<!--
%\VignetteEngine{knitr}
%\VignetteIndexEntry{Make Genome-level Trellis Graph}
-->

Make Genome-level Trellis Graph
========================================

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

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

**Package version**: `r installed.packages()["gtrellis", "Version"]`

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

```{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)
library(ComplexHeatmap)
```

[Trellis graph](http://www.statmethods.net/advgraphs/trellis.html) 
is a type of graph which splits data by certain conditions and visualizes 
subset of data in each category parallel. In genomic data analysis, the conditional variable
is mostly the chromosome. The advantage of Trellis graph is
it can easily reveal multiple variable relationship behind the data. 
In R, **lattice** and **ggplot2** package can make Trellis graph, 
however, specially for whole genome level plot, they are limited in:

- Chromosomes have different length. But in **lattice**/**ggplot2**, panel width
  for each chromosome are the same, so short chromosomes will be extended with 
  empty areas which sometimes is a waste of space. (just find out **latticeExtra** allows different panel width.)
- Genomic data are more multiple dimensional, which means, there are always more 
  than one tracks for chromosomes that to be compared. Unfortunately, **lattice**/**ggplot2**
  can only plot one track at one time.

For a single continuous region, multiple tracks are supported in **ggbio** and **Gviz**. 
But if you want to compare more than one regions, things will be complex.
Due to the design of **ggbio** or **Gviz**, it will not be so efficient to visualize e.g. larger than 10
regions at a same time.

Here, **gtrellis** provides a flexible way to arrange genomic categories. It has following advantages:

- The layout is flexible that it is good at visualizing many genomic categories at a same time (e.g. 24 human chromosomes).
- Genomic category is not restricted in chromosome, it can be any type (e.g. genes).
- Support multiple tracks.
- Graphics in tracks can be highly customized.

## Basic design

**gtrellis** aims to arrange genomic categories as Trellis style and supports multiple
tracks for visualization. In this package, initializating 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 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, users need to use low-level graphic functions 
(`grid.points`, `grid.lines`, `grid.rect`, ...) which are quite similar as those in 
classic graphic system.

## Initialize the layout

`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.

```{r, fig.width = 10, fig.height = 6}
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, just for demonstration purpose in this vignette.

```{r, fig.width = 6, fig.height = 6}
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, if `category` is not specified, `gtrellis` will first remove these
short scaffolds before making the plot. Also non-normal chromosomes (e.g. "chr1_xxxxxx") will also be removed.
Sometimes this detection is not always correct, if you find chromosomes shown on the plot is not what you expect, set `category` manually.

```{r, fig.width = 10, fig.height = 6}
gtrellis_layout(species = "mm10")
gtrellis_show_index()
```

You can put chromosomes on multiple rows by specifying `nrow` or/and `ncol`. 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.

```{r, fig.width = 8, fig.height = 8}
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. 

```{r, fig.width = 8, fig.height = 8}
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.

```{r, fig.width = 10, fig.height = 6}
gtrellis_layout(equal_width = TRUE)
gtrellis_show_index()
```

Make all columns having equal width and also set multiple rows.

```{r, fig.width = 8, fig.height = 8}
gtrellis_layout(ncol = 5, byrow = FALSE, equal_width = TRUE)
gtrellis_show_index()
```

There is also a 'compact' mode of the layout that
when there are multiple rows, chromosomes on a same row can be put compactly without being aligned to 
corresponding columns. This mode saves a lot of white space but the drawback is that
it is not easy to directly compare positions among chromosomes.

```{r, fig.width = 10, fig.height = 8}
gtrellis_layout(nrow = 3, compact = TRUE)
gtrellis_show_index()
```

Set gaps between chromosomes. Note if it is set as a numeric value,
it should only be 0 (no gap).

```{r, fig.width = 10, fig.height = 6}
gtrellis_layout(gap = 0)
```

Or `gap` can be a `unit` object.

```{r, fig.width = 10, fig.height = 6}
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.

```{r, fig.width = 8, fig.height = 8}
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.

```{r, fig.width = 10, fig.height = 6}
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.

