R/circos_plot.R
In mattlee821/EpiCircos: An implementation of Circos plots for epidemiologists
Defines functions
circos_plot
Documented in
circos_plot
# source function ====
#' Create a Circos plot with up to 3 tracks
#'
#' @param track_number The number of tracks you wnat your circos plot to have. Maximum number of tracks is 3.
#' @param track1_data The data frame of your first track.
#' @param track2_data The data frame of your second track.
#' @param track3_data The data frame of your third track.
#' @param track1_type The type of plot for the first track. One of "scatter", "lines", "bar" and "histogram".
#' @param track2_type The type of plot for the second track. One of "scatter", "lines", "bar" and "histogram".
#' @param track3_type The type of plot for the third track. One of "scatter", "lines", "bar" and "histogram".
#' @param label_column The column in your data frames that you will use as the labels for the circos plot. This will likely be the column with your exposure/outcome name in. Labels go on the outside of the plot.
#' @param section_column The column in your data frames that you will use to group/section your data/plot by.
#' @param order Do you want MR Viz to organise your columns alphabetically. Default is TRUE
#' @param order_column If order = FALSE what column do you want to order your sections by
#' @param estimate_column The column in your data frames with the estimates in.
#' @param pvalue_column The column in your data frames with the p-values in.
#' @param pvalue_adjustment The threshold you want to set your p-value 'significance' threshold too. This can be in the format of a single value or a function e.g. 0.05/22. The p-value thrshold is only used for scatter plots.
#' @param lower_ci The column in your data frames with the lower confidence intervals in.
#' @param upper_ci The column in your data frames with the upper confidence intervals in.
#' @param lines_column The column in your data frames that you want to plot as lines.
#' @param lines_type The type of line plot you want. "l" = straight lines; "o" = straight lines with points; "s" = boxed lines; "h" = vertical lines. Default is "o".
#' @param bar_column The column in your data frames that you want to plot as bars.
#' @param histogram_column The column in your data frames that you want to plot as histograms.
#' @param histogram_binsize The binsize of the histogram. Default is 0.01.
#' @param histogram_densityplot Do you want your histogram to be a density plot or not. Default is FALSE.
#' @param legend Do you want a legend = TRUE or FALSE. FALSE by default
#' @param track1_label What do you want the label for the first track to be e.g. "Body Mass Index"
#' @param track2_label What do you want the label for the first track to be e.g. "Corondary Heart Disease"
#' @param track3_label What do you want the label for the first track to be e.g. "Breast Cancer"
#' @param pvalue_label What do you want the label for the p-value to read e.g. "p-value <= 0.05"
#' @param circle_size The size of the circos plot. Smaller numbers make larger circos plots. Default is 25. You will need to adjust this number a few times to get the perfect size for your specific circos plot requirements. If you have long labels then try ≥ 25. If your labels are short try 1-10.
#' @param track1_height Size of the track as a percent of the whole circle. Default is 20 percent (0.20)
#' @param track2_height Size of the track as a percent of the whole circle. Default is 20 percent (0.20)
#' @param track3_height Size of the track as a percent of the whole circle. Default is 20 percent (0.20). It is sometimes worth increasing the size of track 3 to 30 percent
#'
#' @export
#'
#' @examples circos_plot(track_number = 3,
#' track1_data = EpiCircos_data,
#' track2_data = EpiCircos_data,
#' track3_data = EpiCircos_data,
#' track1_type = "points",
#' track2_type = "lines",
#' track3_type = "bar",
#' label_column = 1,
#' section_column = 2,
#' estimate_column = 4,
#' pvalue_column = 5,
#' pvalue_adjustment = 0.05,
#' lower_ci = 7,
#' upper_ci = 8,
#' lines_column = 4,
#' lines_type = "o",
#' bar_column = 4,
#' histogram_column = 4,
#' histogram_binsize = 0.01,
#' histogram_densityplot = F,
#' legend = FALSE,
#' circle_size = 25)
#'
circos_plot <- function(track_number,
track1_data,
track2_data,
track3_data,
track1_type,
track2_type,
track3_type,
label_column,
section_column,
order = TRUE,
order_column,
estimate_column,
pvalue_column,
pvalue_adjustment,
lower_ci,
upper_ci,
lines_column,
lines_type = "o",
bar_column,
histogram_column,
histogram_binsize = 0.01,
histogram_densityplot = FALSE,
legend = FALSE,
track1_label = NA,
track2_label = NA,
track3_label = NA,
pvalue_label = NA,
circle_size = 25,
track1_height = 0.20,
track2_height = 0.20,
track3_height = 0.20){
# Default plot paramaters ====
track1 <- 1 # track 1 is your section header
track2 <- 2 # you start plotting your data on track 2
track3 <- 3
track4 <- 4
x_axis_index <- 1
track_axis_reference <- 0
margins <- c(0.5, 0.5, 0.5, 0.5) * 25 # A numerical vector of the form c(bottom, left, top, right) which gives the number of lines of margin to be specified on the four sides of the plot
start_gap <- 17 # indicates gap at start for Y axis scale, this is a percentage so the larger the number the larger the empty gap
start_degree <- 90 # starting point of the cirlce in degrees (90 is top)
section_track_height <- 0.10 # size of secton header track as percent of whole circle
# Customisable paramaters ====
## Colours
discrete_palette <- c("#00378f", # track 1 colour
"#ffc067", # track 2 colour
"#894300") # track 3 colour
## section header specifics
section_fill_colour <- "snow2"
section_text_colour <- "black"
section_line_colour <- "grey"
section_line_thickness <- 1.5
section_line_type <- 1
## reference lines that go around the tracks
reference_line_colour <- "deeppink"
reference_line_thickness <- 1.5
reference_line_type <- 1
## point specifics
point_pch <- 21
point_cex <- 1.5
point_col1 <- discrete_palette[1]
point_bg1 <- "white"
point_col1_sig <- "white"
point_bg1_sig <- discrete_palette[1]
point_col2 <- discrete_palette[2]
point_bg2 <- "white"
point_col2_sig <- "white"
point_bg2_sig <- discrete_palette[2]
point_col3 <- discrete_palette[3]
point_bg3 <- "white"
point_col3_sig <- "white"
point_bg3_sig <- discrete_palette[3]
## confidence intervals
ci_lwd <- 5
ci_lty <- 1
ci_col1 <- discrete_palette[1]
ci_col2 <- discrete_palette[2]
ci_col3 <- discrete_palette[3]
## lines specifics
lines_col1 <- discrete_palette[1]
lines_col2 <- discrete_palette[2]
lines_col3 <- discrete_palette[3]
lines_lwd <- 3
lines_lty <- 1
## y axis specifics
y_axis_location <- "left"
y_axis_tick <- FALSE
y_axis_tick_length <- 0
y_axis_label_cex <- 0.75
## label specifics
label_distance <- 1.5 # distance from track 0 to plot labels
label_col <- "black"
label_cex <- 0.6
# plot set-up ====
## the plot region is set-up based on track1_data
data <- track1_data
## 1. organise the data based on section and alphabetically within that based on label
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
## 2. add column of positions of where each data point will be in th track - this is position of values within the track and should be 1:n depedning on number of variables in each section and is based also on individual sections. this will give a number 1-n for each individual metabolite in each section as a position within the section for plotting values.
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN = seq_along))
## 3. set parameter - this sets the paramater of the sections of the circos plot so that you can plot within individual sections
npercat <- as.vector(table(data[[section_column]]))
## 4. Standardise data$x so that the axis is from 0-1 - this provides for each section an individual x axis
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
## 5. add column that codes sections as numbers - this will be the label for section headings. This can then be translated in the legend or figure title
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
## 6. set gap for axis - spacing between sections. It is 1-n where n is 1 minus the total number of sections - last number indicates gap at start for Y axis scale, this is a percentage so the larger the number the larger the empty gap
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 7. clear plotting area
circlize::circos.clear()
## 8. initiate blank page to plot on top of
graphics::par(mar = c(0.6, 0.5, 0.5, 0.5) * circle_size,
cex = 0.8,
xpd = NA) # A logical value or NA. If FALSE, all plotting is clipped to the plot region, if TRUE, all plotting is clipped to the figure region, and if NA, all plotting is clipped to the device region. See also clip.
