Last updated: 2023-05-01

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File Version Author Date Message
Rmd 3933b92 steveped 2022-11-18 Corrected section ids
html 3933b92 steveped 2022-11-18 Corrected section ids
html 3a59bc5 steveped 2022-11-18 Build site.
Rmd 36a1cb8 steveped 2022-11-18 Added enrichment tables
html 0b3ca1b steveped 2022-11-17 Build site.
Rmd 68c392f steveped 2022-11-17 Added down & up genes separately
html f2ee30e steveped 2022-11-14 Build site.
Rmd 61ec31f Steve Pederson 2022-11-01 Added enrichment testing
html 61ec31f Steve Pederson 2022-11-01 Added enrichment testing
Rmd 0892674 Steve Pederson 2022-10-26 Started adding heatmaps
Rmd 8745ba2 Steve Pederson 2022-10-26 Shifted to workflowr 
knitr::opts_chunk$set(
  warning = FALSE, message = FALSE,
  fig.width = 10, fig.height = 8,
  dev = c("png", "pdf")
)
library(tidyverse)
library(limma)
library(edgeR)
library(RColorBrewer)
library(pheatmap)
library(magrittr)
library(clusterProfiler)
library(org.Mm.eg.db)
library(reactable)
library(htmltools)
library(glue)
library(scales)
with_tooltip <- function(value, width = 30) {
  tags$span(title = value, str_trunc(value, width))
}
top_tables <- read_rds(here::here("output", "top_tables.rds"))
dgeFilt <- read_rds(here::here("output", "dgeFilt.rds"))
universe <- dgeFilt$genes$entrezid %>%
  unlist() %>%
  extract(!is.na(.)) %>% #keep non NA gene
  unique() %>%
  as.character()

Enrichment Results

ego_results <- top_tables %>% 
  lapply(
    function(x) {
      de <- dplyr::filter(x, adj.P.Val < 0.05, abs(logFC) > 1)
      ids <- unique(unlist(de$entrezid))
      enrichGO(
        ids,
        universe = universe,
        OrgDb = org.Mm.eg.db,
        ont = "ALL",
        keyType = "ENTREZID",
        pAdjustMethod = "bonferroni",
        pvalueCutoff = 0.05,
        qvalueCutoff = 0.05,
        readable = TRUE
      )
    }
  )

Enrichment testing was performed using all three available Gene Ontologies and the function enrichGO() from the package clusterProfiler. Ontologies were only considered to be enriched amongst the differentially expressed genes if a Bonferroni-adjusted p-value < 0.05 was returned during enrichment testing, with no regard to the sign of fold change.

DN Vs. Lag3+

dotplot(ego_results$DNvLAG3) +
  ggtitle(expression(paste("DN Vs. Lag", 3^{textstyle("+")}))) +
  scale_y_discrete(labels = label_wrap(25)) +
  theme(
    plot.title = element_text(hjust = 0.5),
    text = element_text(size = 16)
  )

Version Author Date
f2ee30e steveped 2022-11-14
61ec31f Steve Pederson 2022-11-01

DN Vs. DP

dotplot(ego_results$DNvDP) +
  ggtitle("DN Vs. DP") +
  scale_y_discrete(labels = label_wrap(25)) +
  theme(
    plot.title = element_text(hjust = 0.5),
    text = element_text(size = 16)
  )

Version Author Date
f2ee30e steveped 2022-11-14
61ec31f Steve Pederson 2022-11-01

DN Vs. Cd49b+

dotplot(ego_results$DNvCD49b) +
  ggtitle(expression(paste("DN Vs. Cd49", b^{textstyle("+")}))) +
  scale_y_discrete(labels = label_wrap(25)) +
  theme(
    plot.title = element_text(hjust = 0.5),
    text = element_text(size = 16)
  )

Version Author Date
f2ee30e steveped 2022-11-14
61ec31f Steve Pederson 2022-11-01

Lag3+ Vs Cd49+

dotplot(ego_results$LAG3vCD49b) +
  ggtitle(
    expression(
      paste("Lag", 3^{textstyle("+")}, " Vs. CD49", b^{textstyle("+")})
    )
  ) +
  scale_y_discrete(labels = label_wrap(25)) +
  theme(
    plot.title = element_text(hjust = 0.5),
    text = element_text(size = 16)
  )

Version Author Date
f2ee30e steveped 2022-11-14
61ec31f Steve Pederson 2022-11-01

Lag3+ Vs. DP

dotplot(ego_results$LAG3vDP) +
  ggtitle(expression(paste("Lag", 3^{textstyle("+")}, " Vs. DP"))) +
  scale_y_discrete(labels = label_wrap(25)) +
  theme(
    plot.title = element_text(hjust = 0.5),
    text = element_text(size = 16)
  )

