<!--
%\VignetteEngine{knitr}
%\VignetteIndexEntry{Make Enriched Heatmaps}
-->

Make Enriched Heatmaps
========================================

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

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

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

```{r, 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")
options(markdown.HTML.stylesheet = "custom.css")

options(width = 100)
suppressPackageStartupMessages(library(circlize))
```

Enriched heatmap is a special type of heatmap which visualizes the enrichment
of genomic signals over specific target regions. It is broadly used to
visualize e.g. how histone modifications are enriched at transcription start
sites.

There are already several tools that are able to make such heatmap (e.g.
[ngs.plot](https://github.com/shenlab-sinai/ngsplot),
[deepTools](https://github.com/fidelram/deepTools) or [genomation](https://bioconductor.org/packages/release/bioc/html/genomation.html)). Here we implement
Enriched heatmap by **ComplexHeatmap** package. Since this type of heatmap is
just a normal heatmap but with some special settings, with the functionality
of **ComplexHeatmap**, it would be much easier to customize the heatmaps as
well as concatenating to a list of heatmaps to show correspondance between
different data sources.

```{r, echo = FALSE, message = FALSE}
suppressWarnings(suppressPackageStartupMessages(library(EnrichedHeatmap)))
```

## Basic

```{r, eval = FALSE}
library(EnrichedHeatmap)
```

First load the example data that we will use for demostration. The data is for human lung tissue and is from [Roadmap dataset](http://www.roadmapepigenomics.org/data/).

```{r}
set.seed(123)
load(system.file("extdata", "chr21_test_data.RData", package = "EnrichedHeatmap"))
ls()
```

There are following R objects:

- `H3K4me3`: coverage for H3K4me3 histone modification from the ChIPSeq data
- `cgi`: CpG islands
- `genes`: genes
- `meth`: methylation for CpG sites from WGBS
- `rpkm`: gene expression from RNASeq

In order to build the vignette fast, the data only includes chromosome 21. Also we downsampled 100000 CpG
sites for methylation data.

We first visualize how H3K4me3 histone modification is enriched around gene TSS.
First we extract TSS of genes (note `tss` has strand information):

```{r, fig.width = 3}
tss = promoters(genes, upstream = 0, downstream = 1)
tss[1:5]
H3K4me3[1:5]
```

Similar as other tools, the task of visualization are separated into two steps:

1. get association between genomic signals and targets by normalizing into a matrix.
2. visualize the matrix by heatmap.

```{r}
mat1 = normalizeToMatrix(H3K4me3, tss, value_column = "coverage", 
    extend = 5000, mean_mode = "w0", w = 50)
mat1
class(mat1)
```

`normalizeToMatrix()` converts the association between genomic signals
(`H3K4me3`) and targets(`tss`) into a matrix (actually `mat1` is just a
normal matrix with several additional attributes). It first splits the
extended targets regions (the extension to upstream and downstream is
controlled by `extend` argument) into a list of small windows (the width of
the windows is controlled by `w`), then overlaps genomic signals to these
small windows and calculates the value for every small window which is the
mean value of genomic signals that intersects with the window (the value
corresponds to genomic signals are controlled by `value_column` and how to
calcualte the mean value is controlled by `mean_mode`.).

There are several modes for `mean_mode` according to different types of
genomic signals. It will be explained in later sections.

With `mat1`, we can visualize it as a heatmap:

```{r, fig.width = 3}
EnrichedHeatmap(mat1, name = "H3K4me3")
```

By default, rows are ordered according to the enrichment to the target regions. On top
of the heatmap there is a specific type of annotation which summarize the enrichment
patterns as a line plot.

`EnrichedHeatmap()` returns an `EnrichedHeatmap` class instance which is inherited from `Heatmap` class,
so parameters and methods for `Heatmap` class can be directly applied to `EnrichedHeatmap` class. Users can
go to the [**ComplexHeatmap** package](http://www.bioconductor.org/packages/devel/bioc/html/ComplexHeatmap.html) to get a more comprehensive help.