## 9. creat circle paramaters
circlize::circos.par(cell.padding = c(0, 0.5, 0, 0.5),
start.degree = start_degree, # starting point of the circle
gap.degree = gap, # gap between two neighbour sections
track.margin = c(0.012, 0.012), #blank area outside of the plotting area
points.overflow.warning = FALSE, #this dictates whether warnings will pop up if plots are plotted outside of the plotting region (this is to do with teh plotting region not being circular and instead being rectangular) - keep this as FALSE
track.height = section_track_height, #height of the section track as percent of whole circle
clock.wise = TRUE) #direction to add sections
## 10. initiate circle
circlize::circos.initialize(factors = data$section_numbers,
xlim = c(0, 1),
sector.width = npercat)
## 11. create and plot section headers
circlize::circos.trackPlotRegion(factors = data$section_numbers, #we plot the first region based on the column in our data set which we creat the sections from
track.index = track1, #the track you are plotting
x = data$ncat, #the x axis is dictated by the number of values in our data set
ylim = c(0, 1), #the y axis is set based on the track width you want as it will impact the size of text you can have in this track
track.height = 0.05, #size of track as % of whole circle
panel.fun = function(x, y) {
chr = circlize::get.cell.meta.data("sector.index") #dont change as this gathers all of the info you need automatically
xlim = circlize::get.cell.meta.data("xlim") #dont change as this gathers all of the info you need automatically
ylim = circlize::get.cell.meta.data("ylim") #dont change as this gathers all of the info you need automatically
circlize::circos.rect(xlim[1], 0, xlim[2], 1, # n (+ and -) length of track away from centre (low number means smaller) - want it large enough to encompass text
border = NA, #give the track a border
col = section_fill_colour) #colour of track
circlize::circos.text(mean(xlim), #text location on x axis
mean(ylim), #text location on y axis
chr, #colour of text, default blue
cex = 1, #text size
facing = "outside", #diretion of text
niceFacing = TRUE, #flip so text is readable
col = section_text_colour)
},
bg.border = NA) #background border colour
## 12. add labels
circlize::circos.trackText(factors = data$section_numbers,
track.index = track1, #choose labels based on the track we have just made as you can only plot text once a track has been created
x = data$ncat, #location on the x axis where we will plot the lables
y = data$b * 0 + label_distance, #dictates where the labels are plotted - * 0 to give 0 and then choose how far away from the 0 we want to plot the text (this will be trial and error before you get what works best for your data set)
labels = data[[label_column]], #where you are taking the names for the labels from
facing = "reverse.clockwise",
niceFacing = TRUE, #flip the text so it is readable
adj = c(1, 1),
col = label_col,
cex = label_cex) #size of the text
# track 1 ====
## points ====
if(track_number >= 1 && track1_type == "points"){
data <- track1_data
track.index <- 2
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
a <- min(data[[lower_ci]])
b <- min(data[[upper_ci]])
axis_min <- min(a,b)
axis_min <- round(axis_min, 3)
axis_min <- round(axis_min + (axis_min * 0.1), 3)
axis_min_half <- round(axis_min/2, 3)
a <- max(data[[lower_ci]])
b <- max(data[[upper_ci]])
axis_max <- max(a,b)
axis_max <- round(axis_max, 3)
axis_max <- round(axis_max + (axis_max * 0.01), 3)
axis_max_half <- round(axis_max/2, 3)
## 3. create the track and plot the confidence intervals
for(i in 1:nlevels(data$section_numbers)){
data1 = subset(data, section_numbers == i)
circlize::circos.trackPlotRegion(factors = data1$section_numbers, #we plot the first region based on the column in our data set which we creat the sections from
track.index = track.index,
x = data1$ncat, #set this as ncat as ncat dictates the location of the variable you want to plot within each section and within the circle as a whole
y = data1[[estimate_column]], #variable you want to plot
ylim = c(axis_min, axis_max), #co-ordinates of the Y axis of the track
track.height = track1_height, #how big is the track as % of circle
#Set sector background
bg.border = NA,
bg.col = NA,
#Map values
panel.fun = function(x, y) { #this sets x and y as the above defined variables for the following lines of code
# plot '0' reference line
circlize::circos.lines(x = x,
y = y * 0 + track_axis_reference,
col = reference_line_colour, #set the 0 line colour to something distinctive
lwd = reference_line_thickness, #set the thickness of the line so it's a bit smaller than your point (looks better)
lty = reference_line_type)
# confidence interval
circlize::circos.segments(x0 = data1$ncat, # x coordinates for starting point
x1 = data1$ncat, # x coordinates for end point
y0 = data1[[estimate_column]] * 0 - -(data1[[lower_ci]]), # y coordinates for start point
y1 = data1[[estimate_column]] * 0 + data1[[upper_ci]], # y coordinates for end point
col = ci_col1,
lwd = ci_lwd,
lty = ci_lty,
sector.index = i)})}
## 4. layer on top of the confidence intervals the effect estimates
### a. points not reaching signfiicance
circlize::circos.trackPoints(factors = subset(data, data[[pvalue_column]] > pvalue_adjustment)$section_numbers,
track.index = track.index,
x = subset(data, data[[pvalue_column]] > pvalue_adjustment)$ncat,
y = subset(data, data[[pvalue_column]] > pvalue_adjustment)[[estimate_column]],
cex = point_cex,
pch = point_pch,
col = point_col1,
bg = point_bg1)
### b. points reaching significance
circlize::circos.trackPoints(factors = subset(data, data[[pvalue_column]] < pvalue_adjustment)$section_numbers,
track.index = track.index,
x = subset(data, data[[pvalue_column]] < pvalue_adjustment)$ncat,
y = subset(data, data[[pvalue_column]] < pvalue_adjustment)[[estimate_column]],
cex = point_cex,
pch = point_pch,
col = point_col1_sig,
bg = point_bg1_sig)
## 5. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
## lines ====
if(track_number >= 1 && track1_type == "lines"){
data <- track1_data
track.index <- 2
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
axis_min <- round(min(data[[lines_column]]), 3)
axis_min <- round(axis_min + (axis_min * 0.1), 3)
axis_min_half <- round(axis_min/2, 3)
axis_max <- round(max(data[[lines_column]]), 3)
axis_max <- round(axis_max + (axis_max * 0.1), 3)
axis_max_half <- round(axis_max/2, 3)
## 3. create the track and plot the confidence intervals
circlize::circos.trackPlotRegion(factors = data$section_numbers, #we plot the first region based on the column in our data set which we creat the sections from
track.index = track.index,
x = data$ncat, #set this as ncat as ncat dictates the location of the variable you want to plot within each section and within the circle as a whole
y = data[[lines_column]], #variable you want to plot
ylim = c(axis_min, axis_max), #co-ordinates of the Y axis of the track
track.height = track1_height, #how big is the track as % of circle
#Set sector background
bg.border = NA,
bg.col = NA,
#Map values
panel.fun = function(x, y) { #this sets x and y as the above defined variables for the following lines of code
circlize::circos.lines(x = x,
y = y,
col = lines_col1,
lwd = lines_lwd,
lty = lines_lty,
type = lines_type)
# plot '0' reference line
# circlize::circos.lines(x = x,
# y = y * 0 + track_axis_reference,
# col = reference_line_colour, #set the 0 line colour to something distinctive
# lwd = reference_line_thickness, #set the thickness of the line so it's a bit smaller than your point (looks better)
# lty = reference_line_type)
})
## 4. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
## barplot ====
if(track_number >= 1 && track1_type == "bar"){
data <- track1_data
track.index <- 2
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
axis_min <- round(min(data[[bar_column]]), 3)
axis_max <- round(max(data[[bar_column]]), 3)
## 3. create the track and plot the confidence intervals
circlize::circos.trackPlotRegion(factors = data$section_numbers, #we plot the first region based on the column in our data set which we creat the sections from
track.index = track.index,
x = data$ncat, #set this as ncat as ncat dictates the location of the variable you want to plot within each section and within the circle as a whole
y = data[[bar_column]], #variable you want to plot
ylim = c(axis_min, axis_max), #co-ordinates of the Y axis of the track
track.height = track1_height, #how big is the track as % of circle
#Set sector background
bg.border = NA,
bg.col = NA,
#Map values
panel.fun = function(x, y) { #this sets x and y as the above defined variables for the following lines of code
circlize::circos.lines(x = x,
y = y,
col = lines_col1,
lwd = 1,
lty = lines_lty,
type = "s",
area = T,
border = "White",
baseline = "bottom")
# # plot '0' reference line
# circlize::circos.lines(x = x,
# y = y * 0 + track_axis_reference,
# col = reference_line_colour, #set the 0 line colour to something distinctive
# lwd = reference_line_thickness, #set the thickness of the line so it's a bit smaller than your point (looks better)
# lty = reference_line_type)
})
## 4. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
## histogram ====
if(track_number >= 1 && track1_type == "histogram"){
data <- track1_data
track.index <- 2
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
