +
geom_point(aes(x = group, y = reorder(Description, logFDR), colour = logFDR, size = Count, shape = ONTOLOGY %>% as.factor())) +
scale_color_gradientn(colors = rev(c("#FB8072","#FDB462","#8DD3C7","#80B1D3")),
values = scales::rescale(c(min(go_dot_all$logFDR), max(go_dot_all$logFDR))),
limits = c(min(go_dot_all$logFDR), max(go_dot_all$logFDR)),
breaks = scales::pretty_breaks(n = 5)) +
scale_x_discrete(drop = F)+
scale_size(range = c(2,8), limits = c(min(go_dot_all$Count), max(go_dot_all$Count))) +
labs(x = "", y = "", color = expression("-log"[10] * "FDR"), size = "Counts", shape = "Ontology")+
bossTheme(base_size = 14,legend = "right")
combine_go_2 <- ggplot(go_dot_all[26:62,]) +
geom_point(aes(x = group, y = reorder(Description, logFDR), colour = logFDR, size = Count, shape = ONTOLOGY %>% as.factor())) +
scale_color_gradientn(colors = rev(c("#FB8072","#FDB462","#8DD3C7","#80B1D3")),
values = scales::rescale(c(min(go_dot_all$logFDR), max(go_dot_all$logFDR))),
limits = c(min(go_dot_all$logFDR), max(go_dot_all$logFDR)),
breaks = scales::pretty_breaks(n = 5)) +
scale_x_discrete(drop = F)+
scale_size(range = c(2,8), limits = c(min(go_dot_all$Count), max(go_dot_all$Count))) +
labs(x = "", y = "", color = expression("-log"[10] * "FDR"), size = "Counts", shape = "Ontology")+
bossTheme(base_size = 14,legend = "right")
combine_go <- list(combine_go_1,combine_go_2) %>% patch(.,legend = "bottom")
# if(savePlots == TRUE) {
# ggsave(filename = paste0("immune_go.svg"), plot = combine_go, path = here::here("2_plots/4_paper"), width = 20, height = 25, units = "cm")
# }
combine_go
| Version | Author | Date |
|---|---|---|
| d519e7f | Ha Tran | 2024-12-03 |
# ggsave(filename = paste0("immune_go.svg"), plot = combine_go, path = here::here("2_plots/4_paper"), width = 33, height = 45, units = "cm")Supp Fig 2
enrichKEGG_sig <- readRDS(here::here("0_data/RDS_objects/enrichKEGG_sig.rds"))
# combine all df in list into one df
kegg_dot_all <- as.data.frame(do.call(rbind, enrichKEGG_sig[c(2,3,4)])) %>%
rownames_to_column("group")
# kegg_dot_all <- kegg_dot_all[! kegg_dot_all$group %in% c("DT+Treg vs veh"),]
# clean group names and change to factor
kegg_dot_all$group <- gsub(pattern = "\\..*", "", kegg_dot_all$group) %>% as.factor()
kegg_dot_all$group <- gsub(pattern = " vs SVX_VAS", "", kegg_dot_all$group) %>% as.factor()
kegg_dot_all$group <- factor(kegg_dot_all$group, levels = c("INT", "SVX", "VAS"))
# kegg_dot_all$group <- factor(kegg_dot_all$group,levels = c("DT vs veh", "DT+Treg vs veh", "DT+Treg vs DT" ))
kegg_dot_all$Description <- kegg_dot_all$Description %>% str_wrap(38)
combine_kegg <- ggplot(kegg_dot_all) +
geom_point(aes(x = group, y = reorder(Description, logFDR), colour = logFDR, size = Count)) +
scale_color_gradientn(colors = rev(c("#FB8072","#FDB462","#8DD3C7","#80B1D3")),
values = scales::rescale(c(min(kegg_dot_all$logFDR), max(kegg_dot_all$logFDR))),
breaks = scales::pretty_breaks(n = 5)) +
scale_x_discrete(drop = F)+
scale_size(range = c(2,8),limits = c(2, 11)) +
labs(x = "", y = "", color = expression("-log"[10] * "FDR"), size = "Counts")+
bossTheme(base_size = 14,legend = "right")
if(savePlots == TRUE) {
