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OncoPrint
========================================

**Author**: Zuguang Gu ( z.gu@dkfz.de )

**Date**: `r Sys.Date()`

-------------------------------------------------------------

```{r global_settings, echo = FALSE, message = FALSE}
library(markdown)
options(markdown.HTML.options = c(options('markdown.HTML.options')[[1]], "toc"))

library(knitr)
knitr::opts_chunk$set(
    error = FALSE,
    tidy  = FALSE,
    message = FALSE,
    fig.align = "center",
    fig.width = 5,
    fig.height = 5)
options(markdown.HTML.stylesheet = "custom.css")

options(width = 100)
```

<a href="http://www.cbioportal.org/faq.jsp#what-are-oncoprints">OncoPrint</a> is a way to visualize 
multiple genomic alteration events by heatmap. Here the **ComplexHeatmap** package provides a `oncoPrint()` function.
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 control oncoPrint with
more flexibilities.

There are two different forms of input data. The first is represented as a matrix in which 
element would include multiple alterations in a form of a complex 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 = "\t", stringsAsFactors = FALSE)
mat = as.matrix(mat)
mat
```

In this case, we need to define a function to extract different alteration types and pass the function
to `get_type` argument. The function should return a vector of alteration types.

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 self-defined functions.
Here if the graphics have no transparency, orders of how to add
graphics matters. In following example, snv are first drawn and then the indel. You can see rectangles
for indels are actually smaller than that for snvs so that you can visualiza both snvs and indels if they
are in a same grid. Names in the list of functions should correspond to the alteration types (here, `snv` and `indel`).

For the self-defined graphic function, there should be four arguments which are positions of the grids 
on the heatmap (`x` and `y`), and widths and heights of the grids (`w` and `h`).

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}
library(ComplexHeatmap)
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"))
```

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 matrix in `mat_list` having same row names and column names. Users can use `unify_mat_list()`
to adjust the matrix list.

```{r}
mat_list$indel = mat_list$indel[1:2, 1:2]
mat_list
mat_list = unify_mat_list(mat_list)
mat_list
```

Same as the first example, but here we also define `background` in `alter_fun_list` argument. This function defines
how to add graphics when there is no alteration and it is always put as the first in the list.

```{r}
oncoPrint(mat_list,
	alter_fun_list = 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"))
```

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

```{r}
oncoPrint(mat_list)
```

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:

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 results 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"

First we read the data and do some pre-processing.

```{r}
mat = read.table(paste0(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 which correspond to different alterations. 

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

Also colors for different alterations which will be used for barplots.

```{r}
col = c("MUT" = "#008000", "AMP" = "red", "HOMDEL" = "blue")
```

Make the oncoPrint and adjust heatmap components such as the title and the legend.

```{r, fig.width = 12, fig.height = 8}
oncoPrint(mat, get_type = function(x) strsplit(x, ";")[[1]],
	alter_fun_list = alter_fun_list, 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")))
```

As you see, the genes and samples are sorted automatically. Rows are sorted based on the frequency
of the alterations in all samples and columns are sorted to visualize the mutual exclusivity across genes
based on the "memo sort" method which is
kindly provided by [B. Arman Aksoy](https://gist.github.com/armish/564a65ab874a770e2c26). If you want
to turn off the default sorting, set `row_order` or `column_order` to `NULL`.


By default, if one sample has no alteration, it will still remain in the heatmap, but you can set
`remove_empty_columns` to `TRUE` to remove it:

```{r, fig.width = 12, fig.height = 8}
oncoPrint(mat, get_type = function(x) strsplit(x, ";")[[1]],
	alter_fun_list = alter_fun_list, 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"), 
		labels = c("Amplification", "Deep deletion", "Mutation")))
```

As the normal `Heatmap()` function, `row_order` or `column_order` can be assigned with a vector of 
orders (either numeric or character). Following 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, get_type = function(x) strsplit(x, ";")[[1]],
	alter_fun_list = alter_fun_list, 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",
	heatmap_legend_param = list(title = "Alternations", at = c("AMP", "HOMDEL", "MUT"), 
		labels = c("Amplification", "Deep deletion", "Mutation")))
```

`oncoPrint()` actually returns a `HeatmapList` object, so you can add more Heatmaps or row annotations
to it to visualize more complicated information.

Following example splits the heatmap into two halves and add a new heatmap to the right.

```{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, 
	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"), 
		labels = c("Amplification", "Deep deletion", "Mutation")),
	split = sample(letters[1:2], nrow(mat), replace = TRUE)) +
Heatmap(matrix(rnorm(nrow(mat)*10), ncol = 10), width = unit(4, "cm"))
draw(ht_list, row_sub_title_side = "left")
```

## Session info

```{r}
sessionInfo()
```