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Data Setup

# working with data
library(dplyr)
library(magrittr)
library(readr)
library(tibble)
library(reshape2)
library(tidyverse)

# Visualisation:
library(kableExtra)
library(ggplot2)
library(grid)
library(DT)
library(extrafont)
library(VennDiagram)
# Custom ggplot
library(gridExtra)
library(ggbiplot)
library(ggrepel)
library(rrvgo)
library(d3treeR)
library(plotly)
library(GOSemSim)
library(data.table)

# Bioconductor packages:
library(edgeR)
library(limma)
library(Glimma)
library(clusterProfiler)
library(org.Mm.eg.db)
library(enrichplot)
library(patchwork)

library(pandoc)
library(knitr)

# 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
}

Gene ontology (GO) Analysis

Functional enrichment analysis is a method used to identify biological functions or processes overrepresented in a set of genes or proteins.

Gene Ontology (GO) is a standardized system for annotating genes and their products with terms from a controlled vocabulary, organized into three main categories: Molecular Function, Biological Process, and Cellular Component.

Biological Process (BP): Describes the larger, coordinated biological events or processes in which a gene product is involved. This category represents a series of molecular events that contribute to a specific function.
Molecular Function (MF): Describes the specific molecular activities that a gene product performs, such as catalytic or binding activities.
Cellular Component (CC): Describes the location or structure within the cell where a gene product is active, such as the nucleus, cytoplasm, or membrane.

Each of these three main categories is further organized into a hierarchical structure with more specific terms. The terms become more specialized as you move down the hierarchy (ontology level). Comparing a gene list to a reference database offers critical insights into the biological significance of gene expression changes.

enrichGO <- readRDS(here::here("0_data/RDS_objects/enrichGO_new.rds"))
enrichGO_sig <- readRDS(here::here("0_data/RDS_objects/enrichGO_sig_new.rds"))

# `goSummaries` is a package created by Dr Stevie Pederson for filtering GO terms based on ontology level.
goSummaries <- url("https://uofabioinformaticshub.github.io/summaries2GO/data/goSummaries.RDS") %>%
  readRDS()
minPath <- 3
rm(comp)
mmGO <- lapply(c("BP","MF","CC"), function(ont){godata('org.Mm.eg.db', ont=ont)}) %>% setNames(c("BP","MF","CC"))

enrichGO_new=list()
enrichGO_sig_new <- list()

for (comp in Comp) {
  # find enriched GO terms
  enrichGO_new[[comp]] <- clusterProfiler::enrichGO(
    gene =lm_sig[[comp]]$entrezid,
    universe = lm_all[[comp]]$entrezid,
    OrgDb = org.Mm.eg.db,
    keyType = "ENTREZID",
    ont = "ALL",
    pAdjustMethod = "fdr",
    pvalueCutoff = 0.05
  )
}

for (comp in Comp) {
  enrichGO_new[[comp]] <- pairwise_termsim(enrichGO_new[[comp]], method = "Wang",semData = mmGO[[1]], showCategory = nrow(enrichGO_new[[comp]]@result))
  
}

for (comp in Comp) {
  # bind to goSummaries to elminate go terms with ontology levels 1 and 2.
  enrichGO_sig_new[[comp]] <- enrichGO_new[[comp]] %>% 
    clusterProfiler::setReadable(OrgDb = org.Mm.eg.db, keyType = "auto")
  
  enrichGO_sig_new[[comp]] <- enrichGO_sig_new[[comp]] %>%
    as.data.frame() %>%
    rownames_to_column("id") %>%
    left_join(goSummaries) %>%
    # dplyr::filter(shortest_path >= minPath) %>%
    column_to_rownames("id")
  
  # adjust go results, separate compound column, add FDR column, adjust the GeneRatio column
  enrichGO_sig_new[[comp]] <- enrichGO_sig_new[[comp]] %>%
    separate(col = BgRatio, sep = "/", into = c("Total", "Universe")) %>%
    dplyr::mutate(
      logFDR = -log(p.adjust, 10),
      GeneRatio = Count / as.numeric(Total))
  # %>% 
  #   dplyr::select(c("Description", "ontology", "GeneRatio", "pvalue", "p.adjust", "logFDR", "qvalue", "geneID", "Count"))
  
  
  enrichGO_sig_new[[comp]]$Description <- enrichGO_sig_new[[comp]]$Description %>% firstCap() %>% str_wrap(width = 45)
  
  
}

saveRDS(object = enrichGO_sig_new,file = here::here("0_data/RDS_objects/enrichGO_sig_new.rds"))
saveRDS(object = enrichGO_new,file = here::here("0_data/RDS_objects/enrichGO_new.rds"))

simMatrix <- list()
scores <- list()
reducedTerms <- list()

enrichGO <- enrichGO_new
enrichGO_sig <- enrichGO_sig_new

simMatrix[["BP"]] <- lapply(Comp[1:5], function(comp) {
    calculateSimMatrix(enrichGO[[comp]]@result$ID,
                       orgdb="org.Mm.eg.db",
                       ont="BP",
                       method="Wang",
                       semdata = mmGO[["BP"]])
  }) %>% setNames(Comp[1:5])


