---
title: "How interactive complex heatmap is implemented"
author: "Zuguang Gu ( z.gu@dkfz.de )"
date: "`r Sys.Date()`"
output: 
    rmarkdown::html_vignette:
        width: 8
        fig_width: 5
vignette: >
  %\VignetteIndexEntry{2. How interactive complex heatmap is implemented}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---


```{r, echo = FALSE}
library(knitr)
knitr::opts_chunk$set(
    error = FALSE,
    tidy  = FALSE,
    message = FALSE,
    warning = FALSE,
    fig.align = "center"
)
```


<style>
p {
    margin: 1em 0;
}
img {
    background-color: #FFFFFF;
    padding: 2px;
    border: 1px solid #DDDDDD;
    border-radius: 3px;
    border: 1px solid #CCCCCC;
    margin: 0 5px;
}
</style>

Heatmaps are mainly for visualizing common patterns that are shared by groups
of rows and columns. After the patterns are observed, the next step is
to extract the corresponding groups of rows and columns from the heatmap,
which requires interactivity on the heatmaps. The **ComplexHeatmap** package
is well known for generating **static heatmaps** (a single heatmap or a list
of heatmaps, possibly with complex annotations). Here the package
**InteractiveComplexHeatmap** brings interactivity to **ComplexHeatmap**. The
new functionalities allow users to capture sub-heatmaps by clicking/hovering single cells or
selecting areas from heatmaps.

Unlike other packages which support interactive heatmaps based on JavaScript,
_e.g._, **iheatmapr**, **heatmaply** and **d3heatmap**,
the package **InteractiveComplexHeatmap** has a special way to capture the positions that
users selected and to extract the corresponding values from the matrices. In
this vignette, I will explain in details how the interactivity is implemented based on
**ComplexHeatmap**.

To demonstrate it, I first generate a list of two heatmaps and apply _k_-means
clustering on the numeric heatmap.


```{r}
library(ComplexHeatmap)
library(InteractiveComplexHeatmap)
set.seed(123)
mat1 = matrix(rnorm(100), 10)
rownames(mat1) = colnames(mat1) = paste0("a", 1:10)
mat2 = matrix(sample(letters[1:10], 100, replace = TRUE), 10)
rownames(mat2) = colnames(mat2) = paste0("b", 1:10)

ht_list = Heatmap(mat1, name = "mat_a", row_km = 2, column_km = 2) +
    Heatmap(mat2, name = "mat_b")
```

**InteractiveComplexHeatmap** implements two types of interactivity: 

1. on the interactive graphics device,
2. on a Shiny app.

The interactivity on the interactive graphics device is the basis of the
interactivity of the Shiny app, so in the following sections, I will first introduce
how the interactivity is implemneted with the interactive graphics device.

## On the interactive graphics device

Here the "interactive graphics device" is the window that is opened for
generating plots in your R session if you use R in the terminal or in a native R
GUI, or the figure panel in Rstudio IDE.

I will first explain how **InteractiveComplexHeatmap** captures the positions
that user clicked on the device and how it is associated to the values in the
matrix.

When user clicks on the device, the physical locations relative in the device (offsets to the bottom left of the device on both x and y directions)
are captured by `grid::grid.locator()`. The physical locations of the heatmaps
(more precisely, the heatmap slices) can also be captured by `grid::deviceLoc()`.
With knowing the exact positions of the clicked points and the heatmaps, it is possible
to tell which heatmap the clicked points are in. Furthermore, by calculating
the relative distance of the clicked points in that heatmap, it is also possible to
know which rows and columns the clicked points correspond to.

For associating user's clicked points and the heatmaps, we first need to calculate the
positions of all heatmaps. There is a helper function `htPositionsOnDevice()`
that does this job.

Before executing `htPositionsOnDevice()`, the heatmap should be drawn on the
device and the layout of heatmaps should have been generated so that `htPositionsOnDevice()` can
access various viewports of the plot. Thus, the heatmap object
`ht_list` should be updated explicitly by the `draw()` function.

