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+#' Feature Selection Optimization for sPLS method 
+#' 
+#' This function identify the number of feautures to keep per component and thus by cluster in \code{mixOmics::spls} by optimizing the silhouette coefficient, which assesses the quality of clustering.
+#' 
+#' @param X numeric matrix (or data.frame) with features in columns and samples in rows
+#' @param Y numeric matrix (or data.frame) with features in columns and samples in rows (same rows as \code{X})
+#' @param ncomp integer, number of component to include in the model
+#' @param test.keepX vector of integer containing the different value of keepX to test for block \code{X}.
+#' @param test.keepY vector of integer containing the different value of keepY to test for block \code{Y}.
+#' @param ... other parameters to be included in the spls model (see \code{mixOmics::spls})
+#' 
+#' @return 
+#' \item{silhouette}{silhouette coef. computed for every combinasion of keepX/keepY}
+#' \item{ncomp}{number of component included in the model}
+#' \item{test.keepX}{list of tested keepX}
+#' \item{test.keepY}{list of tested keepY}
+#' \item{block}{names of blocks}
+#' \item{slopes}{"slopes" computed from the silhouette coef. for each keepX and keepY, used to determine the best keepX and keepY}
+#' \item{choice.keepX}{best \code{keepX} for each component}
+#' \item{choice.keepY}{best \code{keepY} for each component}
+#' 
+#'
+#' @details
+#' For each component and for each keepX/keepY value, a spls is done from these parameters.
+#' Then the clustering is performed and the silhouette coefficient is calculated for this clustering.
+#'
+#' We then calculate "slopes" where keepX/keepY are the coordinates and the silhouette is the intensity.
+#' A z-score is assigned to each slope.
+#' We then identify the most significant slope which indicates a drop in the silhouette coefficient and thus a deterioration of the clustering.
+#'
+#' 
+#' @seealso 
+#' \code{\link[mixOmics]{spls}}, \code{\link[timeOmics]{getCluster}}, \code{\link[timeOmics]{plotLong}}
+#'
+#' @examples
+#' demo <- suppressWarnings(get_demo_cluster())
+#' X <- demo$X
+#' Y <- demo$Y
+#' 
+#' # tuning
+#' tune.spls <- tuneCluster.spls(X, Y, ncomp= 2, test.keepX= c(5,10,15,20), test.keepY= c(2,4,6))
+#' keepX <- tune.spls$choice.keepX
+#' keepY <- tune.spls$choice.keepY
+#' 
+#' # final model
+#' spls.res <- mixOmics::spls(X, Y, ncomp= 2, keepX= keepX, keepY= keepY)
+#' 
+#' # get clusters and plot longitudinal profile by cluster
+#' spls.cluster <- getCluster(spls.res)
+#' plotLong(spls.res)
+#' 
+#'
+#' @export
+#' @import mixOmics
+#' @importFrom dplyr left_join
+#' @importFrom dplyr mutate
+#' @importFrom dplyr filter
+
+tuneCluster.spls <- function(X, Y, ncomp = 2, test.keepX = rep(ncol(X), ncomp),
+                             test.keepY = rep(ncol(Y), ncomp), ...){
+    #-- checking input parameters ------------  ---------------------------------#
+    #--------------------------------------------------------------------------#
+
+    #-- X
+    X <- validate_matrix_X(X)
+    Y <- validate_matrix_Y(Y)
+
+    #-- ncomp
+    ncomp <- validate_ncomp(ncomp, list(X,Y))
+
+    #-- test.keepX
+    test.keepX <- validate_test_keepX(test.keepX = test.keepX, X = X)
+
+    #-- test.keepY
+    test.keepY <- validate_test_keepY(test.keepY = test.keepY, Y = Y)
+
+    list.keepX.keepY <- list("keepX" = test.keepX, "keepY" = test.keepY) %>%