```{r, fig.width = 10, fig.height = 6}
gtrellis_layout(n_track = 3, track_height = c(1, 2, 3))
```

`track_height` can also be a `unit` object.

```{r, fig.width = 10, fig.height = 6}
gtrellis_layout(n_track = 3, 
    track_height = unit.c(unit(1, "cm"), unit(1, "null"), grobHeight(textGrob("chr1"))))
```

`track_axis` controls whether to show y-axes. If certain value is set to `FALSE`, 
y-axis on corresponding track will not be drawn.

```{r, fig.width = 10, fig.height = 6}
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.

```{r, fig.width = 10, fig.height = 6}
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.)

```{r, fig.width = 10, fig.height = 6}
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`.

```{r, fig.width = 10, fig.height = 6}
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.

```{r, fig.width = 8, fig.height = 10}
gtrellis_layout(n_track = 3, ncol = 4)
gtrellis_show_index()
```

Set `border` to `FALSE` to remove borders.

```{r, fig.width = 8, fig.height = 10}
gtrellis_layout(n_track = 3, ncol = 4, border = FALSE, xaxis = FALSE, track_axis = FALSE, xlab = "")
gtrellis_show_index()
```

## Add graphics

After the initialization of the layout, each cell can be thought as an ordinary coordinate
system. Then graphics can be added in afterwards.

### Pre-defined track

First we will introduce functions which add fixed types of graphics.

`add_points_track()` directly adds points at the middle points of corresponding genomic regions.
The genomic region variable can be either a data frame or a `GRanges` object.

```{r, fig.width = 10, fig.height = 6}
library(circlize)
bed = generateRandomBed()
gtrellis_layout(track_ylim = range(bed[[4]]), nrow = 3, byrow = FALSE)
add_points_track(bed, bed[[4]], gp = gpar(col = ifelse(bed[[4]] > 0, "red", "green")))
```

`add_segments_track()` adds segments for corresponding regions.

```{r, fig.width = 10, fig.height = 6}
bed = generateRandomBed(nr = 100)
gtrellis_layout(track_ylim = range(bed[[4]]), nrow = 3, byrow = FALSE)
add_segments_track(bed, bed[[4]], gp = gpar(col = ifelse(bed[[4]] > 0, "red", "green"), lwd = 4))
```

`add_lines_track()` adds lines. Also it can draw areas below the lines (or above, depending on `baseline`). 

```{r, fig.width = 10, fig.height = 6}
bed = generateRandomBed(200)
gtrellis_layout(n_track = 2, track_ylim = rep(range(bed[[4]]), 2), nrow = 3, byrow = FALSE)
add_lines_track(bed, bed[[4]])
add_lines_track(bed, bed[[4]], area = TRUE, gp = gpar(fill = "grey", col = NA))
```

`add_rect_track()` adds rectangles which is useful to draw bars.

```{r, fig.width = 10, fig.height = 6}
col_fun = colorRamp2(c(-1, 0, 1), c("green", "black", "red"))
gtrellis_layout(track_ylim = range(bed[[4]]), nrow = 3, byrow = FALSE)
add_rect_track(bed, h1 = bed[[4]], h2 = 0, 
    gp = gpar(col = NA, fill = col_fun(bed[[4]])))
```

`add_heatmap_track()` adds heatmap. Heatmap will fill the whole track vertically.

```{r, fig.width = 10, fig.height = 6}
gtrellis_layout(nrow = 3, byrow = FALSE, track_axis = FALSE)
mat = matrix(rnorm(nrow(bed)*4), ncol = 4)
add_heatmap_track(bed, mat, fill = col_fun)
```

By default, these pre-defined graphic functions draw in the next track.
However, different types of graphics can be drawn in a same track by manually setting
`track`.