axis_min <- round(min(data[[histogram_column]]), 3)
axis_min <- round(axis_min + (axis_min * 0.1), 3)
axis_min_half <- round(axis_min/2, 3)
axis_max <- round(max(data[[histogram_column]]), 3)
axis_max <- round(axis_max + (axis_max * 0.1), 3)
axis_max_half <- round(axis_max/2, 3)
## 3. create the track and plot the confidence intervals
circlize::circos.trackHist(factors = data$section_numbers,
x = data[[histogram_column]],
track.height = track1_height,
track.index = NULL,
col = lines_col1,
border = lines_col1,
bg.border = NA,
draw.density = histogram_densityplot,
bin.size = histogram_binsize)
## 4. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
# track 2 ====
## points ====
if(track_number >= 2 && track2_type == "points"){
data <- track2_data
track.index <- 3
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
a <- min(data[[lower_ci]])
b <- min(data[[upper_ci]])
axis_min <- min(a,b)
axis_min <- round(axis_min, 3)
axis_min <- round(axis_min + (axis_min * 0.1), 3)
axis_min_half <- round(axis_min/2, 3)
a <- max(data[[lower_ci]])
b <- max(data[[upper_ci]])
axis_max <- max(a,b)
axis_max <- round(axis_max, 3)
axis_max <- round(axis_max + (axis_max * 0.01), 3)
axis_max_half <- round(axis_max/2, 3)
## 3. create the track and plot the confidence intervals
for(i in 1:nlevels(data$section_numbers)){
data1 = subset(data, section_numbers == i)
circlize::circos.trackPlotRegion(factors = data1$section_numbers, #we plot the first region based on the column in our data set which we creat the sections from
track.index = track.index,
x = data1$ncat, #set this as ncat as ncat dictates the location of the variable you want to plot within each section and within the circle as a whole
y = data1[[estimate_column]], #variable you want to plot
ylim = c(axis_min, axis_max), #co-ordinates of the Y axis of the track
track.height = track2_height, #how big is the track as % of circle
#Set sector background
bg.border = NA,
bg.col = NA,
#Map values
panel.fun = function(x, y) { #this sets x and y as the above defined variables for the following lines of code
# plot '0' reference line
circlize::circos.lines(x = x,
y = y * 0 + track_axis_reference,
col = reference_line_colour, #set the 0 line colour to something distinctive
lwd = reference_line_thickness, #set the thickness of the line so it's a bit smaller than your point (looks better)
lty = reference_line_type)
# confidence interval
circlize::circos.segments(x0 = data1$ncat, # x coordinates for starting point
x1 = data1$ncat, # x coordinates for end point
y0 = data1[[estimate_column]] * 0 - -(data1[[lower_ci]]), # y coordinates for start point
y1 = data1[[estimate_column]] * 0 + data1[[upper_ci]], # y coordinates for end point
col = ci_col2,
lwd = ci_lwd,
lty = ci_lty,
sector.index = i)})}
## 4. layer on top of the confidence intervals the effect estimates
### a. points not reaching signfiicance
circlize::circos.trackPoints(factors = subset(data, data[[pvalue_column]] > pvalue_adjustment)$section_numbers,
track.index = track.index,
x = subset(data, data[[pvalue_column]] > pvalue_adjustment)$ncat,
y = subset(data, data[[pvalue_column]] > pvalue_adjustment)[[estimate_column]],
cex = point_cex,
pch = point_pch,
col = point_col2,
bg = point_bg2)
### b. points reaching significance
circlize::circos.trackPoints(factors = subset(data, data[[pvalue_column]] < pvalue_adjustment)$section_numbers,
track.index = track.index,
x = subset(data, data[[pvalue_column]] < pvalue_adjustment)$ncat,
y = subset(data, data[[pvalue_column]] < pvalue_adjustment)[[estimate_column]],
cex = point_cex,
pch = point_pch,
col = point_col2_sig,
bg = point_bg2_sig)
## 5. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
## lines ====
if(track_number >= 2 && track2_type == "lines"){
data <- track2_data
track.index <- 3
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
axis_min <- round(min(data[[lines_column]]), 3)
axis_min <- round(axis_min + (axis_min * 0.1), 3)
axis_min_half <- round(axis_min/2, 3)
axis_max <- round(max(data[[lines_column]]), 3)
axis_max <- round(axis_max + (axis_max * 0.1), 3)
axis_max_half <- round(axis_max/2, 3)
## 3. create the track and plot the confidence intervals
circlize::circos.trackPlotRegion(factors = data$section_numbers, #we plot the first region based on the column in our data set which we creat the sections from
track.index = track.index,
x = data$ncat, #set this as ncat as ncat dictates the location of the variable you want to plot within each section and within the circle as a whole
y = data[[lines_column]], #variable you want to plot
ylim = c(axis_min, axis_max), #co-ordinates of the Y axis of the track
track.height = track2_height, #how big is the track as % of circle
#Set sector background
bg.border = NA,
bg.col = NA,
#Map values
panel.fun = function(x, y) { #this sets x and y as the above defined variables for the following lines of code
circlize::circos.lines(x = x,
y = y,
col = lines_col2,
lwd = lines_lwd,
lty = lines_lty,
type = lines_type)
# # plot '0' reference line
# circlize::circos.lines(x = x,
# y = y * 0 + track_axis_reference,
# col = reference_line_colour, #set the 0 line colour to something distinctive
# lwd = reference_line_thickness, #set the thickness of the line so it's a bit smaller than your point (looks better)
# lty = reference_line_type)
})
## 4. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
## barplot ====
if(track_number >= 2 && track2_type == "bar"){
data <- track1_data
track.index <- 3
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
axis_min <- round(min(data[[bar_column]]), 3)
axis_max <- round(max(data[[bar_column]]), 3)
## 3. create the track and plot the confidence intervals
circlize::circos.trackPlotRegion(factors = data$section_numbers, #we plot the first region based on the column in our data set which we creat the sections from
track.index = track.index,
x = data$ncat, #set this as ncat as ncat dictates the location of the variable you want to plot within each section and within the circle as a whole
y = data[[bar_column]], #variable you want to plot
ylim = c(axis_min, axis_max), #co-ordinates of the Y axis of the track
track.height = track2_height, #how big is the track as % of circle
#Set sector background
bg.border = NA,
bg.col = NA,
#Map values
panel.fun = function(x, y) { #this sets x and y as the above defined variables for the following lines of code
circlize::circos.lines(x = x,
y = y,
col = lines_col2,
lwd = 1,
lty = lines_lty,
type = "s",
area = T,
border = "White",
baseline = "bottom")
# # plot '0' reference line
# circlize::circos.lines(x = x,
# y = y * 0 + track_axis_reference,
# col = reference_line_colour, #set the 0 line colour to something distinctive
# lwd = reference_line_thickness, #set the thickness of the line so it's a bit smaller than your point (looks better)
# lty = reference_line_type)
})
## 4. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
## histogram ====
if(track_number >= 2 && track2_type == "histogram"){
data <- track2_data
track.index <- 3
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
axis_min <- round(min(data[[histogram_column]]), 3)
axis_min <- round(axis_min + (axis_min * 0.1), 3)
axis_min_half <- round(axis_min/2, 3)
axis_max <- round(max(data[[histogram_column]]), 3)
axis_max <- round(axis_max + (axis_max * 0.1), 3)
axis_max_half <- round(axis_max/2, 3)
## 3. create the track and plot the confidence intervals
circlize::circos.trackHist(factors = data$section_numbers,
x = data[[histogram_column]],
track.height = track2_height,
track.index = NULL,
col = lines_col2,
border = lines_col2,
bg.border = NA,
draw.density = histogram_densityplot,
bin.size = histogram_binsize)
## 4. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
# track 3 ====
## points ====
if(track_number >= 3 && track3_type == "points"){
data <- track3_data
track.index <- 4
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
a <- min(data[[lower_ci]])
b <- min(data[[upper_ci]])
axis_min <- min(a,b)
axis_min <- round(axis_min, 3)
axis_min <- round(axis_min + (axis_min * 0.1), 3)
axis_min_half <- round(axis_min/2, 3)
a <- max(data[[lower_ci]])
b <- max(data[[upper_ci]])
axis_max <- max(a,b)
axis_max <- round(axis_max, 3)
axis_max <- round(axis_max + (axis_max * 0.01), 3)
axis_max_half <- round(axis_max/2, 3)
## 3. create the track and plot the confidence intervals
for(i in 1:nlevels(data$section_numbers)){
data1 = subset(data, section_numbers == i)
circlize::circos.trackPlotRegion(factors = data1$section_numbers, #we plot the first region based on the column in our data set which we creat the sections from
track.index = track.index,
x = data1$ncat, #set this as ncat as ncat dictates the location of the variable you want to plot within each section and within the circle as a whole
y = data1[[estimate_column]], #variable you want to plot
ylim = c(axis_min, axis_max), #co-ordinates of the Y axis of the track
track.height = track3_height, #how big is the track as % of circle
#Set sector background
bg.border = NA,
bg.col = NA,
#Map values
panel.fun = function(x, y) { #this sets x and y as the above defined variables for the following lines of code
# plot '0' reference line
circlize::circos.lines(x = x,
y = y * 0 + track_axis_reference,
col = reference_line_colour, #set the 0 line colour to something distinctive
lwd = reference_line_thickness, #set the thickness of the line so it's a bit smaller than your point (looks better)