### Colors

Similar as the normal heatmap, the simplest way to set colors is to provide a vector of length two.

```{r, fig.width = 3}
EnrichedHeatmap(mat1, col = c("white", "red"), name = "H3K4me3")
```

You may wonder why the color looks so light. The reason is in coverage values in `H3K4me3`, there exist
some extreme values, which results in extreme value in `mat1`.

```{r}
quantile(H3K4me3$coverage, c(0, 0.25, 0.5, 0.75, 0.99, 1))
quantile(mat1, c(0, 0.25, 0.5, 0.75, 0.99, 1))
```

If a vector of colors is specified, sequential values from minimal to maximal are mapped to the colors,
and other values are linearly interpolated. To get rid of such extreme values, there are two ways. 
The first is to specify `keep` option which trims extreme values both at lower and upper bounds.
(In following, it means only to trim values larger than 99th percentile.)

```{r, fig.width = 3}
mat1_trim = normalizeToMatrix(H3K4me3, tss, value_column = "coverage", 
    extend = 5000, mean_mode = "w0", w = 50, keep = c(0, 0.99))
EnrichedHeatmap(mat1_trim, col = c("white", "red"), name = "H3K4me3")
```

The second way is to define a color mapping function which only maps colors to values less than 99th percentile
and the value larger than the 99th percentile uses same color as the 99th percentile.
The advantage of using a color mapping function is that if you have more than one heatmaps to make, it
makes colors in heatmaps comparable.

```{r, fig.width = 3}
library(circlize)
col_fun = colorRamp2(quantile(mat1, c(0, 0.99)), c("white", "red"))
EnrichedHeatmap(mat1, col = col_fun, name = "H3K4me3")
```

To sum it up, the first way directly modified values in `mat1` while the second way keeps the original values
but uses a robust color mapping.

If `col` is not specified in `EnrichedHeatmap()`, blue-white-red is mapped to 1st quantile, mean and 99th quantile by default.

In following sections, we will also use the matrix to do row-clustering, thus we directly use the trimmed matrix.

```{r}
mat1 = mat1_trim
```

### Split on rows

Split rows by a vector or a data frame by specifying `split` option.

```{r, fig.width = 3}
EnrichedHeatmap(mat1, col = col_fun, name = "H3K4me3", 
    split = sample(c("A", "B"), length(genes), replace = TRUE),
    column_title = "Enrichment of H3K4me3") 
```

Split rows by k-means clustering by specifying `km` option.

```{r, fig.width = 4}
set.seed(123)
EnrichedHeatmap(mat1, col = col_fun, name = "H3K4me3", km = 3,
    column_title = "Enrichment of H3K4me3", row_title_rot = 0)
```

In each row cluster, rows are still ordered by the enrichment.

When rows are split, graphic parameters for the enriched annotation can be a vector with length
as the number of row clusters.

```{r, fig.width = 4}
set.seed(123)
EnrichedHeatmap(mat1, col = col_fun, name = "H3K4me3", km = 3,
    top_annotation = HeatmapAnnotation(enriched = anno_enriched(gp = gpar(col = 2:4, lty = 1:3))),
    column_title = "Enrichment of H3K4me3", row_title_rot = 0)
```

### Cluster on rows

Cluster on rows. By default `show_row_dend` is turned off, so you don't need to specify it here.
More options for row clustering can be found in the help page of `Heatmap()`.

```{r, fig.width = 3}
EnrichedHeatmap(mat1, col = col_fun, name = "H3K4me3", 
    cluster_rows = TRUE, column_title = "Enrichment of H3K4me3")     
```

Vignette [row_ordering.html](row_ordering.html) compares different row ordering methods and clustering methods,
and discusses which might be the proper way to show the enrichment patterns.