scores[["BP"]] <- lapply(enrichGO[1:5], function(x) {setNames(-log10(x@result$p.adjust), x@result$ID)}) %>% setNames(names(enrichGO[1:5]))

reducedTerms[["BP"]] <- lapply(Comp[1:5], function(comp) {
  reduced_tb <- reduceSimMatrix(simMatrix[["BP"]][[comp]],
                                scores[["BP"]][[comp]],
                                threshold=0.7,
                                orgdb="org.Mm.eg.db")
  
  reduced_tb$parentTerm <- reduced_tb$parentTerm %>% firstCap() %>% str_wrap(width = 45)
  reduced_tb$term <- reduced_tb$term %>% firstCap() %>% str_wrap(width = 35)
  return(reduced_tb)
}) %>% setNames(Comp[1:5])
    

semSim_df <- list()
reduced_semSim_df <- list()
for(ont in "BP") {
  for (comp in Comp[1:5]) {
    subset_df <- subset(reducedTerms[[ont]][[comp]], parent == rownames(reducedTerms[[ont]][[comp]]))

    parentTerm_size <- reducedTerms[[ont]][[comp]]$parentTerm %>% as.factor() %>% summary(500)
    semSim_df[[ont]][[comp]] <- cbind(subset_df, parentTerm_size)
  }
  
  reduced_semSim_df[[ont]] <- as.data.frame(do.call(rbind, semSim_df[[ont]])) %>%
    rownames_to_column("comparison")
  reduced_semSim_df[[ont]]$comparison <- gsub(pattern = "\\..*", "", reduced_semSim_df[[ont]]$comparison) %>% as.factor()
}

reducedTerms_all <- as.data.frame(do.call(rbind, reduced_semSim_df)) %>%
  rownames_to_column("ont")

reducedTerms_all$ont <- gsub(pattern = "\\..*", "", reducedTerms_all$ont) %>% as.factor()


saveRDS(simMatrix, here::here("0_data/RDS_objects/simMatrix_ora.rds"))
saveRDS(scores, here::here("0_data/RDS_objects/scores_ora.rds"))
saveRDS(reducedTerms, here::here("0_data/RDS_objects/reducedTerms_ora.rds"))
saveRDS(semSim_df, here::here("0_data/RDS_objects/semSim_df.rds"))
saveRDS(reduced_semSim_df, here::here("0_data/RDS_objects/reduced_semSim_df.rds"))
saveRDS(reducedTerms_all, here::here("0_data/RDS_objects/reducedTerms_all.rds"))

Visualisations

The following visualisations are GO enrichment analysis performed with set of DE genes significantly below FDR 0.1 without FC threshold (TREAT). IMPORTANTLY, these GO terms are all significantly enriched (FDR <0.05)

Dot plot: illustrates the top 25 enriched GO terms.
- \(Gene ratio =\) the number of significant DE gene in the term / the total of number of genes in the term. Indicated by the size
- The shapes represents the three main GO categories, either BP, MP, or CC
Table: list of all the significant GO terms
Upset: illustrate the overlap of gene between different functional terms

Semantic similarity plots - GO specific

Due to the hierarchical structure of Gene Ontologies, the enriched sets generated often exhibit redundancy and pose challenges in interpretation. The subsequent analyses and visualizations seek to alleviate this redundancy in GO sets by grouping comparable terms based on their semantic similarity. The underlying concept behind measuring semantic similarity is grounded in the idea that genes sharing similar functions should possess analogous annotation vocabulary and exhibit close relationships within the ontology structure.

NOTE: the following semantic similarity analyses are performed using Graph-based method (Wang et al. 2007)

Dendrogram plot: performs hierarchical clustering on the semantic similarity of GO terms.
- NOTE: to maintain readability, only the top 50 most significant GO terms are clustered. These clusters are then divided into 9 clades and labeled using the top 4 high-frequency words.
Scatter plot: illustrates the UMAP space between semantically similar significant GO terms
- Distances represent the similarity between terms,
- Size represents the significance (in \(-\log_{10}FDR\)))
- NOTE: to maintain reability, only the top 15 most significant parent terms are labeled. Parent terms are the most significant term in a particular cluster
Treemap plot: Visualise the of hierarchical structures of semantically similar GO terms.
- The terms are colored based on their parent term,
- The size of the term is proportional to the significance.

I recommend reading through the full list of significant GO terms and selecting the most biologically relevant for better visualisation

INT vs CONT

Dot plot

dot <- list()
tab <- list()
upset <- list()

for (comp in Comp) {
  if (nrow(enrichGO_sig[[comp]]) > 20) { n <- 20 } else { n <-  nrow(enrichGO_sig[[comp]]) }
  dot[[comp]] <- ggplot(enrichGO_sig[[comp]][1:n, ]) +
    geom_point(aes(x = GeneRatio, y = reorder(Description, GeneRatio), colour = logFDR, size = Count, shape = ontology %>% as.factor())) +
    scale_color_gradientn(colors = rev(c("#FB8072","#FDB462","#8DD3C7","#80B1D3")),
                          values = scales::rescale(c(min(enrichGO_sig[[comp]]$logFDR), max(enrichGO_sig[[comp]]$logFDR))),
                          breaks = scales::pretty_breaks(n = 5)) +
    scale_size(range = c(2,5)) +
    labs(x = "Gene ratio", y = "", color = expression("-log"[10] * "FDR"), size = "Gene Counts", shape = "Ontology")+
    bossTheme(base_size = 14,legend = "right")
  
  tab[[comp]] <- enrichGO_sig[[comp]] %>% 
    dplyr::mutate_if(is.numeric, funs(as.character(signif(.,3)))) %>% 
    DT(., caption = "Significantly enriched GO terms")
  
  
  upset[[comp]] <- upsetplot(x = enrichGO[[comp]], 10)
  
