Load the required R packages.
##library
library(magrittr)
library(grid)
library(Seurat)
library(ggplot2)
library(ggsector)Use original coordinates with grid.polygon
coordinates of single sector
type of percent, start = 0, r_start = 0
tmp_df <- sector_df(x = 0.5, y = 0.5, theta = 25, r = 0.4, start = 0, r_start = 0)
head(tmp_df)
#> x y
#> 1 0.5000000 0.5000000
#> 2 0.5000000 0.9000000
#> 3 0.5251162 0.8992107
#> 4 0.5501333 0.8968459
#> 5 0.5749525 0.8929149
#> 6 0.5994760 0.8874333
grid.newpage()
grid.polygon(
tmp_df$x, tmp_df$y,
vp = viewport(height = unit(1, "snpc"), width = unit(1, "snpc"))
)
type of percent, start = 50, r_start = 0.2
tmp_df <- sector_df(x = 0.5, y = 0.5, theta = 25, r = 0.4, start = 50, r_start = 0.2)
head(tmp_df)
#> x y
#> 1 0.5000000 0.1000000
#> 2 0.4748838 0.1007893
#> 3 0.4498667 0.1031541
#> 4 0.4250475 0.1070851
#> 5 0.4005240 0.1125667
#> 6 0.3763932 0.1195774
grid.newpage()
grid.polygon(
tmp_df$x, tmp_df$y,
vp = viewport(height = unit(1, "snpc"), width = unit(1, "snpc"))
)
type of degree, start = 90, r_start = 0
tmp_df <- sector_df(
x = 0.5, y = 0.5, theta = 180, r = 0.4,
start = 90, r_start = 0, type = "degree"
)
head(tmp_df)
#> x y
#> 1 0.5000000 0.5000000
#> 2 0.9000000 0.5000000
#> 3 0.8999391 0.4930190
#> 4 0.8997563 0.4860402
#> 5 0.8994518 0.4790656
#> 6 0.8990256 0.4720974
grid.newpage()
grid.polygon(
tmp_df$x, tmp_df$y,
vp = viewport(height = unit(1, "snpc"), width = unit(1, "snpc"))
)
type of degree, start = 180, r_start = 0.2
tmp_df <- sector_df(
x = 0.5, y = 0.5, theta = 180, r = 0.4,
start = 270, r_start = 0.2, type = "degree"
)
head(tmp_df)
#> x y
#> 1 0.1000000 0.5000000
#> 2 0.1000609 0.5069810
#> 3 0.1002437 0.5139598
#> 4 0.1005482 0.5209344
#> 5 0.1009744 0.5279026
#> 6 0.1015221 0.5348623
grid.newpage()
grid.polygon(
tmp_df$x, tmp_df$y,
vp = viewport(height = unit(1, "snpc"), width = unit(1, "snpc"))
)
Coordinates of Multiple Sectors
tmp_df <- sector_df_multiple(
x = c(0.2, 0.5, 0.8),
theta = c(25, 50, 75),
r = 0.15,
start = c(75, 50, 100),
r_start = c(0, 0.05, 0.1),
type = "percent"
)
head(tmp_df)
#> x y group
#> 1 0.20000000 0.5000000 1
#> 2 0.05000000 0.5000000 1
#> 3 0.05029599 0.5094186 1
#> 4 0.05118279 0.5188000 1
#> 5 0.05265691 0.5281072 1
#> 6 0.05471253 0.5373035 1
grid.newpage()
grid.polygon(
tmp_df$x,
tmp_df$y,
id = tmp_df$group,
vp = viewport(height = unit(1, "snpc"), width = unit(1, "snpc")),
gp = gpar(
fill = 3:1, col = 1:3
)
)
Use ggsector with grid
sectorGrob
sectorGrob with units of “cm” and type of “degree”
grid.newpage()
gp <- sectorGrob(
x = unit(c(3, 5, 7), "cm"),
y = unit(c(3, 5, 7), "cm"),
theta = c(90, 180, 270),
r = 1,
start = c(180, 180, 270),
r_start = c(0.6, 0.3, 0),
type = "degree",
group = factor(1:3, levels = c(2, 3, 1)),
gp = gpar(fill = c("green", "red", "blue"))
)
grid.draw(gp)
grid.sector
grid.sector with units of “npc” and type of “percent”
grid.newpage()
grid.sector(
x = c(0.1, 0.5, 0.9),
y = c(0.9, 0.6, 0.1),
theta = c(25, 50, 90),
r = .1,
start = c(25, 50, 100),
r_start = c(0.06, 0.03, 0),
type = "percent",
group = factor(1:3, levels = c(2, 3, 1)),
gp = gpar(col = c("green", "red", "blue"), fill = 2:4),
default.units = "npc"
)
Use ggsector with ComplexHeatmap
Prepare data
## install ComplexHeatmap
# if (!require("ComplexHeatmap", quietly = TRUE)) {
# if (!require("BiocManager", quietly = TRUE)) {
# install.packages("BiocManager")
# }
# BiocManager::install("ComplexHeatmap")
# }
## run
library(magrittr)
library(ComplexHeatmap)
#> ========================================
#> ComplexHeatmap version 2.20.0
#> Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
#> Github page: https://github.com/jokergoo/ComplexHeatmap
#> Documentation: http://jokergoo.github.io/ComplexHeatmap-reference
#>
#> If you use it in published research, please cite either one:
#> - Gu, Z. Complex Heatmap Visualization. iMeta 2022.
#> - Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
#> genomic data. Bioinformatics 2016.
#>
#>
#> The new InteractiveComplexHeatmap package can directly export static
#> complex heatmaps into an interactive Shiny app with zero effort. Have a try!
#>
#> This message can be suppressed by:
#> suppressPackageStartupMessages(library(ComplexHeatmap))
#> ========================================
t0 <- cor(mtcars) %>%
set_colnames(paste("y_", colnames(.))) %>%
set_rownames(paste("x_", rownames(.)))
mat <- abs(t0)
mat[1:5, 1:5]
#> y_ mpg y_ cyl y_ disp y_ hp y_ drat
#> x_ mpg 1.0000000 0.8521620 0.8475514 0.7761684 0.6811719
#> x_ cyl 0.8521620 1.0000000 0.9020329 0.8324475 0.6999381
#> x_ disp 0.8475514 0.9020329 1.0000000 0.7909486 0.7102139
#> x_ hp 0.7761684 0.8324475 0.7909486 1.0000000 0.4487591
#> x_ drat 0.6811719 0.6999381 0.7102139 0.4487591 1.0000000cell_fun + viewport
Realized by modifying the [grid::viewport()] with cell_fun, the sector can be set with a fixed width and height
set.seed(1)
Heatmap(
mat,
name = "vp",
rect_gp = gpar(type = "none"),
cell_fun = function(j, i, x, y, width, height, fill) {
grid.rect(
x = x, y = y, width = width, height = height,
gp = gpar(col = "grey", fill = NA)
)
grid.sector(
theta = mat[i, j] * 100,
r = 0.5,
start = mat[i, j] * 100 * runif(1),
r_start = mat[i, j] * 0.49 * runif(1),
vp = viewport(x, y, width, height),
gp = gpar(fill = fill, col = "transparent")
)
},
width = unit(.7, "snpc"),
height = unit(.7, "snpc")
)
cell_fun + xy
Realized in the form of coordinates + radius with cell_fun.
# The default viewport locks the horizontal and vertical axes
# so that the sector does not deform, which needs to be removed here.
# The radius 'r' is half the min(length, width).
set.seed(2)
Heatmap(
mat,
name = "xy + r",
rect_gp = gpar(type = "none"),
cell_fun = function(j, i, x, y, width, height, fill) {
grid.rect(
x = x, y = y, width = width, height = height,
gp = gpar(col = "grey", fill = NA)
)
r <- as.numeric(min(width, height)) / 2
grid.sector(
x,
y,
theta = mat[i, j] * 100,
r = r,
start = mat[i, j] * 100 * runif(1),
r_start = mat[i, j] * r * 0.9 * runif(1),
vp = NULL,
gp = gpar(fill = fill, col = "transparent")
)
},
width = unit(.7, "snpc"),
height = unit(.7, "snpc")
)
layer_fun + xy
Realized With layer fun
# The input matrix needs to be extracted with pindex(mat, i, j)
set.seed(3)
Heatmap(
mat,
name = "layer",
rect_gp = gpar(type = "none"),
layer_fun = function(j, i, x, y, width, height, fill) {
grid.rect(
x = x, y = y, width = width, height = height,
gp = gpar(col = "grey", fill = NA)
)
r <- as.numeric(min(width, height)) / 2
grid.sector(
x,
y,
theta = pindex(mat, i, j) * 100,
r = r,
start = pindex(mat, i, j) * 100 * runif(nrow(mat) * ncol(mat)),
r_start = pindex(mat, i, j) * r * 0.9 * runif(nrow(mat) * ncol(mat)),
vp = NULL,
gp = gpar(fill = fill, col = "transparent")
)
},
width = unit(.7, "snpc"),
height = unit(.7, "snpc")
)
Use ggsector with ggplot2
prepare data
library(ggsector)
library(reshape2)
df <- cor(mtcars)[1:3, 1:5] %>%
abs() %>%
melt(varnames = c("x", "y"))
## Note, for better display effect, please always add coord_fixed()
## Note, for better display effect, please always add coord_fixed()
## Note, for better display effect, please always add coord_fixed()theta
The sector angle parameter, used in combination with the type parameter, the type parameter defaults to “percent”.
