R/clusterSim.R

In BioCor: Functional similarities

# clusterSim ####
#' Similarity score between clusters of genes based on pathways similarity
#'
#' Looks for the similarity between genes in groups
#'
#' Once the pathways for each cluster are found they are combined using
#' \code{\link{combineScores}}.
#' @param cluster1,cluster2 A vector with genes.
#' @inheritParams geneSim
#' @inheritParams combineScores
#' @inheritParams pathSim
#' @export
#' @author Lluís Revilla
#' @seealso For a different approach see \code{\link{clusterGeneSim}},
#' \code{\link{combineScores}} and \code{\link{conversions}}
#' @return \code{clusterSim} returns a similarity score of the two clusters
#' @examples
#' if (require("org.Hs.eg.db")) {
#'     # Extract the paths of all genes of org.Hs.eg.db from KEGG (last update in
#'     # data of June 31st 2011)
#'     genes.kegg <- as.list(org.Hs.egPATH)
#'     clusterSim(c("9", "15", "10"), c("33", "19", "20"), genes.kegg)
#'     clusterSim(c("9", "15", "10"), c("33", "19", "20"), genes.kegg, NULL)
#'     clusterSim(c("9", "15", "10"), c("33", "19", "20"), genes.kegg, "avg")
#' } else {
#'     warning("You need org.Hs.eg.db package for this example")
#' }
clusterSim <- function(cluster1, cluster2, info, method = "max", ...) {
    if (length(unique(cluster1)) == 1L & length(unique(cluster2)) == 1L) {
        stop(
            "Introduce several genes in each cluster!\n",
            "If you want to calculate similarities ",
            "between two genes use geneSim"
        )
    }
    if (!all(is.character(cluster1)) | !all(is.character(cluster2))) {
        stop("The input genes should be characters")
    }
    cluster1 <- unique(cluster1)
    cluster2 <- unique(cluster2)

    if (!is.list(info)) {
        stop("info should be a list. See documentation.")
    }

    clust1_logic <- !cluster1 %in% names(info)
    clust2_logic <- !cluster2 %in% names(info)

    if (all(clust1_logic) & all(clust2_logic)) {
        warning("At least one gene should be in the list provided")
        return(NA)
    } else if (any(clust1_logic) | any(clust2_logic)) {
        warning("Some genes are not in the list provided.")
    }

    # Extract all pathways for each gene
    pathways1 <- lapply(cluster1, getElement, object = info)
    pathways2 <- lapply(cluster2, getElement, object = info)

    # Remove duplicated and NA
    pathways1 <- unique(unlist(pathways1, use.names = FALSE))
    pathways2 <- unique(unlist(pathways2, use.names = FALSE))
    pathways1 <- pathways1[!is.na(pathways1)]
    pathways2 <- pathways2[!is.na(pathways2)]

    if (is.null(pathways1) & is.null(pathways2)) {
        return(NA)
    }

    pathways <- unique(c(pathways1, pathways2))

    sim_all <- mpathSim(pathways, info, NULL)
    sim <- sim_all[pathways1, pathways2]
    if (!is.null(method)) {
        combineScoresPar(sim, method, ...)
    } else {
        sim
    }
}


#' @describeIn clusterSim Calculates all the similarities of the
#' GeneSetCollection and combine them using \code{\link{combineScoresPar}}
#' @export
setMethod(
    "clusterSim",
    c(
        info = "GeneSetCollection", cluster1 = "character",
        cluster2 = "character"
    ),
    function(cluster1, cluster2, info, method, ...) {
        if (length(unique(cluster1)) == 1L & length(unique(cluster2)) == 1L) {
            stop(
                "Introduce several genes in each cluster!\n",
                "If you want to calculate similarities ",
                "between two genes use geneSim"
            )
        }

        # Check they are unique
        cluster1 <- unique(cluster1)
        cluster2 <- unique(cluster2)

        # Extract the ids
        origGenes <- geneIds(info)
        # Check that the genes are in the GeneSetCollection
        genes <- unique(unlist(origGenes, use.names = FALSE))
        if (all(!cluster1 %in% genes)) {
            warning("At least one gene should be in the GeneSetCollection provided")
            return(NA)
        }

        if (all(!cluster2 %in% genes)) {
            warning("At least one gene should be in the GeneSetCollection provided")
            return(NA)
        }

        # Simplify the GeneSetCollection
        keep <- sapply(origGenes, function(x) {
            any(c(cluster1, cluster2) %in% x)
        })
        gscGenes <- info[names(keep[keep])]

        # Search for the paths of each gene
        clusters <- list(cluster1 = cluster1, cluster2 = cluster2)
        ids <- geneIds(gscGenes)
        paths <- sapply(clusters, function(x) {
            keepPaths <- sapply(ids, function(y) {
                any(x %in% y)
            })
            names(keepPaths[keepPaths])
        })

        # Calculate the pathSim of all the implied pathways
        pathsSim <- mpathSim(info = gscGenes, method = NULL)
        # Summarize the information
        out <- combineScoresPar(pathsSim, method, subSets = paths)
        out["cluster1", "cluster2"]
    }
)