  # if(savePlots == TRUE) {
  #   ggsave(filename = paste0("dot_", comp, ".svg"), plot = dot[[comp]], path = here::here("2_plots/3_FA/go/"),
  #          width = 18, height = 20, units = "cm")
  #   ggsave(filename = paste0("upset_", comp, ".svg"), plot = upset[[comp]], path = here::here("2_plots/3_FA/go/"), width = 20, height = 14, units = "cm")

  # }
}

saveRDS(dot, here::here("0_data/rds_plots/go_dotPlot.rds"))

dot[[1]]

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

Table

tab[[1]]

Upset plot

upset[[1]]

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

Dendrogram

den <- lapply(enrichGO[1:5], function(x) {
  treeplot(x,showCategory = 50, fontsize = 3, cex_category = 0.5,
         cluster.params      = list(method = "ward.D", n = 9, label_words_n = 4, label_format = 30),
         hilight.params      = list(hilight = F, align = "both"),
         clusterPanel.params = list(clusterPanel = "heatMap", pie = "equal", legend_n = 3),
         offset.params       = list(bar_tree = rel(3), tiplab = rel(4), extend = 0.1, hexpand = 0.4)) +
  theme(legend.position = "bottom")
}) %>% setNames(enrichGO[1:5])

# saveRDS(den, here::here("0_data/rds_objects/ora_dendrogram.rds"))
# den <- readRDS(here::here("0_data/rds_objects/ora_dendrogram.rds"))

den[[6]] <- ggplot() + theme_void()
den[[1]]

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

for(i in 1:length(Comp[1:5])){
  if(savePlots == TRUE) {
    ggsave(filename = paste0("semSim_dendrogram_", Comp[i], ".svg"), plot = den[[i]], path = here::here("2_plots/3_FA/go/"),
           width = 20, height = 25, units = "cm")
  }
}

Scatter plot

simMatrix <- readRDS(here::here("0_data/rds_objects/simMatrix_ora.rds"))
scores <- readRDS(here::here("0_data/rds_objects/scores_ora.rds"))
reducedTerms <- readRDS(here::here("0_data/rds_objects/reducedTerms_ora.rds"))

revigo_dimReduction <- function(simMatrix, reducedTerms, algorithm = c("pca", "umap"), size = "score") {
  
  x <- switch(match.arg(algorithm), pca = cmdscale(as.matrix(as.dist(1 - simMatrix)), eig = TRUE, k = 3)$points, umap = umap::umap(as.matrix(as.dist(1 - simMatrix)),n_components = 3)$layout)
  df <- cbind(as.data.frame(x), reducedTerms[match(rownames(x), reducedTerms$go), c("term", "parent", "parentTerm", size)])
  
  return(df)
}
dimReduced <- list()
scatter <- list()
for (comp in Comp[c(1:3,5)]){
  set.seed(3)
  dimReduced[[comp]] <- revigo_dimReduction(simMatrix[["BP"]][[comp]], reducedTerms[["BP"]][[comp]], algorithm = "umap")
  
  m <- subset(dimReduced[[comp]], parent == rownames(dimReduced[[comp]]))[1:10,]
  
  scatter[[comp]] <- ggplot(dimReduced[[comp]], aes(x = V1, y = V2, color = parentTerm)) +
    geom_point(aes_string(size = "score"), alpha = 0.5, stroke = 0) + 
    scale_color_discrete(guide = "none") +
    # scale_fill_discrete(guide = "none") +
    scale_size_continuous(name = expression("-log"[10] * "FDR"), range = c(0,12)) + 
    scale_x_continuous(name = "UMAP1") +
    scale_y_continuous(name = "UMAP2") +
    guides(x = ggh4x::guide_axis_truncated(trunc_lower = unit(0, "npc"),trunc_upper = unit(3, "cm")), 
           y = ggh4x::guide_axis_truncated(trunc_lower = unit(0, "npc"),trunc_upper = unit(3, "cm"))) +
    geom_label_repel(aes(label = m$parentTerm),data = m, box.padding = grid::unit(1,"lines"), size = 3, label.size = 0.15) + 
    bossTheme(14) +
    theme(
      line = element_blank(),
      rect = element_blank(),
      panel.border = element_blank(),
      axis.text.x = element_blank(),
      axis.text.y = element_blank(),
      axis.ticks = element_blank(),
      legend.position = "right",
      axis.line = element_line(arrow = arrow()),
      axis.title = element_text(hjust = 0),
      axis.title.y = element_text(
        family = "Arial Narrow",
        face = "plain",
        size = 13,
        angle = 90,
        vjust = 1
      ),
      axis.title.x = element_text(
        family = "Arial Narrow",
        face = "plain",
        size = 13,
        angle = 0,
        vjust = 0
      )
    )
  
  if(savePlots == TRUE) {
    ggsave(filename = paste0("semSim_scatter_", comp, ".svg"), plot = scatter[[comp]], path = here::here("2_plots/3_FA/go/"),
           width = 18, height = 18, units = "cm")

