---
title: "Introduction to heatmaply"
date: "`r Sys.Date()`"
author: "Tal Galili and Alan O'Callaghan"
output:
 html_document:
  fig_width: 12
  fig_height: 9
  self_contained: yes
  toc: true
---
<!--
%\VignetteEngine{knitr::rmarkdown}
%\VignetteIndexEntry{Introduction to heatmaply}
-->


```{r, echo = FALSE, message = FALSE}
library("heatmaply")
library("knitr")
knitr::opts_chunk$set(
  # cache = TRUE,
  dpi = 60,
  comment = "#>",
  tidy = FALSE)

# http://stackoverflow.com/questions/24091735/why-pandoc-does-not-retrieve-the-image-file
# < ! -- rmarkdown v1 -->

```


**Author**: [Tal Galili](http://www.r-statistics.com/) (Tal.Galili@gmail.com)



Introduction
============

A heatmap is a popular graphical method for visualizing high-dimensional data,
in which a table of numbers are encoded as a grid of colored cells. The rows 
and columns of the matrix are ordered to highlight patterns and are often 
accompanied by dendrograms. Heatmaps are used in many fields for visualizing 
observations, correlations, missing values patterns, and more.

Interactive heatmaps allow the inspection of specific value by hovering the 
mouse over a cell, as well as zooming into a region of the heatmap by dragging 
a rectangle around the relevant area.

This work is based on ggplot2 and plotly.js engine. It produces similar 
heatmaps as d3heatmap, with the advantage of speed (plotly.js is able to 
handle larger size matrix), and the ability to zoom from the dendrogram.

heatmaply also provides an interface based around the 
[plotly R package](https://CRAN.R-project.org/package=plotly).
This interface can be used by choosing `plot_method = "plotly"` instead of
the default `plot_method = "ggplot"`. This interface can provide smaller
objects and faster rendering to disk in many cases and provides otherwise almost
identical features.

Documentation for this package is also available as a 
[pkgdown](https://cran.r-project.org/package=pkgdown)
site: <http://talgalili.github.io/heatmaply/>

Installation
============

To install the stable version on CRAN:

```{r, eval = FALSE}
install.packages('heatmaply')
```

To install the GitHub version:

```{r, eval = FALSE}
# You'll need devtools
install.packages.2 <- function (pkg) if (!require(pkg)) install.packages(pkg);
install.packages.2('devtools')
# make sure you have Rtools installed first! if not, then run:
#install.packages('installr'); install.Rtools()

devtools::install_github("ropensci/plotly") 
devtools::install_github('talgalili/heatmaply')

```


And then you may load the package using:

```{r}
library("heatmaply")
```

Basic usage
===========


Default
-------

The default settings in heatmaply attempt to be both useful yet not too computationally intensive. Here is an example based on the `mtcars` dataset:

> The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973-74 models).


```{r}
library("heatmaply")
heatmaply(mtcars)
```

<!-- The margins parameter
-------------


By default heatmaply tries to fix the margins of the plot based on the length of the labels, but this system is not perfect. Hence, we sometimes need to manually fix the margins (hopefully this will be fixed in future versions of plotly). This is also helpful when including xlab/ylab/main texts.

```{r}
heatmaply(mtcars, xlab = "Features", ylab = "Cars", 
  main = "An example of title and xlab/ylab")
```
 -->

Correlation heatmaps
--------------------

We can use the margins parameter with correlation heatmaps. heatmaply includes
the `heatmaply_cor` function, which is a wrapper around `heatmaply` with 
arguments optimised for use with correlation matrices. Notice how we color 
the branches (see dendextend::color_branches for further detail on `k_row` and 
`k_col`):

```{r}
heatmaply_cor(
  cor(mtcars),
  xlab = "Features",
  ylab = "Features",
  k_col = 2,
  k_row = 2
)
```

We can also do a more advanced correlation heatmap where the p-value from
the correlation test is mapped to point size: 

```{r}

r <- cor(mtcars)
## We use this function to calculate a matrix of p-values from correlation tests
## https://stackoverflow.com/a/13112337/4747043
cor.test.p <- function(x){
    FUN <- function(x, y) cor.test(x, y)[["p.value"]]
    z <- outer(
      colnames(x), 
      colnames(x), 
      Vectorize(function(i,j) FUN(x[,i], x[,j]))
    )
    dimnames(z) <- list(colnames(x), colnames(x))
    z
}
p <- cor.test.p(mtcars)

heatmaply_cor(
  r,
  node_type = "scatter",
  point_size_mat = -log10(p), 
  point_size_name = "-log10(p-value)",
  label_names = c("x", "y", "Correlation")
)
```



