Assemble Network

PPI Network assembly from Metanalysis genes

We took all genes resulting from the metanalysis and assembled a PPI network for them by querying STRINGdb v12.0.

Evidence channels used to gather the interactions were experimental, co-expression and databases. Said interactions were also filtered for a confidence level of 0.5.

Load libraries and data

# remotes::install_github("daniloimparato/easylayout", ref = "dadamorais")
library(easylayout)
library(tidygraph)
library(igraph)
library(ggraph)
library(dplyr)
library(tidyr)
library(vroom)
library(here)
source(here("R/stringdb.R"))

meta <- vroom(here("data/meta_analysis_genes.csv")) %>%
  select(gene_symbol, MD, pvalor, `Grouped process`)

Assemble Network

string_ids <- get_string_ids(meta$gene_symbol)

meta_merged <- meta %>%
  left_join(string_ids %>% select(queryItem, stringId),
            by = c("gene_symbol" = "queryItem")) %>%
  mutate(stringId = stringr::str_remove(stringId, "9606.")) %>%
  separate_rows(`Grouped process`, sep = " \\| ")

encoded_source <- meta_merged %>%
  mutate(n = 1) %>%
  pivot_wider(
    id_cols     = stringId,
    names_from  = `Grouped process`,
    values_from = n,
    values_fn   = list(n = length),
    values_fill = list(n = 0),
    names_prefix = "From "
  ) %>%
  mutate(source_count = starts_with("From ") %>% across %>% rowSums)

network <- get_string_network(string_ids$stringId)

network_separated <-  network %>%
  separate(stringId_A,
           into = c("ncbi_taxon_id", "stringId_A"),
           sep = "\\.") %>%
  separate(stringId_B,
           into = c("ncbi_taxon_id", "stringId_B"),
           sep = "\\.")

nodelist <-
  data.frame(node = unique(c(network_separated$stringId_A, network_separated$stringId_B))) %>%
  left_join(meta_merged, by = c("node" = "stringId")) %>%
  left_join(encoded_source, by = c("node" = "stringId")) %>%
  distinct(node, gene_symbol, .keep_all = TRUE)

network_filtered <- network %>%
  combinescores(.,
                evidences = c("ascore", "escore", "dscore"),
                confLevel = 0.5) %>%
  separate(stringId_A,
           into = c("ncbi_taxon_id", "stringId_A"),
           sep = "\\.") %>%
  separate(stringId_B,
           into = c("ncbi_taxon_id", "stringId_B"),
           sep = "\\.") %>%
  dplyr::select(stringId_A, stringId_B)

Compute Network Layout

graph <-
  graph_from_data_frame(network_filtered, directed = FALSE, vertices = nodelist)

layout <- easylayout::vivagraph(graph)
layout <- easylayout::vivagraph(graph, layout = layout, pin_nodes = TRUE, lcc_margin_left = 10)
V(graph)$x <- layout[, 1]
V(graph)$y <- layout[, 2]

save(graph, nodelist, file = here("results/full_network.rda"))

Network Plots

By significance

load(here("results/full_network.rda"))

graph_colored <- graph %>%
  as_tbl_graph() %>%
  activate(nodes) %>%
  mutate(signif_md = ifelse(pvalor < 0.05, MD, NA),
         signif_label = ifelse(pvalor < 0.05, gene_symbol, NA))

# Write edge-less nodes
graph %>%
  as_tbl_graph() %>%
  activate(nodes) %>%
  mutate(degree = centrality_degree()) %>%
  filter(degree == 0) %N>%
  as_tibble() %>%
  select(name, gene_symbol, MD, pvalor) %>%
  vroom_write(here("results/nodes_without_neighbors.tsv"))

p1 <- ggraph(graph_colored,
       "manual",
       x = V(graph)$x,
       y = V(graph)$y) +
  geom_edge_link0(edge_width = 0.2, color = "#90909020") +
  geom_node_point(aes(color = signif_md)) +
  scale_colour_gradientn(colours = RColorBrewer::brewer.pal(9, "OrRd")) +
  coord_fixed() +
  theme_void() +
  theme(
    legend.key.size = unit(0.5, 'cm'),
    legend.key.height = unit(0.5, 'cm'),
    legend.key.width = unit(0.5, 'cm'),
    legend.title = element_text(size=6),
    legend.text = element_text(size=6),
    plot.title = element_text(size = 4, face = "bold")
  ) +
  labs(
    color = "Expression"
  )
p1

p2 <- p1 +
  geom_node_label(aes(label = signif_label), repel = TRUE, label.size = 0.1)

ggsave(
  plot = p1,
  here("results/full_network.pdf"),
  width = 8,
  height = 8
)
ggsave(
  plot = p1,
  here("results/full_network.png"),
  width = 8,
  height = 8,
  bg = "white"
)

ggsave(
  plot = p2,
  here("results/full_network_labels.pdf"),
  width = 8,
  height = 8
)
ggsave(
  plot = p2,
  here("results/full_network_labels.png"),
  width = 8,
  height = 8,
  bg = "white"
)

By grouped process

ggraph(graph, "manual", x = V(graph)$x, y = V(graph)$y) +
  geom_edge_link0(color = "#90909020") +
  scatterpie::geom_scatterpie(
    cols = colnames(nodelist)[startsWith(colnames(nodelist), "From ")],
    data = igraph::as_data_frame(graph, "vertices"),
    colour = NA,
    pie_scale = 0.2
  ) +
  coord_fixed() +
  theme_void() +
  theme(
    legend.key.size = unit(0.5, 'cm'),
    legend.key.height = unit(0.5, 'cm'),
    legend.key.width = unit(0.5, 'cm'),
    legend.title = element_text(size=6),
    legend.text = element_text(size=6),
    legend.position = "bottom",
    plot.title = element_text(size = 4, face = "bold")
  ) +
  labs(
    fill = "Source:"
  )

ggsave(here("results/network_processes.pdf"), width = 8, height = 8)
ggsave(here("results/network_processes.png"), width = 8, height = 8, bg = "white")