  }
  
}

for(comp in Comp[c(4,6)]) {scatter[[comp]] <- ggplot() + theme_void()}


saveRDS(scatter, here::here("0_data/rds_plots/go_parTerm_scatter.rds"))

scatter[[Comp[1]]]

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

scatter_plotly <- list()
for (comp in Comp[c(1:3,5)]){
  scatter_plotly[[comp]] <- ggplotly(scatter[[comp]] + bossTheme(14)) %>% add_markers(size = 5) %>% layout(showlegend = FALSE)
}

for(comp in Comp[c(4,6)]) {scatter_plotly[[comp]] <- ggplot() + theme_void()}

scatter_plotly[[Comp[1]]]

3D Interactive scatter

scatter_3d <- list()

for(comp in Comp[c(1:3,5)]){
  scatter_3d[[comp]] <- plot_ly(dimReduced[[comp]], x = ~ V1, y = ~ V2, z = ~ V3, color = ~ parentTerm, size = ~ score,
  marker = list(symbol = 'circle', sizemode = 'diameter'),
  sizes = c(5, 70),
  text = ~paste('Term    :', term,'<br>P. Term:', parentTerm, '<br>Sig       :', score),
  hoverinfo = 'text') %>% 
  layout(showlegend = FALSE,
         title = 'Semantically similar GO terms',
         scene = list(xaxis = list(title = 'UMAP 1',
                      gridcolor = 'rgb(255, 255, 255)',
                      zerolinewidth = 1,
                      ticklen = 5,
                      gridwidth = 2),
               yaxis = list(title = 'UMAP 2',
                      gridcolor = 'rgb(255, 255, 255)',
                      zerolinewidth = 1,
                      ticklen = 5,
                      gridwith = 2),
               zaxis = list(title = 'UMAP 3',
                            gridcolor = 'rgb(255, 255, 255)',
                            zerolinewidth = 1,
                            ticklen = 5,
                            gridwith = 2)))
  
}
for(comp in Comp[c(4,6)]) {scatter_3d[[comp]] <- ggplot() + theme_void()}
scatter_3d[[Comp[1]]]

Parent terms

semSim_df <- readRDS(here::here("0_data/rds_objects/semSim_df.rds"))

semSim_dot <- lapply(Comp[1:5], function(comp){
  if (nrow(semSim_df[["BP"]][[comp]]) > 20) { n <- 20 } else { n <- nrow(semSim_df[["BP"]][[comp]]) }
  df <- semSim_df[["BP"]][[comp]][1:n,]
  df$parentTerm <- df$parentTerm %>% str_wrap(50)
  
  plot <- ggplot(df) +
    geom_point(aes(x = parentTerm_size, y = reorder(parentTerm, score), colour = score, size = parentTerm_size)) +
    scale_color_gradientn(colors = rev(c("#FB8072","#FDB462","#8DD3C7","#80B1D3")),
                          values = scales::rescale(c(min(df$score), max(df$score))),
                          breaks = scales::pretty_breaks(n = 5)) +
    scale_size(range = c(2,5), guide = F) +
    labs(x = "Term size", y = "", color = expression("-log"[10] * "FDR"))+
    bossTheme(base_size = 14,legend = "right")
  
  if(savePlots == TRUE) {
    ggsave(filename = paste0("parTerm_dot_", comp, ".svg"), plot = plot, path = here::here("2_plots/3_FA/go/"),
           width = 18, height = 20, units = "cm")
  }
  
  return(plot)
  
}) 
semSim_dot[["BP"]][[Comp[6]]] <- ggplot() + theme_void()

saveRDS(semSim_dot, here::here("0_data/rds_plots/go_parTerm_dotPlot.rds"))

semSim_dot[1]

[[1]]

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

Treemap plot

adjTreemap <- function (x, size = "score", title = "", ...) {
  treemap::treemap(
    x,
    index = c("parentTerm", "term"),
    vSize = size,
    type = "index",
    title = title,
    palette = "Set2",
    fontcolor.labels = c("grey85","#00000080"),
    bg.labels = 0,
    border.col = "grey10",
    border.lwds = c(1,0.5),
    fontfamily.labels = "Arial Narrow"
  )
}

adjTreemap(reducedTerms[[1]][[1]])

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

Interactive Tree

# png(filename=here::here("2_plots/tree.png"),width=20, height=20,units = "cm",res = 900)
# tree <- adjTreemap(reducedTerms)
# dev.off()

library(htmltools)

interactive_treemap <- function(x){
  browsable(
    tagList(
      tags$head(
        tags$style('text.label{font-size: 20px !important}')
      ),
      d3tree3(adjTreemap(x), rootname = "General")
    )
  )
}

interactive_treemap(reducedTerms[[1]][[4]])