The following code draws the heatmap in a device with 6 inches width and 4 inches height.

```{r, fig.width = 6, fig.height = 4}
ht_list = draw(ht_list)
pos = htPositionsOnDevice(ht_list)
```

The returned object `pos` is a `DataFrame` object that contains the positions
of all heatmap slices. A `DataFrame` object (the `DataFrame` class is defined
in [**S4Vectors** package](https://bioconductor.org/packages/release/bioc/html/S4Vectors.html))
is bacially very similar to a data frame, but it can store more complex data
types, such as the `simpleUnit`  vectors (generated by `grid::unit()`).

```{r}
pos
```

We can confirm whether the positions are correctly captured by the following
code. In the next figure, black rectangles correspond to the heatmap slices
and the dashed rectangle corresponds to the border of the whole image.

```{r, fig.width = 6, fig.height = 4, echo = FALSE}
grid.newpage()
grid.rect(gp = gpar(lty = 2))
for(i in seq_len(nrow(pos))) {
    x_min = pos[i, "x_min"]
    x_max = pos[i, "x_max"]
    y_min = pos[i, "y_min"]
    y_max = pos[i, "y_max"]
    pushViewport(viewport(x = x_min, y = y_min, name = pos[i, "slice"],
        width = x_max - x_min, height = y_max - y_min,
        just = c("left", "bottom")))
    grid.rect()
    upViewport()
}
```

```{r, fig.width = 6, fig.height = 4, eval = FALSE}
dev.new(width = 6, height = 4)
grid.newpage()
grid.rect(gp = gpar(lty = 2))
for(i in seq_len(nrow(pos))) {
    x_min = pos[i, "x_min"]
    x_max = pos[i, "x_max"]
    y_min = pos[i, "y_min"]
    y_max = pos[i, "y_max"]
    pushViewport(viewport(x = x_min, y = y_min, name = pos[i, "slice"],
        width = x_max - x_min, height = y_max - y_min,
        just = c("left", "bottom")))
    grid.rect()
    upViewport()
}
```

Yes, the positions of all heatmap slices are correctly captured!

Since now we know the location of the clicked points (by
`grid::grid.locator()`) and the positions of all heatmap slices, it is
possible to calculate which row and which column in the original matrix user's
click corresponds to.

In the next figure, the blue point with the coordinate $(a, b)$ is clicked by
user. The heatmap slice where user clicked into has range $(x_1,x_2)$ on x
direction and range $(y_1, y_2)$ on y direction and this heatmap slice can be easily
found by comparing the locations of every heatmap slice to the position of the click. There are $n_r$ rows ($n_r
=8$) and $n_c$ columns ($n_c = 5$) in this heatmap slice and they are marked
by dashed lines. Note all the coordinate values (_i.e._, $a$, $b$, $x_1$,
$y_1$, $x_2$ and $y_2$) are measured as the physical positions in the graphics
device.

```{r, echo = FALSE, fig.width = 6, fig.height = 4}
source("model.R")
```

In this heatmap slice, the row index $i_r$ and column index $i_c$ of
the cell where the point is in can be calculated as (assume the left bottom
corresponds to the index of 1 for both rows and columns):

$$ i_c = \lceil \frac{a - x_1}{x_2 - x_1} \cdot n_c \rceil $$
$$ i_r = \lceil \frac{b - y_1}{y_2 - y_1} \cdot n_r \rceil $$

where the symbol $\lceil x \rceil$ means the ceiling of the numeric value $x$.
In **ComplexHeatmap**, the row with index 1 is always put on the top of the
heatmap, then $i_r$ should be adjusted as:

$$ i_r = n_r - \lceil \frac{b - y_1}{y_2 - y_1} \cdot n_r \rceil + 1 $$

The subset of row and column indices of the original matrix that belongs to
the selected heatmap slice is already stored in `ht_list` object (they can be
retrieved by `row_order()` and `column_order()` function), thus, we can obtain
the row and column index of the original matrix that corresponds to user's
point easily with $i_r$ and $i_c$.

Denote the matrix for the complete heatmap (without slicing) as $M$, and denote the subset
of row and column indices in that heatmap as $o^{\mathrm{row}}$ and $o^{\mathrm{col}}$. Note, $o^{\mathrm{row}}$ and $o^{\mathrm{col}}$ can be 
reordered due to clustering. Then the row and column indices ($j_r$ and $j_c$) for the selected point in $M$ are

$$j_r = o^{\mathrm{row}}_{i_r}$$
$$j_c = o^{\mathrm{col}}_{i_c}$$

And the corresponding value in $M$ is $M_{j_r, j_c}$.

**InteractiveComplexHeatmap** has two functions `selectPosition()` and
`selectArea()` which allow users to pick single positions or select areas
from the heatmaps. Under the interactive graphics device, users do not need to
run `htPositionsOnDevice()` explicitly. The positions of heatmaps are
automatically calculated, cached and reused if the heatmaps are the same and
the device has not changed its size. If users changed the device size, `htPositionsOnDevice()`
will be automatically re-executed.

The next image shows an example of using `selectPosition()`. 


