Author: Zuguang Gu ( z.gu@dkfz.de )
Date: 2017-04-24
Enriched heatmap is a special type of heatmap which visualizes the enrichment of genomic signals to specific target regions. It is broadly used to visualize e.g. how histone modifications are enriched to transcription start sites.
There are several tools that can make such heatmap (e.g. ngs.plot, deepTools or ChIPseeker). 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 heatmap as well as concatenating to a list of heatmaps to show correspondance between different data sources.
library(EnrichedHeatmap)
Load the example data that we will use for demostration:
set.seed(123)
load(paste0(system.file("extdata", "chr21_test_data.RData", package = "EnrichedHeatmap")))
ls()
## [1] "H3K4me3" "cgi" "genes" "meth" "rpkm"
The example data are all GRanges objects:
H3K4me3: coverage for H3K4me3 histone markscgi: CpG islandsgenes: genesmeth: methylationrpkm: gene expressionIn 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 TSS.
First we extract TSS of genes (note tss has strand information):
tss = promoters(genes, upstream = 0, downstream = 1)
tss[1:5]
## GRanges object with 5 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## ENSG00000141956.9 chr21 [43299591, 43299591] -
## ENSG00000141959.12 chr21 [45719934, 45719934] +
## ENSG00000142149.4 chr21 [33245628, 33245628] +
## ENSG00000142156.10 chr21 [47401651, 47401651] +
## ENSG00000142166.8 chr21 [34696734, 34696734] +
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
H3K4me3[1:5]
## 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, 9825489] * | 12
## [4] chr21 [9825490, 9825490] * | 13
## [5] chr21 [9825491, 9825493] * | 14
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
Similar as other tools, the task of visualization are separated into two steps:
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)
## Not include target regions
## 720 signal regions
class(mat1)
## [1] "normalizedMatrix" "matrix"
normalizeToMatrix() converts the association between genomic signals (H3K4me3) and targets(tss) in to 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:
EnrichedHeatmap(mat1, name = "H3K4me3")
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 to get a more comprehensive help.
Similar as the normal heatmap, colors can be controlled by a vector.
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.
quantile(H3K4me3$coverage, c(0, 0.25, 0.5, 0.75, 0.99, 1))
## 0% 25% 50% 75% 99% 100%
## 10 18 29 43 87 293
quantile(mat1, c(0, 0.25, 0.5, 0.75, 0.99, 1))
## 0% 25% 50% 75% 99% 100%
## 0.00000 0.00000 0.00000 0.00000 38.96001 166.82353
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 trim option which trims extreme values both at lower and upper bounds.
(In following, it means only to trim values larger than 99th quantile.)
mat1_trim = normalizeToMatrix(H3K4me3, tss, value_column = "coverage",
extend = 5000, mean_mode = "w0", w = 50, trim = c(0, 0.01))
EnrichedHeatmap(mat1_trim, col = c("white", "red"), name = "H3K4me3")
The second way is to define a color mapping function which is robust to extreme values. Another advantage of using a color mapping function is that if you have more than one heatmaps to make, it makes colors in heatmaps comparable.
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 using modified color mappings.
If col is not specified in EnrichedHeatmap(), blue-white-red is mapped to 1st quantile, median 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.
mat1 = mat1_trim
Split rows by a vector or a data frame by specifying split option.
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.
set.seed(123)
EnrichedHeatmap(mat1, col = col_fun, name = "H3K4me3", km = 3,
column_title = "Enrichment of H3K4me3", row_title_rot = 0)
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().
EnrichedHeatmap(mat1, col = col_fun, name = "H3K4me3",
cluster_rows = TRUE, column_title = "Enrichment of H3K4me3")
There is a special column annotation function anno_enriched() which shows mean values of columns
(i.e. mean signals across target regions) in the normalized matrix.
EnrichedHeatmap(mat1, col = col_fun, name = "H3K4me3",
top_annotation = HeatmapAnnotation(lines = anno_enriched()),
top_annotation_height = unit(2, "cm"))
If rows are split, the column annotation will show enrichment lines for all row clusters.
set.seed(123)
EnrichedHeatmap(mat1, col = col_fun, name = "H3K4me3",
# note we have three row-clusters, so we assign three colors for the annotation lines
top_annotation = HeatmapAnnotation(lines = anno_enriched(gp = gpar(col = 2:4))),
top_annotation_height = unit(2, "cm"),
km = 3, row_title_rot = 0)
You can also add error areas (1 se) for each line. The color of error areas always have the same color of corresponding line but with 75 percent transparency.
Users should be careful with show_error. It only makes sense when patterns in heatmap or row clusters
are homogeneous.