Data transformation (scaling, normalize, and percentize)
========================================================

The variables in mtcars includes values that reflect different types of 
measurement, each with its own range (and meaning) of values. In such a case, 
it is best to transform the data so to have all the variables have comparable 
values.

scale
-----

If we would assume all variables come from some normal distribution, then 
scaling (i.e.: subtract the mean and divide by the standard deviation) would 
bring them all close to the standard normal distribution. In such a case, each 
value would reflect the distance from the mean in units of standard deviation. 
The "scale" parameter in heatmaply supports column and row scaling, and can be 
used as follows:

```{r}
heatmaply(
  mtcars, 
  xlab = "Features",
  ylab = "Cars", 
  scale = "column",
  main = "Data transformation using 'scale'"
)
```

normalize
---------



When variables in the data comes from possibly different (and non-normal)
distributions, other transformations may be in order. For example, scaling on 
a binary variable with many 0's and just a few 1's will lead to a column with a 
very extreme value that would cause the color legend to be very skewed by that 
variable, leading to a masking of the distribution of the rest of the variables.

Another possibility is to use the `normalize` function to brings data to the 0 
to 1 scale by subtracting the minimum and dividing by the maximum of all 
observations. This preserves the shape of each variable's distribution while 
making them easily comparable on the same "scale". Using the function on 
mtcars easily reveals columns with only two (`am`, `vs`) or three (`gear`, 
`cyl`) variables compared with variables that have a higher resolution of 
possible values:


```{r}
heatmaply(
  normalize(mtcars),
  xlab = "Features",
  ylab = "Cars", 
  main = "Data transformation using 'normalize'"
)
```

percentize
----------

An alternative to `normalize` is the `percentize` function. This is similar 
to ranking the variables, but instead of keeping the rank values, divide them 
by the maximal rank. This is done by using the `ecdf` of the variables on their 
own values, bringing each value to its  empirical percentile. The benefit of 
the percentize function is that each value has a relatively clear 
interpretation, it is the percent of observations with that value or below 
it.

```{r}
heatmaply(
  percentize(mtcars),
  xlab = "Features",
  ylab = "Cars", 
  main = "Data transformation using 'percentize'"
)
```

Notice that for binary variables (0 and 1), `percentize` will turn all 0 values
to their proportion and all 1 values will remain 1. This means the 
transformation is not symmatric for 0 and 1. Hence, if scaling for clustering, 
it might be better to use `rank` for dealing with tie values (if no ties 
are present, then `percentize` will perform similarly to `rank`).



is.na10 (missing values)
------------------------

Reviewing missing values can easily be done using the `is.na10` function. 
When using it with heatmaply, it is often helpful to use a non-zero `grid_gap`
to place gaps between cells of the heatmap. Similar to `heatmaply_cor`,
`heatmaply_na` is a wrapper around `heatmaply` with arguments optimised for the
visualisation of missingness:

```{r}
heatmaply_na(
  airquality[1:30, ],
  showticklabels = c(TRUE, FALSE),
  k_col = 3,
  k_row = 3
)


# warning - using grid_color cannot handle a large matrix! For example:
# airquality[1:10, ] %>% is.na10 %>% 
#   heatmaply(color = c("white", "black"), grid_color = "grey",
#             k_col =3, k_row = 3,
#             margins = c(40, 50)) 
# airquality %>% is.na10 %>% 
#   heatmaply(color = c("grey80", "grey20"), # grid_color = "grey",
#             k_col =3, k_row = 3,
#             margins = c(40, 50)) 
# 

```




Changing color palettes
-----------------------

We can use colors other than the default `viridis`. The packages
[`cetcolor`](https://CRAN.R-project.org/package=cetcolor) and 
[`RColorBrewer`](https://CRAN.R-project.org/package=RColorBrewer)
provide a number of excellent options for continuous and discrete colour 
palettes. These are generally designed to be perceptually uniform, and often
also colorblind-friendly.

