R/createClusters.R

In Krutik6/TimiRGeN: Time sensitive microRNA-mRNA integration, analysis and network generation tool

#' @title createClusters
#' @description Creates soft clusters to assess changes in gene abundance during
#' the time course in many pathways. createClusters will create 3 data files.
#' 1) Clusters will contain cluster logistics information and will be stored as
#' metadata, 2) MfuzzData will contain fuzzy clustering information and will be
#' stored as an experiment, 3) ClusterData will contain cluster-pathway fit
#' information and will be stored as an assay. This function may take some time
#' as it downloads pathway information.
#' @param MAE MultiAssayExperiment which will store the results from
#' createClusters. It is recommended to use the MAE object which stores the
#' output of by turnPercent.
#' @param method Either "c" or "s", respectively for combined or separated
#' analysis.
#' @param percentMatrix A matrix containing wikipathway-data information. It
#' is output from the turnPercent function and will be stored as an assay
#' within the MAE used in the turnPercent function.
#' @param noClusters Number of clusters to create, the default is 5.
#' @param dataString Only for use in "s" analysis. Insert the prefix string e.g.
#' "mRNA" or "miR". The string added should be the same as the prefixString
#' added during the addPrefix function.
#' @param variance Numeric value from 0-1 to control strictness of filtering.
#' Higher variance means more pathways will be excluded from the analysis.
#' @return 3 new objects in the input MAE.
#' Clusters(metadata): A list to be used as the input in checkClusters
#' and quickFuzz.
#' MfuzzData(ExperimentList): An ExpressionSet object to be used as input for
#' quickFuzz.
#' ClusterData(assay): An assay to be used as input for returnCluster.
#' @export
#' @importFrom Mfuzz filter.std standardise mestimate mfuzz
#' @importFrom stats na.omit
#' @importFrom rWikiPathways getPathwayInfo
#' @usage createClusters(MAE, method, percentMatrix, noClusters,
#' dataString = '', variance)
#' @examples
#' MAE <- MultiAssayExperiment()
#'
#' metadata(MAE)[["e_list"]] <- e_list_mouse
#'
#' metadata(MAE)[["w_list"]] <- w_list_mouse[1:10]
#'
#' MAE <- wikiMatrix(MAE, ID_list = metadata(MAE)[[1]],
#'                   wp_list = metadata(MAE)[[2]])
#'
#' MAE <- turnPercent(MAE = MAE,
#'                    wikiMatrix = assay(MAE, 1))
#'
#'MAE <- createClusters(MAE, method = "c",
#'                     percentMatrix = assay(MAE, 2),
#'                     noClusters = 2, variance = 0.99)
createClusters <- function(MAE, method, percentMatrix, noClusters = 5,
                           dataString, variance = 0){

    if (missing(MAE)) stop('MAE is missing. Add MultiAssayExperiment. Data from createClusters will be stored in this MAE object. Please use turnPercent first.')

    if (missing(method)) stop('method is missing. Please add method "c" for combined analysis or "s" for separated analysis')

    if (missing(percentMatrix)) stop('percentMatrix is missing. Add dataframe which contains pathways-sample information as percentages. Please use the turnPercent function first. Results from turnPercent will be stored as an assay within the MAE used in the turnPercent function.')

    metadata <- `metadata<-` <- NULL

    # ready the percentMatrix
    X <- percentMatrix

    df <- as.data.frame(t(X))

    df$Total <- NULL

    # Convert factors into numeric
    df <- data.matrix(frame = df, rownames.force = NA)

    df <- round(df, 0)

    df <- na.omit(df)

    # If == s then subset data by common string e.g mRNA, miR
    if (method == 's') {

      if (missing(dataString)) stop('dataString is missing.  Add prefix which was added in the addPrefix function e.g. "mRNA" or "miR".')

      df <- as.data.frame(df)

      df2 <- df[, grepl(dataString, names(df))]

    # If == c do nothing
    } else if (method == 'c') {

      df2 <- df

    } else print('Enter c or s as method.')

    # standardise data using standard Mfuzz code
    Eset <- new('ExpressionSet', exprs = as.matrix(df2))

    Eset_sd <- Mfuzz::filter.std(Eset, min.std = variance)

    Eset_st <- Mfuzz::standardise(Eset_sd)

    m <- Mfuzz::mestimate(Eset_st)

    # Perform mfuzz
    Clusters <- Mfuzz::mfuzz(Eset_st, centers = noClusters, m=m)

    # Get cluster membership information
    X <- as.data.frame(Clusters$membership)

    # retrieve pathway names using rWikipathways using membership
    for (i in seq_along(rownames(X))) {
      X$Description[i] <- rWikiPathways::getPathwayInfo(rownames(X)[i])[[3]]
        }

    # Save data in MAE
    MAE2 <- suppressWarnings(suppressMessages(MultiAssayExperiment(list("ClusterData" = X,
                                                       "MfuzzData" = Eset_st))))
    metadata(MAE2)[["Clusters"]] <- Clusters

    MAE <- suppressWarnings(c(MAE, MAE2))

return(MAE)
}