R/CAMMGCluster.R

In debCAM: Deconvolution by Convex Analysis of Mixtures

#' MG cluster detection for CAM
#'
#' This function finds corner clusters as MG clusters
#' (clusters containing marker genes).
#' @param K The candidate subpopulation number.
#' @param PrepResult An object of class "\code{\link{CAMPrepObj}}" obtained
#'     from \code{\link{CAMPrep}} function.
#' @param generalNMF If TRUE, the decomposed proportion matrix has no sum-to-one
#'     constraint for each row. Without this constraint, the scale ambiguity of
#'     corner cluster centers will not be removed when computing
#'     reconstruction errors.
#'     The default is FALSE.
#' @param nComb The number of possible combinations of clusters as corner
#'     clusters. Within these possible combinations ranked by margin errors,
#'     we can further select the best one based on reconstruction errors.
#'     The default is 200.
#' @details This function is used internally by \code{\link{CAM}} function to
#' detect clusters containing marker genes,
#' or used when you want to perform CAM step by step.
#'
#' This function provides two solutions. The first is the combination of
#' clusters yielding the minimum sum of margin-of-errors for cluster centers.
#' In the second, nComb possible combinations are selected by ranking sum of
#' margin-of-errors for cluster centers. Then the best one is selected based on
#' reconstruction errors of all data points in original space.
#' @return An object of class "\code{\link{CAMMGObj}}" containing the
#' following components:
#' \item{idx}{Two numbers which are two solutions' ranks by sum of
#'     margin-of-error.}
#' \item{corner}{The indexes of clusters as detected corners. Each row is a
#'     solution.}
#' \item{error}{Two rows. The first row is sum of margin-of-errors for nComb
#'     possible combinations. The second row is reconstruction errors for
#'     nComb possible combinations.}
#' @export
#' @examples
#' #obtain data
#' data(ratMix3)
#' data <- ratMix3$X
#'
#' #preprocess data
#' rPrep <- CAMPrep(data, dim.rdc = 3, thres.low = 0.30, thres.high = 0.95)
#'
#' #Marker gene cluster detection with a fixed K = 3
#' rMGC <- CAMMGCluster(3, rPrep)
CAMMGCluster <- function(K, PrepResult, generalNMF = FALSE, nComb = 200) {
    X <- PrepResult@centers
    if (K > nrow(X) || K > ncol(X)){
        warning("Return NULL for K = ", K, " larger than ", max(dim(X)))
        return(NULL)
    }
    if (K == ncol(X)){
        return(new("CAMMGObj", idx=c(1,1),
                    corner=matrix(as.integer(colnames(X)),1)[c(1,1),]))
    }

    M <- nrow(X)
    topconv <- cornerSort(X, K, nComb)
    idx <- topconv$idx
    error1 <- topconv$error
    nComb <- length(error1)
    scaleRecover <- !generalNMF


    Xall <- PrepResult@Xprep[,!(PrepResult@cluster$cluster %in%
                                PrepResult@c.outlier)]

    errCalcu <- function (p, idx, X, Xall, W, scaleRecover) {
        out <- tryCatch({
            A <- X[,idx[,p]]
            if (scaleRecover) {
                #scale <- as.vector(coef(nnls(A,matrix(W,ncol=1))))
                scale <- c(.fcnnls(A, matrix(W,ncol=1))$coef)
                scale[scale<1e-10] <- 0.01/(sqrt(colSums(A^2)))[scale<1e-10]
                A<-A%*%diag(scale)
            }
            #sum(apply(Xall,2, function(x) (nnls(A,x))$deviance))
            err <- sum((Xall - A%*%(.fcnnls(A, Xall)$coef))^2)
            return(err)
        }, error=function(e) {
            err <- sum(Xall^2)
            return(err)
        })

    }
    error2 <- unlist(lapply(seq_len(nComb), errCalcu,
                            idx, X, Xall, PrepResult@W%*%PrepResult@SW,
                            scaleRecover))

    idx1 <- which.min(error1)
    idx2 <- which.min(error2)
    ind1 <- as.integer(colnames(X[,idx[,idx1]]))
    ind2 <- as.integer(colnames(X[,idx[,idx2]]))


    return(new("CAMMGObj", idx=c(idx1,idx2), corner=rbind(ind1,ind2),
                    error=cbind(error1,error2)))
}