For example, we may want to use other color palettes in order to get divergent colors for the correlations (these will, sadly, often be less useful for 
colorblind people):

```{r}
heatmaply_cor(
  cor(mtcars),
  k_col = 2, 
  k_row = 2
)

```


Another example for using colors:


```{r, eval = FALSE}
heatmaply(
  percentize(mtcars),
  colors = heat.colors(100)
)
```

Or even more customized colors using `scale_fill_gradient_fun`:

```{r}
heatmaply(
  mtcars,
  scale_fill_gradient_fun = ggplot2::scale_fill_gradient2(
    low = "blue", 
    high = "red", 
    midpoint = 200, 
    limits = c(0, 500)
  )
)

```



Customized dendrograms and annotation
=====================================


Various seriation options
-------------------------

heatmaply uses the `seriation` package to find an optimal ordering of rows 
and columns. Optimal means to optimize the Hamiltonian path length that 
is restricted by the dendrogram structure. This, in other words, means to 
rotate the branches so that the sum of distances between each adjacent leaf 
(label) will be minimized. This is related to a restricted version of the 
travelling salesman problem. 

The default options is `"OLO"` (Optimal leaf ordering) which optimizes the 
above criterion (in O(n^4)). Another option is `"GW"`
([Gruvaeus and Wainer](https://www.researchgate.net/publication/230266994_Two_additions_to_hierarchical_cluster_analysis)) 
which aims for the same goal but uses a potentially faster heuristic. The 
option `"mean"` gives the output we would get by default from heatmap functions 
in other packages such as `gplots::heatmap.2`. The option `"none"` gives us 
the dendrograms without any rotation that is based on the data matrix. 

```{r}
# The default of heatmaply:
heatmaply(
  percentize(mtcars)[1:10, ],
  seriate = "OLO"
)
```

```{r}
# Similar to OLO but less optimal (since it is a heuristic)
heatmaply(
  percentize(mtcars)[1:10, ],
  seriate = "GW"
)
```

```{r}
# the default by gplots::heatmaply.2
heatmaply(
  percentize(mtcars)[1:10, ],
  seriate = "mean"
)
```

```{r}
# the default output from hclust
heatmaply(
  percentize(mtcars)[1:10, ],
  seriate = "none"
)
```

This works heavily relies on the `seriation` package (their 
[vignette](https://CRAN.R-project.org/package=seriation/vignettes/seriation.pdf)
is well worth the read), and also lightly on the `dendextend` package (see
[vignette](http://talgalili.github.io/dendextend/articles/dendextend.html) )


Customized dendrograms using dendextend
---------------------------------------

A user can supply their own dendrograms for the rows/columns of the heatmaply
using the `Rowv` and the `Colv` parameters:

```{r}

x  <- as.matrix(datasets::mtcars)

# now let's spice up the dendrograms a bit:
library("dendextend")

row_dend  <- x %>% 
  dist %>% 
  hclust %>% 
  as.dendrogram %>%
  set("branches_k_color", k = 3) %>% 
  set("branches_lwd", c(1, 3)) %>%
  ladderize
# rotate_DendSer(ser_weight = dist(x))
col_dend  <- x %>% 
  t %>% 
  dist %>% 
  hclust %>% 
  as.dendrogram %>%
  set("branches_k_color", k = 2) %>% 
  set("branches_lwd", c(1, 2)) %>%
  ladderize
#    rotate_DendSer(ser_weight = dist(t(x)))

heatmaply(
  percentize(x),
  Rowv = row_dend,
  Colv = col_dend
)


```



Replicating the dendrogram ordering of heatmap.2
------------------------------------------------

The following example shows how to get the same result in heatmaply as with 
`gplots::heatmap.2`:

```{r}
x  <- as.matrix(datasets::mtcars)
gplots::heatmap.2(
  x,
  trace = "none",
  col = viridis(100),
  key = FALSE
)
```

With heatmaply, the only difference is the side of the row dendrogram. 
This is because the `ggplotly` function from plotly does not (yet) handle 
axes placed in different locations than the default.

```{r, eval = FALSE}
heatmaply(
  x,
  seriate = "mean"
)
```

We can get a more similar version using:

```{r}
heatmaply(x,
  seriate = "mean", 
  row_dend_left = TRUE,
  plot_method = "plotly"
)
```

Some aspects of heatmaply may not function identically when using 
`plot_method = "plotly"` relative to how they function when using
`plot_method = "ggplot"` (the default), however in the majority of cases
the output is equivalent. Using this option results in faster heatmaps capable 
of handling larger matrices.