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

## this function is basically creating chunks within chunks, and then
## I use results='asis' so that the html image code is rendered 
kexpand <- function(wd, ht, cap, res, echo) {
  cat(knit(text = knit_expand(text = 
     sprintf("```{r %s, results='%s', echo = '%s',fig.keep='all', fig.width=%s, fig.height=%s}\n.pl\n```", cap, res, echo, wd, ht)
)))}

special_kexpand <- function(wd, ht, cap, res, echo) {
  cat(knit(text = knit_expand(text = 
     sprintf("```{r %s, results='%s', echo = '%s',fig.keep='all', fig.width=%s, fig.height=%s}\ninteractive_treemap(reducedTerms[[1]][[i]])\n```", cap, res, echo, wd, ht)
)))}

# Loop through each FC value
headers <- Comp[1:5]
types <- c("Dot plot", "Table", "Upset plot", "Dendrogram", "Scatter", "Parent term", "Treemap")

for (i in 2:length(headers)) {
  cat(paste0("### ",headers[i],"{.tabset .tabset-pills} \n\n"))
  
  cat(paste0("#### ",types[[1]]," \n"))
  .pl <- dot[[Comp[i]]] 
  kexpand(wd = 8,ht = 10,cap = paste0("dot",i),res = "markup",echo = "TRUE")
  cat("\n\n")
  
  cat(paste0("#### ",types[[2]]," \n"))
  .pl <- tab[[Comp[i]]]
  kexpand(wd = 11,ht = 8,cap = paste0("tab",i), res = "markup",echo = "TRUE")
  cat("\n\n")
  
  cat(paste0("#### ",types[[3]]," \n"))
  .pl <- upset[[Comp[i]]] 
  kexpand(wd = 11,ht = 9,cap = paste0("upset",i),res = "markup",echo = "TRUE")
  cat("\n\n")
  
  cat(paste0("#### ",types[[4]]," \n"))
  .pl <- den[[Comp[i]]] 
  kexpand(wd = 8,ht = 11,cap = paste0("den",i),res = "markup",echo = "TRUE")
  cat("\n\n")
  
  cat(paste0("#### ",types[[5]]," \n"))
  .pl <- scatter[[Comp[i]]] 
  kexpand(wd = 8,ht = 8,cap = paste0("scatter",i),res = "markup",echo = "TRUE")
  cat("\n\n")
  
  cat(paste0("Interactive Scatter \n"))
  .pl <- scatter_plotly[[Comp[i]]] 
  kexpand(wd = 9,ht = 9,cap = paste0("scatter_interactive",i),res = "markup",echo = "TRUE")
  cat("\n\n")
  
  cat(paste0("3D scatter \n"))
  .pl <- scatter_3d[[Comp[i]]] 
  kexpand(wd = 9,ht = 9,cap = paste0("scatter_3d",i),res = "markup",echo = "TRUE")
  cat("\n\n")
  
  cat(paste0("#### ",types[[6]]," \n"))
  .pl <- semSim_dot[[Comp[i]]]
  kexpand(wd = 9,ht = 12 ,cap = paste0("parentTerm",i),res = "markup",echo = "TRUE")
  cat("\n\n")
  
  cat(paste0("#### ",types[[7]]," \n"))
  # special_kexpand(wd = 9,ht = 9,cap = paste0("treemap",i),res = "hide",echo = "FALSE")
  # cat("\n\n")
  
  # cat(paste0("Interactive Treemap\n"))
  # .pl <- interactive_treemap(reducedTerms[[i]])
  special_kexpand(wd = 9,ht = 9,cap = paste0("treemap",i),res = "markup",echo = "FALSE")
  cat("\n\n")
}

INT vs SVX_VAS

Dot plot

| | | 0% | |………………………………………………………| 100% [dot2]

.pl

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

Table

| | | 0% | |………………………………………………………| 100% [tab2]

.pl

Upset plot

| | | 0% | |…………………………………………………….| 100% [upset2]

.pl

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

Dendrogram

| | | 0% | |………………………………………………………| 100% [den2]

.pl

NULL

Scatter

| | | 0% | |…………………………………………………..| 100% [scatter2]

.pl

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

Interactive Scatter | | | 0% | |………………………………………..| 100% [scatter_interactive2]

.pl

3D scatter | | | 0% | |………………………………………………..| 100% [scatter_3d2]

.pl

Parent term

| | | 0% | |………………………………………………..| 100% [parentTerm2]

.pl

NULL

Treemap

| | | 0% | |…………………………………………………..| 100% [treemap2]

interactive_treemap(reducedTerms[[1]][[i]])

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

SVX vs SVX_VAS

Dot plot

| | | 0% | |………………………………………………………| 100% [dot3]

.pl

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

Table

| | | 0% | |………………………………………………………| 100% [tab3]

.pl

Upset plot

| | | 0% | |…………………………………………………….| 100% [upset3]

.pl

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

Dendrogram

| | | 0% | |………………………………………………………| 100% [den3]

.pl

NULL

Scatter

| | | 0% | |…………………………………………………..| 100% [scatter3]

.pl

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

Interactive Scatter | | | 0% | |………………………………………..| 100% [scatter_interactive3]

.pl

3D scatter | | | 0% | |………………………………………………..| 100% [scatter_3d3]

.pl

Parent term

| | | 0% | |………………………………………………..| 100% [parentTerm3]

.pl

NULL

Treemap

| | | 0% | |…………………………………………………..| 100% [treemap3]

interactive_treemap(reducedTerms[[1]][[i]])