Version Author Date
f2ee30e steveped 2022-11-14
61ec31f Steve Pederson 2022-11-01

Cd49b+ Vs. DP

dotplot(ego_results$CD49bvDP) +
  ggtitle(expression(paste("Cd49", b^{textstyle("+")}, " Vs. DP"))) +
  scale_y_discrete(labels = label_wrap(25)) +
  theme(
    plot.title = element_text(hjust = 0.5),
    text = element_text(size = 16)
  )

Version Author Date
f2ee30e steveped 2022-11-14
61ec31f Steve Pederson 2022-11-01

Enrichment Tables

htmltools::tagList(
  names(ego_results) %>% 
    lapply(
      function(x) {
        df <- ego_results[[x]] %>% 
          as_tibble() %>% 
          dplyr::filter(p.adjust < 0.05) %>% 
          mutate(
            geneID = str_replace_all(geneID, "\\/", "; "),
            `% DE` = vapply(GeneRatio, function(x) eval(parse(text = x)), numeric(1)),
            `% BG` = vapply(BgRatio, function(x) eval(parse(text = x)), numeric(1))
          ) %>% 
          dplyr::select(ONTOLOGY, ID, Description, Count, starts_with("%"), p.adjust, geneID) %>% 
          arrange(p.adjust) %>% 
          distinct(ONTOLOGY, geneID, .keep_all = TRUE) 
        
        if (nrow(df)) {
          cp <- glue(
            "All {nrow(df)} ontologies considered enriched in the set of ",
            length(unique(unlist(dplyr::filter(top_tables[[x]], DE)$entrezid))),
            " DE genes mapped to EntrezIDs from the comparison {x}. Enrichment ",
            "testing was performed using Fisher's Exact Test compared to the set of ",
            comma(length(unique(universe))), 
            " unique EntrezIDs mapped to genes considered as detectable. ",
            "The percentage of nonDE genes mapped to the ontology are also given. ",
            "P-values are Bonferroni adjusted and all pathways are considered enriched ",
            "using an adjusted p-value < 0.05. In order to see all DE genes, hover your ", 
            "mouse over the final column. ",
            "Where the identical genes map to multiple terms within each ontology, only ", 
            "the term with the lowest p-value (i.e. strongest enrichment) is shown. ",
            "As a result, some of the pathways seen in the above dotplots may not be ",
            "presented in these tables."
          )
          htmltools::div(
            htmltools::div(
              id = x, 
              class="section level3",
              htmltools::h3(class = "tabset", x),
              htmltools::tags$em(cp),
              df %>% 
                reactable(
                  filterable = TRUE,
                  columns = list(
                    ONTOLOGY = colDef(name = "Ontology", maxWidth = 100),
                    ID = colDef(name = "GO ID", maxWidth = 125),
                    Description = colDef(maxWidth = 250),
                    Count = colDef(name = "N<sub>DE</sub>", html = TRUE, maxWidth = 80),
                    `% DE` = colDef(format = colFormat(percent = TRUE, digits = 1), maxWidth = 85),
                    `% BG` = colDef(format = colFormat(percent = TRUE, digits = 1), maxWidth = 85),
                    p.adjust = colDef(
                      name = "p<sub>adj</sub>", html = TRUE, maxWidth = 100,
                      cell = function(value) {
                        fm <- ifelse(value < 0.001, "%.2e", "%.3f")
                        sprintf(fm, value)
                      }
                    ),
                    geneID = colDef(
                      name = "DE Genes",
                      cell = function(value) with_tooltip(value, width = 100)
                    )
                  )
                )
            )
          )
        } else {
          cat(glue("No enrichment was found for {x}\n\n"))
        }
      }
    )
)

DNvLAG3

All 55 ontologies considered enriched in the set of 73 DE genes mapped to EntrezIDs from the comparison DNvLAG3. Enrichment testing was performed using Fisher's Exact Test compared to the set of 10,006 unique EntrezIDs mapped to genes considered as detectable. The percentage of nonDE genes mapped to the ontology are also given. P-values are Bonferroni adjusted and all pathways are considered enriched using an adjusted p-value < 0.05. In order to see all DE genes, hover your mouse over the final column. Where the identical genes map to multiple terms within each ontology, only the term with the lowest p-value (i.e. strongest enrichment) is shown. As a result, some of the pathways seen in the above dotplots may not be presented in these tables.

