@@ -106,6 +106,7 @@ importFrom("colorspace", diverge_hcl)

 importFrom("RColorBrewer", brewer.pal)

 importFrom("dendextend", get_branches_heights)

 importFrom("dendextend", nnodes)

+importFrom("dendextend", "labels<-")

 import(graphics)

 import(stats)

 import(grDevices)

@@ -5,6 +5,7 @@ CHANEGS in VERSION 1.9.3

 * add `range` option in `densityHeatmap()`

 * when `gap` is set for the main heatmap, other heatmps also adjust their `gap` values to it

 * fixed a bug that when rownames/colnames are not complete, dendrograms are corrupted

+* `alter_fun` now support adding graphics grid by grid

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

@@ -235,7 +235,6 @@ setMethod(f = "make_layout",

     }

     nr = nrow(object@ht_list[[i_main]]@matrix)

-    object@ht_list_param$main_heatmap = i_main

     if(n > 1) {

         if(length(gap) == 1) {

@@ -307,7 +306,8 @@ setMethod(f = "make_layout",

         gap = gap[seq_len(n-1)]

     }

     object@ht_list_param$gap = gap

-

+    object@ht_list_param$main_heatmap = i_main

+    

     n = length(object@ht_list)

     ## orders of other heatmaps should be changed

@@ -379,8 +379,6 @@ setMethod(f = "make_layout",

     for(i in seq_len(n)) {

         if(inherits(object@ht_list[[i]], "Heatmap")) {

             object@ht_list[[i]]@matrix_param$gap = ht_main@matrix_param$gap

-        } else {

-            object@ht_list[[i]]@gap = ht_main@matrix_param$gap

         }

     }

@@ -8,8 +8,9 @@

 #      You can use `unify_mat_list` to make all matrix having same row names and column names.

 # -get_type If different alterations are encoded in the matrix, this self-defined function

 #           determines how to extract them. Only work when ``mat`` is a matrix.

-# -alter_fun_list a list of functions which define how to add graphics for different alterations.

-#                 The names of the list should cover all alteration types.

+# -alter_fun a single function or a list of functions which define how to add graphics for different alterations.

+#                 If it is a list, the names of the list should cover all alteration types.

+# -alter_fun_list deprecated, use ``alter_run`` instead.

 # -col a vector of color for which names correspond to alteration types.

 # -row_order order of genes. By default it is sorted by frequency of alterations decreasingly.

 #                            Set it to ``NULL`` if you don't want to set the order

@@ -17,6 +18,7 @@

 #                                 the mutual exclusivity across genes. Set it to ``NULL`` if you don't want to set the order

 # -show_column_names whether show column names

 # -pct_gp graphic paramters for percent row annotation

+# -pct_digits digits for percent values

 # -axis_gp graphic paramters for axes

 # -show_row_barplot whether show barplot annotation on rows

 # -row_barplot_width width of barplot annotation on rows. It should be a `grid::unit` object

@@ -44,11 +46,11 @@

 # Zuguang Gu <z.gu@dkfz.de>

 #

 oncoPrint = function(mat, get_type = function(x) x,

-	alter_fun_list, col, 

+	alter_fun = alter_fun_list, alter_fun_list = NULL, col, 

 	row_order = oncoprint_row_order(),

 	column_order = oncoprint_column_order(),

 	show_column_names = FALSE,

-	pct_gp = gpar(), 

+	pct_gp = gpar(), pct_digits = 0,

 	axis_gp = gpar(fontsize = 8), 

 	show_row_barplot = TRUE, 

 	row_barplot_width = unit(2, "cm"),

@@ -64,6 +66,10 @@ oncoPrint = function(mat, get_type = function(x) x,

 			stop("`show_column_barplot` and `column_barplot_height` is deprecated, please configure `top_annotation` directly.")

 		}

 	}

+

+	if(!is.null(alter_fun_list)) {

+		warning("`alter_fun_list` is deprecated, please `alter_fun` instead.")