ggsave(filename = paste0("combine_kegg_dot.svg"), plot = combine_kegg, path = here::here("2_plots/3_FA/kegg/"),
width = 18, height = 20, units = "cm")
}
# combine_keggreactome_sig <- readRDS(here::here("0_data/RDS_objects/reactome_sig.rds"))
react <- collatedPath %>% filter(str_detect(Database,"Reactome")) %>%
filter(`Pathway description` %in% reactome_sig$`INT vs SVX_VAS`$Description)
# combine all df in list into one df
react_dot_all <- as.data.frame(do.call(rbind, reactome_sig[c(2,3,4)])) %>%
rownames_to_column("group") %>%
filter(Description %in% react$`Pathway description`)
# clean group names and change to factor
react_dot_all$group <- gsub(pattern = "\\..*", "", react_dot_all$group) %>% as.factor()
# react_subset <- lapply(reactome_sig, function(x) { x %>% filter(Description %in% react_dot_all$Description)})
# factor the descriptions
top <- do.call(rbind, lapply(reactome_sig[c(2, 3, 4)], function(x) {
x %>% as.data.frame() %>%
filter(Description %in% react_dot_all$Description) %>%
arrange(p.adjust) %>%
dplyr::select(1)
})) %>% rownames_to_column("group")
top <- melt(top, "group")
terms <- top$value %>% as.factor() %>% levels()
react_dot_all <- react_dot_all[react_dot_all$Description %in% terms,]
react_dot_all$group <- gsub(pattern = " vs SVX_VAS", "", react_dot_all$group) %>% as.factor()
react_dot_all$group <- factor(react_dot_all$group,levels = c("INT", "SVX", "VAS"))
react_dot_all$Description <- react_dot_all$Description %>% str_wrap(38)
combine_react <- ggplot(react_dot_all) +
geom_point(aes(x = group, y = reorder(Description, logFDR), colour = logFDR, size = Count)) +
scale_color_gradientn(colors = rev(c("#FB8072","#FDB462","#8DD3C7","#80B1D3")),
values = scales::rescale(c(min(react_dot_all$logFDR), max(react_dot_all$logFDR))),
limits = c(min(react_dot_all$logFDR), max(react_dot_all$logFDR)),
breaks = scales::pretty_breaks(n = 5)) +
scale_x_discrete(drop = F)+
scale_size(range = c(2,8), limits = c(2,11)) +
labs(x = "", y = "", color = expression("-log"[10] * "P value"), size = "Counts")+
bossTheme(base_size = 14,legend = "right")
# combine_react
# if(savePlots == TRUE) {
# ggsave(filename = paste0("immune_react.svg"), plot = combine_react, path = here::here("2_plots/4_paper"), width = 20, height = 25, units = "cm")
# }
# ggsave(filename = paste0("immune_react.svg"), plot = combine_react, path = here::here("2_plots/4_paper"), width = 33, height = 45, units = "cm")vol <- readRDS(here::here("0_data/RDS_plots/vol_plots.rds"))
patch(list(combine_kegg, combine_react),ncol = 2) 
| Version | Author | Date |
|---|---|---|
| d519e7f | Ha Tran | 2024-12-03 |
# %>% ggsave(plot = .,filename = "kegg_react.svg", path = here::here("2_plots/4_paper/"), width = 33, height = 15, units = "cm")
design = "
AABB
CCDD
CCDD
"
patch(list(vol[[3]], vol[[4]]),ncol = 2)
| Version | Author | Date |
|---|---|---|
| d519e7f | Ha Tran | 2024-12-03 |
# %>% ggsave(plot = .,filename = "volcanos.svg", path = here::here("2_plots/4_paper/"), width = 33, height = 30, units = "cm")
# ggsave(plot = vol[[3]] + theme(legend.position = "right"),filename = "volcano_SVX.svg", path = here::here("2_plots/4_paper/"), width = 11, height = 11, units = "cm")
# ggsave(plot = vol[[4]] + theme(legend.position = "right"),filename = "volcano_VAS.svg", path = here::here("2_plots/4_paper/"), width = 11, height = 11, units = "cm")Fig 2
genes <- c("Trdc", "Trgc1", "Cd3e", "Trbc2", "Cd3g",