### Extensions to target regions

Extension to upstream and downstream can be controled by `extend` either by a single value
or a vector of length 2.

```{r, fig.width = 3}
# upstream 1kb, downstream 2kb
mat12 = normalizeToMatrix(H3K4me3, tss, value_column = "coverage", 
    extend = c(1000, 2000), mean_mode = "w0", w = 50)
EnrichedHeatmap(mat12, name = "H3K4me3", col = col_fun)
```

Either upstream or downstream can be set to 0.

```{r, fig.width = 3}
mat12 = normalizeToMatrix(H3K4me3, tss, value_column = "coverage", 
    extend = c(0, 2000), mean_mode = "w0", w = 50)
EnrichedHeatmap(mat12, name = "H3K4me3", col = col_fun)
mat12 = normalizeToMatrix(H3K4me3, tss, value_column = "coverage", 
    extend = c(1000, 0), mean_mode = "w0", w = 50)
EnrichedHeatmap(mat12, name = "H3K4me3", col = col_fun)
```


### Mean mode

When normalizing genomic signals to target regions, upstream and downstream
(also target regions themselves if they are included) of the targets are split
into small windows. Then genomic signals are overlapped to each window and
mean signal for each window is calculated. When a window is not completely
covered by the regions for the genomic signales, proper averaging method
should be applied to summarize the value in the window.

Depending on different scenarios, **EnrichedHeatmap** provides three metrics
for averaging.

The overlapping model is illustrated in the following plot. The red line in
the bottom represents the small window. Black lines on the top are the regions
for genomic signals that overlap with the window. The thick lines indicate the
intersected part between the signal regions and the window.

```{r, fig.height = 3, echo = FALSE}
library(grid)
grid.lines(c(0.1, 0.9), c(0.3, 0.3), gp = gpar(col = "red", lwd = 4))
grid.text("window", 0.5, 0.25, just = "top", gp = gpar(col = "red"))
grid.lines(c(0, 0.2), c(0.6, 0.6))
grid.lines(c(0.3, 0.5), c(0.6, 0.6))
grid.lines(c(0.7, 1), c(0.6, 0.6))
grid.lines(c(0.1, 0.2), c(0.6, 0.6), gp = gpar(lwd = 4))
grid.lines(c(0.3, 0.5), c(0.6, 0.6), gp = gpar(lwd = 4))
grid.lines(c(0.7, 0.9), c(0.6, 0.6), gp = gpar(lwd = 4))
grid.lines(c(0.15, 0.35), c(0.45, 0.45), gp = gpar(lwd = 4))
grid.lines(c(0.6, 0.75), c(0.45, 0.45), gp = gpar(lwd = 4))
grid.lines(c(0.1, 0.1), c(0, 1), gp = gpar(lty = 2, col = "grey"))
grid.lines(c(0.9, 0.9), c(0, 1), gp = gpar(lty = 2, col = "grey"))
grid.text("genomic signal regions", 0.5, 0.7, just = "bottom")
```

For a given window, $n$ is the number of signal regions which overlap with the window (it is 5 in the above plot), 
$w_i$ is the width of the intersected segments (black thick lines), and $x_i$ is the signal value associated with the original regions. If there is no value associated with the signal regions, $x_i = 1$ by default.

The "absolute" method is denoted as $v_a$ and is simply calculated as the mean of all signal regions regardless of their width:

$$ v_a = \frac{\sum_i^n{x_i}}{n} $$

The "weighted" method is denoted as $v_w$ and is calculated as the mean of all signal regions weighted by the width of their intersections: 

$$ v_w = \frac{\sum_i^n{x_iw_i}}{\sum_i^n{w_i}} $$

"Absolute" and "weighted" methods should be applied when background values should not be taken into consideration. For example, when summarizing
the mean methylation in a small window, non-CpG background should be ignored, because methylation is only associated with CpG sites and not with other positions.

The "w0" method is the weighted mean between the intersected parts and un-intersected parts: 

 $$ v_{w0} = \frac{v_wW}{W+W'} $$

$W$ is sum of width of the intersected parts ($\sum_i^n{w_i}$) and $W'$ is the sum of width for the non-intersected parts. 

