Make Enriched Heatmaps
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, deepTools or genomation). 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.
Basic usages
First let’s load the example dataset that we will use for demonstration. The data for human lung tissue is from Roadmap dataset.
set.seed(123)
load(system.file("extdata", "chr21_test_data.RData", package = "EnrichedHeatmap"))
ls()## [1] "H3K4me3" "cgi" "genes" "meth" "rpkm"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):
## GRanges object with 5 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## ENSG00000141956.9 chr21 43299591 -
## ENSG00000141959.12 chr21 45719934 +
## ENSG00000142149.4 chr21 33245628 +
## ENSG00000142156.10 chr21 47401651 +
## ENSG00000142166.8 chr21 34696734 +
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths## GRanges object with 5 ranges and 1 metadata column:
## seqnames ranges strand | coverage
## <Rle> <IRanges> <Rle> | <integer>
## [1] chr21 9825468-9825470 * | 10
## [2] chr21 9825471-9825488 * | 13
## [3] chr21 9825489 * | 12
## [4] chr21 9825490 * | 13
## [5] chr21 9825491-9825493 * | 14
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsSimilar as other tools, the task of visualization are separated into two steps:
- get association between genomic signals and targets by normalizing into a matrix.
- visualize the matrix by heatmap.
mat1 = normalizeToMatrix(H3K4me3, tss, value_column = "coverage",
extend = 5000, mean_mode = "w0", w = 50)
mat1## Normalize H3K4me3 to tss:
## Upstream 5000 bp (100 windows)
## Downstream 5000 bp (100 windows)
## Include target regions (width = 1)
## 720 target regions## [1] "normalizedMatrix" "matrix"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:
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 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.
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.
## 0% 25% 50% 75% 99% 100%
## 10 18 29 43 87 293## 0% 25% 50% 75% 99% 100%
## 0.00000 0.00000 0.00000 0.00000 38.92176 174.78000If 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.)
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.
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.
Split on rows
Split rows by a vector or a data frame by specifying
row_split option.
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.
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.
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.
EnrichedHeatmap(mat1, col = col_fun, name = "H3K4me3",
cluster_rows = TRUE, column_title = "Enrichment of H3K4me3") 
Vignette 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.
# 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.
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.
mat_f = normalizeToMatrix(H3K4me3, tss, value_column = "coverage",
extend = 5000, mean_mode = "w0", w = 50, flip_upstream = TRUE)
mat_f## Normalize H3K4me3 to tss:
## Extension 5000 bp (100 window)
## upstream is flipped to downstream.
## Include target regions (width = 1)
## 720 target regions
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().
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.
For a given window, n is the number of signal regions which overlap with the window (it is 5 in the above plot), wi is the width of the intersected segments (black thick lines), and xi is the signal value associated with the original regions. If there is no value associated with the signal regions, xi = 1 by default.
The “absolute” method is denoted as va 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 vw 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 vc 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 xi = 1, vc 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)/17Smoothing
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 +.
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).
## GRanges object with 5 ranges and 1 metadata column:
## seqnames ranges strand | meth
## <Rle> <IRanges> <Rle> | <numeric>
## [1] chr21 9432427 * | 0.267104
## [2] chr21 9432428 * | 0.267107
## [3] chr21 9432964 * | 0.272710
## [4] chr21 9432965 * | 0.272735
## [5] chr21 9433315 * | 0.285115
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengthsmat2 = 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.
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:
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 = (n1 + n2) * r/(1 − r)
where n1 is the
number of upstream windows, n2 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.
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)## Warning: Smoothing is failed for one row because there are very few signals overlapped to it.
## Please use `failed_rows(mat)` to get the index of the failed row and consider to remove
## it.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.
mat3 = normalizeToMatrix(meth, cgi, value_column = "meth", mean_mode = "absolute",
extend = 0, k = 20, background = NA, smooth = TRUE, target_ratio = 1)## Warning: Smoothing is failed for one row because there are very few signals overlapped to it.
## Please use `failed_rows(mat)` to get the index of the failed row and consider to remove
## it.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 :
## [1] 5and 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.
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.
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.
load(system.file("extdata", "H3K4me1_corr_normalize_to_tss.RData", package = "EnrichedHeatmap"))
mat_H3K4me1## Normalize to target:
## Upstream 5000 bp (100 windows)
## Downstream 10000 bp (200 windows)
## Include target regions (width = 1)
## 677 target regionsIn 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.
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.