When type = “percent”, the complete circle is a polygon composed of 100 scattered points, and theta takes a value of 0-100.
When type = “degree”, the complete circle is a polygon composed of 360 scattered points, and theta takes a value of 0-360.
ggplot(df) +
## type = "percent", theta = 0-100
geom_sector(
aes(y, x, theta = value * 100),
type = "percent",
color = "blue",
individual = TRUE
) +
## type = "degree", theta = 0-360
geom_sector(
aes(y, x, theta = value * 360),
type = "degree",
color = "red",
alpha = 0.5,
individual = TRUE
) +
coord_fixed() +
theme_bw() +
theme(axis.title = element_blank())
#> For better display effect, please add `coord_fixed()`
#> For better display effect, please add `coord_fixed()`
Careful observation reveals:
The sectors shapes in the two modes are not completely overlapped, this is because:
when type = “percent”, the circumference is 100 scattered points, and the input value will be round() to an integer of 0-100,
when type = “degree”, the circumference is 360 scattered points, and the input value will be round() to an integer of 0-360.
The more circle points, the higher the precision, but also means the slower drawing speed.
r
Radius of the outer circle of the sector(0-0.5)
ggplot(df) +
geom_sector(
aes(y, x, theta = value * 100),
r = rep(c(0.15, 0.3, 0.45), 5),
fill = 2,
individual = TRUE
) +
coord_fixed() +
theme_bw() +
theme(axis.title = element_blank())
#> For better display effect, please add `coord_fixed()`
start
Starting angle of sector.
ggplot(df) +
geom_sector(
aes(y, x, theta = value * 100),
start = rep(c(60, 40, 20), 5),
fill = 2,
individual = TRUE
) +
coord_fixed() +
theme_bw() +
theme(axis.title = element_blank())
#> For better display effect, please add `coord_fixed()`
r_start
The starting position parameter of the fan radius, the value is between 0 and the radius length, and the default is 0, which means drawing a fan shape. If it is greater than 0, it is to draw a sector, and the following are different displays
ggplot(df) +
geom_sector(
aes(y, x, theta = value * 100),
r_start = rep(c(0.15, 0.25, 0.35), 5),
fill = 2,
individual = TRUE
) +
coord_fixed() +
theme_bw() +
theme(axis.title = element_blank())
#> For better display effect, please add `coord_fixed()`
individual
The default is FALSE, mainly to control whether to draw sector by sector with a single coordinate point or to draw as a whole on the drawing board in the form of vector.
When individual = TRUE, draw one by one, the sector will not be deformed, but when there are too many sectors drawn, the overall drawing speed will be much slower.
When individual = FALSE, drawing in vector form is faster, but it needs to be used with coord_fixed() or ratio to lock the aspect ratio of the drawing board, otherwise the sector will be deformed.
individual with coord_fixed()
For better display effect, please always add coord_fixed().