DNvDP

All 3 ontologies considered enriched in the set of 53 DE genes mapped to EntrezIDs from the comparison DNvDP. Enrichment testing was performed using Fisher's Exact Test compared to the set of 10,006 unique EntrezIDs mapped to genes considered as detectable. The percentage of nonDE genes mapped to the ontology are also given. P-values are Bonferroni adjusted and all pathways are considered enriched using an adjusted p-value < 0.05. In order to see all DE genes, hover your mouse over the final column. Where the identical genes map to multiple terms within each ontology, only the term with the lowest p-value (i.e. strongest enrichment) is shown. As a result, some of the pathways seen in the above dotplots may not be presented in these tables.

DNvCD49b

All 10 ontologies considered enriched in the set of 16 DE genes mapped to EntrezIDs from the comparison DNvCD49b. Enrichment testing was performed using Fisher's Exact Test compared to the set of 10,006 unique EntrezIDs mapped to genes considered as detectable. The percentage of nonDE genes mapped to the ontology are also given. P-values are Bonferroni adjusted and all pathways are considered enriched using an adjusted p-value < 0.05. In order to see all DE genes, hover your mouse over the final column. Where the identical genes map to multiple terms within each ontology, only the term with the lowest p-value (i.e. strongest enrichment) is shown. As a result, some of the pathways seen in the above dotplots may not be presented in these tables.

LAG3vCD49b

All 36 ontologies considered enriched in the set of 151 DE genes mapped to EntrezIDs from the comparison LAG3vCD49b. Enrichment testing was performed using Fisher's Exact Test compared to the set of 10,006 unique EntrezIDs mapped to genes considered as detectable. The percentage of nonDE genes mapped to the ontology are also given. P-values are Bonferroni adjusted and all pathways are considered enriched using an adjusted p-value < 0.05. In order to see all DE genes, hover your mouse over the final column. Where the identical genes map to multiple terms within each ontology, only the term with the lowest p-value (i.e. strongest enrichment) is shown. As a result, some of the pathways seen in the above dotplots may not be presented in these tables.

CD49bvDP

All 2 ontologies considered enriched in the set of 33 DE genes mapped to EntrezIDs from the comparison CD49bvDP. Enrichment testing was performed using Fisher's Exact Test compared to the set of 10,006 unique EntrezIDs mapped to genes considered as detectable. The percentage of nonDE genes mapped to the ontology are also given. P-values are Bonferroni adjusted and all pathways are considered enriched using an adjusted p-value < 0.05. In order to see all DE genes, hover your mouse over the final column. Where the identical genes map to multiple terms within each ontology, only the term with the lowest p-value (i.e. strongest enrichment) is shown. As a result, some of the pathways seen in the above dotplots may not be presented in these tables.

LAG3vDP

All 3 ontologies considered enriched in the set of 33 DE genes mapped to EntrezIDs from the comparison LAG3vDP. Enrichment testing was performed using Fisher's Exact Test compared to the set of 10,006 unique EntrezIDs mapped to genes considered as detectable. The percentage of nonDE genes mapped to the ontology are also given. P-values are Bonferroni adjusted and all pathways are considered enriched using an adjusted p-value < 0.05. In order to see all DE genes, hover your mouse over the final column. Where the identical genes map to multiple terms within each ontology, only the term with the lowest p-value (i.e. strongest enrichment) is shown. As a result, some of the pathways seen in the above dotplots may not be presented in these tables.

Enrichment Results (Up Only)

ego_results <- top_tables %>% 
  lapply(
    function(x) {
      de <- dplyr::filter(x, adj.P.Val < 0.05, logFC > 1)
      ids <- unique(unlist(de$entrezid))
      enrichGO(
        ids,
        universe = universe,
        OrgDb = org.Mm.eg.db,
        ont = "ALL",
        keyType = "ENTREZID",
        pAdjustMethod = "bonferroni",
        pvalueCutoff = 0.05,
        qvalueCutoff = 0.05,
        readable = TRUE
      )
    }
  )

Enrichment testing was repeated using all three available Gene Ontologies and the function enrichGO() from the package clusterProfiler. Ontologies were only considered to be enriched amongst the up-regulated genes if a Bonferroni-adjusted p-value < 0.05 was returned during enrichment testing. Please note that an up-regulated gene does not always correspond to an increase in activity of a pathway.