"Lat", "Itk", "Grap2", "Blk", "Cd226", "Prkcq", "Havcr2", "Rasgrp1", "Itgal", "Btla",
"Scart1", "Rorc", "Irf4",
"Cxcr6", "Cxcr3", "Sell",
"Il7r", "Il12rb1", "Il18rap")
degs <- do.call(rbind, lapply(lm_all[c(2,3,4)], function(x){
x %>% as.data.frame() %>%
dplyr::select(c("gene_name","logFC","AveExpr","description", "P.Value","adj.P.Val","entrezid","expression")) %>%
dplyr::filter(gene_name %in% genes)
})) %>% rownames_to_column("group")
degs$group <- gsub(pattern = "\\..*", "", degs$group) %>% as.factor()
degs$group <- gsub(pattern = " vs SVX_VAS", "", degs$group) %>% as.factor()
degs$group <- factor(degs$group, levels = c("INT", "SVX", "VAS"))
degs_mat <- degs %>% dplyr::select(c("gene_name", "group", "logFC")) %>% dplyr::arrange(factor(gene_name, levels = genes)) %>%
pivot_wider(.,names_from = "group", values_from = "logFC") %>% column_to_rownames("gene_name")
degs_anno <- degs %>% dplyr::select(c("gene_name", "group", "adj.P.Val")) %>% dplyr::arrange(factor(gene_name, levels = genes)) %>%
pivot_wider(.,names_from = "group", values_from = "adj.P.Val") %>% column_to_rownames("gene_name") %>%
dplyr::mutate(across(where(is.numeric), ~ as.character(signif(., 3)))) %>%
dplyr::mutate(across(c(INT, SVX, VAS), ~ case_when(
as.numeric(.) < 0.00001 ~ "****",
as.numeric(.) < 0.0001 ~ "***",
as.numeric(.) < 0.001 ~ "**",
as.numeric(.) < 0.01 ~ "*",
TRUE ~ as.character(.)
)))
anno_degs <- tibble("name" = rownames(degs_mat), "Annotation" = c(rep("TCR complex", 5),
rep("TCR signaling", 10),
rep("Lymphocyte\ndifferentiation/\nmaturation", 3),
rep("Cell homing\nand migration", 3),
rep("Cytokine signaling", 3))) %>%
dplyr::mutate(Annotation = factor(Annotation, levels = c("TCR complex",
"TCR signaling",
"Lymphocyte\ndifferentiation/\nmaturation",
"Cell homing\nand migration",
"Cytokine signaling")))
# colorRampPalette(rev(c("#FB8072","#FDB462","#ffffd5","#8DD3C7","#80B1D3")))(300)
anno_colours <- colorRampPalette(brewer.pal(5, "Set1"))(length(levels(anno_degs$Annotation)))
names(anno_colours) <- levels(anno_degs$Annotation)
degs_plot <- ComplexHeatmap::pheatmap(
mat = degs_mat %>% as.matrix(),
color = colorRampPalette(rev(c("#FB8072","#FDB462","#ffffd5","#8DD3C7","#80B1D3")))(300),
scale = "row",
display_numbers = degs_anno %>% as.matrix(),
cluster_cols = F,
border_color = "white",
show_colnames = T,
cluster_rows = F,
gaps_col = c(1,2),
gaps_row = c(5,15,18,21),
legend = T,
heatmap_legend_param = list(title = "Fold change Z-score",
direction= "horizontal",
merge_legend = T,
legend_direction = "horizontal",
legend_width = unit(5, "cm")),
# Annotation
annotation_legend = F,
# legend_labels = T,
annotation_row = anno_degs %>% remove_rownames() %>% column_to_rownames("name"),
annotation_colors = list("Annotation" = anno_colours),
annotation_names_row = F,
# annotation_legend_param = list(direction = "horizontal"),
fontfamily = "Arial",
fontsize = 12,
fontsize_col = 12,
fontsize_number = 12,