Adding annotation based on additional factors using `RowSideColors`
-------------------------------------------------------------------

With heatmap.2

```{r}
# Example for using RowSideColors

x  <- as.matrix(datasets::mtcars)
rc <- colorspace::rainbow_hcl(nrow(x))

library("gplots")
library("viridis")
heatmap.2(
  x,
  trace = "none",
  col = viridis(100),
  RowSideColors = rc,
  key = FALSE
)

```

With `heatmaply`:

```{r, eval = FALSE}
heatmaply(
  x,
  seriate = "mean",
  RowSideColors = rc
)
```

A more sophisticated heatmap:

```{r}
heatmaply(
  x[, -c(8, 9)],
  seriate = "mean",
  col_side_colors = c(rep(0, 5), rep(1, 4)),
  row_side_colors = x[, 8:9]
)

```

Text annotations
----------------

`heatmaply` includes the `cellnote` argument, which allows us to display
character values overlaid on the heatmap. By default, the colour of the text
on each cell is chosen to ensure legibility, with black text shown over light
cells and white text shown over dark cells.

```{r}
heatmaply(
  mtcars,
  cellnote = mtcars
)
```


Hover text
----------

As hovertext is one of the most useful aspects of interactive heatmaps,
`heatmaply` allows users to append custom hovertext:

```{r}
mat <- mtcars
mat[] <- paste("This cell is", rownames(mat))
mat[] <- lapply(colnames(mat), function(colname) {
    paste0(mat[, colname], ", ", colname)
})
heatmaply(
  mtcars,
  custom_hovertext = mat
)
```


ggheatmap
---------

`heatmaply` also has the option to produce (static) vector graphic equivalents using
the excellent [`egg`](https://CRAN.R-project.org/package=egg) 
package. This feature is somewhat experimental, and all feedback and 
contributions to improve it are highly appreciated.

```{r}
ggheatmap(
  mtcars,
  scale = "column",
  row_side_colors = mtcars[, c("cyl", "gear")]
)
```


Saving your heatmaply into a file
=================================

You can save an interactive version of your `heatmaply` into an HTML file using
the following code:

```{r, eval = FALSE}
dir.create("folder")
heatmaply(mtcars, file = "folder/heatmaply_plot.html")
browseURL("folder/heatmaply_plot.html")
```


Similar code can be used for saving a static file (png/jpeg/pdf)

```{r, eval = FALSE}
dir.create("folder")
# Before the first time using this code you may need to first run:
# webshot::install_phantomjs() or to install 
# [plotly's orca](https://github.com/plotly/orca) program.
heatmaply(mtcars, file = "folder/heatmaply_plot.png")
browseURL("folder/heatmaply_plot.png")
```

If you only wish to save the file, without plotting it in the console,
you can assign the output to a temporary object name:

```{r, eval = FALSE}
# This saves the file, but does not plot it in the RStudio viewer
tmp <- heatmaply(mtcars, file = "folder/heatmaply_plot.png")
rm(tmp)
```


Sidenotes
=========

We considered using the fastcluster package for clustering, but the time gain 
was too small compared to the rest of the bottlenecks in the package.

```{r, eval = FALSE}

library("microbenchmark")


library("heatmaply")
x <- matrix(1:1000, 500, 2)

microbenchmark(
  heatmaply(x, hclustfun = stats::hclust),
  heatmaply(x, hclustfun = fastcluster::hclust),
  times = 10
)

x <- matrix(1:1000, 1000, 2)
microbenchmark(
  stats::hclust(dist(x)),
  fastcluster::hclust(dist(x)),
  times = 10
)


```








Credit
======

This package is thanks to the amazing work done by many people in the open 
source community. Beyond the many people working on the pipeline of R, thanks 
should go to the plotly team, and especially to Carson Sievert and others 
working on the R package of plotly. Also, many of the design elements were 
inspired by the work done on heatmap, heatmap.2 and d3heatmap, so special 
thanks goes to the R core team, Gregory R. Warnes, and Joe Cheng from RStudio. 
The dendrogram side of the package is based on the work in dendextend, in which
special thanks should go to Andrie de Vries for his original work on the 
ggdendro package was the first to bring dendrograms to ggplot2 (this later 
evolved into the richer ggdend objects, as implemented in dendextend). 
Thanks should also go to Alan O'Callaghan for his many contributions to getting 
the package to work better with plotly, as well as for Jonathan Sidi for his 
work on the shinyHeatmply package. Lastely, my thanks goes to Yoav Benjamini 
for his support and helpful comments on this work.


Contact
=======

You are welcome to:

* submit suggestions and bug-reports at: 
  <https://github.com/talgalili/heatmaply/issues>
* send a pull request on: <https://github.com/talgalili/heatmaply/>
* compose a friendly e-mail to: <tal.galili@gmail.com>


Latest news
===========

You can see the most recent changes to the package in the [NEWS.md file](https://talgalili.github.io/heatmaply/news)



Session info
============

```{r, cache = FALSE}
sessionInfo()
```
