R/import_panm_names.R

In ampatzia/pasaR: Pangenome Statistical Analysis for R

#Import names of genomes  datasets functions

#'  Organism Names - fami
#'
#' This function outputs the genome names of a fami clustering type input
#'
#'
#' @param file_path Path to file in disk
#' @export
#' @examples \dontrun{organism_names_panmatrix_fami(file_path)}
#'

organism_names_panmatrix_fami <- function (file_path) {

  Organism<-NULL
  Protein<-NULL
  Cluster<-NULL
  cluster<-NULL
  Proteins<-NULL
  cluster_composition<-NULL
  value<-NULL
  V1<-NULL

  work_list <- readr::read_delim(file_path, "\t", escape_double = FALSE,
                                 col_names = FALSE, trim_ws = TRUE)
  split <- as.data.frame(stringr::str_split_fixed(work_list$X1, " ",
                                                  n = Inf))
  work_list <- dplyr::bind_cols(split, work_list[, -1])
  work_list$V2 <- as.character(work_list$V2)
  work_list <- tidyr::gather(work_list, V1)
  work_list <- work_list[, c(1, 3)]
  colnames(work_list) <- c("Cluster", "V1")
  work_list <- work_list[stats::complete.cases(work_list), ]
  work_list <- tidyr::separate(work_list, V1, into = c("n1", "n2",
                                                       "version", "PID"), sep = "-")
  work_list$n1 <- paste0(work_list$n1, "_", work_list$n2)
  work_list$Cluster <- as.numeric(unlist(stringr::str_extract_all(work_list$Cluster,
                                                                  "\\(?[0-9,.]+\\)?")))
  work_list <- work_list[, c(1, 2, 5)]
  colnames(work_list) <- c("Cluster", "Organism", "Protein")
  cluster_composition <- function(x) {
    result_df_cl1 <- x %>% dplyr::group_by(., Cluster, Organism) %>%
      dplyr::summarise(., Proteins = length(Protein))
    return(result_df_cl1)
  }
  panm <- work_list %>% cluster_composition(.) %>% tidyr::spread(.,
                                                                 Cluster, Proteins, fill = 0)
  org_names <- panm[, 1]
  return(org_names)
}


#'  Organism Names - MCL
#'
#' This function outputs the genome names of a MCL clustering type input
#'
#'
#' @param file_path Disk Path to file
#' @export
#' @examples \dontrun{organism_names_panmatrix(file_path)}

org_names <- function(file_path){
  Organism<-NULL
  Protein<-NULL
  Cluster<-NULL
  cluster<-NULL
  Proteins<-NULL
  cluster_composition<-NULL
  value<-NULL
  V1<-NULL
  x1<-NULL
  make_base_df <- function(x){

    work_list <- scan(file=x,what="character,n=195,",sep=" ", allowEscapes = TRUE)%>%
      stringr::str_split_fixed(.," ", n = Inf) %>%
      sapply(.,stringi::stri_escape_unicode) %>% #Escapes all Unicode (not ASCII-printable) code points ie. single /
      sapply(., function(x) stringr::str_split_fixed(x,"[\\\\]+t|[^[:print:]]" , n = Inf)) %>%
      lapply(., function(x) stringr::str_split_fixed(x,"\\|", n=Inf))%>%
      lapply(., function(x) data.frame(x, stringsAsFactors=FALSE)) %>%
      lapply(., function(x){colnames(x)[1] <- "x1"; x}) %>%
      lapply(., function(x) tidyr::separate(x,x1,into = c("Organism", "Protein", "Other"), sep="\\$"))%>%
      lapply(., function(x) x[,names(x) %in% c("Organism", "Protein") ])

    for (i in 1:length(work_list)){

      work_list[[i]]<-transform(work_list[[i]],Cluster=i)}


    #Make list to dataframe
    result_df <- dplyr::bind_rows(work_list)
    rm(work_list)

    return(result_df)

  }

  cluster_composition <- function(x){
    result_df_cl1 <- x %>% dplyr::group_by(.,Cluster,Organism) %>%
      dplyr::summarise(.,Proteins=length(Protein))
    return(result_df_cl1)}

  panm<-file_path %>% make_base_df(.) %>% cluster_composition(.) %>%tidyr::spread(.,Cluster,Proteins,fill=0)
  organism_names<-panm[,1]
  rm(panm)
  return(organism_names)

}


#'  Organism Names - MCL (fami2)
#'
#' This function outputs the genome names of a MCL clustering type input
#'
#'
#' @param file_path Disk Path to file
#' @export
#' @examples \dontrun{organism_names_fami2(file_path)}


org_names_fami2 <- function (file_path){
  Organism<-NULL
  Protein<-NULL
  Cluster<-NULL
  cluster<-NULL
  Proteins<-NULL
  cluster_composition<-NULL
  value<-NULL
  V1<-NULL
  work_list <- readr::read_delim(file_path, "\t", escape_double = FALSE,
                                 col_names = FALSE, trim_ws = TRUE)

  work_list$V1<-(paste0("cluster",1:nrow(work_list)))
  colvals<-paste0("X",1:(ncol(work_list)-1))
  work_list <- tidyr::gather(work_list,cluster,value,1:(ncol(work_list)-1))
  work_list <- work_list[, c(1, 3)]
  colnames(work_list) <- c("Cluster", "V1")
  work_list <- work_list[stats::complete.cases(work_list), ]
  work_list <- tidyr::separate(work_list, V1, into = c("n1", "n2",
                                                       "version", "PID"), sep = "-")
  work_list$n1 <- paste0(work_list$n1, "_", work_list$n2)
  work_list$Cluster <- as.numeric(unlist(stringr::str_extract_all(work_list$Cluster,
                                                                  "\\(?[0-9,.]+\\)?")))
  work_list <- work_list[, c(1, 2, 5)]
  colnames(work_list) <- c("Cluster", "Organism", "Protein")
  cluster_composition <- function(x) {
    result_df_cl1 <- x %>% dplyr::group_by(., Cluster, Organism) %>%
      dplyr::summarise(., Proteins = length(Protein))
    return(result_df_cl1)
  }
  panm <- work_list %>% cluster_composition(.) %>% tidyr::spread(.,
                                                                 Cluster, Proteins, fill = 0)
  org_names <- panm[, 1]
  return(org_names)
}