```{r, fig.width = 10, fig.height = 6}
col_fun = colorRamp2(c(-1, 0, 1), c("green", "black", "red"))
gtrellis_layout(track_ylim = range(bed[[4]]), nrow = 3, byrow = FALSE)
add_rect_track(bed, h1 = bed[[4]], h2 = 0, gp = gpar(col = NA, fill = col_fun(bed[[4]])))
add_lines_track(bed, bed[[4]], track = current_track())
add_points_track(bed, bed[[4]], track = current_track(), size = unit(abs(bed[[4]])*5, "mm"))
```

### Customized track

More generally, `add_track()` allows adding self-defined graphics. Actually this is how
`add_points_track()`, `add_segments_track()`, `add_lines_track()`, `add_rect_track()` and `add_heatmap_track()` are
implemented.

The self-defined graphics are added by `panel_fun` argument which should be a function. `panel_fun`
is applied to every genomic categories (e.g. chromosomes) and the input
value of `panel_fun` is a subset of data which corresponds to the current chromosome. Following example
simply shows how to add points by `panel_fun`.

```{r, fig.width = 10, fig.height = 6}
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(1, "mm"))
})
```

If the input data is a `GRanges` object, the input variable in `panel_fun` is also a `GRanges` object.

```{r, fig.width = 10, fig.height = 6, message = FALSE}
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(1, "mm"))
})
```

Initialization and adding graphics are actually independent. Following example uses
same code to add graphics but with different layout.

```{r, fig.width = 8, fig.height = 8}
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(1, "mm"))
})
```

## Examples

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.

```{r, fig.width = 8, fig.height = 12}
load(system.file("extdata", "DMR.RData", package = "circlize"))
DMR_hyper_density = circlize::genomicDensity(DMR_hyper, window.size = 1e7)
head(DMR_hyper_density)
```

Initialize the layout and add following four tracks:

1. chromosome names, simple text.
2. rainfall plot, first apply rainfall transformation and then add points.
3. genomic density, lines with area (actually it is polygons).
4. ideogram, rectangles.

```{r, fig.width = 10, fig.height = 12}
gtrellis_layout(n_track = 4, ncol = 4, byrow = FALSE,
    track_axis = c(FALSE, TRUE, TRUE, FALSE), 
    track_height = unit.c(2*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) {
    # the use of `get_cell_meta_data()` will be introduced later
    chr = get_cell_meta_data("name")  
    grid.rect(gp = gpar(fill = "#EEEEEE"))
    grid.text(chr)
})

# track for rainfall plots
DMR_hyper_rainfall = circlize::rainfallTransform(DMR_hyper)
add_points_track(DMR_hyper_rainfall, log10(DMR_hyper_rainfall[[4]]),
  pch = 16, size = unit(1, "mm"), gp = gpar(col = "red"))

# track for genomic density
add_lines_track(DMR_hyper_density, DMR_hyper_density[[4]], area = TRUE, 
  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 performed.
Since rainfall plots for both hyper-DMR and hypo-DMR are added in a same track, 
we explicitly specify value of `track` argument to `current_track()` in  `add_track()`.

```{r, fig.width = 10, fig.height = 12}
DMR_hypo_density = circlize::genomicDensity(DMR_hypo, window.size = 1e7)
DMR_hypo_rainfall = circlize::rainfallTransform(DMR_hypo)

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)

# put into a function and we will use it later
add_graphics = function() {
    add_points_track(DMR_hyper_rainfall, log10(DMR_hyper_rainfall[[4]]),
        pch = 16, size = unit(1, "mm"), gp = gpar(col = "#FF000080"))
    add_points_track(DMR_hypo_rainfall, log10(DMR_hypo_rainfall[[4]]), track = current_track(),
        pch = 16, size = unit(1, "mm"), gp = gpar(col = "#0000FF80"))

    # track for genomic density
    add_lines_track(DMR_hyper_density, DMR_hyper_density[[4]], area = TRUE, 
        gp = gpar(fill = "#FF000080"))
    add_lines_track(DMR_hypo_density, DMR_hypo_density[[4]], area = TRUE, track = current_track(),
        gp = gpar(fill = "#0000FF80"))
}

add_graphics()
```

Next we change the layout to the 'compact' mode without changing the code that adds graphics.

```{r, fig.width = 14, fig.height = 10}
gtrellis_layout(n_track = 2, nrow = 4, compact = TRUE,
    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_graphics()
```

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`.

```{r, fig.width = 8, fig.height = 8}
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))
    })
}
```

Following code plots coverage for a tumor sample, its companion normal sample
and the ratio of coverage. First prepare the data:

```{r}
tumor_df = readRDS(system.file("extdata", "df_tumor.rds", package = "gtrellis"))
control_df = readRDS(system.file("extdata", "df_control.rds", package = "gtrellis"))

# 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.

