Author: Zuguang Gu ( z.gu@dkfz.de )
Date: 2015-04-16
Trellis graph is a type of graph which splits data by certain conditions and visualizes subset of data in each category parallel. The advantage of Trellis graph is that it can easily reveal multiple variable relationship behind the data. For genomic data, chromosomes are always the category variable. In R, lattice and ggplot2 package can make Trellis graph, however, specially for whole genome level plot, they are limited in:
For single continuous region, multiple tracks are supported in ggbio and Gviz.
But if you want to compare more than one continuous regions, things will be complex.
Anyway, you can use grid.layout to arrange multiple continuous regions on the plot with using ggbio and Gviz,
but the solution is not so straightforward.
Here, gtrellis provides a flexible way to arrange genomic categories and supports adding self-defined graphics on the plot.
gtrellis aims to arrange genomic categories as Trellis style and supports multiple tracks for visualization. In this package, initialization the layout and adding graphics are independent. After initialization of the layout, intersection between tracks and genomic categories are named cell or panel, and each cell is an independent plotting region (actually, each cell is a data viewport in grid system) that self-defined graphics can be added afterwards.
gtrellis is implemented in grid graphic system, so, in order
to add graphics in each cell, you only need to use low-level graphic functions
(grid.points, grid.lines, grid.rect, …) which are quite similar as those in
classic graphic system. There is only one thing you need to take care of is the use of unit object
in grid system.
gtrellis_layout() is used to create the global layout. By default, it initializes the layout
with hg19 and puts all chromosomes in one row. Each chromosome has only one track and
range on y-axis is 0 to 1.
library(gtrellis)
gtrellis_layout()
category can be used to set subset of chromosomes as well as the order of chromosomes.
gtrellis_show_index() here is an assistant function to add the information to each cell.
gtrellis_layout(category = c("chr3", "chr1"))
gtrellis_show_index()
Other species are also supported as long as corresponding chromInfo files exist on UCSC ftp.
E.g. chromInfo file for mouse (mm10) is http://hgdownload.cse.ucsc.edu/goldenpath/mm10/database/chromInfo.txt.gz.
Since there may be many short scaffolds in chromInfo file, gtrellis will first remove these
short scaffolds before making the plot.
gtrellis_layout(species = "mm10")
gtrellis_show_index()
You can put chromosomes on multiple rows by specifying nrow or/and ncol in case
you feel the width for some chromosomes are too short. For chromosomes in the same column,
the corresponding width is the width of the longest chromosome in that column and short
chromosomes will be extended with empty areas.
gtrellis_layout(nrow = 3)
gtrellis_show_index()
gtrellis_layout(ncol = 5)
gtrellis_show_index()
You can set byrow argument to arrange chromosomes either by rows or by columns.
As explained before, by default chromosomes in the same column will share
the length of the longest one. It is better to put chromosomes with similar length
in a same column.
gtrellis_layout(ncol = 5, byrow = FALSE)
gtrellis_show_index()
If equal_width is set to TRUE, the layout will be a 'standard' Trellis layout.
All chromosomes will share the same range on x-axis (length of the longest chromosome)
and short chromosomes will be extended with empty areas.
gtrellis_layout(equal_width = TRUE)
gtrellis_show_index()
Make all columns having equal width and also set multiple rows.
gtrellis_layout(ncol = 5, byrow = FALSE, equal_width = TRUE)
gtrellis_show_index()
Set gaps between chromosomes. Note if it is set as a numeric value, it should only be 0 (no gap).
gtrellis_layout(gap = 0)
Or gap can be a unit object.
gtrellis_layout(gap = unit(5, "mm"))
When you arrange the layout with multiple rows, you can also set gap as length of two.
In this case, the first element corresponds to the gaps between rows
and the second corresponds to the gaps between columns.
gtrellis_layout(ncol = 5, gap = unit(c(5, 2), "mm"))
There may be multiple tracks for chromosomes which describe multiple dimensional data.