DN Vs. Lag3+

dotplot(ego_results$DNvLAG3) +
  ggtitle(expression(paste("DN Vs. Lag", 3^{textstyle("+")}, "(Up Only)"))) +
  scale_y_discrete(labels = label_wrap(25)) +
  theme(
    plot.title = element_text(hjust = 0.5),
    text = element_text(size = 16)
  )

Version Author Date
0b3ca1b steveped 2022-11-17

DN Vs. DP

No significant results were obtained for up-regulated genes in this comparison.

dotplot(ego_results$DNvDP) +
  ggtitle("DN Vs. DP (Up Only)") +
  scale_y_discrete(labels = label_wrap(25)) +
  theme(
    plot.title = element_text(hjust = 0.5),
    text = element_text(size = 16)
  )

DN Vs. Cd49b+

dotplot(ego_results$DNvCD49b) +
  ggtitle(expression(paste("DN Vs. Cd49", b^{textstyle("+")}, "(Up Only)"))) +
  scale_y_discrete(labels = label_wrap(25)) +
  theme(
    plot.title = element_text(hjust = 0.5),
    text = element_text(size = 16)
  )

Version Author Date
0b3ca1b steveped 2022-11-17

Lag3+ Vs Cd49+

dotplot(ego_results$LAG3vCD49b) +
  ggtitle(
    expression(
      paste("Lag", 3^{textstyle("+")}, " Vs. CD49", b^{textstyle("+")}, "(Up Only)")
    )
  ) +
  scale_y_discrete(labels = label_wrap(25)) +
  theme(
    plot.title = element_text(hjust = 0.5),
    text = element_text(size = 16)
  )

Version Author Date
0b3ca1b steveped 2022-11-17

Lag3+ Vs. DP

No significant results were obtained for up-regulated genes in this comparison.

dotplot(ego_results$LAG3vDP) +
  ggtitle(expression(paste("Lag", 3^{textstyle("+")}, " Vs. DP", "(Up Only)"))) +
  scale_y_discrete(labels = label_wrap(25)) +
  theme(
    plot.title = element_text(hjust = 0.5),
    text = element_text(size = 16)
  )

Cd49b+ Vs. DP

dotplot(ego_results$CD49bvDP) +
  ggtitle(expression(paste("Cd49", b^{textstyle("+")}, " Vs. DP (Up Only)"))) +
  scale_y_discrete(labels = label_wrap(25)) +
  theme(
    plot.title = element_text(hjust = 0.5),
    text = element_text(size = 16)
  )

Version Author Date
0b3ca1b steveped 2022-11-17

Enrichment Tables (Up Only)

ego_results <- ego_results[vapply(ego_results, nrow, integer(1)) > 0]
htmltools::tagList(
  names(ego_results) %>% 
    lapply(
      function(x) {
        df <- ego_results[[x]] %>% 
          as_tibble() %>% 
          dplyr::filter(p.adjust < 0.05) %>% 
          mutate(
            geneID = str_replace_all(geneID, "\\/", "; "),
            `% DE` = vapply(GeneRatio, function(x) eval(parse(text = x)), numeric(1)),
            `% BG` = vapply(BgRatio, function(x) eval(parse(text = x)), numeric(1))
          ) %>% 
          dplyr::select(ONTOLOGY, ID, Description, Count, starts_with("%"), p.adjust, geneID) %>% 
          arrange(p.adjust) %>% 
          distinct(ONTOLOGY, geneID, .keep_all = TRUE) 
        
        if (nrow(df)) {
          cp <- glue(
            "All {nrow(df)} ontologies considered enriched in the set of ",
            length(unique(unlist(dplyr::filter(top_tables[[x]], DE, logFC > 1)$entrezid))),
            " up-regulated genes mapped to EntrezIDs from the comparison {x}. Enrichment ",
            "testing was performed using Fisher's Exact Test compared to the set of ",
            comma(length(unique(universe))), 
            " unique EntrezIDs mapped to genes considered as detectable. ",
            "The percentage of nonDE genes mapped to the ontology are also given. ",
            "P-values are Bonferroni adjusted and all pathways are considered enriched ",
            "using an adjusted p-value < 0.05. In order to see all DE genes, hover your ", 
            "mouse over the final column. ",
            "Where the identical genes map to multiple terms within each ontology, only ", 
            "the term with the lowest p-value (i.e. strongest enrichment) is shown. ",
            "As a result, some of the pathways seen in the above dotplots may not be ",
            "presented in these tables."
          )
          htmltools::div(
            htmltools::div(
              id = glue("{x}-up"), 
              class="section level3",
              htmltools::h3(class = "tabset", x),
              htmltools::tags$em(cp),
              df %>% 
                reactable(
                  filterable = TRUE,
                  columns = list(
                    ONTOLOGY = colDef(name = "Ontology", maxWidth = 100),
                    ID = colDef(name = "GO ID", maxWidth = 125),
                    Description = colDef(maxWidth = 250),
                    Count = colDef(name = "N<sub>DE</sub>", html = TRUE, maxWidth = 80),
                    `% DE` = colDef(format = colFormat(percent = TRUE, digits = 1), maxWidth = 85),
                    `% BG` = colDef(format = colFormat(percent = TRUE, digits = 1), maxWidth = 85),
                    p.adjust = colDef(
                      name = "p<sub>adj</sub>", html = TRUE, maxWidth = 100,
                      cell = function(value) {
                        fm <- ifelse(value < 0.001, "%.2e", "%.3f")
                        sprintf(fm, value)
                      }
                    ),
                    geneID = colDef(
                      name = "DE Genes",
                      cell = function(value) with_tooltip(value, width = 100)
                    )
                  )
                )
            )
          )
        } else {
          cat(glue("No enrichment was found for {x}\n\n"))
        }
      }
    )
)