```{r, fig.width = 10, fig.height = 8}
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_points_track(tumor_df, tumor_df[[4]], pch = 16, size = unit(2, "bigpts"), 
    gp = gpar(col = "#00000020"))
add_points_track(control_df, tumor_df[[4]], 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.lines(unit(c(0, 1), "npc"), unit(c(0, 0), "native"), gp = gpar(col = "#0000FF80", lty = 2))
    grid.points(x, y, pch = 16, size = unit(2, "bigpts"), gp = gpar(col = col_fun(y)))
})
```

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.

```{r, fig.width = 10, fig.height = 8}
gene = readRDS(system.file("extdata", "gencode_v19_protein_coding_genes.rds", package = "gtrellis"))
gene_density = genomicDensity(gene)

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_lines_track(gene_density, gene_density[[4]])

col_fun = circlize::colorRamp2(seq(0, max(gene_density[[4]]), length = 11), 
                               rev(brewer.pal(11, "RdYlBu")))
add_heatmap_track(gene_density, gene_density[[4]], fill = col_fun)
```

Following two plots compares the Manhattan plot with default and enhanced layout.  
The GWAS data was downloaded from [UCSC Table Browser](https://genome.ucsc.edu/cgi-bin/hgTables). The parameters for downloading were:

```
clade: Mammal
genome: Human
assembly: Feb. 2009(GRCh37/hg19)
group: Phenotype and Literature
track: GWAS Catalog
table: gwasCatalog
```

```{r}
load(system.file("extdata", "gwasCatalog.RData", package = "gtrellis"))
head(gwas)
v = -log10(gwas[, "p-value"])
# remove outliers
q95 = quantile(v, 0.95)
v[v > q95] = q95
```

First we make the normal Manhattan plot. From the plot below, basically we can only see there are 
SNPs that show high significance and on chromosome 6 there exists a cluster where the SNP density
are very high.

```{r, fig.width = 14, fig.height = 6}
library(gtrellis)
gtrellis_layout(category = paste0("chr", 1:22), track_ylim = range(v), track_ylab = "-log10(p)")
add_points_track(gwas, v, gp = gpar(col = "#00000080"))
```

Next we adjust the layout, also we add another track which shows the number of SNPs in
5MB genomic windows. In the new layout, width for each chromosome is much wider than the previous plot,
thus, it shows very clearly for the distribution pattern of highly significant SNPs
in the genome (in the previous plot, due to the narrow plotting area for each chromosome, 
the distribution of SNPs seems random). The additional track gives an exact view that SNP density is dominantly high
in a cluster on chromosome 6 and there are also many small hotspot mutation areas spreading the genome.

```{r, fig.width = 14, fig.height = 10}
library(circlize)
# how many SNPs in every 5MB window
d = genomicDensity(gwas, 5e6)
d[, 4] = d[, 4] * 5e6

gtrellis_layout(nrow = 4, byrow = FALSE, n_track = 2, category = paste0("chr", 1:22),
  add_ideogram_track = TRUE, add_name_track=TRUE, track_ylim = c(range(v), range(d[, 4])),
  track_height = c(2, 1), track_ylab = c("-log10(p)", "#SNP"))
add_points_track(gwas, v, gp = gpar(col = "#00000080"))
add_lines_track(d, d[, 4], area = TRUE, gp = gpar(fill = "#999999", 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. 

```{r, fig.width = 8, fig.height = 8}
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"))
})
```

## Get meta data for each cell

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:

```{r, eval = FALSE}
gtrellis_layout()
add_track(panel_fun = function(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, e.g. the chromosome 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.

