07-oncoprint.Rmd

# OncoPrint {#oncoprint}

<a href="http://www.cbioportal.org/faq#what-are-oncoprints">OncoPrint</a> is a
way to visualize multiple genomic alteration events by heatmap. Here the
**ComplexHeatmap** package provides a `oncoPrint()` function which makes
oncoPrints. Besides the default style which is provided by <a href="http://www.cbioportal.org/index.do">cBioPortal</a>, there are additional
barplots at both sides of the heatmap which show numbers of different
alterations for each sample and for each gene. Also with the functionality of
**ComplexHeatmap**, you can concatenate oncoPrints with additional heatmaps
and annotations to correspond more types of information.

```{r, echo = FALSE}
knitr::opts_chunk$set(
  fig.align = 'center',
  fig.width = 4,
  fig.height = 4,
  message = FALSE
)
```

## General settings {#oncoprint-general-settings}


### Input data format {#input-data-format}

There are two different formats of input data. The first is represented as a
matrix in which each value can include multiple alterations in a form of a
complicated string. In follow example, 'g1' in 's1' has two types of
alterations which are 'snv' and 'indel'.

```{r}
mat = read.table(textConnection(
"s1,s2,s3
g1,snv;indel,snv,indel
g2,,snv;indel,snv
g3,snv,,indel;snv"), row.names = 1, header = TRUE, sep = ",", stringsAsFactors = FALSE)
mat = as.matrix(mat)
mat
```

In this case, we need to define a function to extract different alteration
types from these long strings. The definition of such function is always
simple, it accepts the complicated string and returns a vector of alteration
types.

For `mat`, we can define the function as:

```{r}
get_type_fun = function(x) strsplit(x, ";")[[1]]
get_type_fun(mat[1, 1])
get_type_fun(mat[1, 2])
```

So, if the alterations are encoded as `snv|indel`, you can define the function
as `function(x) strsplit(x, "|")[[1]]`. This self-defined function is assigned
to the `get_type` argument in `oncoPrint()`.

**Since in most cases, the separators are only single characters, If the
separators are in `;:,|`, `oncoPrint()` automatically spit the alteration
strings so that you don't need to explicitely specify `get_type` in
`oncoPrint()` function.**

For one gene in one sample, since different alteration types may be drawn into
one same grid in the heatmap, we need to define how to add the graphics by
providing a list of self-defined functions to `alter_fun` argument. Here if
the graphics have no transparency, order of adding graphics matters. In
following example, snv are first drawn and then the indel. You can see
rectangles for indels are actually smaller (`0.4*h`) than that for snvs
(`0.9*h`) so that you can visualize both snvs and indels if they are in a same
grid. Names of the function list should correspond to the alteration types
(here, `snv` and `indel`).

For the self-defined graphic function (the functions in `alter_fun`, there
should be four arguments which are positions of the grids on the oncoPrint
(`x` and `y`), and widths and heights of the grids (`w` and `h`, which is
measured in `npc` unit). Proper values for the four arguments are sent to these
functions automatically from `oncoPrint()`.

Colors for different alterations are defined in `col`. It should be a named
vector for which names correspond to alteration types. It is used to generate
the barplots and the legends.

```{r}
col = c(snv = "red", indel = "blue")
oncoPrint(mat,
	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)
```

You can see the order in barplots also correspond to the order defined in
`alter_fun`.

If you are confused of how to generated the matrix, there is a second way. The
second type of input data is a list of matrix for which each matrix contains
binary value representing whether the alteration is absent or present. The
list should have names which correspond to the alteration types.

```{r}
mat_list = list(snv = matrix(c(1, 0, 1, 1, 1, 0, 0, 1, 1), nrow = 3),
	            indel = matrix(c(1, 0, 0, 0, 1, 0, 1, 0, 0), nrow = 3))
rownames(mat_list$snv) = rownames(mat_list$indel) = c("g1", "g2", "g3")
colnames(mat_list$snv) = colnames(mat_list$indel) = c("s1", "s2", "s3")
mat_list
```

`oncoPrint()` expects all matrices in `mat_list` having same row names and
column names.