+	}

 	# convert mat to mat_list

 	if(inherits(mat, "matrix")) {

@@ -103,19 +109,42 @@ oncoPrint = function(mat, get_type = function(x) x,

 		stop("Incorrect type of 'mat'")

 	}

-	if(missing(alter_fun_list) && missing(col)) {

+	if(missing(alter_fun) && missing(col)) {

 		if(length(mat_list) == 1) {

-			alter_fun_list = list(function(x, y, w, h) grid.rect(x, y, w*0.9, h*0.9, gp = gpar(fill = "red", col = NA)))

+			af = function(x, y, w, h, v) {

+				grid.rect(x, y, w, h, gp = gpar(fill = "#CCCCCC", col = NA))

+				if(v[1]) grid.rect(x, y, w*0.9, h*0.9, gp = gpar(fill = "red", col = NA))

+			}

 			col = "red"

 		} else if(length(mat_list) == 2) {

-			alter_fun_list = list(

-		        function(x, y, w, h) grid.rect(x, y, w*0.9, h*0.9, gp = gpar(fill = "red", col = NA)),

-		        function(x, y, w, h) grid.rect(x, y, w*0.9, h*0.4, gp = gpar(fill = "blue", col = NA))

-		    )

+			af = function(x, y, w, h, v) {

+				grid.rect(x, y, w, h, gp = gpar(fill = "#CCCCCC", col = NA))

+				if(v[1]) grid.rect(x, y, w*0.9, h*0.9, gp = gpar(fill = "red", col = NA))

+		        if(v[2]) grid.rect(x, y, w*0.9, h*0.4, gp = gpar(fill = "blue", col = NA))

+		    }

 		    col = c("red", "blue")

+		} else {

+			stop("`alter_fun` should be specified.")

 		}

-		names(alter_fun_list) = names(mat_list)

 		names(col) = names(mat_list)

+	} else if(is.list(alter_fun)) {

+

+		# validate the list first

+		if(is.null(alter_fun$background)) alter_fun$background = function(x, y, w, h) grid.rect(x, y, w, h, gp = gpar(fill = "#CCCCCC", col = NA))

+		sdf = setdiff(all_type, names(alter_fun))

+		if(length(sdf) > 0) {

+			stop(paste0("You should define shape function for: ", paste(sdf, collapse = ", ")))

+		}

+

+		af = function(x, y, w, h, v) {

+			if(!is.null(alter_fun$background)) alter_fun$background(x, y, w, h)

+			alter_fun = alter_fun[names(alter_fun) != "background"]

+			for(nm in names(alter_fun)) {

+				if(v[nm]) alter_fun[[nm]](x, y, w, h)

+			}

+		}

+	} else {

+		af = alter_fun

 	}

 	# type as the third dimension

@@ -157,31 +186,15 @@ oncoPrint = function(mat, get_type = function(x) x,

 		column_order = structure(seq_len(sum(l)), names = which(l))[as.character(intersect(column_order, which(l)))]

 	}

-	# validate alter_fun_list

-	if(is.null(alter_fun_list$background)) alter_fun_list$background = function(x, y, w, h) grid.rect(x, y, w, h, gp = gpar(fill = "#CCCCCC", col = NA))

-	sdf = setdiff(all_type, names(alter_fun_list))

-	if(length(sdf) > 0) {

-		stop(paste0("You should define shape function for: ", paste(sdf, collapse = ", ")))

-	}

-

-	all_type = intersect(all_type, names(alter_fun_list))

-	all_type = setdiff(all_type, "background")

-

-	arr = arr[, , all_type, drop = FALSE]

-

 	# validate col

 	sdf = setdiff(all_type, names(col))

 	if(length(sdf) > 0) {

 		stop(paste0("You should define colors for:", paste(sdf, collapse = ", ")))

 	}

-	add_oncoprint = function(type, x, y, width, height) {

-		alter_fun_list[[type]](x, y, width, height)

-	}

-

 	# for each gene, percent of samples that have alterations

 	pct = rowSums(apply(arr, 1:2, any)) / ncol(mat_list[[1]])

-	pct = paste0(round(pct * 100), "%")

+	pct = paste0(round(pct * 100, digits = pct_digits), "%")

 	ha_pct = rowAnnotation(pct = row_anno_text(pct, just = "right", offset = unit(1, "npc"), gp = pct_gp), width = grobWidth(textGrob("100%", gp = pct_gp)))

 	#####################################################################

@@ -244,14 +257,17 @@ oncoPrint = function(mat, get_type = function(x) x,

 	dim(pheudo) = dim(arr)[1:2]

 	dimnames(pheudo) = dimnames(arr)[1:2]

+	if(length(list(...))) {

+		if(names(list(...)) %in% c("rect_gp", "cluster_rows", "cluster_columns", "cell_fun")) {

+			stop("'rect_gp', 'cluster_rows', 'cluster_columns', 'cell_fun' are not allowed to use in `oncoPrint()`.")