lty = reference_line_type)
# confidence interval
circlize::circos.segments(x0 = data1$ncat, # x coordinates for starting point
x1 = data1$ncat, # x coordinates for end point
y0 = data1[[estimate_column]] * 0 - -(data1[[lower_ci]]), # y coordinates for start point
y1 = data1[[estimate_column]] * 0 + data1[[upper_ci]], # y coordinates for end point
col = ci_col3,
lwd = ci_lwd,
lty = ci_lty,
sector.index = i)})}
## 4. layer on top of the confidence intervals the effect estimates
### a. points not reaching signfiicance
circlize::circos.trackPoints(factors = subset(data, data[[pvalue_column]] > pvalue_adjustment)$section_numbers,
track.index = track.index,
x = subset(data, data[[pvalue_column]] > pvalue_adjustment)$ncat,
y = subset(data, data[[pvalue_column]] > pvalue_adjustment)[[estimate_column]],
cex = point_cex,
pch = point_pch,
col = point_col3,
bg = point_bg3)
### b. points reaching significance
circlize::circos.trackPoints(factors = subset(data, data[[pvalue_column]] < pvalue_adjustment)$section_numbers,
track.index = track.index,
x = subset(data, data[[pvalue_column]] < pvalue_adjustment)$ncat,
y = subset(data, data[[pvalue_column]] < pvalue_adjustment)[[estimate_column]],
cex = point_cex,
pch = point_pch,
col = point_col3_sig,
bg = point_bg3_sig)
## 5. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
## lines ====
if(track_number >= 3 && track3_type == "lines"){
data <- track3_data
track.index <- 4
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
axis_min <- round(min(data[[lines_column]]), 3)
axis_min <- round(axis_min + (axis_min * 0.1), 3)
axis_min_half <- round(axis_min/2, 3)
axis_max <- round(max(data[[lines_column]]), 3)
axis_max <- round(axis_max + (axis_max * 0.1), 3)
axis_max_half <- round(axis_max/2, 3)
## 3. create the track and plot the confidence intervals
circlize::circos.trackPlotRegion(factors = data$section_numbers, #we plot the first region based on the column in our data set which we creat the sections from
track.index = track.index,
x = data$ncat, #set this as ncat as ncat dictates the location of the variable you want to plot within each section and within the circle as a whole
y = data[[lines_column]], #variable you want to plot
ylim = c(axis_min, axis_max), #co-ordinates of the Y axis of the track
track.height = track3_height, #how big is the track as % of circle
#Set sector background
bg.border = NA,
bg.col = NA,
#Map values
panel.fun = function(x, y) { #this sets x and y as the above defined variables for the following lines of code
circlize::circos.lines(x = x,
y = y,
col = lines_col3,
lwd = lines_lwd,
lty = lines_lty,
type = lines_type)
# # plot '0' reference line
# circlize::circos.lines(x = x,
# y = y * 0 + track_axis_reference,
# col = reference_line_colour, #set the 0 line colour to something distinctive
# lwd = reference_line_thickness, #set the thickness of the line so it's a bit smaller than your point (looks better)
# lty = reference_line_type)
})
## 4. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
## barplot ====
if(track_number >= 3 && track3_type == "bar"){
data <- track1_data
track.index <- 4
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
axis_min <- round(min(data[[bar_column]]), 3)
axis_max <- round(max(data[[bar_column]]), 3)
## 3. create the track and plot the confidence intervals
circlize::circos.trackPlotRegion(factors = data$section_numbers, #we plot the first region based on the column in our data set which we creat the sections from
track.index = track.index,
x = data$ncat, #set this as ncat as ncat dictates the location of the variable you want to plot within each section and within the circle as a whole
y = data[[bar_column]], #variable you want to plot
ylim = c(axis_min, axis_max), #co-ordinates of the Y axis of the track
track.height = track3_height, #how big is the track as % of circle
#Set sector background
bg.border = NA,
bg.col = NA,
#Map values
panel.fun = function(x, y) { #this sets x and y as the above defined variables for the following lines of code
circlize::circos.lines(x = x,
y = y,
col = lines_col3,
lwd = 1,
lty = lines_lty,
type = "s",
area = T,
border = "White",
baseline = "bottom")
# # plot '0' reference line
# circlize::circos.lines(x = x,
# y = y * 0 + track_axis_reference,
# col = reference_line_colour, #set the 0 line colour to something distinctive
# lwd = reference_line_thickness, #set the thickness of the line so it's a bit smaller than your point (looks better)
# lty = reference_line_type)
})
## 4. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
## histogram ====
if(track_number >= 3 && track3_type == "histogram"){
data <- track3_data
track.index <- 4
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
axis_min <- round(min(data[[histogram_column]]), 3)
axis_min <- round(axis_min + (axis_min * 0.1), 3)
axis_min_half <- round(axis_min/2, 3)
axis_max <- round(max(data[[histogram_column]]), 3)
axis_max <- round(axis_max + (axis_max * 0.1), 3)
axis_max_half <- round(axis_max/2, 3)
## 3. create the track and plot the confidence intervals
circlize::circos.trackHist(factors = data$section_numbers,
x = data[[histogram_column]],
track.height = track3_height,
track.index = NULL,
col = lines_col3,
border = lines_col3,
bg.border = NA,
draw.density = histogram_densityplot,
bin.size = histogram_binsize)
## 4. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
# this is the end of the plotting aspect of the script ====
# Legend ====
## 1. Assign the legend points
if(legend == TRUE && track_number == 1){
legend1 <- ComplexHeatmap::Legend(at = c(track1_label),
labels_gp = grid::gpar(fontsize = 15),
ncol = 1,
border = NA, # color of legend borders, also for the ticks in the continuous legend
background = NA, # background colors
legend_gp = grid::gpar(col = c(discrete_palette[1])), # graphic parameters for the legend
type = "points", # type of legends, can be grid, points and lines
pch = 19, # type of points
size = grid::unit(15, "mm"), # size of points
grid_height = grid::unit(15, "mm"),
grid_width = grid::unit(15, "mm"),
direction = "vertical")}
## 6.A2 - Assign the legend points
if(legend == TRUE && track_number >= 2){
legend1 <- ComplexHeatmap::Legend(at = c(track1_label, track2_label),
labels_gp = grid::gpar(fontsize = 15),
ncol = 1,
border = NA, # color of legend borders, also for the ticks in the continuous legend
background = NA, # background colors
legend_gp = grid::gpar(col = c(discrete_palette[1], discrete_palette[2])), # graphic parameters for the legend
type = "points", # type of legends, can be grid, points and lines
pch = 19, # type of points
size = grid::unit(15, "mm"), # size of points
grid_height = grid::unit(15, "mm"),
grid_width = grid::unit(15, "mm"),
direction = "vertical")}
## 6.A3 - Assign the legend points
if(legend == TRUE && track_number >= 3){
legend1 <- ComplexHeatmap::Legend(at = c(track1_label, track2_label, track3_label),
labels_gp = grid::gpar(fontsize = 15),
ncol = 1,
border = NA, # color of legend borders, also for the ticks in the continuous legend
background = NA, # background colors
legend_gp = grid::gpar(col = c(discrete_palette[1], discrete_palette[2], discrete_palette[3])), # graphic parameters for the legend
type = "points", # type of legends, can be grid, points and lines
pch = 19, # type of points
size = grid::unit(15, "mm"), # size of points
grid_height = grid::unit(15, "mm"),
grid_width = grid::unit(15, "mm"),
direction = "vertical")}
## 6.B - Assign Pvalue legend point
if(legend == TRUE){
legend2 <- ComplexHeatmap::Legend(at = pvalue_label, # breaks, can be wither numeric or character
labels_gp = grid::gpar(fontsize = 15),
ncol = 1,
border = NA, # color of legend borders, also for the ticks in the continuous legend
background = NA, # background colors
legend_gp = grid::gpar(col = c("black")), # graphic parameters for the legend
type = "points", # type of legends, can be grid, points and lines
pch = 1, # type of points
size = grid::unit(15, "mm"), # size of points
grid_height = grid::unit(15, "mm"),
grid_width = grid::unit(15, "mm"),
direction = "vertical")
## 6.C - Assign legend section labelling
names <- levels(as.factor(data[[section_column]]))
names <- paste(1:nlevels(data[[section_column]]), names, sep=". ")
legend3 <- ComplexHeatmap::Legend(at = names,
labels_gp = grid::gpar(fontsize = 15),
nrow = 4,
ncol = 7,
border = NA, # color of legend borders, also for the ticks in the continuous legend
background = NA, # background colors
legend_gp = grid::gpar(col = c("black")), # graphic parameters for the legend
size = grid::unit(15, "mm"), # size of points
grid_height = grid::unit(15, "mm"),
grid_width = grid::unit(10, "mm"),
direction = "horizontal")
## 6.D - Pack lagend together
legend4 <- ComplexHeatmap::packLegend(legend1, legend2, direction = "vertical", gap = grid::unit(0, "mm"))
legend <- ComplexHeatmap::packLegend(legend4, legend3, direction = "horizontal", gap = grid::unit(0, "mm"))
legend_height <- legend@grob[["vp"]][["height"]]
legend_width <- legend@grob[["vp"]][["width"]]
# ## 6.E - Layer legend ontop of plot
grid::pushViewport(grid::viewport(x = grid::unit(0.5, "npc"),
y = grid::unit(0.08, "npc"),
width = legend_width,
height = legend_height,
just = c("center", "top")))
grid::grid.draw(legend)
grid::upViewport()}
}
mattlee821/EpiCircos documentation built on Jan. 6, 2020, 7:13 a.m.