Pass `mat_list` to `oncoPrint()`:

```{r}
# now you don't need `get_type`
oncoPrint(mat_list,
	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)
```

In following parts of this chapter, we still use the single matrix form `mat`
to specify the input data.

### Define the alter_fun() {#define-the-alter-fun}

`alter_fun` is a list of functons which add graphics layer by layer (i.e.
first draw for `snv`, then for `indel`). 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.

Let's assume in a grid there is only snv event, then `v` for this grid is:

```{r, echo = FALSE}
print(c("snv" = TRUE, "indel" = FALSE))
```

```{r}
oncoPrint(mat,
	alter_fun = function(x, y, w, h, v) {
		if(v["snv"]) grid.rect(x, y, w*0.9, h*0.9, # v["snv"] is a logical value
			gp = gpar(fill = col["snv"], col = NA))
		if(v["indel"]) grid.rect(x, y, w*0.9, h*0.4, # v["indel"] is a logical value
			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, the blue rectangles can have different height
in different grid.

```{r}
oncoPrint(mat,
    alter_fun = function(x, y, w, h, v) {
		n = sum(v)  # how many alterations for current gene in current sample
		h = h*0.9
		# use `names(which(v))` to correctly map between `v` and `col`
		if(n) grid.rect(x, y - h*0.5 + 1:n/n*h, w*0.9, 1/n*h, 
			gp = gpar(fill = col[names(which(v))], col = NA), just = "top")
    }, col = col)
```

In some cases, you might need to define `alter_fun` for many alteration types.
If you are not sure about the visual effect of your `alter_fun`, you can use
`test_alter_fun()` to test your `alter_fun`. In following example, we defined
seven alteration functions:

```{r, fig.width = 3, fig.height = 3}
alter_fun = list(
	mut1 = function(x, y, w, h) 
		grid.rect(x, y, w, h, gp = gpar(fill = "red", col = NA)),
	mut2 = function(x, y, w, h) 
		grid.rect(x, y, w, h, gp = gpar(fill = "blue", col = NA)),
	mut3 = function(x, y, w, h) 
		grid.rect(x, y, w, h, gp = gpar(fill = "yellow", col = NA)),
	mut4 = function(x, y, w, h) 
		grid.rect(x, y, w, h, gp = gpar(fill = "purple", col = NA)),
	mut5 = function(x, y, w, h) 
		grid.rect(x, y, w, h, gp = gpar(fill = NA, lwd = 2)),
	mut6 = function(x, y, w, h) 
		grid.points(x, y, pch = 16),
	mut7 = function(x, y, w, h) 
		grid.segments(x - w*0.5, y - h*0.5, x + w*0.5, y + h*0.5, gp = gpar(lwd = 2))
)
test_alter_fun(alter_fun)
```

For the combination of alteration types, `test_alter_fun()` randomly samples
some of them.

`test_alter_fun()` works both for `alter_fun` as a list and as a single
function.

### Background {#oncoprint-background}

If `alter_fun` is specified as a list, the order of the elements controls the
order of adding graphics. There is a special element called `background` which
defines how to draw background and it should be always put as the first
element in the `alter_fun` list. In following example, backgrond color is
changed to light green with borders.

```{r}
oncoPrint(mat,
	alter_fun = list(
		background = function(x, y, w, h) grid.rect(x, y, w, h, 
			gp = gpar(fill = "#00FF0020")),
		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)
```

Or just remove the background (don't set it to `NULL`. Setting `background`
directly to `NULL` means to use the default style of background whch is in
grey):