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

VAS vs SVX_VAS

Dot plot

| | | 0% | |………………………………………………………| 100% [dot4]

.pl

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

Table

| | | 0% | |………………………………………………………| 100% [tab4]

.pl

Upset plot

| | | 0% | |…………………………………………………….| 100% [upset4]

.pl

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

Dendrogram

| | | 0% | |………………………………………………………| 100% [den4]

.pl

NULL

Scatter

| | | 0% | |…………………………………………………..| 100% [scatter4]

.pl

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

Interactive Scatter | | | 0% | |………………………………………..| 100% [scatter_interactive4]

.pl

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

3D scatter | | | 0% | |………………………………………………..| 100% [scatter_3d4]

.pl

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

Parent term

| | | 0% | |………………………………………………..| 100% [parentTerm4]

.pl

NULL

Treemap

| | | 0% | |…………………………………………………..| 100% [treemap4]

interactive_treemap(reducedTerms[[1]][[i]])

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

SVX_VAS vs CONT

Dot plot

| | | 0% | |………………………………………………………| 100% [dot5]

.pl

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

Table

| | | 0% | |………………………………………………………| 100% [tab5]

.pl

Upset plot

| | | 0% | |…………………………………………………….| 100% [upset5]

.pl

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

Dendrogram

| | | 0% | |………………………………………………………| 100% [den5]

.pl

NULL

Scatter

| | | 0% | |…………………………………………………..| 100% [scatter5]

.pl

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

Interactive Scatter | | | 0% | |………………………………………..| 100% [scatter_interactive5]

.pl

3D scatter | | | 0% | |………………………………………………..| 100% [scatter_3d5]

.pl

Parent term

| | | 0% | |………………………………………………..| 100% [parentTerm5]

.pl

NULL

Treemap

| | | 0% | |…………………………………………………..| 100% [treemap5]

interactive_treemap(reducedTerms[[1]][[i]])

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

Combined

Venn diagram

Dot plot

# combine all df in list into one df
go_dot_all <- as.data.frame(do.call(rbind, enrichGO_sig)) %>%
  rownames_to_column("group")

# clean group names and change to factor
go_dot_all$group <- gsub(pattern = "\\..*", "", go_dot_all$group) %>% as.factor()

# factor the descriptions
top10 <- as.data.frame(do.call(rbind, lapply(enrichGO_sig, "[", 1:15,3))) %>% rownames_to_column("group")
top10 <- reshape2::melt(top10, "group")
terms <- top10$value %>% as.factor() %>% levels()
go_dot_all <- go_dot_all[go_dot_all$Description %in% terms,]
go_dot_all$group <- factor(go_dot_all$group,levels = c("INT vs CONT", "INT vs SVX_VAS","SVX vs SVX_VAS",
                                                       "VAS vs SVX_VAS","SVX_VAS vs CONT", "INT vs VAS"))
go_dot_all$Description <- go_dot_all$Description %>% str_wrap(55)
combine_go <- ggplot(go_dot_all) +
    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))),
                          breaks = scales::pretty_breaks(n = 5)) +
    scale_size(range = c(2,5)) +
    labs(x = "", y = "", color = expression("-log"[10] * "FDR"), size = "Counts", shape = "Ontology")+
    bossTheme(base_size = 14,legend = "right")
  
  
  if(savePlots == TRUE) {
    ggsave(filename = paste0("combine_go_dot.svg"), plot = combine_go, path = here::here("2_plots/3_FA/go/"),
           width = 20, height = 25, units = "cm")
  }

saveRDS(combine_go, here::here("0_data/rds_plots/go_combined_dotPlot.rds"))


combine_go

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

Parent term Venn

 # cat.pos = c(-27, 27, 135),
 #               cat.dist = c(0.055, 0.055, 0.085)
futile.logger::flog.threshold(futile.logger::ERROR, name = "VennDiagramLogger")

NULL

# INT vs CONT SVX_VAS vs CONT  INT vs SVX_VAS  SVX vs SVX_VAS 
#       "#BEBADA"       "#FFFFB3"       "#8DD3C7"       "#FB8072" 
#  VAS vs SVX_VAS      INT vs VAS 
#       "#FDB462"       "#FCCDE5" 
cat.pos <- list(BP =c(0,0,180),
                MF =c(-170,170,0),
                CC =c(-27, 27, 135))
cat.dist <- list(BP =c(0.05, .05, .05),
                 MF =c(0.085, 0.06, 0.085),
                 CC =c(0.055, 0.055, 0.085))

for(ont in "BP"){
  venn.diagram(x = list("INT vs SVX_VAS"   = reducedTerms[[ont]][[2]]$parentTerm %>% as.character(), 
                        "SVX vs SVX_VAS" = reducedTerms[[ont]][[3]]$parentTerm %>% as.character(),
                        "VAS vs SVX_VAS"  = reducedTerms[[ont]][[4]]$parentTerm %>% as.character()),
               filename = here::here(paste0("docs/assets/go_parTerm_",ont,"_venn.png")),
               lwd = 2,
               disable.logging = T,
               fill = compColour[c(3,4,5)],
               alpha = 0.75,
               lty = 'blank',
               imagetype = "png",
               # main = paste0(ont, "parent terms"),
               