The "coverage" method is denoted as $v_c$ and is defined as the mean signal averged by the length of the window:

$$ v_c = \frac{\sum_i^n{x_iw_i}}{L} $$

where $L$ is the length of the window itself. Note when $x_i = 1$, $v_c$ is the mean coverage for the signal regions overlapped in the window.

 
Following illustrates different settings for ``mean_mode`` (note there is a signal region overlapping with other signal regions):

       40      50     20     values in signal regions
     ++++++   +++    +++++   signal regions
            30               values in signal regions
          ++++++             signal regions
       =================     window (17bp), there are 4bp not overlapping to any signal region.
         4  6  3      3      overlap

     absolute: (40 + 30 + 50 + 20)/4
     weighted: (40*4 + 30*6 + 50*3 + 20*3)/(4 + 6 + 3 + 3)
     w0:       (40*4 + 30*6 + 50*3 + 20*3)/(4 + 6 + 3 + 3 + 4)
     coverage: (40*4 + 30*6 + 50*3 + 20*3)/17


### Smoothing

Rows can be smoothed by setting `smooth` to `TRUE` when generating the matrix. 
Later we will demonstrate smoothing can also help to impute `NA` values.

As smoothing may change the original data range, the color mapping function `col_fun`
here ensures that the color palette is still the same as the unsmoothed one.

`background` corresponds to the regions that have no signal overlapped. The proper value
depends on specific scenarios. Here since we visualize coverage from ChIP-Seq data, it is reasonable
to assign 0 to regions with no H3K4me3 signal.

In following example, since a enriched heatmap is also a heatmap, we can concatenate two heamtaps by `+`.

```{r smooth, fig.width = 6}
mat1_smoothed = normalizeToMatrix(H3K4me3, tss, value_column = "coverage", 
    extend = 5000, mean_mode = "w0", w = 50, background = 0, smooth = TRUE)
EnrichedHeatmap(mat1_smoothed, col = col_fun, name = "H3K4me3_smoothed",
    column_title = "smoothed") +
EnrichedHeatmap(mat1, col = col_fun, name = "H3K4me3", column_title = "unsmoothed")
```

In above plots, you might feel the left heatmap is better than the right unsmoothed heatmap.
In following, we will demonstrate smoothing can significantly improve the enrichment pattern for methylation datasets.

Following heatmap visualizes the enrichment of low methylated regions on TSS. The grey colors
represent the windows with no CpG sites (note we set `NA` to `background` and grey is the default color
for `NA` values by **ComplexHeatmap**).

```{r, fig.width = 3}
meth[1:5]
mat2 = normalizeToMatrix(meth, tss, value_column = "meth", mean_mode = "absolute",
    extend = 5000, w = 50, background = NA)
meth_col_fun = colorRamp2(c(0, 0.5, 1), c("blue", "white", "red"))
EnrichedHeatmap(mat2, col = meth_col_fun, name = "methylation", column_title = "methylation near TSS")
```

When overlapping CpG positions to segmented target regions, it is possible
that there is no CpG sites in some windows, especially for `meth` which only
contains 100000 CpG sites which are randomly sampled in chromosome 21. The
values for these windows which contain no CpG sites can be imputed by
smoothing. Although it seems not proper to assign methylation values to non-
CpG windows, but it will improve the visualization a lot.

```{r, fig.width = 3}
mat2 = normalizeToMatrix(meth, tss, value_column = "meth", mean_mode = "absolute",
    extend = 5000, w = 50, background = NA, smooth = TRUE)
EnrichedHeatmap(mat2, col = meth_col_fun, name = "methylation", column_title = "methylation near TSS")
```

To do the smoothing, by default, `locfit()` is first applied to each row in
the original matrix. If it is failed, `loess()` smoothing is applied
afterwards. If both smoothing methods are failed, there will be a warning and
the original value is kept.

Users can provides their own smoothing function by `smooth_fun` argument. This
self-defined function accepts a numeric vector (may contains `NA` values) and
returns a vector with same length. If the smoothing is failed, the function
should call `stop()` to throw errors so that `normalizeToMatrix()` can catch
how many rows are failed in smoothing. Take a look at the source code of
`default_smooth_fun()` to get an example.