DNvLAG3

All 55 ontologies considered enriched in the set of 45 up-regulated genes mapped to EntrezIDs from the comparison DNvLAG3. Enrichment testing was performed using Fisher's Exact Test compared to the set of 10,006 unique EntrezIDs mapped to genes considered as detectable. The percentage of nonDE genes mapped to the ontology are also given. P-values are Bonferroni adjusted and all pathways are considered enriched using an adjusted p-value < 0.05. In order to see all DE genes, hover your mouse over the final column. Where the identical genes map to multiple terms within each ontology, only the term with the lowest p-value (i.e. strongest enrichment) is shown. As a result, some of the pathways seen in the above dotplots may not be presented in these tables.

DNvCD49b

All 11 ontologies considered enriched in the set of 14 up-regulated genes mapped to EntrezIDs from the comparison DNvCD49b. Enrichment testing was performed using Fisher's Exact Test compared to the set of 10,006 unique EntrezIDs mapped to genes considered as detectable. The percentage of nonDE genes mapped to the ontology are also given. P-values are Bonferroni adjusted and all pathways are considered enriched using an adjusted p-value < 0.05. In order to see all DE genes, hover your mouse over the final column. Where the identical genes map to multiple terms within each ontology, only the term with the lowest p-value (i.e. strongest enrichment) is shown. As a result, some of the pathways seen in the above dotplots may not be presented in these tables.

LAG3vCD49b

All 13 ontologies considered enriched in the set of 99 up-regulated genes mapped to EntrezIDs from the comparison LAG3vCD49b. Enrichment testing was performed using Fisher's Exact Test compared to the set of 10,006 unique EntrezIDs mapped to genes considered as detectable. The percentage of nonDE genes mapped to the ontology are also given. P-values are Bonferroni adjusted and all pathways are considered enriched using an adjusted p-value < 0.05. In order to see all DE genes, hover your mouse over the final column. Where the identical genes map to multiple terms within each ontology, only the term with the lowest p-value (i.e. strongest enrichment) is shown. As a result, some of the pathways seen in the above dotplots may not be presented in these tables.

CD49bvDP

All 1 ontologies considered enriched in the set of 6 up-regulated genes mapped to EntrezIDs from the comparison CD49bvDP. Enrichment testing was performed using Fisher's Exact Test compared to the set of 10,006 unique EntrezIDs mapped to genes considered as detectable. The percentage of nonDE genes mapped to the ontology are also given. P-values are Bonferroni adjusted and all pathways are considered enriched using an adjusted p-value < 0.05. In order to see all DE genes, hover your mouse over the final column. Where the identical genes map to multiple terms within each ontology, only the term with the lowest p-value (i.e. strongest enrichment) is shown. As a result, some of the pathways seen in the above dotplots may not be presented in these tables.

Enrichment Results (Down Only)

ego_results <- top_tables %>% 
  lapply(
    function(x) {
      de <- dplyr::filter(x, adj.P.Val < 0.05, logFC < -1)
      ids <- unique(unlist(de$entrezid))
      enrichGO(
        ids,
        universe = universe,
        OrgDb = org.Mm.eg.db,
        ont = "ALL",
        keyType = "ENTREZID",
        pAdjustMethod = "bonferroni",
        pvalueCutoff = 0.05,
        qvalueCutoff = 0.05,
        readable = TRUE
      )
    }
  )

Enrichment testing was repeated using all three available Gene Ontologies and the function enrichGO() from the package clusterProfiler. Ontologies were only considered to be enriched amongst the down-regulated genes if a Bonferroni-adjusted p-value < 0.05 was returned during enrichment testing. Please note that a down-regulated gene does not always correspond to an decrease in activity of a pathway.

