<!--
%\VignetteEngine{knitr}
%\VignetteIndexEntry{1. 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 the
enriched heatmap is essentially 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 usages
```{r, eval = FALSE}
library(EnrichedHeatmap)
```
First let's load the example dataset that we will use for demonstration. The
data for human lung tissue 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 ChIP-seq 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. We extract TSS of genes (note `tss` has strand information):
```{r}
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, implemented b `anno_enriched()`.
`EnrichedHeatmap()` internally calls `Heatmap()` and returns a `Heatmap` class
object, so parameters for `Heatmap()` can be directly applied to
`EnrichedHeatmap()`. Users can go to the [**ComplexHeatmap**
package](https://jokergoo.github.io/ComplexHeatmap-reference/book/)
to get a more comprehensive help.
### Colors
Similar as the normal heatmap, the simplest way to set colors is to provide a
vector of colors.
```{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.
```{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 `row_split` option.
```{r, fig.width = 3}
EnrichedHeatmap(mat1, col = col_fun, name = "H3K4me3",
row_split = sample(c("A", "B"), length(genes), replace = TRUE),
column_title = "Enrichment of H3K4me3")
```
Split rows by k-means clustering by specifying `row_km` option.
```{r, fig.width = 4}
set.seed(123)
EnrichedHeatmap(mat1, col = col_fun, name = "H3K4me3", row_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", row_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)
```
Users can go to https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#heatmap-split for more controls
of splitting heatmaps.
### 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()` or https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#clustering.
```{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)
```
Sometimes whether the signals are on the upstream or the downstream of the targets
are not important and users only want to show the relative distance to targets. If `flip_upstream` is set
to `TRUE`, the upstream part in the normalized matrix is flipped and added to the downstream part.
The flipping is only allowed when the targets are single-point targets or the targets are excluded
in the normalized matrix (by setting `include_target = FALSE`). If the extension for the upstream
and downstream is not equal, the smaller extension is used for the final matrix.
```{r, fig.width = 3}
mat_f = normalizeToMatrix(H3K4me3, tss, value_column = "coverage",
extend = 5000, mean_mode = "w0", w = 50, flip_upstream = TRUE)
mat_f
EnrichedHeatmap(mat_f, name = "H3K4me3", col = col_fun)
```
### Axis of the enriched annotation
Axis of the enriched annotation is controlled by `axis_param` in `anno_enriched()`.
All the parameters that can be set can be found in `ComplexHeatmap::default_axis_param()`.
```{r, fig.width = 3}
EnrichedHeatmap(mat1, col = col_fun, name = "H3K4me3",
top_annotation = HeatmapAnnotation(
enriched = anno_enriched(
ylim = c(0, 10),
axis_param = list(
at = c(0, 5, 10),
labels = c("zero", "five", "ten"),
side = "left",
facing = "outside"
)))
)
```
### 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 over 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.
Another thing for smoothing methylation data is, methylation is already in a fixed range, i.e. [0, 1],
smoothing on raw methylation might produce new values exceeding [0, 1]. Thus, by default,
for methylation data (by a guess internally), after smoothing, values will be restricted within [0, 1], as
you already saw in the output message in previous 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}
failed_rows(mat3)
```
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. `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(axis_param = list(side = "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))),
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(15, "mm"),
top_annotation = HeatmapAnnotation(summary = anno_summary(gp = gpar(fill = 2:4),
outline = FALSE, axis_param = list(side = "right"))))
draw(ht_list, split = partition, annotation_legend_list = list(lgd),
ht_gap = unit(c(2, 8, 8), "mm"))
```
## 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(
enrich = anno_enriched(gp = gpar(neg_col = "darkgreen", pos_col = "red"),
axis_param = list(side = "left"))
), column_title = "separate neg and pos") +
EnrichedHeatmap(mat_H3K4me1, col = corr_col_fun, show_heatmap_legend = FALSE,
top_annotation = HeatmapAnnotation(enrich = anno_enriched(value = "abs_mean")),
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 `row_km` or `row_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)
```
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_opt(...)
EnrichedHeatmap(...)
ht_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](https://jokergoo.github.io/ComplexHeatmap-reference/book/).
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.gif' /></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
```
To make the conversion easier, users can directly use `as.normalizedMatrix()` for the conversion.
```{r}
attributes(mat2) = NULL
dim(mat2) = dim(mat1)
as.normalizedMatrix(mat2,
k_upstream = 100,
k_downstream = 100,
k_target = 0,
extend = c(5000, 5000),
signal_name = "H3K4me3",
target_name = "TSS"
)
```
In `as.normalizedMatrix()`, you can also perform smoothing by specifying `smooth` and `smooth_fun`.
Pleas check the documentation of this function.
## Reduce the size of the plot
You can set `use_raster` to
`TRUE` to replace the heatmap bodies with raster images. Check https://jokergoo.github.io/ComplexHeatmap-reference/book/a-single-heatmap.html#heatmap-as-raster-image.
```{r, eval = FALSE}
# code not run
EnrichedHeatmap(mat, use_raster = TRUE, raster_device = ..., raster_device_param = ...)
```
## Error caused from smoothing
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()
```