### Targets are regions

In the example of H3K4me3, the target regions are single points. 
The targets can also be regions with width larger than 1.
Following heatmap visualizes the enrichment of low methylation on CpG islands:

```{r, fig.width = 3}
mat3 = normalizeToMatrix(meth, cgi, value_column = "meth", mean_mode = "absolute",
    extend = 5000, w = 50, background = NA, smooth = TRUE, target_ratio = 0.3)
EnrichedHeatmap(mat3, col = meth_col_fun, name = "methylation", axis_name_rot = 90,
    column_title = "methylation near CGI")
```

Width of the target regions shown on heatmap can be controlled by `target_ratio` which is relative to
the width of the complete heatmap. 

Target regions are also splitted into small windows. Due to the unequal width
of target regions, each target is split into $k$ equal windows with $k = (n_1 +n_2)*r/(1-r)$ 
where $n_1$ is the number of upstream windows, $n_2$ is the number of
downstream windows and $r$ is the ratio of target columns presented in the
matrix. There is a `k` argument in `normalizeToMatrix()`, but it is only used
when there is no upstream nor downstream for the targets.

When genomic targets are regions, upstream and/or downstream can be excluded in the heatmap.

```{r extension, fig.width = 3}
mat3 = normalizeToMatrix(meth, cgi, value_column = "meth", mean_mode = "absolute",
    extend = c(0, 5000), w = 50, background = NA, smooth = TRUE, target_ratio = 0.5)
EnrichedHeatmap(mat3, col = meth_col_fun, name = "methylation", 
    column_title = "methylation near CGI")
mat3 = normalizeToMatrix(meth, cgi, value_column = "meth", mean_mode = "absolute",
    extend = c(5000, 0), w = 50, background = NA, smooth = TRUE, target_ratio = 0.5)
EnrichedHeatmap(mat3, col = meth_col_fun, name = "methylation", 
    column_title = "methylation near CGI")
```

When there is no upstream nor downstream, the number of columns in the heatmap is controlled by `k` argument.


```{r, fig.width = 3}
mat3 = normalizeToMatrix(meth, cgi, value_column = "meth", mean_mode = "absolute",
    extend = 0, k = 20, background = NA, smooth = TRUE, target_ratio = 1)
EnrichedHeatmap(mat3, col = meth_col_fun, name = "methylation",
    column_title = "methylation near CGI")
```

You may notice there are warnings when executing above code, that is because there are very few signals overlapped to some rows,
which results too many `NA` values and failed with the smoothing. Corresponding index for failed rows can be get by :

```{r}
attr(mat3, "failed_rows")
```

and maybe you can remove this row in the matrix beforehand.

## Multiple heatmaps

The power of **EnrichedHeatmap** package is that parallel heatmaps can be concatenated,
both for enriched heatmap, normal heatmap as well the row annotations, which
provides a very efficient way to visualize multiple sources of information.

With the functionality of **ComplexHeatmap** package, heatmaps can be concatenated
by `+` operator. `EnrichedHeatmap` objects, `Heatmap` objects and `HeatmapAnnotation` objects can be mixed.

Following heatmaps visualizes correspondance between H3K4me3 modification, methylation and
gene expression. It is quite straightforward to see high expression correlates
with low methylation and high H3K4me3 signal around TSS.

```{r, fig.width = 6}
EnrichedHeatmap(mat1, col = col_fun, name = "H3K4me3",
    top_annotation = HeatmapAnnotation(enrich = anno_enriched(yaxis_facing = "left"))) + 
EnrichedHeatmap(mat2, col = meth_col_fun, name = "methylation") +
Heatmap(log2(rpkm+1), col = c("white", "orange"), name = "log2(rpkm+1)", 
    show_row_names = FALSE, width = unit(5, "mm"))
```

Of course you can split rows by partition variables or k-means clustering in the main heatmap. In following heatmaps,
the most right color bar can be corresponded to the colors in column annotation on both
histone modification heatmap and methylation heatmap.