```{r}
oncoPrint(mat,
	alter_fun = list(
		background = function(...) NULL,
		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)
```

### Complex alteration types {#complex-alteration-types}

It is very easy to have many more different alteration types when integrating
information from multiple analysis results. It is sometimes difficult to
design graphics and assign different colors for them (e.g. [see plot in this
link](https://user-images.githubusercontent.com/13846735/51974483-fab20b00-24ba-11e9-91d3-3358e5f1017e.png).
On the other hand, in these alteration types, there are primary classes of
alteration types which is more important to distinguish, while there are
secondary classes which is less important. For example, we may have alteration
types of "intronic snv", "exonic snv", "intronic indel" and "exonic indel".
Actually we can classify them into two classes where "snv/indel" is more
important and they belong to the primary class, and "intronic/exonic" is less
important and they belong to the secondary class. Reflecting on the oncoPrint,
for the "intronic snv" and "exonic snv", we want to use similar graphics
because they are snvs and we want them visually similar, and we add slightly
different symbols to represent "intronic" and "exonic", E.g. we can use red
rectangle for snv and above the red rectangles, we use dots to represent
"intronic" and cross lines to represent "exonic". On the barplot annotations
which summarize the number of different alteration types, we don't want to
separate "intronic snv" and "exonic snv" while we prefer to simply get the
total number of snv to get rid of too many categories in the barplots.

Let's demonstrate this scenario by following simulated data. To simplify the
example, we assume for a single gene in a single sample, it only has either
snv or indel and it can only be either intronic or exonic. If there is no
"intronic" or "exonic" attached to the gene, it basically means we don't have
this gene-related information (maybe it is an intergenic snv/indel).

```{r}
set.seed(123)
x1 = sample(c("", "snv"), 100, replace = TRUE, prob = c(8, 2))
x2 = sample(c("", "indel"), 100, replace = TRUE, prob = c(8, 2))
x2[x1 == "snv"] = ""
x3 = sample(c("", "intronic"), 100, replace = TRUE, prob = c(5, 5))
x4 = sample(c("", "exonic"), 100, replace = TRUE, prob = c(5, 5))
x3[x1 == "" & x2 == ""] = ""
x4[x1 == "" & x2 == ""] = ""
x4[x3 == "intronic"] = ""
x = apply(cbind(x1, x2, x3, x4), 1, function(x) {
	x = x[x != ""]
	paste(x, collapse = ";")
})
m = matrix(x, nrow = 10, ncol = 10, dimnames = list(paste0("g", 1:10), paste0("s", 1:10)))
m[1:4, 1:4]
```

Now in `m`, there are four different alteration types: `snv`, `indel`,
`intronic` and `exonic`. Next we define `alter_fun` for the four alterations.

```{r}
alter_fun = list(
	background = function(x, y, w, h) 
		grid.rect(x, y, w*0.9, h*0.9, gp = gpar(fill = "#CCCCCC", col = NA)),
	# red rectangles
	snv = function(x, y, w, h) 
		grid.rect(x, y, w*0.9, h*0.9, gp = gpar(fill = "red", col = NA)),
	# blue rectangles
	indel = function(x, y, w, h) 
		grid.rect(x, y, w*0.9, h*0.9, gp = gpar(fill = "blue", col = NA)),
	# dots
	intronic = function(x, y, w, h) 
		grid.points(x, y, pch = 16),
	# crossed lines
	exonic = function(x, y, w, h) {
		grid.segments(x - w*0.4, y - h*0.4, x + w*0.4, y + h*0.4, gp = gpar(lwd = 2))
		grid.segments(x + w*0.4, y - h*0.4, x - w*0.4, y + h*0.4, gp = gpar(lwd = 2))
	}
)
```

For the alteration types in the primary class (`snv` and `indel`), we use
colorred rectangles to represent them because the rectangles are visually
obvious, while for the alteration types in the secondary class (`intronic` and
`exonic`), we only use simple symbols (dots for `intronic` and crossed
diagonal lines for `exonic`). Since there is no color corresponding to
`intronic` and `exonic`, we don't need to define colors for these two types,
and on the barplot annotation for genes and samples, only `snv` and `indel`
are visualized (so the height for `snv` in the barplot corresponds the number
of intronic snv plus exonic snv).

In following code which draws the oncoPrint, we add another legend for the
`intronic`/`exonic` types. note a `pch` value of 16 corresponds to a dot and a
value of 28 corresponds to crossed diagonal lines (see the last plot in
Section \@ref(discrete-legends) for `pch` 26, 27, 28).

```{r, fig.width = 5.5}
# we only define color for snv and indel, so barplot annotations only show snv and indel
ht = oncoPrint(m, alter_fun = alter_fun, col = c(snv = "red", indel = "blue"))
draw(ht, heatmap_legend_list = list(
	Legend(labels = c("intronic", "exonic"), type = "points", pch = c(16, 28))
))
```

### Other heatmap-related settings {#other-heatmap-related-settings}

Column names are by default not drawn in the plot. It is can be turned on by
setting `show_column_names = TRUE`.