               # Numbers
               cex = 2,
               fontface = "plain",
               fontfamily = "Arial Narrow",
               
               # Set names
               cat.cex = 2,
               cat.fontface = "bold",
               cat.fontfamily = "Arial Narrow",
               cat.default.pos = "outer",
               cat.pos = cat.pos[[ont]],
               cat.dist = cat.dist[[ont]]
  )
}

Biological Processes parent terms

Parent term dot

reducedTerms_all <- readRDS(here::here("0_data/rds_objects/reducedTerms_all.rds"))
reducedTerms_all_BP <- reducedTerms_all[!reducedTerms_all$ont %in% c("MF", "CC"),] 
reducedTerms_all_BP <- reducedTerms_all_BP[reducedTerms_all_BP$comparison %in% c(Comp[c(2,3,4)]),] %>% dplyr::arrange(parentTerm)
reducedTerms_all_BP$parentTerm <- reducedTerms_all_BP$parentTerm %>% str_wrap(38)

dotplot <- function(data){
  ggplot(data) +
    geom_point(aes(x = comparison, y = parentTerm, colour = score, size = parentTerm_size), alpha=0.8) +
    scale_color_gradientn(colours = rev(c("#FB8072","#FDB462","#8DD3C7","#80B1D3")),
                          limits = c(min(reducedTerms_all_BP$score), max(reducedTerms_all_BP$score)),
                          breaks = scales::pretty_breaks(n = 5)) +
    scale_size(range = c(2,5),limits = c(min(reducedTerms_all_BP$parentTerm_size), max(reducedTerms_all_BP$parentTerm_size))) +
    labs(x = "", y = "", color = expression("-log"[10] * "FDR"), size = "Term size")+
    bossTheme(base_size = 14,legend = "bottom")
}


t <- wrap_plots(list(dotplot(reducedTerms_all_BP[1:32, ]), dotplot(reducedTerms_all_BP[32:nrow(reducedTerms_all_BP), ]))) + 
  plot_layout(guides = "collect") &
  bossTheme(base_size = 12, legend = "none") &
  theme(legend.box.margin = margin(-5, 0, 0, -50, unit = "mm"),
        # plot.margin = margin(1, 1, 1, 0, unit = "pt"),
        axis.text.y = element_text(family = "Arial Narrow", face = "plain", size = 10, hjust = 1),
        axis.text.x = element_text(family =  "Arial", face = "plain", size = 11, hjust = 1, vjust = 1, angle = 40))

t

Biological processes parent terms

Version	Author	Date
d71eeb4	Ha Tran	2024-10-16

saveRDS(t, here::here("0_data/rds_plots/go_combined_parTerm_dotPlot.rds"))

ggsave(filename = "parentTerm_all.png", plot = t, path = here::here("2_plots/3_FA/go/"),
           width = 21, height = 27, units = "cm")

Export Data

The following are exported:

GO.xlsx - This spreadsheet contains all significantly enriched GO terms. NOTE:

# save to excel
writexl::write_xlsx(x = lapply(enrichGO_sig[c(2,3,4)], function(x) {x %>% dplyr::select(c(2,1,3:11,19,12:14))}) , here::here("3_output/Gene Ontology.xlsx"))
writexl::write_xlsx(x = reducedTerms[["BP"]], here::here("3_output/semSim_GO_sig.xlsx"))

# saveRDS(object = enrichGO_sig,file = here::here("0_data/RDS_objects/enrichGO_sig.rds"))
# saveRDS(object = enrichGO,file = here::here("0_data/RDS_objects/enrichGO.rds"))

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] stats4    grid      stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] htmltools_0.5.8.1      knitr_1.50             pandoc_0.2.0          
 [4] patchwork_1.3.2        enrichplot_1.26.6      org.Mm.eg.db_3.20.0   
 [7] AnnotationDbi_1.68.0   IRanges_2.40.1         S4Vectors_0.44.0      
[10] Biobase_2.66.0         BiocGenerics_0.52.0    clusterProfiler_4.14.6
[13] Glimma_2.16.0          edgeR_4.4.2            limma_3.62.2          
[16] data.table_1.17.8      GOSemSim_2.32.0        plotly_4.11.0         
[19] d3treeR_0.1            rrvgo_1.18.0           ggrepel_0.9.6         
[22] ggbiplot_0.6.2         gridExtra_2.3          VennDiagram_1.7.3     
[25] futile.logger_1.4.3    extrafont_0.19         DT_0.34.0             
[28] kableExtra_1.4.0       lubridate_1.9.4        forcats_1.0.0         
[31] stringr_1.5.2          purrr_1.1.0            tidyr_1.3.1           
[34] ggplot2_4.0.0          tidyverse_2.0.0        reshape2_1.4.4        
[37] tibble_3.3.0           readr_2.1.5            magrittr_2.0.4        
[40] dplyr_1.1.4           