+		}

+	}

+

 	ht = Heatmap(pheudo, col = col, rect_gp = gpar(type = "none"), 

 		cluster_rows = FALSE, cluster_columns = FALSE, row_order = row_order, column_order = column_order,

 		cell_fun = function(j, i, x, y, width, height, fill) {

 			z = arr[i, j, ]

-			add_oncoprint("background", x, y, width, height)

-			for(type in all_type[z]) {

-				add_oncoprint(type, x, y, width, height)

-			}

+			af(x, y, width, height, z)

 		}, show_column_names = show_column_names,

 		top_annotation = top_annotation,

 		heatmap_legend_param = heatmap_legend_param, ...)

@@ -21,10 +21,10 @@

 # # No example for this function

 # NULL

 # 

-selectArea = function(mark = FALSE) {

+selectArea = function(mark = TRUE) {

 	if(!interactive()) {

-		stop("`select()` can only be used under interactive mode.")

+		stop("`selectArea()` can only be used under interactive mode.")

 	}

 	x = dev.cur()

@@ -54,8 +54,8 @@ selectArea = function(mark = FALSE) {

 		pos1$y = tmp

 	}

-	grid.rect( (0.5*pos1$x + 0.5*pos2$x), (0.5*pos1$y + 0.5*pos2$y),

-		abs_width(pos2$x - pos1$x), abs_height(pos2$y - pos1$y), gp = gpar(col = "orange") )

+	# grid.rect( (0.5*pos1$x + 0.5*pos2$x), (0.5*pos1$y + 0.5*pos2$y),

+	# 	abs_width(pos2$x - pos1$x), abs_height(pos2$y - pos1$y), gp = gpar(col = "orange") )

 	# calcualte each heatmap's position under main_heatmap_list viewport

 	vp_cumsum = unit(0, "mm")

@@ -69,7 +69,7 @@ selectArea = function(mark = FALSE) {

 			seekViewport("main_heatmap_list")

 			pos1_cp = list()

 			pos2_cp = list()

-browser()

+

 			# relative to current heatmap body

 			pos1_cp$x = pos1$x - convertWidth(vp$x, "mm") - sum(component_width(ht, 1:3))

 			pos1_cp$y = pos1$y - convertHeight(vp$y, "mm") - sum(component_height(ht, 6:9))

@@ -8,7 +8,7 @@ Using boxplot as annotation

 }

 \usage{

 anno_boxplot(x, which = c("column", "row"), border = TRUE,

-    gp = gpar(fill = "#CCCCCC"), ylim = NULL,

+    gp = gpar(fill = "#CCCCCC"), ylim = NULL, outline = TRUE,

     pch = 16, size = unit(2, "mm"), axis = FALSE, axis_side = NULL,

     axis_gp = gpar(fontsize = 8), axis_direction = c("normal", "reverse"))

 }

@@ -19,6 +19,7 @@ anno_boxplot(x, which = c("column", "row"), border = TRUE,

   \item{border}{whether show border of the annotation compoment}

   \item{gp}{graphic parameters}

   \item{ylim}{data ranges.}

+  \item{outline}{whether draw outliers}

   \item{pch}{point type}

   \item{size}{point size}

   \item{axis}{whether add axis}

@@ -8,11 +8,11 @@ Make oncoPrint

 }

 \usage{

 oncoPrint(mat, get_type = function(x) x,

-    alter_fun_list, col,

+    alter_fun = alter_fun_list, alter_fun_list = NULL, col,

     row_order = oncoprint_row_order(),

     column_order = oncoprint_column_order(),

     show_column_names = FALSE,

-    pct_gp = gpar(),

+    pct_gp = gpar(), pct_digits = 0,

     axis_gp = gpar(fontsize = 8),

     show_row_barplot = TRUE,

     row_barplot_width = unit(2, "cm"),

@@ -27,12 +27,14 @@ oncoPrint(mat, get_type = function(x) x,

   \item{mat}{a character matrix which encodes mulitple alterations or a list of matrix for which every matrix contains binary value representing the alteration is present or absent. When it is a list, the names represent alteration types. You can use \code{\link{unify_mat_list}} to make all matrix having same row names and column names.}

   \item{get_type}{If different alterations are encoded in the matrix, this self-defined function determines how to extract them. Only work when \code{mat} is a matrix.}

