ggideogram is an extended ggplot2 package to draw graphics to visualize and map genome-wide data on idiograms.
To install the package from Github, you need to install the devtools package first.
install.packages("devtools")
library(devtools)Then you can install the ggideogram package.
devtools::install_github('dxsbiocc/ggideogram')This is a simple package with only one geom geom_ideogram, and some help functions to process data.
First, you need to load the package after you installed it.
require(ggideogram)
Then, you can load the data from the ggideogram package.
data(karyotype_hg19, package="ggideogram")
head(karyotype_hg19)
# Chrom Start End Stain Arm
# 1 chr1 0 2300000 gneg p
# 2 chr1 2300000 5400000 gpos25 p
# 3 chr1 5400000 7200000 gneg p
# 4 chr1 7200000 9200000 gpos25 p
# 5 chr1 9200000 12700000 gneg p
# 6 chr1 12700000 16200000 gpos50 p
data(chromosome.size.hg19, package="ggideogram")
head(chromosome.size.hg19)
# # A tibble: 6 × 3
# Chrom Start End
# <fct> <int> <int>
# 1 chr1 0 249250621
# 2 chr2 0 243199373
# 3 chr3 0 198022430
# 4 chr4 0 191154276
# 5 chr5 0 180915260
# 6 chr6 0 171115067
Now, let's begin.
First, we draw a idiogram using the karyotype_hg19 data.
ggplot(karyotype_hg19) +
geom_ideogram(aes(x = Chrom, ymin = Start, ymax = End,
chrom = Chrom, fill = Stain, arm = Arm),
radius = unit(6, 'pt'), width = 0.5, linewidth = .2,
colour = 'black', show.legend = FALSE) +
scale_fill_manual(values = cytoband_colors) +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.background = element_blank()
)
flip the coordinate to horizontal.
karyotype_hg19 %>%
ggplot() +
geom_ideogram(aes(x = Chrom, ymin = Start, ymax = End,
chrom = Chrom, fill = Stain, arm = Arm),
radius = unit(4, 'pt'), width = 0.3, linewidth = .2,
colour = 'black', show.legend = FALSE) +
scale_fill_manual(values = cytoband_colors) +
coord_flip() +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.background = element_blank()
)
select some chromosomes to draw and show the chromosome name.
karyotype_hg19 %>%
dplyr::filter(Chrom %in% c('chr1', 'chr2', 'chr3', 'chr4', 'chr5')) %>%
ggplot() +
geom_ideogram(aes(x = Chrom, ymin = Start, ymax = End,
chrom = Chrom, fill = Stain, arm = Arm),
radius = unit(10, 'pt'), width = 0.3,
linewidth = .2, colour = 'black',
show.legend = FALSE) +
scale_fill_manual(values = cytoband_colors) +
scale_y_continuous(expand = expansion(mult = c(0, 0.01))) +
theme(
axis.title = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
panel.background = element_blank()
)
draw the gene density heatmap on the idiogram.
we need to load the gene density data from the RIdeogram package.
data(gene_density, package="RIdeogram")
mutate(gene_density, Chr = factor(Chr, levels = c(1:22, 'X', 'Y'))) %>%
ggplot() +
geom_ideogram(aes(x = Chr, ymin = Start, ymax = End,
chrom = Chr, fill = Value),
radius = unit(4, 'pt'), width = 0.5, linewidth = 1,
show.legend = FALSE) +
scale_fill_gradient2(low = "#6fda97", mid = "#e6f972", high = "#38acaf") +
scale_y_continuous(expand = expansion(mult = c(0, 0.01))) +
theme(
axis.title = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
panel.background = element_blank()
)
We can display the information of chromosomes in a stacked manner. Then, in the blank area of the panel, we show the values corresponding to the positions of the chromosomes.
mutate(gene_density, Chrom = factor(Chr, levels = c(1:22, 'X', 'Y'))) %>%
dplyr::filter(Chrom %in% c(1:3)) %>%
put_togather(width = 5e6) %>%
mutate(Mid = (Start + End) / 2) %>%
ggplot() +
geom_ideogram(aes(x = -10, ymin = Start, ymax = End,
chrom = Chrom, fill = Value),
radius = unit(4, 'pt'), width = 10, linewidth = 1,
chrom.col = 'grey80',
show.legend = FALSE) +
scale_fill_gradient2(low = "#6fda97", mid = "#e6f972", high = "#38acaf") +
geom_line(aes(x = Value, y = Mid, colour = Chrom, group = Chrom),
orientation = 'y') +
theme(
axis.title = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
panel.background = element_blank()
)
Add some points which represent RNAs on the idiogram.