loaded via a namespace (and not attached):
  [1] splines_4.4.1               later_1.4.4                
  [3] ggplotify_0.1.3             R.oo_1.27.1                
  [5] XML_3.99-0.19               lifecycle_1.0.4            
  [7] rprojroot_2.1.1             NLP_0.3-2                  
  [9] lattice_0.22-7              crosstalk_1.2.2            
 [11] sass_0.4.10                 rmarkdown_2.29             
 [13] jquerylib_0.1.4             yaml_2.3.10                
 [15] ggtangle_0.0.7              httpuv_1.6.16              
 [17] askpass_1.2.1               reticulate_1.43.0          
 [19] cowplot_1.2.0               DBI_1.2.3                  
 [21] RColorBrewer_1.1-3          abind_1.4-8                
 [23] zlibbioc_1.52.0             GenomicRanges_1.58.0       
 [25] R.utils_2.13.0              yulab.utils_0.2.1          
 [27] rappdirs_0.3.3              git2r_0.36.2               
 [29] GenomeInfoDbData_1.2.13     data.tree_1.2.0            
 [31] tm_0.7-16                   tidytree_0.4.6             
 [33] pheatmap_1.0.13             umap_0.2.10.0              
 [35] RSpectra_0.16-2             svglite_2.2.1              
 [37] gridSVG_1.7-6               codetools_0.2-20           
 [39] DelayedArray_0.32.0         DOSE_4.0.1                 
 [41] xml2_1.4.0                  tidyselect_1.2.1           
 [43] aplot_0.2.9                 UCSC.utils_1.2.0           
 [45] farver_2.1.2                matrixStats_1.5.0          
 [47] jsonlite_2.0.0              systemfonts_1.2.3          
 [49] ggnewscale_0.5.2            tools_4.4.1                
 [51] ragg_1.5.0                  treeio_1.30.0              
 [53] Rcpp_1.1.0                  glue_1.8.0                 
 [55] Rttf2pt1_1.3.12             SparseArray_1.6.2          
 [57] here_1.0.2                  xfun_0.53                  
 [59] DESeq2_1.46.0               qvalue_2.38.0              
 [61] MatrixGenerics_1.18.1       GenomeInfoDb_1.42.3        
 [63] withr_3.0.2                 formatR_1.14               
 [65] fastmap_1.2.0               ggh4x_0.3.1                
 [67] openssl_2.3.3               digest_0.6.37              
 [69] timechange_0.3.0            R6_2.6.1                   
 [71] mime_0.13                   gridGraphics_0.5-1         
 [73] textshaping_1.0.3           colorspace_2.1-1           
 [75] GO.db_3.20.0                RSQLite_2.4.3              
 [77] R.methodsS3_1.8.2           generics_0.1.4             
 [79] httr_1.4.7                  htmlwidgets_1.6.4          
 [81] S4Arrays_1.6.0              whisker_0.4.1              
 [83] pkgconfig_2.0.3             gtable_0.3.6               
 [85] blob_1.2.4                  workflowr_1.7.2            
 [87] S7_0.2.0                    XVector_0.46.0             
 [89] fgsea_1.32.4                ggupset_0.4.1              
 [91] scales_1.4.0                png_0.1-8                  
 [93] wordcloud_2.6               ggfun_0.2.0                
 [95] lambda.r_1.2.4              rstudioapi_0.17.1          
 [97] tzdb_0.5.0                  nlme_3.1-168               
 [99] cachem_1.1.0                parallel_4.4.1             
[101] treemap_2.4-4               pillar_1.11.1              
[103] vctrs_0.6.5                 slam_0.1-55                
[105] promises_1.3.3              xtable_1.8-4               
[107] extrafontdb_1.0             evaluate_1.0.5             
[109] cli_3.6.5                   locfit_1.5-9.12            
[111] compiler_4.4.1              futile.options_1.0.1       
[113] rlang_1.1.6                 crayon_1.5.3               
[115] labeling_0.4.3              plyr_1.8.9                 
[117] fs_1.6.6                    writexl_1.5.4              
[119] stringi_1.8.7               viridisLite_0.4.2          
[121] gridBase_0.4-7              BiocParallel_1.40.2        
[123] Biostrings_2.74.1           lazyeval_0.2.2             
[125] Matrix_1.7-4                hms_1.1.3                  
[127] bit64_4.6.0-1               KEGGREST_1.46.0            
[129] statmod_1.5.0               shiny_1.11.1               
[131] SummarizedExperiment_1.36.0 igraph_2.1.4               
[133] memoise_2.0.1               bslib_0.9.0                
[135] ggtree_3.14.0               fastmatch_1.1-6            
[137] bit_4.6.0                   gson_0.1.0                 
[139] ape_5.8-1

GO Analysis

Ha Tran

22/08/2021

Data Setup

Gene ontology (GO) Analysis

Visualisations

INT vs CONT

Dot plot

Table

Upset plot

Dendrogram

Scatter plot

Parent terms

Treemap plot

INT vs SVX_VAS

Dot plot

Table

Upset plot

Dendrogram

Scatter

Parent term

Treemap

SVX vs SVX_VAS

Dot plot

Table

Upset plot

Dendrogram

Scatter

Parent term

Treemap

VAS vs SVX_VAS

Dot plot

Table

Upset plot

Dendrogram

Scatter

Parent term

Treemap

SVX_VAS vs CONT

Dot plot

Table

Upset plot

Dendrogram

Scatter

Parent term

Treemap

Combined

Venn diagram

Dot plot

Parent term Venn

Parent term dot

Export Data