-  \item{alter_fun_list}{a list of functions which define how to add graphics for different alterations. The names of the list should cover all alteration types.}

+  \item{alter_fun}{a single function or a list of functions which define how to add graphics for different alterations. If it is a list, the names of the list should cover all alteration types.}

+  \item{alter_fun_list}{deprecated, use \code{alter_run} instead.}

   \item{col}{a vector of color for which names correspond to alteration types.}

   \item{row_order}{order of genes. By default it is sorted by frequency of alterations decreasingly. Set it to \code{NULL} if you don't want to set the order}

   \item{column_order}{order of samples. By default the order is calculated by the 'memo sort' method which can visualize the mutual exclusivity across genes. Set it to \code{NULL} if you don't want to set the order}

   \item{show_column_names}{whether show column names}

   \item{pct_gp}{graphic paramters for percent row annotation}

+  \item{pct_digits}{digits for percent values}

   \item{axis_gp}{graphic paramters for axes}

   \item{show_row_barplot}{whether show barplot annotation on rows}

   \item{row_barplot_width}{width of barplot annotation on rows. It should be a \code{\link[grid]{unit}} object}

@@ -7,7 +7,7 @@ Select an area in the heatmap

 Select an area in the heatmap

 }

 \usage{

-selectArea(mark = FALSE)

+selectArea(mark = TRUE)

 }

 \arguments{

@@ -70,11 +70,12 @@ to alteration types. It is used to generate the barplots and the legends.

 ```{r}

 library(ComplexHeatmap)

+col = c(snv = "red", indel = "blue")

 oncoPrint(mat, get_type = function(x) strsplit(x, ";")[[1]],

-	alter_fun_list = list(

-		snv = function(x, y, w, h) grid.rect(x, y, w*0.9, h*0.9, gp = gpar(fill = "red", col = NA)),

-		indel = function(x, y, w, h) grid.rect(x, y, w*0.9, h*0.4, gp = gpar(fill = "blue", col = NA))

-	), col = c(snv = "red", indel = "blue"))

+	alter_fun = list(

+		snv = function(x, y, w, h) grid.rect(x, y, w*0.9, h*0.9, gp = gpar(fill = col["snv"], col = NA)),

+		indel = function(x, y, w, h) grid.rect(x, y, w*0.9, h*0.4, gp = gpar(fill = col["indel"], col = NA))

+	), col = col)

 ```

 The second type of input data is a list of matrix for which each matrix contains binary value representing

@@ -104,11 +105,11 @@ how to add graphics when there is no alteration and it is always put as the firs

 ```{r}

 oncoPrint(mat_list,

-	alter_fun_list = list(

+	alter_fun = list(

 		background = function(x, y, w, h) NULL,

-		snv = function(x, y, w, h) grid.rect(x, y, w*0.9, h*0.9, gp = gpar(fill = "red", col = NA)),

-		indel = function(x, y, w, h) grid.rect(x, y, w*0.9, h*0.4, gp = gpar(fill = "blue", col = NA))

-	), col = c(snv = "red", indel = "blue"))

+		snv = function(x, y, w, h) grid.rect(x, y, w*0.9, h*0.9, gp = gpar(fill = col["snv"], col = NA)),

+		indel = function(x, y, w, h) grid.rect(x, y, w*0.9, h*0.4, gp = gpar(fill = col["indel"], col = NA))

+	), col = col)

 ```

 If types of alterations is less than two and the purpose is only to have a quick look at the data, there are default

@@ -118,6 +119,31 @@ graphics added:

 oncoPrint(mat_list)

 ```

+In above examples, `alter_fun` is a list of functons which add graphics layer by layer. Graphics

+can also be added in a grid-by-grid style by specifying `alter_fun` as a single function. The difference

+from the function list is now `alter_fun` should accept a fifth argument which is a logical vector. 

+This logical vector shows whether different alterations exist for current gene in current sample.

+

+```{r}

+oncoPrint(mat_list,

+	alter_fun = function(x, y, w, h, v) {

+		if(v["snv"]) grid.rect(x, y, w*0.9, h*0.9, gp = gpar(fill = col["snv"], col = NA))

+		if(v["indel"]) grid.rect(x, y, w*0.9, h*0.4, gp = gpar(fill = col["indel"], col = NA))

+	}, col = col)

+```

+

+If `alter_fun` is set as a single function, customization can be more flexible. In following example,

+rectangles always fill the whole grid.

+

+```{r}

+oncoPrint(mat_list,

+    alter_fun = function(x, y, w, h, v) {

+		n = sum(v)

+		h = h*0.9

+		if(n) grid.rect(x, y - h*0.5 + 1:n/n*h, w*0.9, 1/n*h, gp = gpar(fill = col[which(v)], col = NA), just = "top")

+    }, col = col)

+```

+

 Now we make an oncoPrint with a real-world data. The data is retrieved from [cBioPortal](http://www.cbioportal.org/). 

 Steps for getting the data are as follows:

@@ -152,7 +178,7 @@ There are three different alterations in `mat`: `HOMDEL`, `AMP` and `MUT`. We fi

 define how to add graphics which correspond to different alterations. 