The tracks can be created by n_track argument.
gtrellis_layout(n_track = 3)
gtrellis_show_index()
By default, tracks share the same height. The height can be customized by track_height argument.
If it is set as numeric values, it will be normalized as percent to the sum.
gtrellis_layout(n_track = 3, track_height = c(1, 2, 3))
track_height can also be a unit object.
gtrellis_layout(n_track = 3,
track_height = unit.c(unit(1, "cm"), unit(1, "null"), grobHeight(textGrob("chr1"))))
Whether to show y-axes by setting track_axis. If certain value is set to FALSE,
y-axis on corresponding track will not be drawn.
gtrellis_layout(n_track = 3, track_axis = c(FALSE, TRUE, FALSE), xaxis = FALSE, xlab = "")
Set y-lim by track_ylim. It should be a two-column matrix. But to make things easy, it can
also be a vector and it will be filled into a two-column matrix by rows. If it is a vector
with length 2, it means all tracks share the same y-lim.
gtrellis_layout(n_track = 3, track_ylim = c(0, 3, -4, 4, 0, 1000000))
Axis ticks are added on one side of rows or columns, asist_ticks controls
whether to add axis ticks on the other sides. (You can compare following figure to the above one.)
gtrellis_layout(n_track = 3, track_ylim = c(0, 3, -4, 4, 0, 1000000), asist_ticks = FALSE)
Set x-label by xlab and set y-labels by track_ylab.
gtrellis_layout(n_track = 3, title = "title", track_ylab = c("", "bbbbbb", "ccccccc"), xlab = "xlab")
Since chromosomes can have more than one tracks, following shows a layout with multiple columns and multiple tracks.
gtrellis_layout(n_track = 3, ncol = 4)
gtrellis_show_index()
Set border to FALSE to remove borders.
gtrellis_layout(n_track = 3, ncol = 4, border = FALSE, xaxis = FALSE, track_axis = FALSE, xlab = "")
gtrellis_show_index()
After the initialization of the layout, each cell can be thought as an ordinary coordinate system. Then graphics can be added in afterwards.
Graphics are added by the self-defined function panel.fun. Similar as circlize
package, panel.fun will be applied in every cell in 'the current track'.
The first argument of add_track() can be either a GRanges object or a data frame,
and the argument in panel.fun is a subset of data in current chromosome.
library(circlize)
bed = generateRandomBed()
gtrellis_layout(track_ylim = range(bed[[4]]))
add_track(bed, panel.fun = function(bed) {
# `bed` inside `panel.fun` is a subset of the main `bed`
x = (bed[[2]] + bed[[3]]) / 2
y = bed[[4]]
grid.points(x, y, pch = 16, size = unit(0.5, "mm"))
})
The input data can be a GRanges object as well.
library(GenomicRanges)
gr = GRanges(seqnames = bed[[1]],
ranges = IRanges(start = bed[[2]],
end = bed[[3]]),
score = bed[[4]])
gtrellis_layout(track_ylim = range(gr$score))
add_track(gr, panel.fun = function(gr) {
x = (start(gr) + end(gr)) / 2
y = gr$score
grid.points(x, y, pch = 16, size = unit(0.5, "mm"))
})
Initialization and adding graphics are actually independent. Following example uses same code to add graphics but with different layout. (Compare to the first figure in this section.)
gtrellis_layout(nrow = 5, byrow = FALSE, track_ylim = range(bed[[4]]))
add_track(bed, panel.fun = function(bed) {
x = (bed[[2]] + bed[[3]]) / 2
y = bed[[4]]
grid.points(x, y, pch = 16, size = unit(0.5, "mm"))
})
In following, we make rainfall plot as well as the density
distribution of genomic regions (in the example below, DMR_hyper contains differentially
methylated regions that show high methylation compared to control samples and
in DMR_hypo the methylation is lower than control samples).
Also, we manually add a track which contains chromosome names
and a track which contains ideograms.