DN Vs. Lag3+

dotplot(ego_results$DNvLAG3) +
  ggtitle(expression(paste("DN Vs. Lag", 3^{textstyle("+")}, "(Down Only)"))) +
  scale_y_discrete(labels = label_wrap(25)) +
  theme(
    plot.title = element_text(hjust = 0.5),
    text = element_text(size = 16)
  )

Version Author Date
0b3ca1b steveped 2022-11-17

DN Vs. DP

dotplot(ego_results$DNvDP) +
  ggtitle("DN Vs. DP (Down Only)") +
  scale_y_discrete(labels = label_wrap(25)) +
  theme(
    plot.title = element_text(hjust = 0.5),
    text = element_text(size = 16)
  )

Version Author Date
0b3ca1b steveped 2022-11-17

DN Vs. Cd49b+

dotplot(ego_results$DNvCD49b) +
  ggtitle(expression(paste("DN Vs. Cd49", b^{textstyle("+")}, "(Down Only)"))) +
  scale_y_discrete(labels = label_wrap(25)) +
  theme(
    plot.title = element_text(hjust = 0.5),
    text = element_text(size = 16)
  )

Version Author Date
0b3ca1b steveped 2022-11-17

Lag3+ Vs Cd49+

dotplot(ego_results$LAG3vCD49b) +
  ggtitle(
    expression(
      paste("Lag", 3^{textstyle("+")}, " Vs. CD49", b^{textstyle("+")}, "(Down Only)")
    )
  ) +
  scale_y_discrete(labels = label_wrap(25)) +
  theme(
    plot.title = element_text(hjust = 0.5),
    text = element_text(size = 16)
  )

Version Author Date
0b3ca1b steveped 2022-11-17

Lag3+ Vs. DP

dotplot(ego_results$LAG3vDP) +
  ggtitle(expression(paste("Lag", 3^{textstyle("+")}, " Vs. DP", "(Down Only)"))) +
  scale_y_discrete(labels = label_wrap(25)) +
  theme(
    plot.title = element_text(hjust = 0.5),
    text = element_text(size = 16)
  )

Version Author Date
0b3ca1b steveped 2022-11-17

Cd49b+ Vs. DP

dotplot(ego_results$CD49bvDP) +
  ggtitle(expression(paste("Cd49", b^{textstyle("+")}, " Vs. DP (Down Only)"))) +
  scale_y_discrete(labels = label_wrap(25)) +
  theme(
    plot.title = element_text(hjust = 0.5),
    text = element_text(size = 16)
  )

Version Author Date
0b3ca1b steveped 2022-11-17

Enrichment Tables (Down Only)

ego_results <- ego_results[vapply(ego_results, nrow, integer(1)) > 0]
htmltools::tagList(
  names(ego_results) %>% 
    lapply(
      function(x) {
        df <- ego_results[[x]] %>% 
          as_tibble() %>% 
          dplyr::filter(p.adjust < 0.05) %>% 
          mutate(
            geneID = str_replace_all(geneID, "\\/", "; "),
            `% DE` = vapply(GeneRatio, function(x) eval(parse(text = x)), numeric(1)),
            `% BG` = vapply(BgRatio, function(x) eval(parse(text = x)), numeric(1))
          ) %>% 
          dplyr::select(ONTOLOGY, ID, Description, Count, starts_with("%"), p.adjust, geneID) %>% 
          arrange(p.adjust) %>% 
          distinct(ONTOLOGY, geneID, .keep_all = TRUE) 
        
        if (nrow(df)) {
          cp <- glue(
            "All {nrow(df)} ontologies considered enriched in the set of ",
            length(unique(unlist(dplyr::filter(top_tables[[x]], DE, logFC < -1)$entrezid))),
            " down-regulated genes mapped to EntrezIDs from the comparison {x}. Enrichment ",
            "testing was performed using Fisher's Exact Test compared to the set of ",
            comma(length(unique(universe))), 
            " unique EntrezIDs mapped to genes considered as detectable. ",
            "The percentage of nonDE genes mapped to the ontology are also given. ",
            "P-values are Bonferroni adjusted and all pathways are considered enriched ",
            "using an adjusted p-value < 0.05. In order to see all DE genes, hover your ", 
            "mouse over the final column. ",
            "Where the identical genes map to multiple terms within each ontology, only ", 
            "the term with the lowest p-value (i.e. strongest enrichment) is shown. ",
            "As a result, some of the pathways seen in the above dotplots may not be ",
            "presented in these tables."
          )
          htmltools::div(
            htmltools::div(
              id = glue("{x}-down"), 
              class="section level3",
              htmltools::h3(class = "tabset", x),
              htmltools::tags$em(cp),
              df %>% 
                reactable(
                  filterable = TRUE,
                  columns = list(
                    ONTOLOGY = colDef(name = "Ontology", maxWidth = 100),
                    ID = colDef(name = "GO ID", maxWidth = 125),
                    Description = colDef(maxWidth = 250),
                    Count = colDef(name = "N<sub>DE</sub>", html = TRUE, maxWidth = 80),
                    `% DE` = colDef(format = colFormat(percent = TRUE, digits = 1), maxWidth = 85),
                    `% BG` = colDef(format = colFormat(percent = TRUE, digits = 1), maxWidth = 85),
                    p.adjust = colDef(
                      name = "p<sub>adj</sub>", html = TRUE, maxWidth = 100,
                      cell = function(value) {
                        fm <- ifelse(value < 0.001, "%.2e", "%.3f")
                        sprintf(fm, value)
                      }
                    ),
                    geneID = colDef(
                      name = "DE Genes",
                      cell = function(value) with_tooltip(value, width = 100)
                    )
                  )
                )
            )
          )
        } else {
          cat(glue("No enrichment was found for {x}\n\n"))
        }
      }
    )
)