Actually I am not quite sure whether we should visualize the errors because the spreadness of the data can already be seen in the heatmaps.
Also we demonstrate how to add legend for the annotation lines by Legend() function from ComplexHeatmap package.
set.seed(123)
lgd = Legend(at = c("cluster1", "cluster2", "cluster3"), title = "Clusters",
type = "lines", legend_gp = gpar(col = 2:4))
ht = EnrichedHeatmap(mat1, col = col_fun, name = "H3K4me3",
# note we have three row-clusters, so we assign three colors for the annotation lines
top_annotation = HeatmapAnnotation(lines = anno_enriched(gp = gpar(col = 2:4), show_error = TRUE)),
top_annotation_height = unit(2, "cm"),
km = 3, row_title_rot = 0)
draw(ht, annotation_legend_list = list(lgd))
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.
empty_value 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.
mat1_smoothed = normalizeToMatrix(H3K4me3, tss, value_column = "coverage",
extend = 5000, mean_mode = "w0", w = 50, empty_value = 0, smooth = TRUE)
EnrichedHeatmap(mat1_smoothed, col = col_fun, name = "H3K4me3")
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)
By default there is an axis on the bottom border of the heatmap and a vertical line which represents the targets. There are several arguments which can be used to customize:
EnrichedHeatmap(mat1, col = col_fun, name = "H3K4me3",
pos_line_gp = gpar(col = "blue", lwd = 2), axis_name = c("-5kb", "TSS", "5kb"),
axis_name_rot = -45, border = FALSE)
Upstream and downstream also the target body are segmented into a list of small windows and overlap to signal regions. Since signal regions and small windows do not always 100 percent overlap, to summarize values in small windows, there are four different average modes:
Following illustrates different settings for mean_mode (note there is a signal region overlapping with other signal regions):
40 50 20 values in signal
++++++ +++ +++++ signal
30 values in signal
++++++ signal
================= 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
To explain it more clearly, let's consider two scenarios:
First, we want to calculate mean methylation from 3 CpG sites in a 20bp window. Since methylation
is only measured at CpG site level, the mean value should only be calculated from the 3 CpG sites while not the non-CpG sites. In this
case, absolute mode should be used here.
Second, we want to calculate mean coverage in a 20bp window. Let's assume coverage is 5 in 1bp ~ 5bp, 10 in 11bp ~ 15bp and 20 in 16bp ~ 20bp.
Since converage is kind of attribute for all bases, all 20 bp should be taken in account. Thus, here w0 mode should be used
which also takes account of the 0 coverage in 6bp ~ 10bp. The mean coverage will be caculated as (5*5 + 10*5 + 20*5)/(5+5+5+5).
Third, genes have multiple transcripts and we want to calculate how many transcripts eixst in a certain position in the gene body.
In this case, values associated to each transcript are binary (either 1 or 0) and coverage mean mode should be used.
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 empty_value and grey is the default color
for NA values by ComplexHeatmap).
meth[1:5]
## GRanges object with 5 ranges and 1 metadata column:
## seqnames ranges strand | meth
## <Rle> <IRanges> <Rle> | <numeric>
## [1] chr21 [9432427, 9432427] * | 0.267104203931852
## [2] chr21 [9432428, 9432428] * | 0.267106771955287
## [3] chr21 [9432964, 9432964] * | 0.272709911227985
## [4] chr21 [9432965, 9432965] * | 0.2727345344132
## [5] chr21 [9433315, 9433315] * | 0.285114797969136
## -------
## seqinfo: 1 sequence from an unspecified genome; no seqlengths
mat2 = normalizeToMatrix(meth, tss, value_column = "meth", mean_mode = "absolute",
extend = 5000, w = 50, empty_value = 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 site in some windows. 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 enhance the effect of visualization a lot.
mat2 = normalizeToMatrix(meth, tss, value_column = "meth", mean_mode = "absolute",
extend = 5000, w = 50, empty_value = 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.
In the example of H3K4me3, the target regions are single points. The target 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, empty_value = NA, smooth = TRUE)
EnrichedHeatmap(mat3, col = meth_col_fun, name = "methylation", 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, but since width of the target regions are different from each other, they are splitted by percent to their full width (the percent value is calculated automatically).