Density for genomic regions is defined as the percent of a genomic window that is covered by the input genomic regions.
load(paste0(system.file("extdata", package = "circlize"), "/DMR.RData"))
DMR_hyper_density = lapply(split(DMR_hyper, DMR_hyper[[1]]), function(gr) {
gr2 = circlize::genomicDensity(gr[2:3], window.size = 5e6)
cbind(chr = rep(gr[1,1], nrow(gr2)), gr2)
})
DMR_hyper_density = do.call("rbind", DMR_hyper_density)
head(DMR_hyper_density)
## chr start end pct
## chr1.1 chr1 1 5000000 0.0060636
## chr1.2 chr1 2500001 7500000 0.0036004
## chr1.3 chr1 5000001 10000000 0.0015556
## chr1.4 chr1 7500001 12500000 0.0024016
## chr1.5 chr1 10000001 15000000 0.0023680
## chr1.6 chr1 12500001 17500000 0.0027954
Initialize the layout and add following four tracks:
gtrellis_layout(n_track = 4, ncol = 4, byrow = FALSE,
track_axis = c(FALSE, TRUE, TRUE, FALSE),
track_height = unit.c(1.5*grobHeight(textGrob("chr1")),
unit(1, "null"),
unit(0.5, "null"),
unit(3, "mm")),
track_ylim = c(0, 1, 0, 8, c(0, max(DMR_hyper_density[[4]])), 0, 1),
track_ylab = c("", "log10(inter_dist)", "density", ""))
# track for chromosome names
add_track(panel.fun = function(gr){
chr = get_cell_meta_data("name")
grid.rect(gp = gpar(fill = "#EEEEEE"))
grid.text(chr)
})
# track for rainfall plots
add_track(DMR_hyper, panel.fun = function(gr) {
df = circlize::rainfallTransform(gr[2:3])
x = (df[[1]] + df[[2]])/2
y = log10(df[[3]])
grid.points(x, y, pch = 16, size = unit(0.5, "mm"), gp = gpar(col = "red"))
})
# track for genomic density
add_track(DMR_hyper_density, panel.fun = function(gr) {
x = (gr[[3]] + gr[[2]])/2
y = gr[[4]]
grid.polygon(c(x[1], x, x[length(x)]),
c(0, y, 0), default.units = "native", gp = gpar(fill = "pink"))
})
# track for ideogram
cytoband_df = circlize::read.cytoband(species = "hg19")$df
add_track(cytoband_df, panel.fun = function(gr) {
cytoband_chr = gr
grid.rect( cytoband_chr[[2]], unit(0, "npc"),
width = cytoband_chr[[3]] - cytoband_chr[[2]], height = unit(1, "npc"),
default.units = "native", hjust = 0, vjust = 0,
gp = gpar(fill = circlize::cytoband.col(cytoband_chr[[5]])) )
grid.rect( min(cytoband_chr[[2]]), unit(0, "npc"),
width = max(cytoband_chr[[3]]) - min(cytoband_chr[[2]]), height = unit(1, "npc"),
default.units = "native", hjust = 0, vjust = 0,
gp = gpar(fill = "transparent") )
})
Actually, you don't need to add name track and ideogram track manually.
Name track and ideogram track can be added by add_name_track and add_ideogram_track arguments.
Name track will be inserted before the first track and ideogram track will be
inserted after the last track. So in following example, although we only specified
n_track to 2, but the name track and ideogram track are also added, thus, the
final number of track is 4.
In following example, we additionally add graphics for hypo-DMR as well so that
direct comparison between different methylation patterns can be applied.
Since rainfall plot for both hyper-DMR and hypo-DMR is added in a same track,
we explicitly specify value of track argument to 2 in add_track().