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]## GRanges object with 2 ranges and 1 metadata column:
## seqnames ranges strand | gene
## <Rle> <IRanges> <Rle> | <character>
## [1] chr1 901460-902041 * | ENSG00000187583.5
## [2] chr1 1238870-1239998 * | ENSG00000131584.14
## -------
## seqinfo: 17 sequences from an unspecified genome; no seqlengths## GRanges object with 2 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## ENSG00000187583.5 chr1 901877 +
## ENSG00000131584.14 chr1 1244989 -
## -------
## seqinfo: 25 sequences (1 circular) from an unspecified genome; no seqlengthsIn this example, neg_cr contains regions that show
negative correlation between methylation and expression for the genes.
The negative correlated regions are detected as:
- extend gene to upstream 5kb and downtream 5kb;
- 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.
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.
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().
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:
To change graphic settings for legends:
# 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:
For more advanced settings, please directly go to the vignettes in the ComplexHeamtap package.
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.
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.
# 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().
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.
# 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.
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.
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.
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]## [,1] [,2] [,3] [,4]
## [1,] 0 0 0 0
## [2,] 0 0 0 0
## [3,] 0 0 0 0
## [4,] 0 0 0 0As 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:
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 twoAnd don’t forget to set mat2 to
normalizedMatrix class. And now mat2 is a
valid object for EnrichedHeamtap().
## Normalize to :
## Upstream 5000 bp (100 windows)
## Downstream 5000 bp (100 windows)
## Not include target regions
## 720 target regionsAbove four attributes are enough for making the heatmaps, there are
several more attributes which can give better information when printing
mat2.
## Normalize H3K4me3 to TSS:
## Upstream 5000 bp (100 windows)
## Downstream 5000 bp (100 windows)
## Not include target regions
## 720 target regionsTo make the conversion easier, users can directly use
as.normalizedMatrix() for the conversion.
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"
)## Normalize H3K4me3 to TSS:
## Upstream 5000 bp (100 windows)
## Downstream 5000 bp (100 windows)
## Not include target regions
## 720 target regionsIn 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.
Error caused from smoothing
If you meet following error when doing smoothing in
normalizeToMatrix():
Error: segfault from C stack overflowYou can either:
- remove target regions for which there are few signals overlapped to them;
- change to another
smooth_fun()or change parameters inlocfit().
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.
Session info
## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
## [4] LC_COLLATE=C LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 grid stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] EnrichedHeatmap_1.37.0 GenomicRanges_1.59.1 GenomeInfoDb_1.43.2 IRanges_2.41.2
## [5] S4Vectors_0.45.2 BiocGenerics_0.53.3 generics_0.1.3 ComplexHeatmap_2.23.0
## [9] circlize_0.4.16 knitr_1.49 markdown_1.13
##
## loaded via a namespace (and not attached):
## [1] sass_0.4.9 shape_1.4.6.1 lattice_0.22-6 digest_0.6.37
## [5] evaluate_1.0.1 RColorBrewer_1.1-3 iterators_1.0.14 fastmap_1.2.0
## [9] foreach_1.5.2 doParallel_1.0.17 jsonlite_1.8.9 GlobalOptions_0.1.2
## [13] httr_1.4.7 UCSC.utils_1.3.0 codetools_0.2-20 jquerylib_0.1.4
## [17] cli_3.6.3 rlang_1.1.4 crayon_1.5.3 XVector_0.47.0
## [21] cachem_1.1.0 yaml_2.3.10 tools_4.4.2 parallel_4.4.2
## [25] colorspace_2.1-1 locfit_1.5-9.10 GenomeInfoDbData_1.2.13 GetoptLong_1.0.5
## [29] buildtools_1.0.0 R6_2.5.1 png_0.1-8 matrixStats_1.4.1
## [33] lifecycle_1.0.4 zlibbioc_1.52.0 clue_0.3-66 cluster_2.1.6
## [37] bslib_0.8.0 Rcpp_1.0.13-1 xfun_0.49 sys_3.4.3
## [41] rjson_0.2.23 htmltools_0.5.8.1 rmarkdown_2.29 maketools_1.3.1
## [45] compiler_4.4.2