In dmontemayor/Rcricvol: Data Processing Tools in R for cricvol project

Generate Ion-Pathway information for enrichment analysis

Before calculating enrichment score: make dataframe of ions and which pathways they are in in: annotated ions out: writes dataframe of ions and which pathways they are in as pathway_df.csv

#library(BiocManager)
#BiocManager::install("hmdbQuery")

find_ions_in_pathways <- function(ann) {
  library(hmdbQuery)

  # load annotated ions
  ann <- read.csv("../data/CRIC_annotation_1mD_neg.csv")

  pathway_df <- data.frame("init" = c(1:(nrow(ann)*100)),
                           "metabolite" = 0, 
                           "ion" = 0,
                           "HMDBID" = 0,
                           "pathway" = 0)
  pathway_df <- pathway_df[-1]
  line <- 1

  metabolites <- c()

  # for every metabolite in ann (i) 
  for (i in 1:nrow(ann)) { 
    met_IDs <- unlist(strsplit(as.character(ann$'ï..id'[i]), '; '))
    met_IDs <- met_IDs[grepl("HMDB", met_IDs)]
    met_IDs <- sub("HMDB", "HMDB00", met_IDs)
    metabolites <- c(metabolites, gsub("[[:punct:]]", "", ann$name[i]))

    for (id in met_IDs) {

      entry <- tryCatch(HmdbEntry(prefix = "http://www.hmdb.ca/metabolites/", id = id), error = function(e) entry <- NA)

      if (!is.na(entry)) {
        pathways <- store(entry)$biological_properties$pathways

        if (pathways != "\n    ") {
          prev_paths <- c()

          for(path in 1:length(pathways)){
            if (!gsub(" ", "", unlist(as.character(pathways[path]$pathway$name))) %in% prev_paths){
              pathway_df$metabolite[line] <- gsub("[[:punct:]]", "", ann$name[i])
              pathway_df$ion[line] <- ann$ion[i]
              pathway_df$HMDBID[line] <- id
              pathway_df$pathway[line] <- gsub(" ", "", unlist(as.character(pathways[path]$pathway$name) ))
              line <- line + 1
              prev_paths <- c(prev_paths, gsub(" ", "", unlist(as.character(pathways[path]$pathway$name))))
            }
          }
        }
      }
    }
    print(paste0("met ", i, ": ", as.character(ann$name[i])))
  }

  pathway_df <- pathway_df[pathway_df$metabolite != 0,]

  return(pathway_df)

  write.csv(pathway_df, "../data/pathway_df.csv", row.names = FALSE)
}

Re format pathway_df as binary matrix in: dataframe of ions and which pathways they are in (from prev) out: writes binary matrix of ions by pathway as ion_inpath.csv

binary_pathways <- function(pathway_df) {
  ion_inpath <- as.data.frame(matrix(nrow = length(unique(ann$ion)), ncol = length(unique(pathway_df$pathway)), 0))
  rownames(ion_inpath) <- unique(ann$ion)
  colnames(ion_inpath) <- unique(pathway_df$pathway)


  for (path in 1:ncol(ion_inpath)){
    ion_inpath[[path]] <- as.numeric(c(rownames(ion_inpath)) %in% unique(pathway_df[pathway_df$pathway == names(ion_inpath)[path],]$ion))
  }

  write.csv(ion_inpath, "../data/ion_inpath.csv", row.names = FALSE)
}

only do these once, takes several hours

ann <- read.csv("../data/CRIC_annotation_1mD_neg.csv")
pathway_df <- find_ions_in_pathways(ann)
binary_pathways(pathway_df = pathway_df)

dmontemayor/Rcricvol documentation built on Sept. 9, 2021, 9:12 a.m.