R Package Documentation
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Defines functions circos_plot
Documented in circos_plot
# source function ====
#' Create a Circos plot with up to 3 tracks
#'
#' @param track_number The number of tracks you wnat your circos plot to have. Maximum number of tracks is 3.
#' @param track1_data The data frame of your first track.
#' @param track2_data The data frame of your second track.
#' @param track3_data The data frame of your third track.
#' @param track1_type The type of plot for the first track. One of "scatter", "lines", "bar" and "histogram".
#' @param track2_type The type of plot for the second track. One of "scatter", "lines", "bar" and "histogram".
#' @param track3_type The type of plot for the third track. One of "scatter", "lines", "bar" and "histogram".
#' @param label_column The column in your data frames that you will use as the labels for the circos plot. This will likely be the column with your exposure/outcome name in. Labels go on the outside of the plot.
#' @param section_column The column in your data frames that you will use to group/section your data/plot by.
#' @param order Do you want MR Viz to organise your columns alphabetically. Default is TRUE
#' @param order_column If order = FALSE what column do you want to order your sections by
#' @param estimate_column The column in your data frames with the estimates in.
#' @param pvalue_column The column in your data frames with the p-values in.
#' @param pvalue_adjustment The threshold you want to set your p-value 'significance' threshold too. This can be in the format of a single value or a function e.g. 0.05/22. The p-value thrshold is only used for scatter plots.
#' @param lower_ci The column in your data frames with the lower confidence intervals in.
#' @param upper_ci The column in your data frames with the upper confidence intervals in.
#' @param lines_column The column in your data frames that you want to plot as lines.
#' @param lines_type The type of line plot you want. "l" = straight lines; "o" = straight lines with points; "s" = boxed lines; "h" = vertical lines. Default is "o".
#' @param bar_column The column in your data frames that you want to plot as bars.
#' @param histogram_column The column in your data frames that you want to plot as histograms.
#' @param histogram_binsize The binsize of the histogram. Default is 0.01.
#' @param histogram_densityplot Do you want your histogram to be a density plot or not. Default is FALSE.
#' @param legend Do you want a legend = TRUE or FALSE. FALSE by default
#' @param track1_label What do you want the label for the first track to be e.g. "Body Mass Index"
#' @param track2_label What do you want the label for the first track to be e.g. "Corondary Heart Disease"
#' @param track3_label What do you want the label for the first track to be e.g. "Breast Cancer"
#' @param pvalue_label What do you want the label for the p-value to read e.g. "p-value <= 0.05"
#' @param circle_size The size of the circos plot. Smaller numbers make larger circos plots. Default is 25. You will need to adjust this number a few times to get the perfect size for your specific circos plot requirements. If you have long labels then try ≥ 25. If your labels are short try 1-10.
#' @param track1_height Size of the track as a percent of the whole circle. Default is 20 percent (0.20)
#' @param track2_height Size of the track as a percent of the whole circle. Default is 20 percent (0.20)
#' @param track3_height Size of the track as a percent of the whole circle. Default is 20 percent (0.20). It is sometimes worth increasing the size of track 3 to 30 percent
#'
#' @export
#'
#' @examples circos_plot(track_number = 3,
#' track1_data = EpiCircos_data,
#' track2_data = EpiCircos_data,
#' track3_data = EpiCircos_data,
#' track1_type = "points",
#' track2_type = "lines",
#' track3_type = "bar",
#' label_column = 1,
#' section_column = 2,
#' estimate_column = 4,
#' pvalue_column = 5,
#' pvalue_adjustment = 0.05,
#' lower_ci = 7,
#' upper_ci = 8,
#' lines_column = 4,
#' lines_type = "o",
#' bar_column = 4,
#' histogram_column = 4,
#' histogram_binsize = 0.01,
#' histogram_densityplot = F,
#' legend = FALSE,
#' circle_size = 25)
#'
circos_plot <- function(track_number,
track1_data,
track2_data,
track3_data,
track1_type,
track2_type,
track3_type,
label_column,
section_column,
order = TRUE,
order_column,
estimate_column,
pvalue_column,
pvalue_adjustment,
lower_ci,
upper_ci,
lines_column,
lines_type = "o",
bar_column,
histogram_column,
histogram_binsize = 0.01,
histogram_densityplot = FALSE,
legend = FALSE,
track1_label = NA,
track2_label = NA,
track3_label = NA,
pvalue_label = NA,
circle_size = 25,
track1_height = 0.20,
track2_height = 0.20,
track3_height = 0.20){
# Default plot paramaters ====
track1 <- 1 # track 1 is your section header
track2 <- 2 # you start plotting your data on track 2
track3 <- 3
track4 <- 4
x_axis_index <- 1
track_axis_reference <- 0
margins <- c(0.5, 0.5, 0.5, 0.5) * 25 # A numerical vector of the form c(bottom, left, top, right) which gives the number of lines of margin to be specified on the four sides of the plot
start_gap <- 17 # indicates gap at start for Y axis scale, this is a percentage so the larger the number the larger the empty gap
start_degree <- 90 # starting point of the cirlce in degrees (90 is top)
section_track_height <- 0.10 # size of secton header track as percent of whole circle
# Customisable paramaters ====
## Colours
discrete_palette <- c("#00378f", # track 1 colour
"#ffc067", # track 2 colour
"#894300") # track 3 colour
## section header specifics
section_fill_colour <- "snow2"
section_text_colour <- "black"
section_line_colour <- "grey"
section_line_thickness <- 1.5
section_line_type <- 1
## reference lines that go around the tracks
reference_line_colour <- "deeppink"
reference_line_thickness <- 1.5
reference_line_type <- 1
## point specifics
point_pch <- 21
point_cex <- 1.5
point_col1 <- discrete_palette[1]
point_bg1 <- "white"
point_col1_sig <- "white"
point_bg1_sig <- discrete_palette[1]
point_col2 <- discrete_palette[2]
point_bg2 <- "white"
point_col2_sig <- "white"
point_bg2_sig <- discrete_palette[2]
point_col3 <- discrete_palette[3]
point_bg3 <- "white"
point_col3_sig <- "white"
point_bg3_sig <- discrete_palette[3]
## confidence intervals
ci_lwd <- 5
ci_lty <- 1
ci_col1 <- discrete_palette[1]
ci_col2 <- discrete_palette[2]
ci_col3 <- discrete_palette[3]
## lines specifics
lines_col1 <- discrete_palette[1]
lines_col2 <- discrete_palette[2]
lines_col3 <- discrete_palette[3]
lines_lwd <- 3
lines_lty <- 1
## y axis specifics
y_axis_location <- "left"
y_axis_tick <- FALSE
y_axis_tick_length <- 0
y_axis_label_cex <- 0.75
## label specifics
label_distance <- 1.5 # distance from track 0 to plot labels
label_col <- "black"
label_cex <- 0.6
# plot set-up ====
## the plot region is set-up based on track1_data
data <- track1_data
## 1. organise the data based on section and alphabetically within that based on label
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
## 2. add column of positions of where each data point will be in th track - this is position of values within the track and should be 1:n depedning on number of variables in each section and is based also on individual sections. this will give a number 1-n for each individual metabolite in each section as a position within the section for plotting values.
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN = seq_along))
## 3. set parameter - this sets the paramater of the sections of the circos plot so that you can plot within individual sections
npercat <- as.vector(table(data[[section_column]]))
## 4. Standardise data$x so that the axis is from 0-1 - this provides for each section an individual x axis
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
## 5. add column that codes sections as numbers - this will be the label for section headings. This can then be translated in the legend or figure title
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
## 6. set gap for axis - spacing between sections. It is 1-n where n is 1 minus the total number of sections - last number indicates gap at start for Y axis scale, this is a percentage so the larger the number the larger the empty gap
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 7. clear plotting area
circlize::circos.clear()
## 8. initiate blank page to plot on top of
graphics::par(mar = c(0.6, 0.5, 0.5, 0.5) * circle_size,
cex = 0.8,
xpd = NA) # A logical value or NA. If FALSE, all plotting is clipped to the plot region, if TRUE, all plotting is clipped to the figure region, and if NA, all plotting is clipped to the device region. See also clip.