```{r, echo = FALSE, fig.width = 8, fig.height = 8}
library(GetoptLong)
gtrellis_layout(category = c("chr1", "chr2", "chr21", "chr22"), equal_width = TRUE, add_name_track = TRUE, add_ideogram_track = TRUE, nrow = 2,
  xpadding = c(0.1, 0.1), ypadding = c(0.1, 0.1))

add_track(panel_fun = function(gr) {
    xlim = get_cell_meta_data("xlim")
    ylim = get_cell_meta_data("ylim")
    extended_xlim = get_cell_meta_data("extended_xlim")
    extended_ylim = get_cell_meta_data("extended_ylim")
    original_xlim = get_cell_meta_data("original_xlim")
    original_ylim = get_cell_meta_data("original_ylim")

    grid.rect(xlim[1], ylim[1], width = xlim[2] - xlim[1], height = ylim[2] - ylim[1], default.unit = "native", just = c(0, 0), gp = gpar(col = "#FF000080", fill = "transparent", lwd = 3))
    grid.rect(extended_xlim[1], extended_ylim[1], width = extended_xlim[2] - extended_xlim[1], height = extended_ylim[2] - extended_ylim[1], default.unit = "native", just = c(0, 0), gp = gpar(col = "#00FF0080", fill = "transparent", lwd = 3))
    grid.rect(original_xlim[1], original_ylim[1], width = original_xlim[2] - original_xlim[1], height = original_ylim[2] - original_ylim[1], default.unit = "native", just = c(0, 0), gp = gpar(col = "#0000FF80", fill = "transparent", lwd = 3))

    grid.text("xlim, ylim", xlim[2], ylim[1], default.unit = "native", just = c(1, 0), gp = gpar(col = "red"))
    grid.text("extended_xlim, extended_ylim", extended_xlim[1], extended_ylim[2], default.unit = "native", just = c(0, 1), gp = gpar(col = "green"))
    grid.text("original_xlim, original_ylim", original_xlim[1], original_ylim[2], default.unit = "native", just = c(0, 1), gp = gpar(col = "blue"))


    name = get_cell_meta_data("name")
    column = get_cell_meta_data("column")
    row = get_cell_meta_data("row")
    track = get_cell_meta_data("track")
    grid.text(qq("name = '@{name}'\ncolumn = @{column}\nrow = @{row}\ntrack = @{track}"), 0.5, 0.5)
})
```

## Legend

Legend can be added by `legend` argument. The value must be a `grob` object. `ColorMapping` class 
and `Legend()` function in **ComplexHeatmap** package or `legendGrob()` in grid package can be used to create a legend grob. 
Or you can consider to use `frameGrob()` and `placeGrob()` to build a legend from ground.

```{r, fig.width = 8, fig.height = 8}
library(ComplexHeatmap)
bed = generateRandomBed()
lgd = Legend(at = c("class1", "class2"), title = "Class", type = "points", legend_gp = gpar(col = 2:3))
gtrellis_layout(nrow = 5, byrow = FALSE, track_ylim = range(bed[[4]]), legend = lgd)
add_points_track(bed, bed[[4]], gp = gpar(col = sample(2:3, nrow(bed), replace = TRUE)))
```

The second example is already shown, but this time we add legend as well:

```{r, fig.width = 8, fig.height = 8}
library(ComplexHeatmap)

bed = generateRandomBed(nr = 10000)
bed = bed[sample(10000, 100), ]
col_fun = colorRamp2(c(-1, 0, 1), c("green", "yellow", "red"))
cm = ColorMapping(col_fun = col_fun)
lgd = color_mapping_legend(cm, plot = FALSE, title = "Value")

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"), legend = lgd)
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"))
})
```

## General genomic categories

Genomic categories are not restricted in chromosomes. It can be any kind,
such as genes. 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.

```{r, fig.width = 10, fig.height = 5}
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
# 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))

# put into a function so that we can use it repeatedly
add_tx = function() {
  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"))
      }
  })
}

add_tx()
```

If you want to put all genes on one column and align them by TSS, you need 
to normalize the genomic coordinate first. In follwoing code, numbers are positions
of TSS of corresponding gene.