Use the Random_RNAs_500 data from the RIdeogram
data(Random_RNAs_500, package="RIdeogram")
mutate(gene_density, Chr = factor(Chr, levels = c(1:22, 'X', 'Y'))) %>%
ggplot() +
geom_ideogram(aes(x = Chr, ymin = Start, ymax = End,
chrom = Chr, fill = Value),
radius = unit(4, 'pt'), width = 0.5,
linewidth = 1, chrom.col = 'grey80',
show.legend = FALSE) +
scale_fill_gradient2(low = "#6fda97", mid = "#e6f972", high = "#38acaf") +
geom_point(data = Random_RNAs_500,
aes(x = Chr, y = (Start + End) / 2,
shape = Type, colour = Type),
position = position_nudge(x = 0.5)) +
scale_colour_manual(values = c("#e66760", "#8c88cd", "#69c5e4")) +
scale_y_continuous(expand = expansion(mult = c(0, 0.01))) +
theme(
axis.title = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
panel.background = element_blank()
)
Draw the idiogram of mm10.
data(karyotype_mm10, package="ggideogram")
ggplot(karyotype_mm10) +
geom_ideogram(aes(x = Chrom, ymin = Start, ymax = End,
chrom = Chrom, fill = Stain, arm = Arm),
radius = unit(6, 'pt'), width = 0.5,
linewidth = .2, colour = 'black',
show.legend = FALSE) +
scale_fill_manual(values = cytoband_colors) +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.background = element_blank()
)
Show gene names on the idiogram.
library(ggrepel)
path <- system.file("extdata", "genes.txt", package = "ggideogram")
gene_list <- read.csv(path, sep = ' ', header = FALSE,
col.names = c('Chrom', 'Start', 'End', 'Name')) %>%
mutate(Chrom = str_replace(Chrom, 'hs', 'chr'),
Name = str_extract(Name, '(.+)_', group = 1),
Mid = (Start + End) / 2) %>%
dplyr::filter(Chrom %in% c('chr1', 'chr2', 'chr3')) %>%
distinct()
karyotype_hg19 %>%
dplyr::filter(Chrom %in% c('chr1', 'chr2', 'chr3')) %>%
ggplot() +
geom_ideogram(aes(x = 1, ymin = Start, ymax = End,
chrom = Chrom, fill = Stain, arm = Arm),
radius = unit(4, 'pt'), width = 0.1,
linewidth = .2, colour = 'black',
show.legend = FALSE) +
geom_text_repel(data = gene_list, aes(x = 1, y = Mid,
label = Name, colour = Chrom),
size = 2, hjust = 0, nudge_x = 0.5,
arrow = arrow(length = unit(0.015, "npc")),
direction = "y",
force_pull = 0,
segment.size = 0.2,
segment.curvature = -0.1,
segment.ncp = 3,
segment.angle = 20) +
scale_fill_manual(values = cytoband_colors) +
scale_x_continuous(expand = expansion(mult = c(0, 0.01)),
limits = c(0.9, 3)) +
facet_wrap(vars(Chrom), scales = 'free') +
theme(
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
panel.background = element_blank()
)
Draw the Fst and Pi data from the RIdeogram package on the idiogram.
data(Fst_between_CE_and_CW, package="RIdeogram")
data(Pi_for_CE, package="RIdeogram")
line.data <- mutate(Pi_for_CE,
Value = (Value - min(Value)) / (max(Value) - min(Value)),
Mid = (Start + End) / 2)
mutate(Fst_between_CE_and_CW, Chr = factor(Chr, levels = c(1:19))) %>%
ggplot() +
geom_ideogram(aes(x = Chr, ymin = Start, ymax = End,
chrom = Chr, fill = Value),
radius = unit(4, 'pt'), width = 0.3,
linewidth = 1, just = 1,
show.legend = FALSE) +
geom_line(aes(x = as.integer(Chr) + Value / 2, y = Mid, group = Chr),
data = line.data,
orientation = 'y', colour = 'orange') +
scale_fill_gradient2(low = "#e5f5f9", mid = "#99d8c9", high = "#2ca25f") +
theme(
axis.title = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
panel.background = element_blank()
)
Draw the LTR and gene density heatmap on the idiogram.
data(LTR_density, package="RIdeogram")
ggplot() +
geom_ideogram(aes(x = Chr, ymin = Start, ymax = End,
chrom = Chr, fill = Value),
data = gene_density,
radius = unit(4, 'pt'), width = 0.4,
linewidth = 1, just = -0.05,
chrom.col = "#252525", chrom.lwd = .5) +
scale_fill_gradient(name = "gene_density", low = "#fef6b5",
high = "#e15383", n.breaks = 3) +
ggnewscale::new_scale_fill() +
geom_ideogram(aes(x = Chr, ymin = Start, ymax = End,
chrom = Chr, fill = Value),
data = LTR_density,
radius = unit(4, 'pt'), width = 0.4,
linewidth = 1, just = 1.05,
chrom.col = "#252525", chrom.lwd = .5) +
scale_fill_gradient(name = "LTR_density", low = "#d3f2a3",
high = "#074050", n.breaks = 3) +
theme(
axis.title = element_blank(),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
panel.background = element_blank(),
legend.position = 'top'
)
Other species' idiograms can be drawn by the same way. If you want to draw the idiogram of other species, you can use the karyotype data from the ggideogram package or download the data from the UCSC website.
The karyotype data is a data frame with columns Chrom, Start, End, Stain(optional), Arm(optional), and Chrom is the chromosome name, Start and End are the start and end positions of the chromosome, Stain is the color of the chromosome, Arm is the arm of the chromosome. You can use the chromosome.size data to set the chromosome size.