## 9. creat circle paramaters
circlize::circos.par(cell.padding = c(0, 0.5, 0, 0.5),
start.degree = start_degree, # starting point of the circle
gap.degree = gap, # gap between two neighbour sections
track.margin = c(0.012, 0.012), #blank area outside of the plotting area
points.overflow.warning = FALSE, #this dictates whether warnings will pop up if plots are plotted outside of the plotting region (this is to do with teh plotting region not being circular and instead being rectangular) - keep this as FALSE
track.height = section_track_height, #height of the section track as percent of whole circle
clock.wise = TRUE) #direction to add sections
## 10. initiate circle
circlize::circos.initialize(factors = data$section_numbers,
xlim = c(0, 1),
sector.width = npercat)
## 11. create and plot section headers
circlize::circos.trackPlotRegion(factors = data$section_numbers, #we plot the first region based on the column in our data set which we creat the sections from
track.index = track1, #the track you are plotting
x = data$ncat, #the x axis is dictated by the number of values in our data set
ylim = c(0, 1), #the y axis is set based on the track width you want as it will impact the size of text you can have in this track
track.height = 0.05, #size of track as % of whole circle
panel.fun = function(x, y) {
chr = circlize::get.cell.meta.data("sector.index") #dont change as this gathers all of the info you need automatically
xlim = circlize::get.cell.meta.data("xlim") #dont change as this gathers all of the info you need automatically
ylim = circlize::get.cell.meta.data("ylim") #dont change as this gathers all of the info you need automatically
circlize::circos.rect(xlim[1], 0, xlim[2], 1, # n (+ and -) length of track away from centre (low number means smaller) - want it large enough to encompass text
border = NA, #give the track a border
col = section_fill_colour) #colour of track
circlize::circos.text(mean(xlim), #text location on x axis
mean(ylim), #text location on y axis
chr, #colour of text, default blue
cex = 1, #text size
facing = "outside", #diretion of text
niceFacing = TRUE, #flip so text is readable
col = section_text_colour)
},
bg.border = NA) #background border colour
## 12. add labels
circlize::circos.trackText(factors = data$section_numbers,
track.index = track1, #choose labels based on the track we have just made as you can only plot text once a track has been created
x = data$ncat, #location on the x axis where we will plot the lables
y = data$b * 0 + label_distance, #dictates where the labels are plotted - * 0 to give 0 and then choose how far away from the 0 we want to plot the text (this will be trial and error before you get what works best for your data set)
labels = data[[label_column]], #where you are taking the names for the labels from
facing = "reverse.clockwise",
niceFacing = TRUE, #flip the text so it is readable
adj = c(1, 1),
col = label_col,
cex = label_cex) #size of the text
# track 1 ====
## points ====
if(track_number >= 1 && track1_type == "points"){
data <- track1_data
track.index <- 2
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
a <- min(data[[lower_ci]])
b <- min(data[[upper_ci]])
axis_min <- min(a,b)
axis_min <- round(axis_min, 3)
axis_min <- round(axis_min + (axis_min * 0.1), 3)
axis_min_half <- round(axis_min/2, 3)
a <- max(data[[lower_ci]])
b <- max(data[[upper_ci]])
axis_max <- max(a,b)
axis_max <- round(axis_max, 3)
axis_max <- round(axis_max + (axis_max * 0.01), 3)
axis_max_half <- round(axis_max/2, 3)
## 3. create the track and plot the confidence intervals
for(i in 1:nlevels(data$section_numbers)){
data1 = subset(data, section_numbers == i)
circlize::circos.trackPlotRegion(factors = data1$section_numbers, #we plot the first region based on the column in our data set which we creat the sections from
track.index = track.index,
x = data1$ncat, #set this as ncat as ncat dictates the location of the variable you want to plot within each section and within the circle as a whole
y = data1[[estimate_column]], #variable you want to plot
ylim = c(axis_min, axis_max), #co-ordinates of the Y axis of the track
track.height = track1_height, #how big is the track as % of circle
#Set sector background
bg.border = NA,
bg.col = NA,
#Map values
panel.fun = function(x, y) { #this sets x and y as the above defined variables for the following lines of code
# plot '0' reference line
circlize::circos.lines(x = x,
y = y * 0 + track_axis_reference,
col = reference_line_colour, #set the 0 line colour to something distinctive
lwd = reference_line_thickness, #set the thickness of the line so it's a bit smaller than your point (looks better)
lty = reference_line_type)
# confidence interval
circlize::circos.segments(x0 = data1$ncat, # x coordinates for starting point
x1 = data1$ncat, # x coordinates for end point
y0 = data1[[estimate_column]] * 0 - -(data1[[lower_ci]]), # y coordinates for start point
y1 = data1[[estimate_column]] * 0 + data1[[upper_ci]], # y coordinates for end point
col = ci_col1,
lwd = ci_lwd,
lty = ci_lty,
sector.index = i)})}
## 4. layer on top of the confidence intervals the effect estimates
### a. points not reaching signfiicance
circlize::circos.trackPoints(factors = subset(data, data[[pvalue_column]] > pvalue_adjustment)$section_numbers,
track.index = track.index,
x = subset(data, data[[pvalue_column]] > pvalue_adjustment)$ncat,
y = subset(data, data[[pvalue_column]] > pvalue_adjustment)[[estimate_column]],
cex = point_cex,
pch = point_pch,
col = point_col1,
bg = point_bg1)
### b. points reaching significance
circlize::circos.trackPoints(factors = subset(data, data[[pvalue_column]] < pvalue_adjustment)$section_numbers,
track.index = track.index,
x = subset(data, data[[pvalue_column]] < pvalue_adjustment)$ncat,
y = subset(data, data[[pvalue_column]] < pvalue_adjustment)[[estimate_column]],
cex = point_cex,
pch = point_pch,
col = point_col1_sig,
bg = point_bg1_sig)
## 5. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
## lines ====
if(track_number >= 1 && track1_type == "lines"){
data <- track1_data
track.index <- 2
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
axis_min <- round(min(data[[lines_column]]), 3)
axis_min <- round(axis_min + (axis_min * 0.1), 3)
axis_min_half <- round(axis_min/2, 3)
axis_max <- round(max(data[[lines_column]]), 3)
axis_max <- round(axis_max + (axis_max * 0.1), 3)
axis_max_half <- round(axis_max/2, 3)
## 3. create the track and plot the confidence intervals
circlize::circos.trackPlotRegion(factors = data$section_numbers, #we plot the first region based on the column in our data set which we creat the sections from
track.index = track.index,
x = data$ncat, #set this as ncat as ncat dictates the location of the variable you want to plot within each section and within the circle as a whole
y = data[[lines_column]], #variable you want to plot
ylim = c(axis_min, axis_max), #co-ordinates of the Y axis of the track
track.height = track1_height, #how big is the track as % of circle
#Set sector background
bg.border = NA,
bg.col = NA,
#Map values
panel.fun = function(x, y) { #this sets x and y as the above defined variables for the following lines of code
circlize::circos.lines(x = x,
y = y,
col = lines_col1,
lwd = lines_lwd,
lty = lines_lty,
type = lines_type)
# plot '0' reference line
# circlize::circos.lines(x = x,
# y = y * 0 + track_axis_reference,
# col = reference_line_colour, #set the 0 line colour to something distinctive
# lwd = reference_line_thickness, #set the thickness of the line so it's a bit smaller than your point (looks better)
# lty = reference_line_type)
})
## 4. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
## barplot ====
if(track_number >= 1 && track1_type == "bar"){
data <- track1_data
track.index <- 2
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
axis_min <- round(min(data[[bar_column]]), 3)
axis_max <- round(max(data[[bar_column]]), 3)
## 3. create the track and plot the confidence intervals
circlize::circos.trackPlotRegion(factors = data$section_numbers, #we plot the first region based on the column in our data set which we creat the sections from
track.index = track.index,
x = data$ncat, #set this as ncat as ncat dictates the location of the variable you want to plot within each section and within the circle as a whole
y = data[[bar_column]], #variable you want to plot
ylim = c(axis_min, axis_max), #co-ordinates of the Y axis of the track
track.height = track1_height, #how big is the track as % of circle
#Set sector background
bg.border = NA,
bg.col = NA,
#Map values
panel.fun = function(x, y) { #this sets x and y as the above defined variables for the following lines of code
circlize::circos.lines(x = x,
y = y,
col = lines_col1,
lwd = 1,
lty = lines_lty,
type = "s",
area = T,
border = "White",
baseline = "bottom")