Here we emphasize again, proper trimming on the matrix will greatly help to reveal the patterns. 
You can try replace `mat1` to a un-trimmed matrix and see whether this patterns shown below still preserves.

```{r, fig.width = 7, fig.height = 8}
partition = paste0("cluster", kmeans(mat1, centers = 3)$cluster)
lgd = Legend(at = c("cluster1", "cluster2", "cluster3"), title = "Clusters", 
    type = "lines", legend_gp = gpar(col = 2:4))
ht_list = Heatmap(partition, col = structure(2:4, names = paste0("cluster", 1:3)), name = "partition",
              show_row_names = FALSE, width = unit(3, "mm")) +
          EnrichedHeatmap(mat1, col = col_fun, name = "H3K4me3",
              top_annotation = HeatmapAnnotation(lines = anno_enriched(gp = gpar(col = 2:4),
                yaxis_facing = "left")), 
              column_title = "H3K4me3") + 
          EnrichedHeatmap(mat2, col = meth_col_fun, name = "methylation",
              top_annotation = HeatmapAnnotation(lines = anno_enriched(gp = gpar(col = 2:4))), 
              column_title = "Methylation") +
          Heatmap(log2(rpkm+1), col = c("white", "orange"), name = "log2(rpkm+1)", 
              show_row_names = FALSE, width = unit(5, "mm"))
draw(ht_list, split = partition, annotation_legend_list = list(lgd))
```

## Visualize positive signals and negative signals separatedly

Sometimes we visualize the general correlation or the group difference
around certain genomic targets. In this case, it makes more sense to visualize the enrichment for the 
positive signals and negative signals separatedly. In following example, variable `mat_H3K4me1`
contains correlation between H3K4me1 signal and gene expression in (-5kb, 10kb)
of the gene TSS.

```{r}
load(system.file("extdata", "H3K4me1_corr_normalize_to_tss.RData", package = "EnrichedHeatmap"))
mat_H3K4me1
```

In `anno_enriched()`, there are two non-standard parameters `neg_col` and `pos_col` for `gp`. If these
two are set, the enrichment lines are drawn for the positive signals and negative signals in the matrix
separatedly.

```{r, fig.width = 5}
corr_col_fun = colorRamp2(c(-1, 0, 1), c("darkgreen", "white", "red"))
EnrichedHeatmap(mat_H3K4me1, col = corr_col_fun, name = "corr_H3K4me1",
    top_annotation = HeatmapAnnotation(
        line = anno_enriched(gp = gpar(neg_col = "darkgreen", pos_col = "red"),
            yaxis_facing = "left")
    ), column_title = "separate neg and pos") +
EnrichedHeatmap(mat_H3K4me1, col = corr_col_fun, show_heatmap_legend = FALSE,
    column_title = "pool neg and pos")
```

## Restrict overlapping by mappings

By default every genomic signal tries to intersect to every target region, but if mapping is
provided, only those genomic signals that are mapped to the corresponding target region will be overlapped.

To illustrate it more clearly, we load the example data. 
`gene` column in `neg_cr` is used to map to the names of `all_tss`.
In following example, `neg_cr` is the signal and `all_tss` is the target.

```{r}
load(system.file("extdata", "neg_cr.RData", package = "EnrichedHeatmap"))
all_tss = promoters(all_genes, upstream = 0, downstream = 1)
all_tss = all_tss[unique(neg_cr$gene)]
neg_cr[1:2]
all_tss[1:2]
```

In this example, `neg_cr` contains regions that show negative correlation between methylation
and expression for the genes. The negative correlated regions are detected as:

1. extend gene to upstream 5kb and downtream 5kb;
2. for every gene, use a sliding window to go through left to right
    and find correlated regions by looking at the correlation between methylation in the window and expression for the gene.

