05.1-expressed-mRNA-top10-GOslim-GO-counts
================
Sam White
2025-07-09

- [1 BACKGROUND](#1-background)

# 1 BACKGROUND

Identify, and plot, the Top 10 GOslims by number of GO term assignments
across all three species.

``` r
# Define file paths and species names
files <- list(
  "D-Apul" = "~/gitrepos/urol-e5/deep-dive-expression/D-Apul/output/30.00-Apul-transcriptome-GOslims/GOslim-counts.tsv",
  "E-Peve" = "~/gitrepos/urol-e5/deep-dive-expression/E-Peve/output/30.00-Peve-transcriptome-GOslims/GOslim-counts.tsv",
  "F-Ptuh" = "~/gitrepos/urol-e5/deep-dive-expression/F-Ptuh/output/30.00-Ptua-transcriptome-GOslims/GOslim-counts.tsv"
)

# Read and combine data
goslim_data <- map2_dfr(
  files,
  names(files),
  ~ read_tsv(.x, show_col_types = FALSE) %>%
    mutate(Species = .y)
)


# Find the top 10 Terms by Count for each species
top_terms <- goslim_data %>%
  group_by(Species) %>%
  slice_max(order_by = Count, n = 10, with_ties = FALSE) %>%
  ungroup() %>%
  distinct(Term)

# Filter the data to only include these Terms
goslim_top <- goslim_data %>%
  filter(Term %in% top_terms$Term)

# Plot: Term on Y, Count on X
ggplot(goslim_top, aes(y = fct_reorder(Term, Count), x = Count, fill = Species)) +
  geom_bar(stat = "identity", position = position_dodge(width = 0.8)) +
  labs(
    title = "Top GOslim Term Counts by Species",
    y = "GOslim Term",
    x = "Count"
  ) +
  theme_bw() +
  theme(
    axis.text.y = element_text(size = 8),
    plot.title = element_text(hjust = 0.5)
  )
```

![](05.1-expressed-mRNA-top10-GOslim-GO-counts_files/figure-gfm/unnamed-chunk-1-1.png)<!-- -->