# # plot '0' reference line
# circlize::circos.lines(x = x,
# y = y * 0 + track_axis_reference,
# col = reference_line_colour, #set the 0 line colour to something distinctive
# lwd = reference_line_thickness, #set the thickness of the line so it's a bit smaller than your point (looks better)
# lty = reference_line_type)
})
## 4. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
## histogram ====
if(track_number >= 1 && track1_type == "histogram"){
data <- track1_data
track.index <- 2
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
axis_min <- round(min(data[[histogram_column]]), 3)
axis_min <- round(axis_min + (axis_min * 0.1), 3)
axis_min_half <- round(axis_min/2, 3)
axis_max <- round(max(data[[histogram_column]]), 3)
axis_max <- round(axis_max + (axis_max * 0.1), 3)
axis_max_half <- round(axis_max/2, 3)
## 3. create the track and plot the confidence intervals
circlize::circos.trackHist(factors = data$section_numbers,
x = data[[histogram_column]],
track.height = track1_height,
track.index = NULL,
col = lines_col1,
border = lines_col1,
bg.border = NA,
draw.density = histogram_densityplot,
bin.size = histogram_binsize)
## 4. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
# track 2 ====
## points ====
if(track_number >= 2 && track2_type == "points"){
data <- track2_data
track.index <- 3
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
a <- min(data[[lower_ci]])
b <- min(data[[upper_ci]])
axis_min <- min(a,b)
axis_min <- round(axis_min, 3)
axis_min <- round(axis_min + (axis_min * 0.1), 3)
axis_min_half <- round(axis_min/2, 3)
a <- max(data[[lower_ci]])
b <- max(data[[upper_ci]])
axis_max <- max(a,b)
axis_max <- round(axis_max, 3)
axis_max <- round(axis_max + (axis_max * 0.01), 3)
axis_max_half <- round(axis_max/2, 3)
## 3. create the track and plot the confidence intervals
for(i in 1:nlevels(data$section_numbers)){
data1 = subset(data, section_numbers == i)
circlize::circos.trackPlotRegion(factors = data1$section_numbers, #we plot the first region based on the column in our data set which we creat the sections from
track.index = track.index,
x = data1$ncat, #set this as ncat as ncat dictates the location of the variable you want to plot within each section and within the circle as a whole
y = data1[[estimate_column]], #variable you want to plot
ylim = c(axis_min, axis_max), #co-ordinates of the Y axis of the track
track.height = track2_height, #how big is the track as % of circle
#Set sector background
bg.border = NA,
bg.col = NA,
#Map values
panel.fun = function(x, y) { #this sets x and y as the above defined variables for the following lines of code
# plot '0' reference line
circlize::circos.lines(x = x,
y = y * 0 + track_axis_reference,
col = reference_line_colour, #set the 0 line colour to something distinctive
lwd = reference_line_thickness, #set the thickness of the line so it's a bit smaller than your point (looks better)
lty = reference_line_type)
# confidence interval
circlize::circos.segments(x0 = data1$ncat, # x coordinates for starting point
x1 = data1$ncat, # x coordinates for end point
y0 = data1[[estimate_column]] * 0 - -(data1[[lower_ci]]), # y coordinates for start point
y1 = data1[[estimate_column]] * 0 + data1[[upper_ci]], # y coordinates for end point
col = ci_col2,
lwd = ci_lwd,
lty = ci_lty,
sector.index = i)})}
## 4. layer on top of the confidence intervals the effect estimates
### a. points not reaching signfiicance
circlize::circos.trackPoints(factors = subset(data, data[[pvalue_column]] > pvalue_adjustment)$section_numbers,
track.index = track.index,
x = subset(data, data[[pvalue_column]] > pvalue_adjustment)$ncat,
y = subset(data, data[[pvalue_column]] > pvalue_adjustment)[[estimate_column]],
cex = point_cex,
pch = point_pch,
col = point_col2,
bg = point_bg2)
### b. points reaching significance
circlize::circos.trackPoints(factors = subset(data, data[[pvalue_column]] < pvalue_adjustment)$section_numbers,
track.index = track.index,
x = subset(data, data[[pvalue_column]] < pvalue_adjustment)$ncat,
y = subset(data, data[[pvalue_column]] < pvalue_adjustment)[[estimate_column]],
cex = point_cex,
pch = point_pch,
col = point_col2_sig,
bg = point_bg2_sig)
## 5. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
## lines ====
if(track_number >= 2 && track2_type == "lines"){
data <- track2_data
track.index <- 3
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
axis_min <- round(min(data[[lines_column]]), 3)
axis_min <- round(axis_min + (axis_min * 0.1), 3)
axis_min_half <- round(axis_min/2, 3)
axis_max <- round(max(data[[lines_column]]), 3)
axis_max <- round(axis_max + (axis_max * 0.1), 3)
axis_max_half <- round(axis_max/2, 3)
## 3. create the track and plot the confidence intervals
circlize::circos.trackPlotRegion(factors = data$section_numbers, #we plot the first region based on the column in our data set which we creat the sections from
track.index = track.index,
x = data$ncat, #set this as ncat as ncat dictates the location of the variable you want to plot within each section and within the circle as a whole
y = data[[lines_column]], #variable you want to plot
ylim = c(axis_min, axis_max), #co-ordinates of the Y axis of the track
track.height = track2_height, #how big is the track as % of circle
#Set sector background
bg.border = NA,
bg.col = NA,
#Map values
panel.fun = function(x, y) { #this sets x and y as the above defined variables for the following lines of code
circlize::circos.lines(x = x,
y = y,
col = lines_col2,
lwd = lines_lwd,
lty = lines_lty,
type = lines_type)
# # plot '0' reference line
# circlize::circos.lines(x = x,
# y = y * 0 + track_axis_reference,
# col = reference_line_colour, #set the 0 line colour to something distinctive
# lwd = reference_line_thickness, #set the thickness of the line so it's a bit smaller than your point (looks better)
# lty = reference_line_type)
})
## 4. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
## barplot ====
if(track_number >= 2 && track2_type == "bar"){
data <- track1_data
track.index <- 3
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
axis_min <- round(min(data[[bar_column]]), 3)
axis_max <- round(max(data[[bar_column]]), 3)
## 3. create the track and plot the confidence intervals
circlize::circos.trackPlotRegion(factors = data$section_numbers, #we plot the first region based on the column in our data set which we creat the sections from
track.index = track.index,
x = data$ncat, #set this as ncat as ncat dictates the location of the variable you want to plot within each section and within the circle as a whole
y = data[[bar_column]], #variable you want to plot
ylim = c(axis_min, axis_max), #co-ordinates of the Y axis of the track
track.height = track2_height, #how big is the track as % of circle
#Set sector background
bg.border = NA,
bg.col = NA,
#Map values
panel.fun = function(x, y) { #this sets x and y as the above defined variables for the following lines of code
circlize::circos.lines(x = x,
y = y,
col = lines_col2,
lwd = 1,
lty = lines_lty,
type = "s",
area = T,
border = "White",
baseline = "bottom")
# # plot '0' reference line
# circlize::circos.lines(x = x,
# y = y * 0 + track_axis_reference,
# col = reference_line_colour, #set the 0 line colour to something distinctive
# lwd = reference_line_thickness, #set the thickness of the line so it's a bit smaller than your point (looks better)
# lty = reference_line_type)
})
## 4. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
## histogram ====
if(track_number >= 2 && track2_type == "histogram"){
data <- track2_data
track.index <- 3
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
axis_min <- round(min(data[[histogram_column]]), 3)
axis_min <- round(axis_min + (axis_min * 0.1), 3)
axis_min_half <- round(axis_min/2, 3)
axis_max <- round(max(data[[histogram_column]]), 3)
axis_max <- round(axis_max + (axis_max * 0.1), 3)
axis_max_half <- round(axis_max/2, 3)
## 3. create the track and plot the confidence intervals
circlize::circos.trackHist(factors = data$section_numbers,
x = data[[histogram_column]],
track.height = track2_height,
track.index = NULL,
col = lines_col2,
border = lines_col2,
bg.border = NA,
draw.density = histogram_densityplot,
bin.size = histogram_binsize)
## 4. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
# track 3 ====
## points ====
if(track_number >= 3 && track3_type == "points"){
data <- track3_data
track.index <- 4
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
a <- min(data[[lower_ci]])
b <- min(data[[upper_ci]])
axis_min <- min(a,b)
axis_min <- round(axis_min, 3)
axis_min <- round(axis_min + (axis_min * 0.1), 3)
axis_min_half <- round(axis_min/2, 3)
a <- max(data[[lower_ci]])
b <- max(data[[upper_ci]])
axis_max <- max(a,b)
axis_max <- round(axis_max, 3)
axis_max <- round(axis_max + (axis_max * 0.01), 3)
axis_max_half <- round(axis_max/2, 3)