mat3 = normalizeToMatrix(meth, cgi, value_column = "meth", mean_mode = "absolute",
extend = 5000, w = 50, empty_value = NA, smooth = TRUE, target_ratio = 0.3)
EnrichedHeatmap(mat3, col = meth_col_fun, name = "methylation", axis_name_rot = 90,
column_title = "methylation near CGI")
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, empty_value = NA, smooth = TRUE, target_ratio = 0.5)
EnrichedHeatmap(mat3, col = meth_col_fun, name = "methylation", axis_name_rot = 90,
column_title = "methylation near CGI")
mat3 = normalizeToMatrix(meth, cgi, value_column = "meth", mean_mode = "absolute",
extend = c(5000, 0), w = 50, empty_value = NA, smooth = TRUE, target_ratio = 0.5)
## Warning in normalizeToMatrix(meth, cgi, value_column = "meth", mean_mode = "absolute", :
## Smoothig is failed for one row because there are very few signals overlapped to it.
## Please use `attr(mat, 'failed_rows')` to get the index of the failed row and consider to
## remove it.
EnrichedHeatmap(mat3, col = meth_col_fun, name = "methylation", axis_name_rot = 90,
column_title = "methylation near CGI")
# since there is not upstream and downstream, the number of columns is controlled by `k` argument
mat3 = normalizeToMatrix(meth, cgi, value_column = "meth", mean_mode = "absolute",
extend = 0, k = 20, empty_value = NA, smooth = TRUE, target_ratio = 1)
## Warning in normalizeToMatrix(meth, cgi, value_column = "meth", mean_mode = "absolute", :
## Smoothig is failed for one row because there are very few signals overlapped to it.
## Please use `attr(mat, 'failed_rows')` to get the index of the failed row and consider to
## remove it.
EnrichedHeatmap(mat3, col = meth_col_fun, name = "methylation", axis_name_rot = 90,
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 means there are too many NA to do the smoothing. Corresponding index for failed rows can be get by :
attr(mat3, "failed_rows")
## [1] 5
and maybe you can remove them beforehand.
The power of EnrichedHeatmap package is that parallel heatmaps can be concatenated, both for enriched heatmap, normal heatmap as well the normal 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.
EnrichedHeatmap(mat1, col = col_fun, name = "H3K4me3", width = 1) +
EnrichedHeatmap(mat2, col = meth_col_fun, name = "methylation", width = 1) +
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 splitting 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 preserve.
set.seed(123)
partition = 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 = as.character(1:3)), name = "partition",
show_row_names = FALSE, width = unit(3, "mm")) +
EnrichedHeatmap(mat1, col = col_fun, name = "H3K4me3", split = partition, width = 1,
top_annotation = HeatmapAnnotation(lines = anno_enriched(gp = gpar(col = 2:4))),
top_annotation_height = unit(2, "cm"), row_title_rot = 0,
column_title = "H3K4me3", combined_name_fun = NULL) +
EnrichedHeatmap(mat2, col = meth_col_fun, name = "methylation", width = 1,
top_annotation = HeatmapAnnotation(lines = anno_enriched(gp = gpar(col = 2:4))),
top_annotation_height = unit(2, "cm"),
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, main_heatmap = "H3K4me3", gap = unit(c(2, 10, 2), "mm"),
annotation_legend_list = list(lgd))
Sometimes we visualize the general correlation around certain genomic targets or the difference
between two subgroups. 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 expression of corresponding genes in (-5kb, 10kb)
of the gene TSS.
load(paste0(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)
## Not include target regions
## 677 signal regions
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.
EnrichedHeatmap(mat_H3K4me1, col = colorRamp2(c(-1, 0, 1), c("darkgreen", "white", "red")), name = "corr_H3K4me1",
top_annotation = HeatmapAnnotation(line = anno_enriched(gp = gpar(neg_col = "darkgreen", pos_col = "red"))),
top_annotation_height = unit(2, "cm"))
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(paste0(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
all_tss[1:2]
## GRanges object with 2 ranges and 0 metadata columns:
## seqnames ranges strand
## <Rle> <IRanges> <Rle>
## ENSG00000187583.5 chr1 [ 901877, 901877] +
## ENSG00000131584.14 chr1 [1244989, 1244989] -
## -------
## seqinfo: 25 sequences (1 circular) from an unspecified genome; no seqlengths
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:
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.
mat4 = normalizeToMatrix(neg_cr, all_tss, mapping_column = "gene", w = 50, mean_mode = "w0")
EnrichedHeatmap(mat4, col = c("white", "green"), name = "neg_cr", cluster_rows = TRUE,
top_annotation = HeatmapAnnotation(lines = anno_enriched(gp = gpar(col = "green"))),
top_annotation_height = unit(2, "cm"))
Above heatmap shows negative correlated regions are enriched at some distance downstream of the TSS. We guess it is because genes have alternative transcripts and negative correlated regions are actually enriched at the start sites of transcripts.
Next we add another heatmap showing how transcripts are distributed to gene TSS. Note here mean_mode
is set to coverage when normalizing the matrix.