<script>
document.write('<img width="100%" src="https://jokergoo.github.io/images/example1.gif" />');
</script>

Interactively, the function asks user to click one position on the heatmap.
The function returns a `DataFrame` which contains the heatmap name, slice name
and the row/column index of the matrix in that heatmap.

```
## DataFrame with 1 row and 6 columns
##       heatmap                  slice row_slice column_slice row_index
##   <character>            <character> <numeric>    <numeric> <integer>
## 1       mat_a mat_a_heatmap_body_1_2         1            2         9
##   column_index
##      <integer>
## 1            1
```

The output means, the position user clicked is in a heatmap called "mat_a", in its first row
slice and the second column slice. Assume `mat` is the matrix sent to heatmap "mat_a", then the
clicked point correspond to the value `mat[9, 1]`.

If the position clicked is not in any of the heatmap slices, the function
returns `NULL`.


Similarly, the `selectArea()` function asks user to click two positions on the
heatmap which defines an area. 


<script>
document.write('<img width="100%" src="https://jokergoo.github.io/images/example2.gif" />');
</script>

Note since the selected area may overlap over multiple heatmaps and slices,
the function returns a `DataFrame` with multiple rows which contains the
heatmap names, slice names and the row/column indices in that heatmap. An
example output is as follows.

```
## DataFrame with 4 rows and 6 columns
##       heatmap                  slice row_slice column_slice     row_index
##   <character>            <character> <numeric>    <numeric> <IntegerList>
## 1       mat_a mat_a_heatmap_body_1_2         1            2     7,5,2,...
## 2       mat_a mat_a_heatmap_body_2_2         2            2           6,3
## 3       mat_b mat_b_heatmap_body_1_1         1            1     7,5,2,...
## 4       mat_b mat_b_heatmap_body_2_1         2            1           6,3
##    column_index
##   <IntegerList>
## 1     2,4,1,...
## 2     2,4,1,...
## 3     1,2,3,...
## 4     1,2,3,...
```

The columns `row_index` and `column_index` are stored in `IntegerList` format.
To get the row indices in _e.g._ `mat_a_heatmap_body_1_2` (in the first row),
user can use either one of the following command (assume the `DataFrame` object
is called `df`):

```{r, eval = FALSE}
df[1, "row_index"][[1]]
unlist(df[1, "row_index"])
df$row_index[[1]]
```

The rectangle and the points that mark the area can be turned off by setting
`mark` argument to `FALSE`.


## On off-screen graphics devices

It is also possible to use `selectPosition()` and `selectArea()` on other
off-screen graphics devices, such as `pdf()` or `png()`. Now you cannot
select the positions interactively, but instead you can specify `pos` argument
in `selectPosition()` and `pos1`/`pos2` in `selectArea()` to simulate clicks.
The values for `pos`, `pos1` and `pos2` all should be a `unit` object of
length two which correspond to the x and y coordinate of the positions.


```{r, fig.width = 6, fig.height = 4, eval = FALSE}
pdf(...)
ht_list = draw(ht_list)
pos = selectPosition(ht_list, pos = unit(c(3, 3), "cm"))
dev.off()
```

```{r, fig.width = 6, fig.height = 4, echo = FALSE}
# pdf(...) or png(...) or other devices, because under this vignette generation, it is
# already under a png() device, I don't need to call `png()` explictly.
ht_list = draw(ht_list)
pos = selectPosition(ht_list, pos = unit(c(3, 3), "cm"))
# remember to dev.off()
```

```{r}
pos
```

```{r, fig.width = 6, fig.height = 4, eval = FALSE}
pdf(...)
ht_list = draw(ht_list)
pos = selectArea(ht_list, pos1 = unit(c(3, 3), "cm"), pos2 = unit(c(5, 5), "cm"))
dev.off()
```

```{r, fig.width = 6, fig.height = 4, echo = FALSE}
# pdf(...) or png(...) or other devices
ht_list = draw(ht_list)
pos = selectArea(ht_list, pos1 = unit(c(3, 3), "cm"), pos2 = unit(c(5, 5), "cm"))
# remember to dev.off()
```

```{r}
pos
```

Users do not need to use this functionality directly with an off-screen
graphics device, however, it is very useful when developing a Shiny app where
the plot is actually generated under an off-screen graphics device. I will explain it in
the next section.

## Shiny app

With the three functions `htPositionsOnDevice()`, `selectPosition()` and
`selectArea()`, it is possible to implement Shiny apps for interactively
working with heatmaps. Now the problem is how does the server side capture the
positions that user clicked on the web page. Luckily, there is a solution for
this. The output heatmap is normally put within a `shiny::plotOutput()` and
`plotOutput()` provides two actions `click` and `brush`. Then on the server
side, it is possible to get the information of the positions that user
clicked. The positions can then be set to `selectPosition()` and `selectArea()` via
`pos` or `pos1`/`pos2` arguments to correctly correspond to
the values in original matrices.


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