DMR_hypo_density = lapply(split(DMR_hypo, DMR_hypo[[1]]), function(gr) {
gr2 = genomicDensity(gr[2:3], window.size = 5e6)
cbind(chr = rep(gr[1,1], nrow(gr2)), gr2)
})
DMR_hypo_density = do.call("rbind", DMR_hypo_density)
gtrellis_layout(n_track = 2, ncol = 4, byrow = FALSE,
track_axis = TRUE,
track_height = unit.c(unit(1, "null"),
unit(0.5, "null")),
track_ylim = c(0, 8, c(0, max(c(DMR_hyper_density[[4]], DMR_hypo_density[[4]])))),
track_ylab = c("log10(inter_dist)", "density"),
add_name_track = TRUE, add_ideogram_track = TRUE)
add_track(DMR_hyper, track = 2, panel.fun = function(gr) {
df = rainfallTransform(gr[2:3])
x = (df[[1]] + df[[2]])/2
y = log10(df[[3]])
grid.points(x, y, pch = 16, size = unit(0.5, "mm"), gp = gpar(col = "#FF000040"))
})
add_track(DMR_hypo, track = 2, panel.fun = function(gr) {
df = rainfallTransform(gr[2:3])
x = (df[[1]] + df[[2]])/2
y = log10(df[[3]])
grid.points(x, y, pch = 16, size = unit(0.5, "mm"), gp = gpar(col = "#0000FF40"))
})
add_track(DMR_hyper_density, track = 3, panel.fun = function(gr) {
x = (gr[[3]] + gr[[2]])/2
y = gr[[4]]
grid.polygon(c(x[1], x, x[length(x)]),
c(0, y, 0), default.units = "native", gp = gpar(fill = "#FF000040"))
})
add_track(DMR_hypo_density, track = 3, panel.fun = function(gr) {
x = (gr[[3]] + gr[[2]])/2
y = gr[[4]]
grid.polygon(c(x[1], x, x[length(x)]),
c(0, y, 0), default.units = "native", gp = gpar(fill = "#0000FF40"))
})
By default, tracks are added from the first track to the last one. You can also add graphics
in any specified chromosomes and tracks by specifying category and track.
all_chr = paste0("chr", 1:22)
letter = strsplit("MERRY CHRISTMAS!", "")[[1]]
gtrellis_layout(nrow = 5)
for(i in seq_along(letter)) {
add_track(category = all_chr[i], track = 1, panel.fun = function(gr) {
grid.text(letter[i], gp = gpar(fontsize = 30))
})
}
Adding legend is not so straightforward, but it is not complex if you use functionality of the grid system.
For example, you can first create a global viewport
which contains a two-column layout, then put Trellis plot in one part and put legend in the
other part. Remember to set newpage to FALSE so that Trellis plot will be
added on the current graphic page.
legd = legendGrob("label", pch = 16)
layout = grid.layout(nrow = 1, ncol = 2, widths = unit.c(unit(1, "null"), grobWidth(legd)))
grid.newpage()
pushViewport(viewport(layout = layout))
pushViewport(viewport(layout.pos.row = 1, layout.pos.col = 1))
gtrellis_layout(nrow = 5, byrow = FALSE, track_ylim = range(bed[[4]]), newpage = FALSE)
add_track(bed, panel.fun = function(bed) {
x = (bed[[2]] + bed[[3]]) / 2
y = bed[[4]]
grid.points(x, y, pch = 16, size = unit(0.5, "mm"))
})
upViewport()
pushViewport(viewport(layout.pos.row = 1, layout.pos.col = 2))
grid.draw(legd)
upViewport()
As a real application, following code plots coverage for a tumor sample, its companion normal sample and the ratio of coverage. First prepare the data:
tumor_df = readRDS(paste0(system.file("extdata", package = "gtrellis"), "/df_tumor.rds"))
control_df = readRDS(paste0(system.file("extdata", package = "gtrellis"), "/df_control.rds"))
# remove regions that have zero coverage
ind = which(tumor_df$cov > 0 & control_df$cov > 0)
tumor_df = tumor_df[ind, , drop = FALSE]
control_df = control_df[ind, , drop = FALSE]
ratio_df = tumor_df
# get rid of small value dividing small value resulting large value
q01 = quantile(c(tumor_df$cov, control_df$cov), 0.01)
ratio_df[[4]] = log2( (tumor_df$cov+q01) / (control_df$cov+q01) *
sum(control_df$cov) / sum(tumor_df$cov) )
names(ratio_df) = c("chr", "start", "end", "ratio")
tumor_df[[4]] = log10(tumor_df[[4]])
control_df[[4]] = log10(control_df[[4]])
Then, initialize the layout and add three tracks.