```{r}
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))
)
oncoPrint(mat, alter_fun = alter_fun, col = col, show_column_names = TRUE)
```

Row names and percent texts can be turned on/off by setting `show_pct`
and `show_row_names`. The side of both according to the oncoPrint is controlled
by `pct_side` and `row_names_side`. Digits of the percent values are controlled
by `pct_digits`.


```{r}
oncoPrint(mat, alter_fun = alter_fun, col = col, 
	row_names_side = "left", pct_side = "right", pct_digits = 2)
```

The barplot annotations on the both side are controlled by
`anno_oncoprint_barplot()` annotation function. Customization such as the size
and the axes can be set directly in `anno_oncoprint_barplot()`. More examples
of setting `anno_oncoprint_barplot()` can be found in Section \@ref(oncoprint-annotations).

```{r}
oncoPrint(mat, alter_fun = alter_fun, col = col, 
	top_annotation = HeatmapAnnotation(
		cbar = anno_oncoprint_barplot(height = unit(1, "cm"))),
	right_annotation = rowAnnotation(
		rbar = anno_oncoprint_barplot(
			width = unit(4, "cm"),
			axis_param = list(at = c(0, 2, 4), 
				labels = c("zero", "two", "four"),
				side = "top",
				labels_rot = 0))),
	)
```

Some people might want to move the right barplots to the left of the oncoPrint:

```{r}
oncoPrint(mat, alter_fun = alter_fun, col = col, 
	left_annotation =  rowAnnotation(
		rbar = anno_oncoprint_barplot(
			axis_param = list(direction = "reverse")
	)),
	right_annotation = NULL)
```


OncoPrints essentially are heatmaps, thus, there are many arguments set in
`Heatmap()` can also be set in `oncoPrint()`. In following section, we use a
real-world dataset to demonstrate more use of `oncoPrint()` function.


```{r, echo = FALSE}
knitr::opts_chunk$set(
  fig.align = 'center',
  fig.width = 6,
  fig.height = 6,
  message = FALSE
)
```

## Apply to cBioPortal dataset {#apply-to-cbioportal-dataset}

We use a real-world dataset to demonstrate advanced usage of `oncoPrint()`.
The data is retrieved from [cBioPortal](http://www.cbioportal.org/). 
Steps for getting the data are as follows:

1. go to http://www.cbioportal.org,
2. search Cancer Study: "_Lung Adenocarcinoma Carcinoma_" and select: "_Lung
   Adenocarcinoma Carcinoma (TCGA, Provisinal)_",
3. in "_Enter Gene Set_" field, select: "_General: Ras-Raf-MEK-Erk/JNK
   signaling (26 genes)_",
4. submit the form.

In the result page,

5. go to "_Download_" tab, download text in "_Type of Genetic alterations
   across all cases_".

The order of samples can also be downloaded from the results page,

6. go to "_OncoPrint_" tab, move the mouse above the plot, click "_download_"
   icon and click "_Sample order_".

The data is already in **ComplexHeatmap** package. First we read the data and
make some pre-processing.

```{r}
mat = read.table(system.file("extdata", package = "ComplexHeatmap", 
	"tcga_lung_adenocarcinoma_provisional_ras_raf_mek_jnk_signalling.txt"), 
	header = TRUE, stringsAsFactors = FALSE, sep = "\t")
mat[is.na(mat)] = ""
rownames(mat) = mat[, 1]
mat = mat[, -1]
mat=  mat[, -ncol(mat)]
mat = t(as.matrix(mat))
mat[1:3, 1:3]
```

There are three different alterations in `mat`: `HOMDEL`, `AMP` and `MUT`. We first 
define how to add graphics for different alterations. 