 ```{r}

-alter_fun_list = list(

+alter_fun = list(

 	background = function(x, y, w, h) {

 		grid.rect(x, y, w-unit(0.5, "mm"), h-unit(0.5, "mm"), gp = gpar(fill = "#CCCCCC", col = NA))

 	},

@@ -178,7 +204,7 @@ Make the oncoPrint and adjust heatmap components such as the title and the legen

 ```{r, fig.width = 12, fig.height = 8}

 oncoPrint(mat, get_type = function(x) strsplit(x, ";")[[1]],

-	alter_fun_list = alter_fun_list, col = col, 

+	alter_fun = alter_fun, col = col, 

 	column_title = "OncoPrint for TCGA Lung Adenocarcinoma, genes in Ras Raf MEK JNK signalling",

 	heatmap_legend_param = list(title = "Alternations", at = c("AMP", "HOMDEL", "MUT"), 

 		labels = c("Amplification", "Deep deletion", "Mutation")))

@@ -196,7 +222,7 @@ By default, if one sample has no alteration, it will still remain in the heatmap

 ```{r, fig.width = 12, fig.height = 8}

 oncoPrint(mat, get_type = function(x) strsplit(x, ";")[[1]],

-	alter_fun_list = alter_fun_list, col = col, 

+	alter_fun = alter_fun, col = col, 

 	remove_empty_columns = TRUE,

 	column_title = "OncoPrint for TCGA Lung Adenocarcinoma, genes in Ras Raf MEK JNK signalling",

 	heatmap_legend_param = list(title = "Alternations", at = c("AMP", "HOMDEL", "MUT"), 

@@ -211,7 +237,7 @@ You can see the difference for the sample order between 'memo sort' and the meth

 sample_order = scan(paste0(system.file("extdata", package = "ComplexHeatmap"), 

     "/sample_order.txt"), what = "character")

 oncoPrint(mat, get_type = function(x) strsplit(x, ";")[[1]],

-	alter_fun_list = alter_fun_list, col = col, 

+	alter_fun = alter_fun, col = col, 

 	row_order = NULL, column_order = sample_order,

 	remove_empty_columns = TRUE,

 	column_title = "OncoPrint for TCGA Lung Adenocarcinoma, genes in Ras Raf MEK JNK signalling",

@@ -226,7 +252,7 @@ Following example splits the heatmap into two halves and add a new heatmap to th

 ```{r, fig.width = 12, fig.height = 8}

 ht_list = oncoPrint(mat, get_type = function(x) strsplit(x, ";")[[1]],

-	alter_fun_list = alter_fun_list, col = col, 

+	alter_fun = alter_fun, col = col, 

 	remove_empty_columns = TRUE,

 	column_title = "OncoPrint for TCGA Lung Adenocarcinoma, genes in Ras Raf MEK JNK signalling",

 	heatmap_legend_param = list(title = "Alternations", at = c("AMP", "HOMDEL", "MUT"), 

@@ -240,7 +266,7 @@ In some scenarios, you don't want to show some of the alterations on the barplot

 `````{r, fig.width = 12, fig.height = 8}

 oncoPrint(mat, get_type = function(x) strsplit(x, ";")[[1]],

-	alter_fun_list = alter_fun_list, col = col, 

+	alter_fun = alter_fun, col = col, 

 	remove_empty_columns = TRUE,

 	column_title = "OncoPrint for TCGA Lung Adenocarcinoma, genes in Ras Raf MEK JNK signalling",

 	heatmap_legend_param = list(title = "Alternations", at = c("AMP", "HOMDEL", "MUT"),