DNvLAG3

All 2 ontologies considered enriched in the set of 29 down-regulated genes mapped to EntrezIDs from the comparison DNvLAG3. Enrichment testing was performed using Fisher's Exact Test compared to the set of 10,006 unique EntrezIDs mapped to genes considered as detectable. The percentage of nonDE genes mapped to the ontology are also given. P-values are Bonferroni adjusted and all pathways are considered enriched using an adjusted p-value < 0.05. In order to see all DE genes, hover your mouse over the final column. Where the identical genes map to multiple terms within each ontology, only the term with the lowest p-value (i.e. strongest enrichment) is shown. As a result, some of the pathways seen in the above dotplots may not be presented in these tables.

DNvDP

All 3 ontologies considered enriched in the set of 33 down-regulated genes mapped to EntrezIDs from the comparison DNvDP. Enrichment testing was performed using Fisher's Exact Test compared to the set of 10,006 unique EntrezIDs mapped to genes considered as detectable. The percentage of nonDE genes mapped to the ontology are also given. P-values are Bonferroni adjusted and all pathways are considered enriched using an adjusted p-value < 0.05. In order to see all DE genes, hover your mouse over the final column. Where the identical genes map to multiple terms within each ontology, only the term with the lowest p-value (i.e. strongest enrichment) is shown. As a result, some of the pathways seen in the above dotplots may not be presented in these tables.

DNvCD49b

All 1 ontologies considered enriched in the set of 2 down-regulated genes mapped to EntrezIDs from the comparison DNvCD49b. Enrichment testing was performed using Fisher's Exact Test compared to the set of 10,006 unique EntrezIDs mapped to genes considered as detectable. The percentage of nonDE genes mapped to the ontology are also given. P-values are Bonferroni adjusted and all pathways are considered enriched using an adjusted p-value < 0.05. In order to see all DE genes, hover your mouse over the final column. Where the identical genes map to multiple terms within each ontology, only the term with the lowest p-value (i.e. strongest enrichment) is shown. As a result, some of the pathways seen in the above dotplots may not be presented in these tables.

LAG3vCD49b

All 14 ontologies considered enriched in the set of 53 down-regulated genes mapped to EntrezIDs from the comparison LAG3vCD49b. Enrichment testing was performed using Fisher's Exact Test compared to the set of 10,006 unique EntrezIDs mapped to genes considered as detectable. The percentage of nonDE genes mapped to the ontology are also given. P-values are Bonferroni adjusted and all pathways are considered enriched using an adjusted p-value < 0.05. In order to see all DE genes, hover your mouse over the final column. Where the identical genes map to multiple terms within each ontology, only the term with the lowest p-value (i.e. strongest enrichment) is shown. As a result, some of the pathways seen in the above dotplots may not be presented in these tables.

CD49bvDP

All 3 ontologies considered enriched in the set of 28 down-regulated genes mapped to EntrezIDs from the comparison CD49bvDP. Enrichment testing was performed using Fisher's Exact Test compared to the set of 10,006 unique EntrezIDs mapped to genes considered as detectable. The percentage of nonDE genes mapped to the ontology are also given. P-values are Bonferroni adjusted and all pathways are considered enriched using an adjusted p-value < 0.05. In order to see all DE genes, hover your mouse over the final column. Where the identical genes map to multiple terms within each ontology, only the term with the lowest p-value (i.e. strongest enrichment) is shown. As a result, some of the pathways seen in the above dotplots may not be presented in these tables.