cov_range = range(c(tumor_df[[4]], control_df[[4]]))
ratio_range = range(ratio_df[[4]])
ratio_range = c(-max(abs(ratio_range)), max(abs(ratio_range)))
gtrellis_layout(n_track = 3, nrow = 3, byrow = FALSE, gap = unit(c(4, 1), "mm"),
track_ylim = c(cov_range, cov_range, ratio_range),
track_ylab = c("tumor, log10(cov)", "control, log10(cov)", "ratio, log2(ratio)"),
add_name_track = TRUE, add_ideogram_track = TRUE)
# track for coverage in tumor
add_track(tumor_df, panel.fun = function(gr) {
x = (gr[[2]] + gr[[3]])/2
y = gr[[4]]
grid.points(x, y, pch = 16, size = unit(2, "bigpts"), gp = gpar(col = "#00000020"))
})
# track for coverage in control
add_track(control_df, panel.fun = function(gr) {
x = (gr[[2]] + gr[[3]])/2
y = gr[[4]]
grid.points(x, y, pch = 16, size = unit(2, "bigpts"), gp = gpar(col = "#00000020"))
})
# track for ratio between tumor and control
library(RColorBrewer)
col_fun = circlize::colorRamp2(seq(-0.5, 0.5, length = 11), rev(brewer.pal(11, "RdYlBu")),
transparency = 0.5)
add_track(ratio_df, panel.fun = function(gr) {
x = (gr[[2]] + gr[[3]])/2
y = gr[[4]]
grid.points(x, y, pch = 16, size = unit(2, "bigpts"), gp = gpar(col = col_fun(y)))
grid.lines(unit(c(0, 1), "npc"), unit(c(0, 0), "native"), gp = gpar(col = "#0000FF80"))
})
Following example visualizes gene density (defined as how much a genomic window is covered by gene regions) on different chromosomes both by a line track and a heatmap track.
gene = readRDS(paste0(system.file(package = "gtrellis"),
"/extdata/gencode_v19_protein_coding_genes.rds"))
gene_density = lapply(split(gene, gene[[1]]), function(gr) {
gr2 = genomicDensity(gr[2:3], window.size = 5e6)
cbind(chr = rep(gr[1,1], nrow(gr2)), gr2)
})
gene_density = do.call("rbind", gene_density)
gtrellis_layout(byrow = FALSE, n_track = 2, ncol = 4,
add_ideogram_track = TRUE, add_name_track = TRUE,
track_ylim = c(0, max(gene_density[[4]]), 0, 1), track_axis = c(TRUE, FALSE),
track_height = unit.c(unit(1, "null"), unit(4, "mm")),
track_ylab = c("density", ""))
add_track(gene_density, panel.fun = function(gr) {
x = (gr[[3]] + gr[[2]])/2
y = gr[[4]]
grid.lines(x, y, default.unit = "native")
})
col_fun = circlize::colorRamp2(seq(0, max(gene_density[[4]]), length = 11),
rev(brewer.pal(11, "RdYlBu")))
add_track(gene_density, panel.fun = function(gr) {
grid.rect(gr[[2]], 0, width = gr[[3]] - gr[[2]], height = 1, just = c(0, 0),
default.units = "native", gp = gpar(fill = col_fun(gr[[4]]), col = NA))
})
Following figure is karyogram view of genomic regions (reproduced from http://www.tengfei.name/ggbio/docs/man/layout_karyogram-method.html). We arrange the layout as one column and create two 'short' tracks, one for genomic regions and one for ideogram. Different values are mapped to continuous colors.