```{r}
tp_family$TP53 = lapply(tp_family$TP53, function(df) {
        data.frame(start = abs(df[[2]] - 7590856),
                   end = abs(df[[1]] - 7590856))
    })
tp_family$TP63 = lapply(tp_family$TP63, function(df) {
        data.frame(start = abs(df[[1]] - 189349205),
                   end = abs(df[[2]] - 189349205))
    })
tp_family$TP73 = lapply(tp_family$TP73, function(df) {
        data.frame(start = abs(df[[1]] - 3569084),
                   end = abs(df[[2]] - 3569084))
    })
```

Then similar code as previous one.

```{r, fig.width = 8, fig.height = 6}
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
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_tx()
```

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. 

```{r, fig.width = 10, fig.height = 8}
col_fun = circlize::colorRamp2(seq(-0.5, 0.5, length = 11), rev(brewer.pal(11, "RdYlBu")),
    transparency = 0.5)
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_points_track(tumor_df, tumor_df[[4]], pch = 16, size = unit(2, "bigpts"), 
        gp = gpar(col = "#00000080"))
    add_points_track(control_df, control_df[[4]], pch = 16, size = unit(2, "bigpts"), 
        gp = gpar(col = "#00000080"))
    add_points_track(ratio_df, ratio_df[[4]], pch = 16, size = unit(2, "bigpts"), 
        gp = gpar(col = col_fun(ratio_df[[4]])))
}

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.

```{r, message = TRUE, error = TRUE}
df = data.frame(chr = c("chr1", "chr2"),
                start = c(1e8, 2e8),
                end = c(2e8, 3e8))
gtrellis_layout(df, ncol = 1)
```

<<<<<<< HEAD
=======
<<<<<<< HEAD
=======
>>>>>>> bioc/master
## Support raster image

When there are, say, huge number of points, if the plot is saved as a PDF file, the file size
would be very huge. A [raster image](https://en.wikipedia.org/wiki/Raster_graphics) is a ideal way
to effectively reduce the file size that the subset of the image is first stored as e.g. a PNG file, 
then the PNG is imported and filled into the original plot. In `add_track()` (also for the five other
customized function: `add_points_track()`, `add_segments_track()`, `add_lines_track()`, 
`add_rect_track()` and `add_heatmap_track()`), there are arguments `use_raster`, `raster_quality`,
`raster_quality` and `raster_device_param` which controls how to use raster image.

When `use_raster` is set to `TRUE`, for every panel, the graphics are firstly written into e.g. a PNG file, 
later the PNG is imported and filled to the panel. When `raster_quality` is 1, the size of the PNG is as same
as the size of the panel, while when `raster_quality` is e.g. 2, the size of the PNG is actually double size
as the panel (or the area is four times as the panel), when it is scaled and fit into the panel, actually, the size
will reduce half size, thus, when `raster_quality` is set to larger than 1, all units defined in `panel_fun`
should be increase by multiplying `raster_quanlity`. See following example (Once you save the image as a PDF or 
visualize in R interactively, you will see the difference between tracks).

```{r}
bed = generateRandomBed()
gtrellis_layout(category = "chr1", n_track = 4, track_ylim = range(bed[[4]]))
add_track(bed, panel_fun = function(bed) {
    grid.points((bed[[2]] + bed[[3]]) / 2, bed[[4]], pch = 16, size = unit(2, "mm"))
})
add_track(bed, panel_fun = function(bed) {
    grid.points((bed[[2]] + bed[[3]]) / 2, bed[[4]], pch = 16, size = unit(2, "mm"))
}, use_raster = TRUE)
add_track(bed, panel_fun = function(bed) {
    grid.points((bed[[2]] + bed[[3]]) / 2, bed[[4]], pch = 16, size = unit(2, "mm"))
}, use_raster = TRUE, raster_quality = 2)
add_track(bed, panel_fun = function(bed) {
    grid.points((bed[[2]] + bed[[3]]) / 2, bed[[4]], pch = 16, size = unit(4, "mm"))
}, use_raster = TRUE, raster_quality = 2)
```

## Session info

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

