Associated and Non-associated genes + Networks

#Associated

#Top lowest P-values
#upload magma p.r3 file 
file_path <- file.choose()
data_matrix <- read.table(file_path, header = TRUE)
# Get the p-values from column X
p_values <- data_matrix[-1, X]

sorted_p_values <- sort(p_values)

top_500_p_values <- head(sorted_p_values, 500)

indices <- which(data_matrix[-1, X] %in% top_500_p_values)

related_names <- data_matrix[indices + 1, 1]  # Adding 1 to indices due to header row

# Choose the file path to save the results
output_file <- file.choose()

write.table(related_names, file = output_file, col.names = FALSE, row.names = FALSE, quote = FALSE)
cat("Result saved to file:", output_file, "\n")

#Non-Associated

#Top highest P-values

file_path <- file.choose()
data_matrix <- read.table(file_path, header = TRUE)
p_values <- data_matrix[-1, X]

sorted_p_values <- sort(p_values, decreasing = TRUE)

top_500_p_values <- head(sorted_p_values, 500)

indices <- which(data_matrix[-1, X] %in% top_500_p_values)

related_names <- data_matrix[indices + 1, 1]  # Adding 1 to indices due to header row

output_file <- file.choose()

write.table(related_names, file = output_file, col.names = FALSE, row.names = FALSE, quote = FALSE)
cat("Result saved to file:", output_file, "\n")

data_matrix <- read.table(file_path, header = TRUE)
p_values <- data_matrix[-1, X]

sorted_p_values <- sort(p_values)

top_500_p_values <- head(sorted_p_values, 500)
indices <- which(data_matrix[-1, X] %in% top_500_p_values)

related_names <- data_matrix[indices + 1, 1]  # Adding 1 to indices due to header row

# Map the related names to IDs using the gconvert function
X <- gconvert(query = related_names, organism = "hsapiens", target = "ENSG")
proteins <- X$name
proteins <- as.character(proteins)

# Map protein symbols to IDs
proteins_mapped <- rba_string_map_ids(ids = proteins, species = 9606)
proteins_ids <- proteins_mapped$stringId

# Create the interaction network
int_net <- rba_string_interactions_network(ids = proteins_ids,
                                           species = 9606,
                                           network_type = "functional",
                                           required_score = 900)

matrix <- int_net[, c(3, 4, 6)]  # Assuming the columns are for gene names, scores, and IDs

library(igraph)

graph <- graph_from_data_frame(matrix, directed = FALSE)

E(graph)$weight <- matrix$score

graph <- simplify(graph)

node_color <- "#808080"  # Gray
edge_color <- "#000000"  # Black

folder_path <- dirname(file.choose())

# Plot the graph
plot(graph, 
     vertex.size = 8, 
     vertex.label.cex = 0.5,
     vertex.label.color = "black",
     vertex.color = node_color,
     edge.color = edge_color,
     edge.width = 5)
# Save the plot as PNG in the chosen folder
png(file.path(folder_path, "graph_plot.png"))
plot(graph, 
     vertex.size = 8, 
     vertex.label.cex = 0.5,
     vertex.label.color = "black",
     vertex.color = node_color,
     edge.color = edge_color,
     edge.width = 5)
dev.off()