We specified n_track to 1, but we also specify add_ideogram_track to TRUE, so actually
there are two tracks. xpadding is specified to make some space on the left of the cells so that
We can manually add chromosome names in the second track.
bed = generateRandomBed(nr = 10000)
bed = bed[sample(10000, 100), ]
col_fun = colorRamp2(c(-1, 0, 1), c("green", "yellow", "red"))
gtrellis_layout(n_track = 1, ncol = 1, track_axis = FALSE, xpadding = c(0.1, 0),
gap = unit(4, "mm"), border = FALSE, asist_ticks = FALSE, add_ideogram_track = TRUE,
ideogram_track_height = unit(2, "mm"))
add_track(bed, panel.fun = function(gr) {
grid.rect((gr[[2]] + gr[[3]])/2, unit(0.2, "npc"), unit(1, "mm"), unit(0.8, "npc"),
hjust = 0, vjust = 0, default.units = "native",
gp = gpar(fill = col_fun(gr[[4]]), col = NA))
})
add_track(track = 2, clip = FALSE, panel.fun = function(gr) {
chr = get_cell_meta_data("name")
if(chr == "chrY") {
grid.lines(get_cell_meta_data("xlim"), unit(c(0, 0), "npc"),
default.units = "native")
}
grid.text(chr, x = 0, y = 0, just = c("left", "bottom"))
})
# add legend
breaks = seq(-1, 1, by = 0.5)
lg = legendGrob(breaks, pch = 15, vgap = 0, gp = gpar(col = col_fun(breaks)))
pushViewport(viewport(0.9, 0.1, width = grobWidth(lg), height = grobHeight(lg), just = c(1, 0)))
grid.draw(lg)
upViewport()
For every cell in the plot, several meta data can be extracted by get_cell_meta_data().
get_cell_meta_data() is always used inside panel.fun to extract information about the
'current cell'. You can also use the function outside panel.fun by explicitly specifying
category and track. Pseudo code are:
gtrellis_layout()
add_track(panel.fun(gr) {
# get xlim of current cell
xlim = get_cell_meta_data("xlim")
})
# get xlim of the specified cell
xlim = get_cell_meta_data("xlim", category = "chr2", track = 1)
Following meta data can be retrieved by get_cell_meta_data():
name: category name.xlim: xlim without including padding, cells in a same column shares the same xlim.ylim: ylim without including padding.extended_xlim: xlim with padding.extended_ylim: ylim with padding.original_xlim: xlim in original data.original_ylim: ylim in original data.column: which column in the layout.row: which row in the layout.track: which track in the layout.Following figure demonstrates difference between different cell meta data. The space between
extended_xlim and xlim is the padding regions on x direction.
Genomic categories are not restricted in chromosomes. It can be any kind,
such as genes. Similar as circlize::circos.genomicInitialize(), you can also specify
genomic categories as well as their ranges as a data frame when
initializing the layout.
In following example, we put three genes in one row and add their transcripts afterwards.
load(paste0(system.file(package = "circlize"), "/extdata/tp_family.RData"))
df = data.frame(gene = names(tp_family),
start = sapply(tp_family, function(x) min(unlist(x))),
end = sapply(tp_family, function(x) max(unlist(x))))
df
## gene start end
## TP73 TP73 3569084 3652765
## TP63 TP63 189349205 189615068
## TP53 TP53 7565097 7590856
# maximum number of transcripts
n = max(sapply(tp_family, length))
gtrellis_layout(data = df, n_track = 1, track_ylim = c(0.5, n+0.5),
track_axis = FALSE, add_name_track = TRUE, xpadding = c(0.05, 0.05), ypadding = c(0.05, 0.05))
add_track(panel.fun = function(gr) {
gn = get_cell_meta_data("name")
tr = tp_family[[gn]] # all transcripts for this gene
for(i in seq_along(tr)) {
# for each transcript
current_tr_start = min(tr[[i]]$start)
current_tr_end = max(tr[[i]]$end)
grid.lines(c(current_tr_start, current_tr_end), c(n - i + 1, n - i + 1),
default.units = "native", gp = gpar(col = "#CCCCCC"))
grid.rect(tr[[i]][[1]], n - i + 1, tr[[i]][[2]] - tr[[i]][[1]], 0.8,
default.units = "native", just = "left",
gp = gpar(fill = "orange", col = "orange"))
}
})
If you want to put all genes on one column and align them by TSS, you need to normalize the genomic coordinate first.