```{r}
col = c("HOMDEL" = "blue", "AMP" = "red", "MUT" = "#008000")
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))
	},
	# big blue
	HOMDEL = function(x, y, w, h) {
		grid.rect(x, y, w-unit(0.5, "mm"), h-unit(0.5, "mm"), 
			gp = gpar(fill = col["HOMDEL"], col = NA))
	},
	# bug red
	AMP = function(x, y, w, h) {
		grid.rect(x, y, w-unit(0.5, "mm"), h-unit(0.5, "mm"), 
			gp = gpar(fill = col["AMP"], col = NA))
	},
	# small green
	MUT = function(x, y, w, h) {
		grid.rect(x, y, w-unit(0.5, "mm"), h*0.33, 
			gp = gpar(fill = col["MUT"], col = NA))
	}
)
```

Now we make the oncoPrint. We save `column_title` and `heatmap_legend_param`
as varaibles because they are used multiple times in following code chunks.

```{r, fig.width = 12, fig.height = 8}
column_title = "OncoPrint for TCGA Lung Adenocarcinoma, genes in Ras Raf MEK JNK signalling"
heatmap_legend_param = list(title = "Alternations", at = c("HOMDEL", "AMP", "MUT"), 
		labels = c("Deep deletion", "Amplification", "Mutation"))
oncoPrint(mat,
	alter_fun = alter_fun, col = col, 
	column_title = column_title, heatmap_legend_param = heatmap_legend_param)
```

As you see, the genes and samples are reordered automatically. Rows are sorted
based on the frequency of the alterations in all samples and columns are
reordered to visualize the mutual exclusivity between samples. The column
reordering is based on the "memo sort" method, adapted from
https://gist.github.com/armish/564a65ab874a770e2c26.

### Remove empty rows and columns {#remove-empty-rows-and-columns}

By default, if samples or genes have no alterations, they will still remain in
the heatmap, but you can set `remove_empty_columns` and `remove_empty_rows` to
`TRUE` to remove them:

```{r, fig.width = 12, fig.height = 8}
oncoPrint(mat,
	alter_fun = alter_fun, col = col, 
	remove_empty_columns = TRUE, remove_empty_rows = TRUE,
	column_title = column_title, heatmap_legend_param = heatmap_legend_param)
```

The number of rows and columns may be reduced after empty rows and columns are
removed. All the components of the oncoPrint are adjusted accordingly. When
the oncoPrint is concatenated with other heatmaps and annotations, this may
cause a problem that the number of rows or columns are not all identical in
the heatmap list. So, if you put oncoPrint into a heatmap list and you don't
want to see empty rows or columns, you need to remove them manually before
sending to `oncoPrint()` function (this preprocess should be very easy for
you!).

### Reorder the oncoPrint {#reorder-the-oncoprint}

As the normal `Heatmap()` function, `row_order` or `column_order` can be
assigned with a vector of orders (either numeric or character). In following
example, the order of samples are gathered from cBio as well. You can see the
difference for the sample order between 'memo sort' and the method used by
cBio.

```{r, fig.width = 12, fig.height = 8}
sample_order = scan(paste0(system.file("extdata", package = "ComplexHeatmap"), 
    "/sample_order.txt"), what = "character")
oncoPrint(mat,
	alter_fun = alter_fun, col = col, 
	row_order = 1:nrow(mat), column_order = sample_order,
	remove_empty_columns = TRUE, remove_empty_rows = TRUE,
	column_title = column_title, heatmap_legend_param = heatmap_legend_param)
```

Again, `row_order` and `column_order` are automatically adjusted if
`remove_empty_rows` and `remove_empty_columns` are set to `TRUE`.

### OncoPrint annotations {#oncoprint-annotations}

The oncoPrint has several pre-defined annotations.

On top and right of the oncoPrint, there are barplots showing the number of
different alterations for each gene or for each sample, and on the left of the
oncoPrint is a text annotation showing the percent of samples that have
alterations for every gene.

The barplot annotation is implemented by `anno_oncoprint_barplot()` where you
can set the the annotation there. Barplots by default draw for all alteration
types, but you can also select subset of alterations to put on barplots by
setting in `anno_oncoprint_barplot()`. See following example:

```{r, fig.width = 12, fig.height = 8}
oncoPrint(mat,
	alter_fun = alter_fun, col = col, 
	top_annotation = HeatmapAnnotation(
		column_barplot = anno_oncoprint_barplot("MUT", border = TRUE, # only MUT
			height = unit(4, "cm"))),
	right_annotation = rowAnnotation(
		row_barplot = anno_oncoprint_barplot(c("AMP", "HOMDEL"),  # only AMP and HOMDEL
			border = TRUE, height = unit(4, "cm"), 
			axis_param = list(side = "bottom", labels_rot = 90))),
	remove_empty_columns = TRUE, remove_empty_rows = TRUE,
	column_title = column_title, heatmap_legend_param = heatmap_legend_param)
```

The percent values and row names are internally constructed as text
annotations. You can set `show_pct` and `show_row_names` to turn them on or
off. `pct_side` and `row_names_side` controls the sides where they are put.

```{r, fig.width = 12, fig.height = 8}
oncoPrint(mat,
	alter_fun = alter_fun, col = col, 
	remove_empty_columns = TRUE, remove_empty_rows = TRUE,
	pct_side = "right", row_names_side = "left",
	column_title = column_title, heatmap_legend_param = heatmap_legend_param)
```

The barplot annotation for oncoPrint are essentially normal annotations, you
can add more annotations in `HeatmapAnnotation()` or `rowAnnotation()` in the
normal way:

```{r, fig.width = 12, fig.height = 8}
oncoPrint(mat,
	alter_fun = alter_fun, col = col, 
	remove_empty_columns = TRUE, remove_empty_rows = TRUE,
	top_annotation = HeatmapAnnotation(cbar = anno_oncoprint_barplot(),
		foo1 = 1:172,
		bar1 = anno_points(1:172)),
	left_annotation = rowAnnotation(foo2 = 1:26),
	right_annotation = rowAnnotation(bar2 = anno_barplot(1:26)),
	column_title = column_title, heatmap_legend_param = heatmap_legend_param)
```

As you see, the percent annotation, the row name annotation and the oncoPrint
annotation are appended to the user-specified annotation by default. Also
annotations are automatically adjusted if `remove_empty_columns` and
`remove_empty_rows` are set to `TRUE`.

### oncoPrint as a Heatmap {#oncoprint-as-a-heatmap}

`oncoPrint()` actually returns a `Heatmap` object, so you can add more
heatmaps and annotations horizontally or vertically to visualize more
complicated associations.

Following example adds a heatmap horizontally. Remember you can always add row
annotations to the heatmap list.

```{r, fig.width = 12, fig.height = 8}
ht_list = oncoPrint(mat,
	alter_fun = alter_fun, col = col, 
	column_title = column_title, heatmap_legend_param = heatmap_legend_param) +
Heatmap(matrix(rnorm(nrow(mat)*10), ncol = 10), name = "expr", width = unit(4, "cm"))
draw(ht_list)
```

or add it vertically:

```{r, fig.width = 12, fig.height = 8}
ht_list = oncoPrint(mat,
	alter_fun = alter_fun, col = col, 
	column_title = column_title, heatmap_legend_param = heatmap_legend_param) %v%
Heatmap(matrix(rnorm(ncol(mat)*10), nrow = 10), name = "expr", height = unit(4, "cm"))
draw(ht_list)
```

Similar as normal heatmap list, you can split the heatmap list:

```{r, fig.width = 12, fig.height = 8}
ht_list = oncoPrint(mat,
	alter_fun = alter_fun, col = col, 
	column_title = column_title, heatmap_legend_param = heatmap_legend_param) +
Heatmap(matrix(rnorm(nrow(mat)*10), ncol = 10), name = "expr", width = unit(4, "cm"))
draw(ht_list, row_split = sample(c("a", "b"), nrow(mat), replace = TRUE))
```

When `remove_empty_columns` or `remove_empty_rows` is set to `TRUE`, the
number of genes or the samples may not be the original number. If the original
matrix has row names and column names. The subset of rows and columns can be
get as follows:

```{r}
ht = oncoPrint(mat,
	alter_fun = alter_fun, col = col, 
	remove_empty_columns = TRUE, remove_empty_rows = TRUE,
	column_title = column_title, heatmap_legend_param = heatmap_legend_param)
rownames(ht@matrix)
colnames(ht@matrix)
```