individual = TRUE + coord_fixed()
# x = x, y = y
ggplot(rbind(
cbind(df, t1 = 1),
cbind(df[1:9, ], t1 = 2)
)) +
facet_wrap(~t1, ncol = 2) +
geom_sector(
aes(x, y),
theta = 75,
fill = 2,
r = 0.5,
individual = TRUE
) +
coord_fixed() +
theme_bw() +
theme(axis.title = element_blank())
#> For better display effect, please add `coord_fixed()`
# x = y, y =x
ggplot(rbind(
cbind(df, t1 = 1),
cbind(df[1:9, ], t1 = 2)
)) +
facet_wrap(~t1, ncol = 2) +
geom_sector(
aes(y, x),
theta = 75,
fill = 2,
r = 0.5,
individual = TRUE
) +
coord_fixed() +
theme_bw() +
theme(axis.title = element_blank())
#> For better display effect, please add `coord_fixed()`
individual = FALSE + coord_fixed()
# x = x, y = y
ggplot(rbind(
cbind(df, t1 = 1),
cbind(df[1:9, ], t1 = 2)
)) +
facet_wrap(~t1, ncol = 2) +
geom_sector(
aes(x, y),
theta = 75,
fill = 2,
r = 0.5,
individual = FALSE
) +
coord_fixed() +
theme_bw() +
theme(axis.title = element_blank())
#> For better display effect, please add `coord_fixed()`
# x = y, y =x
ggplot(rbind(
cbind(df, t1 = 1),
cbind(df[1:9, ], t1 = 2)
)) +
facet_wrap(~t1, ncol = 2) +
geom_sector(
aes(y, x),
theta = 75,
fill = 2,
r = 0.5,
individual = TRUE
) +
coord_fixed() +
theme_bw() +
theme(axis.title = element_blank())
#> For better display effect, please add `coord_fixed()`
individual without coord_fixed()
individual = TRUE without coord_fixed()
If you are in a special situation and cannot use coord_fixed(), then it is recommended that you use individual = TRUE and the r parameter to fine-tune. Also, to reduce the radius, you need to try it manually.
# x = x, y = y
ggplot(rbind(
cbind(df, t1 = 1),
cbind(df[1:9, ], t1 = 2)
)) +
facet_wrap(~t1, ncol = 2) +
geom_sector(
aes(x, y),
theta = 75,
fill = 2,
r = 0.35, ## To reduce the radius, you need to try it manually
individual = TRUE
) +
theme_bw() +
theme(axis.title = element_blank())
#> For better display effect, please add `coord_fixed()`
# x = y, y =x
ggplot(rbind(
cbind(df, t1 = 1),
cbind(df[1:9, ], t1 = 2)
)) +
facet_wrap(~t1, ncol = 2) +
geom_sector(
aes(y, x),
theta = 75,
fill = 2,
r = 0.25, ## To reduce the radius, you need to try it manually
individual = TRUE
) +
theme_bw() +
theme(axis.title = element_blank())
#> For better display effect, please add `coord_fixed()`
individual = FALSE without coord_fixed()
If you really want to use individual = FALSE without coord_fixed(), you might try the experimental parameter ratio' You need to manually adjust theratio` value to prevent sector deformation.
# x = x, y = y
ggplot(rbind(
cbind(df, t1 = 1),
cbind(df[1:9, ], t1 = 2)
)) +
facet_wrap(~t1, ncol = 2) +
geom_sector(
aes(x, y),
theta = 75,
fill = 2,
r = 0.5,
## You need to manually adjust the `ratio` value
## to prevent sector deformation.
ratio = 1.6,
individual = FALSE
) +
theme_bw() +
theme(axis.title = element_blank())
#> For better display effect, please add `coord_fixed()`
#>
#> This is not an error or warning, just a small reminder.
#> The calculated optimal ratio value is: 1.625.
#> The ratio value currently used is: 1.6.
#>
#> This is not an error or warning, just a small reminder.
#> The calculated optimal ratio value is: 1.625.
#> The ratio value currently used is: 1.6.
# x = y, y =x
ggplot(rbind(
cbind(df, t1 = 1),
cbind(df[1:9, ], t1 = 2)
)) +
facet_wrap(~t1, ncol = 2) +
geom_sector(
aes(y, x),
theta = 75,
fill = 2,
r = 0.5,
## You need to manually adjust the `ratio` value
## to prevent sector deformation.
ratio = 1.6,
individual = FALSE
) +
# coord_fixed() +
theme_bw() +
theme(axis.title = element_blank())
#> For better display effect, please add `coord_fixed()`
#>
#> This is not an error or warning, just a small reminder.
#> The calculated optimal ratio value is: 0.615384615384616.
#> The ratio value currently used is: 1.6.
#>
#> This is not an error or warning, just a small reminder.
#> The calculated optimal ratio value is: 0.615384615384616.
#> The ratio value currently used is: 1.6.