tp_family$TP53 = lapply(tp_family$TP53, function(df) {
data.frame(start = 7590856 - df[[2]],
end = 7590856 - df[[1]])
})
tp_family$TP63 = lapply(tp_family$TP63, function(df) {
data.frame(start = df[[1]] - 189349205,
end = df[[2]] - 189349205)
})
tp_family$TP73 = lapply(tp_family$TP73, function(df) {
data.frame(start = df[[1]] - 3569084,
end = df[[2]] - 3569084)
})
Then similar code as previous one.
df = data.frame(gene = names(tp_family),
start = sapply(tp_family, function(x) min(unlist(x))),
end = sapply(tp_family, function(x) max(unlist(x))))
df
## gene start end
## TP73 TP73 0 83681
## TP63 TP63 0 265863
## TP53 TP53 0 25759
n = max(sapply(tp_family, length))
gtrellis_layout(data = df, n_track = 1, ncol = 1, track_ylim = c(0.5, n+0.5),
track_axis = FALSE, add_name_track = TRUE,
xpadding = c(0.01, 0.01), ypadding = c(0.05, 0.05))
add_track(panel.fun = function(gr) {
gn = get_cell_meta_data("name")
tr = tp_family[[gn]] # all transcripts for this gene
for(i in seq_along(tr)) {
# for each transcript
current_tr_start = min(tr[[i]]$start)
current_tr_end = max(tr[[i]]$end)
grid.lines(c(current_tr_start, current_tr_end), c(n - i + 1, n - i + 1),
default.units = "native", gp = gpar(col = "#CCCCCC"))
grid.rect(tr[[i]][[1]], n - i + 1, tr[[i]][[2]] - tr[[i]][[1]], 0.8,
default.units = "native", just = "left",
gp = gpar(fill = "orange", col = "orange"))
}
})
You can create layout with self-defined regions. clip argument controls whether
data points outside of the cell need to be added. Since by default clip is TRUE,
you do not need to make intersection of your full data to the sub-region, which means, you
can use same code to deal with different regions.
zoom = function(df) {
gtrellis_layout(data = df, n_track = 3, nrow = 2,
track_ylim = c(cov_range, cov_range, ratio_range),
track_ylab = c("tumor, log10(cov)", "control, log10(cov)", "ratio, log2(ratio)"),
add_name_track = TRUE, add_ideogram_track = TRUE)
add_track(tumor_df, panel.fun = function(gr) {
x = (gr[[2]] + gr[[3]])/2
y = gr[[4]]
grid.points(x, y, pch = 16, size = unit(2, "bigpts"), gp = gpar(col = "#00000080"))
})
add_track(control_df, panel.fun = function(gr) {
x = (gr[[2]] + gr[[3]])/2
y = gr[[4]]
grid.points(x, y, pch = 16, size = unit(2, "bigpts"), gp = gpar(col = "#00000080"))
})
add_track(ratio_df, panel.fun = function(gr) {
x = (gr[[2]] + gr[[3]])/2
y = gr[[4]]
grid.points(x, y, pch = 16, size = unit(2, "bigpts"), gp = gpar(col = "#FF000080"))
})
}
df = data.frame(chr = c("chr1", "chr2"),
start = c(1e8, 1e8),
end = c(2e8, 2e8))
zoom(df)
df = data.frame(chr = c("chr11", "chr12"),
start = c(4e7, 4e7),
end = c(8e7, 8e7))
zoom(df)
If start positions for two genomic categories are different (e.g. 0~100000 for the first one and 100000~200000 for the second one), you should not put them in a same column. You should normalize start positions in the first place.
The following code will generate an error.
df = data.frame(chr = c("chr1", "chr2"),
start = c(1e8, 2e8),
end = c(2e8, 3e8))
try(gtrellis_layout(df, ncol = 1))