+        expand.grid(stringsAsFactors = FALSE, KEEP.OUT.ATTRS = FALSE)
+
+
+    #-- launch tuning  --------------------------------------------------------#
+    #--------------------------------------------------------------------------#
+
+    #-- 0. set output object
+    result <- as.data.frame(matrix(ncol = 5, nrow = nrow(list.keepX.keepY)*ncomp))
+    colnames(result) <- c("comp", "X", "Y", "pos", "neg")
+    result.index <- 0
+
+    #--1. compute dissimilarity matrix for silhouette coef. (once and for all)
+    all_data <- cbind(X, Y)
+    dmatrix <- dmatrix.spearman.dissimilarity(all_data)
+
+    cluster <- as.data.frame(list("feature" = rownames(dmatrix),
+                                  "block" = c(rep("X", ncol(X)), rep("Y", ncol(Y)))))
+
+    #--2. tuning
+    for(comp in 1:ncomp){
+        for(index.list.kX.kY in 1:nrow(list.keepX.keepY)){
+            # foreach comp, keepX and keepY of other comp is set to minimum
+            kX <- rep(min(test.keepX), ncomp)
+            kY <- rep(min(test.keepY), ncomp)
+            kX[comp] <- list.keepX.keepY[index.list.kX.kY,"keepX"]
+            kY[comp] <- list.keepX.keepY[index.list.kX.kY,"keepY"]
+
+            #--3. run spls
+            spls.res <- mixOmics::spls(X = X, Y = Y, ncomp =  ncomp, keepX = kX, keepY = kY, ...)
+
+            #--4. extract clusters
+            tmp.cluster <- getCluster(spls.res)
+            tmp.cluster <- suppressWarnings(dplyr::left_join(cluster, tmp.cluster[c(1,4)],
+                                                             by = c("feature"="molecule"))) %>%
+                dplyr::mutate(cluster = as.numeric(as.character(cluster))) %>%
+                dplyr::mutate(cluster = ifelse(is.na(cluster), 0, cluster))
+
+            #--5. compute silhouette
+            sil <- silhouette(dmatrix, tmp.cluster$cluster)
+
+            #--6. store
+            result.index <- result.index + 1
+            result[result.index, "comp"] <- comp
+            result[result.index, "X"] <- kX[comp]
+            result[result.index, "Y"] <- kY[comp]
+            # result[result.index, "pos"] <- sil$average.cluster  %>%
+            #     dplyr::filter(cluster == comp) %>% pull(silhouette.coef)
+            # result[result.index, "neg"] <- sil$average.cluster  %>%
+            #     dplyr::filter(cluster == -comp) %>% pull(silhouette.coef)
+            pos.res <-  sil$average.cluster  %>% 
+                dplyr::filter(cluster == comp) %>%
+                dplyr::pull(silhouette.coef)
+            result[result.index, "pos"] <- ifelse(length(pos.res) == 0, NA, pos.res)
+            neg.res <-  sil$average.cluster  %>%
+                dplyr::filter(cluster == -comp) %>%
+                dplyr::pull(silhouette.coef)
+            result[result.index, "neg"] <- ifelse(length(neg.res) == 0, NA, neg.res)
+        }
+    }
+    result <- list("silhouette" = result)
+    result[["ncomp"]] <- ncomp
+    result[["test.keepX"]] <- test.keepX
+    result[["test.keepY"]] <- test.keepY
+    result[["block"]] <- c("X", "Y")
+    class(result) <- "spls.tune.silhouette"
+    
+    #-- 7. choice.keepX / choice.keepY
+    result[["slopes"]] <- tune.silhouette.get_slopes(result)
+    tmp <- tune.silhouette.get_choice_keepX(result) # choice keepX/keepY
+    result[["choice.keepX"]] <- unlist(lapply(tmp, function(x) x$X))
+    result[["choice.keepY"]] <- unlist(lapply(tmp, function(x) x$Y))
+    return(result)
+}