03-gsea.Rmd

# GSEA

This is GSEA.

## Pathways

These are GSEA pathways.

```{r, results='asis'}
global_state$data %<>%
  map(~ update_list(
    .,
    bpn = do_gsea_pod(
      .$bpn,
      gmtfile = paste0("extdata/", global_state$gmt),
      rankWithSignificance = TRUE,
      species = NULL,
      useFDR = FALSE,
    )
  ))


global_state$data %>%
  map(~ knit_child(
    "Rmd/gsea_pathways.Rmd",
    envir = environment(),
    quiet = TRUE
  )) %>%
  list_c() %>%
  cat(sep = "\n")
``` 

## Leading Edge

These are leading edge genes.

```{r, results='asis'}
global_state$data %<>%
   map(~ update_list(., bpn = do_leading_pod(.$bpn)))

global_state$data %>%
  map(~ knit_child(
    "Rmd/gsea_le.Rmd",
    envir = environment(),
    quiet = TRUE
  )) %>%
  list_c() %>%
  cat(sep = "\n")
``` 

## PAVER

These are PAVER plots of the pathways from all comparisons

```{r, results='asis'}
input = global_state$data %>%
  map(~ bind_rows(.$bpn@gsea@sig_pos_enriched, .$bpn@gsea@sig_neg_enriched)) %>%
  bind_rows(.id = "Group") %>%
  select(GOID = pathway, NES, Group) %>%
  mutate(GOID = str_split_i(GOID, "%", -1)) %>%
  distinct(GOID, Group, .keep_all = T) %>%
  pivot_wider(names_from = Group, values_from = NES)

minClusterSize = 5
maxCoreScatter = 0.33
minGap = (1 - maxCoreScatter) * 3 / 4

GSEA_PAVER_result <- quiet(PAVER::prepare_data(input, global_state$embeddings, global_state$term2name) %>%
  PAVER::generate_themes(maxCoreScatter = maxCoreScatter,
  minGap = minGap,
  minClusterSize = minClusterSize))

global_state$results <- c(global_state$results, lst(GSEA_PAVER_result))

list(GSEA_PAVER_result, "NES") %>%
  knit_child(text = readLines("Rmd/paver_report.Rmd"),
             envir = environment(),
             quiet = TRUE) %>%
  cat(sep = "\n")
```