Since genes may be close to each other, it is possible that one correlated region for gene A
overlaps with gene B, and actually we only want to overlap this correlated regions to gene A while not gene B. 
By specifying the mapping, we can correspond correlated regions to the correct genes.

```{r, fig.width = 3}
mat_neg_cr = normalizeToMatrix(neg_cr, all_tss, mapping_column = "gene", w = 50, mean_mode = "w0")
EnrichedHeatmap(mat_neg_cr, col = c("white", "darkgreen"), name = "neg_cr", cluster_rows = TRUE,
    top_annotation = HeatmapAnnotation(lines = anno_enriched(gp = gpar(col = "darkgreen"))))
```

Similarly we can visualize the distribution of transcript to gene TSS. Since there are already
connections between transcripts and their host genes, we need to provide this information 
when normalizing into the matrix.

```{r, fig.width = 3}
mat_tx = normalizeToMatrix(tx, all_tss, mapping_column="gene", extend = c(5000, 10000), w = 50, 
    mean_mode = "coverage", keep = c(0, 0.99))
EnrichedHeatmap(mat_tx, col = c("white", "black"), name = "tx_coverage", cluster_rows = TRUE,
    top_annotation = HeatmapAnnotation(lines2 = anno_enriched(gp = gpar(col = "black"))))
```

## Features coming from ComplexHeatmap package

Since **EnrichedHeatmap** is built upon the **ComplexHeatmap** package, features in **ComplexHeatmap** can be
used directly for **EnrichedHeatmap**. As shown before, heatmaps can be split either by `km` or `spilt` arguments.

The order of rows can be retrieved by `row_order()`.

```{r, eval = FALSE}
# code not run
ht_list = draw(ht_list)
row_order(ht_list)
```

If you are interested in a small cluster, under the interactive mode,
you can use mouse to select this region by `selectArea()` function, and it will give you the index of rows
in the selected sub-region.

```{r, eval = FALSE}
# code not run
draw(ht_list)
pos = selectArea()
```

Since `EnrichedHeatmap` and `EnrichedHeamtapList` class are inherited from `Heamtap` and `HeamtapList` class
respectively, all advanced parameters in the latter two classes can be directly used in the former two classes.

E.g. to change graphic settings for the heatmap title:

```{r, eval = FALSE}
# code not run
EnrichedHeatmap(..., column_title_gp = ...)
```

To change graphic settings for legends:

```{r, eval = FALSE}
# code not run
EnrichedHeatmap(..., heatmap_legend_param = ...)
# or set is globally
ht_global_opt(...)
EnrichedHeatmap(...)
ht_global_opt(RESET = TRUE)
```

To set the width of the heatmaps if there are more than one heatmaps:

```{r, eval = FALSE}
# code not run
EnrichedHeatmap(..., width = ...) + EnrichedHeatmap(...)
```

For more advanced settings, please directly go to [the vignettes in the **ComplexHeamtap** package](http://www.bioconductor.org/packages/devel/bioc/vignettes/ComplexHeatmap/inst/doc/s1.introduction.html).

Together with above features, you can make very complex heatmaps. Following example is from a real-world dataset
and the details of making this plot can be found [in this vigentte](roadmap.html).

<p><img src='roadmap.png' /></p>

## Summarize from a list of matrices

Let's assume you have a list of histone modification signals for different samples and you want
to visualize the mean pattern across samples. You can first normalize histone mark signals for each sample and then
calculate means values across all samples. In following example code, `hm_gr_list` is a list of `GRanges` objects
which contain positions of histone modifications, `tss` is a `GRanges` object containing positions of gene TSS.

```{r, eval = FALSE}
# code not run
mat_list = NULL
for(i in seq_along(hm_gr_list)) {
    mat_list[[i]] = normalizeToMatrix(hm_gr_list[[i]], tss, value_column = ...)
}
```

If we compress the list of matrices as a three-dimension array where the first dimension corresponds to genes,
the second dimension corresponds to windows and the third dimension corresponds to samples, the mean signal
across all sample can be calculated on the third dimension. Here `getSignalsFromList()` simplifies this job.