Maybe here the heatmap is not a nice way for showing transcripts, but according to the annotation graphs on the both top, we can see there is a perfect fitting for the peaks of negative correlated regions and transcripts.
mat5 = normalizeToMatrix(tx, all_tss, mapping_column="gene", extend = c(5000, 10000), w = 50,
mean_mode = "coverage")
ht_list = EnrichedHeatmap(mat4, col = c("white", "green"), name = "neg_cr", cluster_rows = TRUE,
top_annotation = HeatmapAnnotation(lines1 = anno_enriched(gp = gpar(col = "green"))),
top_annotation_height = unit(2, "cm")) +
EnrichedHeatmap(mat5, col = c("white", "black"), name = "tx",
top_annotation = HeatmapAnnotation(lines2 = anno_enriched(gp = gpar(col = "black"))),
top_annotation_height = unit(2, "cm"))
draw(ht_list, gap = unit(1, "cm"))
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().
# 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.
# 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:
# code not run
EnrichedHeatmap(..., column_title_gp = ...)
To change graphic settings for legends:
# 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:
# code not run
EnrichedHeatmap(..., width = ...) + EnrichedHeatmap(...)
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. Some information is hidden because the data is not published yet, but still, you can see it is really easy to correspond different sources of information.
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 = ...)
}
Applying getSignalsFromList() to mat_list, it gives a new normalized matrix which contains mean signals and can
be directly used in EnrichedHeatmap().
# code not run
mat = getSignalsFromList(mat_list)
EnrichedHeatmap(mat)
Next let's consider a second scenario: we want to see the correlation between histone modification and gene expression.
In this case, fun can have a second argument so that users can correspond histone signals to the expression of the
associated gene. In following code, expr is a matrix of expression, columns in expr correspond to elements in hm_gr_list,
rows in expr are same as tss.
# code not run
mat = getSignalsFromList(mat_list, fun = function(x, i) cor(x, expr[i, ], method = "spearman"))
Then mat here can be used to visualize how gene expression is correlated to histone modification around TSS.
# code not run
EnrichedHeatmap(mat)
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 0
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:
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().
class(mat2) = c("normalizedMatrix", "matrix")
mat2
## Normalize to :
## Upstream 5000 bp (100 windows)
## Downstream 5000 bp (100 windows)
## Not include target regions
## 720 signal regions
Above four attributes are enough for making the heatmaps, there are several more attributes which can give
better information when printing mat2.
attr(mat2, "signal_name") = "H3K4me3"
attr(mat2, "target_name") = "TSS"
mat2
## Normalize H3K4me3 to TSS:
## Upstream 5000 bp (100 windows)
## Downstream 5000 bp (100 windows)
## Not include target regions
## 720 signal regions
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 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
# 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:
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.
# 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)))
sessionInfo()
## R version 3.4.0 (2017-04-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.5-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.5-bioc/R/lib/libRlapack.so
##
## 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
##
## attached base packages:
## [1] parallel stats4 grid stats graphics grDevices utils datasets methods
## [10] base
##
## other attached packages:
## [1] circlize_0.3.10 EnrichedHeatmap_1.6.0 locfit_1.5-9.1 GenomicRanges_1.28.0
## [5] GenomeInfoDb_1.12.0 IRanges_2.10.0 S4Vectors_0.14.0 BiocGenerics_0.22.0
## [9] ComplexHeatmap_1.14.0 knitr_1.15.1 markdown_0.8
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.10 highr_0.6 XVector_0.16.0 DEoptimR_1.0-8
## [5] compiler_3.4.0 RColorBrewer_1.1-2 plyr_1.8.4 zlibbioc_1.22.0
## [9] bitops_1.0-6 viridis_0.4.0 class_7.3-14 tools_3.4.0
## [13] prabclus_2.2-6 dendextend_1.5.2 mclust_5.2.3 viridisLite_0.2.0
## [17] evaluate_0.10 tibble_1.3.0 gtable_0.2.0 lattice_0.20-35
## [21] mvtnorm_1.0-6 GenomeInfoDbData_0.99.0 gridExtra_2.2.1 trimcluster_0.1-2
## [25] stringr_1.2.0 cluster_2.0.6 GlobalOptions_0.0.11 fpc_2.1-10
## [29] diptest_0.75-7 nnet_7.3-12 robustbase_0.92-7 GetoptLong_0.1.6
## [33] flexmix_2.3-13 kernlab_0.9-25 ggplot2_2.2.1 magrittr_1.5
## [37] whisker_0.3-2 matrixStats_0.52.2 scales_0.4.1 modeltools_0.2-21
## [41] MASS_7.3-47 shape_1.4.2 colorspace_1.3-2 stringi_1.1.5
## [45] RCurl_1.95-4.8 lazyeval_0.2.0 munsell_0.4.3 rjson_0.2.15