@@ -6,7 +6,7 @@ Date: 2019-03-15

 Author: Zuguang Gu

 Maintainer: Zuguang Gu <z.gu@dkfz.de>

 Depends: R (>= 3.1.2), methods, grid, graphics, stats, grDevices

-Imports: circlize (>= 0.4.5), GetoptLong, colorspace,

+Imports: circlize (>= 0.4.5), GetoptLong, colorspace, clue,

     RColorBrewer, GlobalOptions (>= 0.1.0), parallel

 Suggests: testthat (>= 1.0.0), knitr, markdown, dendsort, 

     Cairo, png, jpeg, tiff, fastcluster,

@@ -270,4 +270,7 @@ importFrom("utils", "combn")

 importFrom("utils", "getFromNamespace")

 importFrom("utils", "packageDescription")

 importFrom("utils", "str")

-

+importFrom("clue", as.cl_hard_partition)

+importFrom("clue", cl_ensemble)

+importFrom("clue", cl_consensus)

+importFrom("clue", cl_class_ids)

@@ -1,6 +1,7 @@

 CHANGES in VERSION 1.99.6

 * adjust the size of heatmap annotations and add testing scripts

+* run multiple times k-means to get a consensus partition

 ========================

@@ -152,8 +152,10 @@ Heatmap = setClass("Heatmap",

 # -split A vector or a data frame by which the rows are split. But if ``cluster_rows`` is a clustering object, ``split`` can be a single number

 #        indicating to split the dendrogram by `stats::cutree`.

 # -row_km Same as ``km``.

+# -row_km_repeats Number of k-means runs to get a consensus k-means clustering.

 # -row_split Same as ``split``.

 # -column_km K-means clustering on columns.

+# -column_km_repeats Number of k-means runs to get a consensus k-means clustering.

 # -column_split Split on columns. For heatmap splitting, please refer to https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#heatmap-split .

 # -gap Gap between row slices if the heatmap is split by rows. The value should be a `grid::unit` object.

 # -row_gap Same as ``gap``.

@@ -254,8 +256,10 @@ Heatmap = function(matrix, col, name,

     km = 1, 

     split = NULL, 

     row_km = km,

+    row_km_repeats = 10,

     row_split = split,

     column_km = 1,

+    column_km_repeats = 10,

     column_split = NULL,

     gap = unit(1, "mm"),

     row_gap = unit(1, "mm"),

@@ -413,8 +417,10 @@ Heatmap = function(matrix, col, name,

     .Object@matrix = matrix

     .Object@matrix_param$row_km = row_km

+    .Object@matrix_param$row_km_repeats = row_km_repeats

     .Object@matrix_param$row_gap = row_gap

     .Object@matrix_param$column_km = column_km

+    .Object@matrix_param$column_km_repeats = column_km_repeats

     .Object@matrix_param$column_gap = column_gap

     ### check row_split and column_split ###

@@ -912,6 +918,7 @@ make_cluster = function(object, which = c("row", "column")) {

     method = slot(object, paste0(which, "_dend_param"))$method

     order = slot(object, paste0(which, "_order"))  # pre-defined row order

     km = getElement(object@matrix_param, paste0(which, "_km"))

+    km_repeats = getElement(object@matrix_param, paste0(which, "_km_repeats"))

     split = getElement(object@matrix_param, paste0(which, "_split"))

     reorder = slot(object, paste0(which, "_dend_param"))$reorder

     cluster = slot(object, paste0(which, "_dend_param"))$cluster

@@ -1108,16 +1115,26 @@ make_cluster = function(object, which = c("row", "column")) {

     if(verbose) qq("clustering object is not pre-defined, clustering is applied to each @{which} slice\n")

     # make k-means clustering to add a split column

+    consensus_kmeans = function(mat, centers, km_repeats) {

+        partition_list = lapply(seq_len(km_repeats), function(i) {

+            as.cl_hard_partition(kmeans(mat, centers))

+        })

+        partition_list = cl_ensemble(list = partition_list)

+        partition_consensus = cl_consensus(partition_list)

+        as.vector(cl_class_ids(partition_consensus)) 

+    }

     if(km > 1 && is.numeric(mat)) {

         if(which == "row") {

-            km.fit = kmeans(mat, centers = km)

-            cl = km.fit$cluster

+            # km.fit = kmeans(mat, centers = km)

+            # cl = km.fit$cluster

+            cl = consensus_kmeans(mat, km, km_repeats)

             meanmat = lapply(sort(unique(cl)), function(i) {

                 colMeans(mat[cl == i, , drop = FALSE])

             })

         } else {

-            km.fit = kmeans(t(mat), centers = km)

-            cl = km.fit$cluster

+            # km.fit = kmeans(t(mat), centers = km)

+            # cl = km.fit$cluster

+            cl = consensus_kmeans(t(mat), km, km_repeats)

             meanmat = lapply(sort(unique(cl)), function(i) {

                 rowMeans(mat[, cl == i, drop = FALSE])

             })

new file mode 100644