Applying `getSignalsFromList()` to `mat_list`, it gives a new normalized matrix which contains mean signals across all samples and can
be directly used in `EnrichedHeatmap()`.

```{r, eval = FALSE}
# code not run
mat_mean = getSignalsFromList(mat_list)
EnrichedHeatmap(mat_mean)
```

The correlation between histone modification and gene expression can
also be calculated on the third dimension of the array. In the user-defined function `fun`, `x` is the vector for gene i
and window j in the array, and `i` is the index of current gene.

```{r, eval = FALSE}
# code not run
mat_corr = getSignalsFromList(mat_list, fun = function(x, i) cor(x, expr[i, ], method = "spearman"))
```

Then `mat_corr` here can be used to visualize how gene expression is correlated to histone modification around TSS.

```{r, eval = FALSE}
# code not run
EnrichedHeatmap(mat_corr)
```

## Use your own matrix

`normalizeToMatrix()` is used to normalize the associations between genomic signals to the targets. The returned value
is just a simple matrix but with several attributes attached. Sometimes, users may have their own way to generate such matrix.
It is easy to add the addtional attributes and send to `EnrichedHeamtap()` for visualization.

Following four attributes should be attached. Basically they are used for making the axes and labels.

```{r, eval = FALSE}
attr(mat, "upstream_index")
attr(mat, "target_index")
attr(mat, "downstream_index")
attr(mat, "extend")
```

To taks as an example, in following code, `mat2` is a simple matrix which only contains `dim` attributes.
`mat2` can be thought as a matrix obtained from other methods.

```{r}
mat1 = normalizeToMatrix(H3K4me3, tss, value_column = "coverage", 
    extend = 5000, mean_mode = "w0", w = 50)
mat2 = mat1
attributes(mat2) = NULL
dim(mat2) = dim(mat1)
mat2[1:4, 1:4]
```

As we already know, in `mat2`, upstream is extended to 5kb by 50bp window, which means the first 100 columns
correspond to the upstream. Similar the last 100 columns for downstream. Here the targets is TSS which can be thought
as with no width. So we can set column index attributes for upstream, target and downstream as follows:

```{r}
attr(mat2, "upstream_index") = 1:100
attr(mat2, "target_index") = integer(0)
attr(mat2, "downstream_index") = 101:200
attr(mat2, "extend") = c(5000, 5000)  # it must be a vector of length two
```

And don't forget to set `mat2` to `normalizedMatrix` class. And now `mat2` is a valid object for `EnrichedHeamtap()`.

```{r}
class(mat2) = c("normalizedMatrix", "matrix")
mat2
```

Above four attributes are enough for making the heatmaps, there are several more attributes which can give
better information when printing `mat2`.

```{r}
attr(mat2, "signal_name") = "H3K4me3"
attr(mat2, "target_name") = "TSS"
mat2
```

## Error caused from smoothing

If you generate the plot for the whole genome, I suggest you first save the figure as pdf format and then
convert to png by [`convert`](http://www.imagemagick.org/script/convert.php) software, instead of directly saving as png format.
A second solution is to set `use_raster` to `TRUE` to replace the heatmap bodies with raster images. Check [this document](http://www.bioconductor.org/packages/devel/bioc/vignettes/ComplexHeatmap/inst/doc/s2.single_heatmap.html#toc_7)

```{r, eval = FALSE}
# code not run
EnrichedHeatmap(mat, use_raster = TRUE, raster_device = ..., raster_device_param = ...)
```

If you meet following error when doing smoothing in `normalizeToMatrix()`:

```
Error: segfault from C stack overflow
```

You can either:

1. remove target regions for which there are few signals overlapped to them;
2. change to another `smooth_fun()` or change parameters in `locfit()`.

For solution 1, you can first calculate the matrix without smoothing and calculate
the percent of `NA` values in each row. Rows having high `NA` values can be removed.

```{r, eval = FALSE}
# code not run
mat = normalizeToMatrix(..., smooth = FALSE)
# the percent of NA values in each row
apply(mat, 1, function(x) sum(is.na(x)/length(x)))
```

## Session info

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