R/convert-functions.R

In harpomaxx/GSgalgoR: An Evolutionary Framework for the Identification and Study of Prognostic Gene Expression Signatures in Cancer

#' Convert galgo.Obj to list
#'
#' The current function transforms a \code{galgo.Obj} to a \code{list}
#'
#' @param output An object of class \code{galgo.Obj}
#'
#' @return The current function restructurates a \code{galgo.Obj} to a more 
#' easy to understand an use \code{list}. This output is particularly useful 
#' if one wants to select a given solution and use its outputs in a new 
#' classifier. The output of type \code{list} has a length equals to the 
#' number of solutions obtained by the \code{\link[GSgalgoR:galgo]{galgo}} 
#' algorithm.
#'
#' Basically this output is a list of lists, where each element of the output 
#' is named after the solution's name (\code{solution.n}, where \code{n} is 
#' the number assigned to that solution), and inside of it, it has all the 
#' constituents for that given solution with the following structure:
#' \enumerate{
#' \item \strong{output$solution.n$Genes}: A vector of the features included 
#' in the solution
#' \item \strong{output$solution.n$k}: The number of partitions found in that 
#' solution
#' \item \strong{output$solution.n$SC.Fit}: The average silhouette coefficient 
#' of the partitions found
#' \item \strong{output$solution.n$Surv.Fit}: The survival fitness value
#' \item \strong{output$solution.n$Rank}: The solution rank
#' \item \strong{CrowD}: The solution crowding distance related to the rest 
#' of the solutions
#' }
#' @export
#' @author Martin E Guerrero-Gimenez, \email{mguerrero@mendoza-conicet.gob.ar}
#'
#' @examples
#' # load example dataset
#' library(breastCancerTRANSBIG)
#' data(transbig)
#' Train <- transbig
#' rm(transbig)
#'
#' expression <- Biobase::exprs(Train)
#' clinical <- Biobase::pData(Train)
#' OS <- survival::Surv(time = clinical$t.rfs, event = clinical$e.rfs)
#'
#' # We will use a reduced dataset for the example
#' expression <- expression[sample(1:nrow(expression), 100), ]
#'
#' # Now we scale the expression matrix
#' expression <- t(scale(t(expression)))
#'
#' # Run galgo
#' output <- GSgalgoR::galgo(generations = 5, population = 15, 
#' prob_matrix = expression, OS = OS)
#' outputDF <- to_dataframe(output)
#' outputList <- to_list(output)
to_list <- function(output) {
    if (!methods::is(output, "galgo.Obj")) {
        stop("object must be of class 'galgo.Obj'")
    }
    OUTPUT <- vector("list", nrow(Solutions(output)))
    Genes <-
        colnames(Solutions(output))[seq_len(ncol(Solutions(output)) - 5)]
    for (i in seq_len(nrow(Solutions(output)))) {
        Sol <- paste("Solution", i, sep = ".")
        names(OUTPUT)[i]<-Sol
        OUTPUT[[Sol]] <- list()
        OUTPUT[[Sol]][["Genes"]] <-
            Genes[as.logical(Solutions(output)[i, seq_len(length(Genes))])]
        OUTPUT[[Sol]]["k"] <- Solutions(output)[i, "k"]
        OUTPUT[[Sol]]["SC.Fit"] <-
            Solutions(output)[i, length(Genes) + 2]
        OUTPUT[[Sol]]["Surv.Fit"] <-
            Solutions(output)[i, length(Genes) + 3]
        OUTPUT[[Sol]]["rank"] <- Solutions(output)[i, "rnkIndex"]
        OUTPUT[[Sol]]["CrowD"] <- Solutions(output)[i, "CrowD"]
    }
    return(OUTPUT)
}

#' Convert galgo.Obj to data.frame
#'
#' The current function transforms a \code{galgo.Obj} to a \code{data.frame}
#'
#' @param output An object of class \code{galgo.Obj}
#'
#' @return The current function restructurates a \code{galgo.Obj} to a more 
#' easy to understand an use \code{data.frame}. The output \code{data.frame} 
#' has \eqn{ m x n} dimensions, were the rownames (\eqn{m}) are the solutions 
#' obtained by the \code{\link[GSgalgoR:galgo]{galgo}} algorithm. 
#' The columns has the following structure:
#' \enumerate{
#' \item \strong{Genes}: The features included in each solution in form 
#' of a \code{list}
#' \item \strong{k}: The number of partitions found in that solution
#' \item \strong{SC.Fit}: The average silhouette coefficient of the 
#' partitions found
#' \item \strong{Surv.Fit}: The survival fitness value
#' \item \strong{Rank}: The solution rank
#' \item \strong{CrowD}: The solution crowding distance related to the 
#' rest of the solutions
#' }
#' @export
#'
#' @author Martin E Guerrero-Gimenez, \email{mguerrero@mendoza-conicet.gob.ar}
#'
#' @examples
#' # load example dataset
#' library(breastCancerTRANSBIG)
#' data(transbig)
#' Train <- transbig
#' rm(transbig)
#'
#' expression <- Biobase::exprs(Train)
#' clinical <- Biobase::pData(Train)
#' OS <- survival::Surv(time = clinical$t.rfs, event = clinical$e.rfs)
#'
#' # We will use a reduced dataset for the example
#' expression <- expression[sample(1:nrow(expression), 100), ]
#'
#' # Now we scale the expression matrix
#' expression <- t(scale(t(expression)))
#'
#' # Run galgo
#' output <- GSgalgoR::galgo(generations = 5, population = 15, 
#' prob_matrix = expression, OS = OS)
#' outputDF <- to_dataframe(output)
#' outputList <- to_list(output)
to_dataframe <- function(output) {
    if (!methods::is(output, "galgo.Obj")) {
        stop("object must be of class 'galgo.Obj'")
    }
    Genes <-
        colnames(Solutions(output))[seq_len(ncol(Solutions(output)) - 5)]
    ListGenes <- vector("list", nrow(Solutions(output)))
    for (i in seq_len(nrow(Solutions(output)))) {
        ListGenes[[i]] <- list()
        ListGenes[[i]] <-
            Genes[as.logical(Solutions(output)[i, seq_len(length(Genes))])]
    }

    OUTPUT <-
        data.frame(
            Genes = I(ListGenes),
            k = Solutions(output)[, "k"],
            SC.Fit = Solutions(output)[, ncol(Solutions(output)) - 3],
            Surv.Fit = Solutions(output)[, ncol(Solutions(output)) - 2],
            Rank = Solutions(output)[, "rnkIndex"],
            CrowD = Solutions(output)[, "CrowD"]
        )
    rownames(OUTPUT) <-
        paste("Solutions", seq_len(nrow(Solutions(output))), sep = ".")
    return(OUTPUT)
}