fontsize_row = 12, angle_col = "0",
labels_row = as.expression(lapply(rownames(degs_mat), function(a) bquote(italic(.(a)))))
) %>% as.ggplot()
ggsave(plot = degs_plot ,filename = "subset_degs.svg", path = here::here("2_plots/4_paper/"), width = 18, height = 25, units = "cm")
degs_plot
| Version | Author | Date |
|---|---|---|
| d519e7f | Ha Tran | 2024-12-03 |
# draw(degs_plot, merge_legend = F, heatmap_legend_side = "bottom", annotation_legend_side = "right" )
# if (savePlots == T) {
# svg(filename = here::here("../2_plots/4_paper/selected_degs.svg"),width = 18,height = 25)
# draw(heat_combined, merge_legend = T, heatmap_legend_side = "bottom",
# annotation_legend_side = "bottom")
# dev.off()
# }
sessionInfo()R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS 26.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
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: Australia/Adelaide
tzcode source: internal
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ggraph_2.2.2 stringi_1.8.7 knitr_1.50
[4] pandoc_0.2.0 ggrepel_0.9.6 ggbiplot_0.6.2
[7] ggplotify_0.1.3 RColorBrewer_1.1-3 ggalluvial_0.12.5
[10] igraph_2.1.4 viridis_0.6.5 viridisLite_0.4.2
[13] cowplot_1.2.0 pander_0.6.6 kableExtra_1.4.0
[16] VennDiagram_1.7.3 futile.logger_1.4.3 patchwork_1.3.2
[19] readxl_1.4.5 extrafont_0.19 DT_0.34.0
[22] ComplexHeatmap_2.22.0 pheatmap_1.0.13 lubridate_1.9.4
[25] forcats_1.0.0 stringr_1.5.2 dplyr_1.1.4
[28] purrr_1.1.0 tidyr_1.3.1 ggplot2_4.0.0
[31] tidyverse_2.0.0 reshape2_1.4.4 tibble_3.3.0
[34] readr_2.1.5 magrittr_2.0.4 showtext_0.9-7
[37] showtextdb_3.0 sysfonts_0.8.9
loaded via a namespace (and not attached):
[1] gridExtra_2.3 formatR_1.14 rlang_1.1.6
[4] clue_0.3-66 GetoptLong_1.0.5 git2r_0.36.2
[7] matrixStats_1.5.0 compiler_4.4.1 png_0.1-8
[10] systemfonts_1.2.3 vctrs_0.6.5 pkgconfig_2.0.3
[13] shape_1.4.6.1 crayon_1.5.3 fastmap_1.2.0
[16] magick_2.9.0 labeling_0.4.3 promises_1.3.3
[19] rmarkdown_2.29 tzdb_0.5.0 ggbeeswarm_0.7.2
[22] ragg_1.5.0 xfun_0.53 cachem_1.1.0
[25] jsonlite_2.0.0 later_1.4.4 tweenr_2.0.3
[28] parallel_4.4.1 cluster_2.1.8.1 R6_2.6.1
[31] bslib_0.9.0 extrafontdb_1.0 jquerylib_0.1.4
[34] cellranger_1.1.0 Rcpp_1.1.0 iterators_1.0.14
[37] IRanges_2.40.1 httpuv_1.6.16 timechange_0.3.0
[40] tidyselect_1.2.1 rstudioapi_0.17.1 yaml_2.3.10
[43] doParallel_1.0.17 codetools_0.2-20 plyr_1.8.9
[46] withr_3.0.2 S7_0.2.0 ggrastr_1.0.2
[49] evaluate_1.0.5 gridGraphics_0.5-1 lambda.r_1.2.4
[52] polyclip_1.10-7 xml2_1.4.0 circlize_0.4.16
[55] pillar_1.11.1 whisker_0.4.1 foreach_1.5.2
[58] stats4_4.4.1 generics_0.1.4 rprojroot_2.1.1
[61] S4Vectors_0.44.0 hms_1.1.3 scales_1.4.0
[64] glue_1.8.0 tools_4.4.1 graphlayouts_1.2.2
[67] fs_1.6.6 tidygraph_1.3.1 Cairo_1.6-5
[70] Rttf2pt1_1.3.12 colorspace_2.1-1 beeswarm_0.4.0
[73] ggforce_0.5.0 vipor_0.4.7 cli_3.6.5
[76] rappdirs_0.3.3 textshaping_1.0.3 workflowr_1.7.2
[79] futile.options_1.0.1 svglite_2.2.1 gtable_0.3.6
[82] yulab.utils_0.2.1 sass_0.4.10 digest_0.6.37
[85] BiocGenerics_0.52.0 rjson_0.2.23 htmlwidgets_1.6.4
[88] farver_2.1.2 memoise_2.0.1 htmltools_0.5.8.1
[91] lifecycle_1.0.4 here_1.0.2 GlobalOptions_0.1.2
[94] MASS_7.3-65