LAG3vDP

All 2 ontologies considered enriched in the set of 17 down-regulated genes mapped to EntrezIDs from the comparison LAG3vDP. Enrichment testing was performed using Fisher's Exact Test compared to the set of 10,006 unique EntrezIDs mapped to genes considered as detectable. The percentage of nonDE genes mapped to the ontology are also given. P-values are Bonferroni adjusted and all pathways are considered enriched using an adjusted p-value < 0.05. In order to see all DE genes, hover your mouse over the final column. Where the identical genes map to multiple terms within each ontology, only the term with the lowest p-value (i.e. strongest enrichment) is shown. As a result, some of the pathways seen in the above dotplots may not be presented in these tables. 

sessionInfo()
R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale:
 [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
 [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
 [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       

time zone: Australia/Adelaide
tzcode source: system (glibc)

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] scales_1.2.1          glue_1.6.2            htmltools_0.5.5      
 [4] reactable_0.4.4       org.Mm.eg.db_3.17.0   AnnotationDbi_1.62.0 
 [7] IRanges_2.34.0        S4Vectors_0.38.0      Biobase_2.60.0       
[10] BiocGenerics_0.46.0   clusterProfiler_4.8.0 magrittr_2.0.3       
[13] pheatmap_1.0.12       RColorBrewer_1.1-3    edgeR_3.42.0         
[16] limma_3.56.0          lubridate_1.9.2       forcats_1.0.0        
[19] stringr_1.5.0         dplyr_1.1.2           purrr_1.0.1          
[22] readr_2.1.4           tidyr_1.3.0           tibble_3.2.1         
[25] ggplot2_3.4.2         tidyverse_2.0.0       workflowr_1.7.0      

loaded via a namespace (and not attached):
  [1] rstudioapi_0.14         jsonlite_1.8.4          farver_2.1.1           
  [4] rmarkdown_2.21          fs_1.6.2                zlibbioc_1.46.0        
  [7] vctrs_0.6.2             memoise_2.0.1           RCurl_1.98-1.12        
 [10] ggtree_3.8.0            gridGraphics_0.5-1      sass_0.4.5             
 [13] bslib_0.4.2             htmlwidgets_1.6.2       plyr_1.8.8             
 [16] cachem_1.0.7            whisker_0.4.1           igraph_1.4.2           
 [19] lifecycle_1.0.3         pkgconfig_2.0.3         gson_0.1.0             
 [22] Matrix_1.5-4            R6_2.5.1                fastmap_1.1.1          
 [25] GenomeInfoDbData_1.2.10 digest_0.6.31           aplot_0.1.10           
 [28] enrichplot_1.20.0       colorspace_2.1-0        patchwork_1.1.2        
 [31] ps_1.7.5                rprojroot_2.0.3         crosstalk_1.2.0        
 [34] RSQLite_2.3.1           reactR_0.4.4            labeling_0.4.2         
 [37] fansi_1.0.4             timechange_0.2.0        httr_1.4.5             
 [40] polyclip_1.10-4         compiler_4.3.0          here_1.0.1             
 [43] bit64_4.0.5             withr_2.5.0             downloader_0.4         
 [46] BiocParallel_1.34.0     viridis_0.6.2           DBI_1.1.3              
 [49] highr_0.10              ggforce_0.4.1           MASS_7.3-59            
 [52] HDO.db_0.99.1           tools_4.3.0             scatterpie_0.1.9       
 [55] ape_5.7-1               httpuv_1.6.9            callr_3.7.3            
 [58] nlme_3.1-162            GOSemSim_2.26.0         promises_1.2.0.1       
 [61] shadowtext_0.1.2        grid_4.3.0              getPass_0.2-2          
 [64] reshape2_1.4.4          fgsea_1.26.0            generics_0.1.3         
 [67] gtable_0.3.3            tzdb_0.3.0              data.table_1.14.8      
 [70] hms_1.1.3               tidygraph_1.2.3         utf8_1.2.3             
 [73] XVector_0.40.0          ggrepel_0.9.3           pillar_1.9.0           
 [76] yulab.utils_0.0.6       later_1.3.0             splines_4.3.0          
 [79] tweenr_2.0.2            treeio_1.24.0           lattice_0.21-8         
 [82] bit_4.0.5               tidyselect_1.2.0        GO.db_3.17.0           
 [85] locfit_1.5-9.7          Biostrings_2.68.0       knitr_1.42             
 [88] git2r_0.32.0            gridExtra_2.3           xfun_0.39              
 [91] graphlayouts_0.8.4      stringi_1.7.12          lazyeval_0.2.2         
 [94] ggfun_0.0.9             yaml_2.3.7              evaluate_0.20          
 [97] codetools_0.2-19        ggraph_2.1.0            qvalue_2.32.0          
[100] ggplotify_0.1.0         cli_3.6.1               munsell_0.5.0          
[103] processx_3.8.1          jquerylib_0.1.4         Rcpp_1.0.10            
[106] GenomeInfoDb_1.36.0     png_0.1-8               parallel_4.3.0         
[109] ellipsis_0.3.2          blob_1.2.4              DOSE_3.26.0            
[112] bitops_1.0-7            tidytree_0.4.2          viridisLite_0.4.1      
[115] crayon_1.5.2            rlang_1.1.0             cowplot_1.1.1          
[118] fastmatch_1.1-3         KEGGREST_1.40.0