## 3. create the track and plot the confidence intervals
for(i in 1:nlevels(data$section_numbers)){
data1 = subset(data, section_numbers == i)
circlize::circos.trackPlotRegion(factors = data1$section_numbers, #we plot the first region based on the column in our data set which we creat the sections from
track.index = track.index,
x = data1$ncat, #set this as ncat as ncat dictates the location of the variable you want to plot within each section and within the circle as a whole
y = data1[[estimate_column]], #variable you want to plot
ylim = c(axis_min, axis_max), #co-ordinates of the Y axis of the track
track.height = track3_height, #how big is the track as % of circle
#Set sector background
bg.border = NA,
bg.col = NA,
#Map values
panel.fun = function(x, y) { #this sets x and y as the above defined variables for the following lines of code
# plot '0' reference line
circlize::circos.lines(x = x,
y = y * 0 + track_axis_reference,
col = reference_line_colour, #set the 0 line colour to something distinctive
lwd = reference_line_thickness, #set the thickness of the line so it's a bit smaller than your point (looks better)
lty = reference_line_type)
# confidence interval
circlize::circos.segments(x0 = data1$ncat, # x coordinates for starting point
x1 = data1$ncat, # x coordinates for end point
y0 = data1[[estimate_column]] * 0 - -(data1[[lower_ci]]), # y coordinates for start point
y1 = data1[[estimate_column]] * 0 + data1[[upper_ci]], # y coordinates for end point
col = ci_col3,
lwd = ci_lwd,
lty = ci_lty,
sector.index = i)})}
## 4. layer on top of the confidence intervals the effect estimates
### a. points not reaching signfiicance
circlize::circos.trackPoints(factors = subset(data, data[[pvalue_column]] > pvalue_adjustment)$section_numbers,
track.index = track.index,
x = subset(data, data[[pvalue_column]] > pvalue_adjustment)$ncat,
y = subset(data, data[[pvalue_column]] > pvalue_adjustment)[[estimate_column]],
cex = point_cex,
pch = point_pch,
col = point_col3,
bg = point_bg3)
### b. points reaching significance
circlize::circos.trackPoints(factors = subset(data, data[[pvalue_column]] < pvalue_adjustment)$section_numbers,
track.index = track.index,
x = subset(data, data[[pvalue_column]] < pvalue_adjustment)$ncat,
y = subset(data, data[[pvalue_column]] < pvalue_adjustment)[[estimate_column]],
cex = point_cex,
pch = point_pch,
col = point_col3_sig,
bg = point_bg3_sig)
## 5. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
## lines ====
if(track_number >= 3 && track3_type == "lines"){
data <- track3_data
track.index <- 4
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
axis_min <- round(min(data[[lines_column]]), 3)
axis_min <- round(axis_min + (axis_min * 0.1), 3)
axis_min_half <- round(axis_min/2, 3)
axis_max <- round(max(data[[lines_column]]), 3)
axis_max <- round(axis_max + (axis_max * 0.1), 3)
axis_max_half <- round(axis_max/2, 3)
## 3. create the track and plot the confidence intervals
circlize::circos.trackPlotRegion(factors = data$section_numbers, #we plot the first region based on the column in our data set which we creat the sections from
track.index = track.index,
x = data$ncat, #set this as ncat as ncat dictates the location of the variable you want to plot within each section and within the circle as a whole
y = data[[lines_column]], #variable you want to plot
ylim = c(axis_min, axis_max), #co-ordinates of the Y axis of the track
track.height = track3_height, #how big is the track as % of circle
#Set sector background
bg.border = NA,
bg.col = NA,
#Map values
panel.fun = function(x, y) { #this sets x and y as the above defined variables for the following lines of code
circlize::circos.lines(x = x,
y = y,
col = lines_col3,
lwd = lines_lwd,
lty = lines_lty,
type = lines_type)
# # plot '0' reference line
# circlize::circos.lines(x = x,
# y = y * 0 + track_axis_reference,
# col = reference_line_colour, #set the 0 line colour to something distinctive
# lwd = reference_line_thickness, #set the thickness of the line so it's a bit smaller than your point (looks better)
# lty = reference_line_type)
})
## 4. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
## barplot ====
if(track_number >= 3 && track3_type == "bar"){
data <- track1_data
track.index <- 4
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
axis_min <- round(min(data[[bar_column]]), 3)
axis_max <- round(max(data[[bar_column]]), 3)
## 3. create the track and plot the confidence intervals
circlize::circos.trackPlotRegion(factors = data$section_numbers, #we plot the first region based on the column in our data set which we creat the sections from
track.index = track.index,
x = data$ncat, #set this as ncat as ncat dictates the location of the variable you want to plot within each section and within the circle as a whole
y = data[[bar_column]], #variable you want to plot
ylim = c(axis_min, axis_max), #co-ordinates of the Y axis of the track
track.height = track3_height, #how big is the track as % of circle
#Set sector background
bg.border = NA,
bg.col = NA,
#Map values
panel.fun = function(x, y) { #this sets x and y as the above defined variables for the following lines of code
circlize::circos.lines(x = x,
y = y,
col = lines_col3,
lwd = 1,
lty = lines_lty,
type = "s",
area = T,
border = "White",
baseline = "bottom")
# # plot '0' reference line
# circlize::circos.lines(x = x,
# y = y * 0 + track_axis_reference,
# col = reference_line_colour, #set the 0 line colour to something distinctive
# lwd = reference_line_thickness, #set the thickness of the line so it's a bit smaller than your point (looks better)
# lty = reference_line_type)
})
## 4. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
## histogram ====
if(track_number >= 3 && track3_type == "histogram"){
data <- track3_data
track.index <- 4
## 1. prepare data
if (order == TRUE){
data <- data[order(data[[section_column]], data[[label_column]]),]
}
else if(order == FALSE){
data[[section_column]] <- stats::reorder(data[[section_column]], data[[order_column]])
data <- data[order(data[[section_column]], data[[label_column]]),]
}
data$x <- with(data, ave(seq_along(data[[section_column]]), data[[section_column]], FUN=seq_along))
npercat <- as.vector(table(data[[section_column]]))
getaxis <- function(data) {
for (i in 1:nrow(data)) {
data$n[i]<-as.numeric(nrow(subset(data, data[[section_column]] == data[[section_column]][i])))
data$ncat[i]<- data$x[i]/data$n[i]
}
return(data)
}
data <- getaxis(data)
data$section_numbers = factor(data[[section_column]],
labels = 1:nlevels(data[[section_column]]))
gap = c(rep(1, nlevels(data[[section_column]])-1), start_gap)
## 2. set axis limits
axis_min <- round(min(data[[histogram_column]]), 3)
axis_min <- round(axis_min + (axis_min * 0.1), 3)
axis_min_half <- round(axis_min/2, 3)
axis_max <- round(max(data[[histogram_column]]), 3)
axis_max <- round(axis_max + (axis_max * 0.1), 3)
axis_max_half <- round(axis_max/2, 3)
## 3. create the track and plot the confidence intervals
circlize::circos.trackHist(factors = data$section_numbers,
x = data[[histogram_column]],
track.height = track3_height,
track.index = NULL,
col = lines_col3,
border = lines_col3,
bg.border = NA,
draw.density = histogram_densityplot,
bin.size = histogram_binsize)
## 4. add axis labels
circlize::circos.yaxis(side = y_axis_location,
sector.index = x_axis_index, #the sector this is plotted in
track.index = track.index,
at = c(axis_min, track_axis_reference, axis_max), #location on the y axis as well as the name of the label
tick = y_axis_tick, tick.length = y_axis_tick_length,
labels.cex = y_axis_label_cex)
}
# this is the end of the plotting aspect of the script ====
# Legend ====
## 1. Assign the legend points
if(legend == TRUE && track_number == 1){
legend1 <- ComplexHeatmap::Legend(at = c(track1_label),
labels_gp = grid::gpar(fontsize = 15),
ncol = 1,
border = NA, # color of legend borders, also for the ticks in the continuous legend
background = NA, # background colors
legend_gp = grid::gpar(col = c(discrete_palette[1])), # graphic parameters for the legend
type = "points", # type of legends, can be grid, points and lines
pch = 19, # type of points
size = grid::unit(15, "mm"), # size of points
grid_height = grid::unit(15, "mm"),
grid_width = grid::unit(15, "mm"),
direction = "vertical")}
## 6.A2 - Assign the legend points
if(legend == TRUE && track_number >= 2){
legend1 <- ComplexHeatmap::Legend(at = c(track1_label, track2_label),
labels_gp = grid::gpar(fontsize = 15),
ncol = 1,
border = NA, # color of legend borders, also for the ticks in the continuous legend
background = NA, # background colors
legend_gp = grid::gpar(col = c(discrete_palette[1], discrete_palette[2])), # graphic parameters for the legend
type = "points", # type of legends, can be grid, points and lines
pch = 19, # type of points
size = grid::unit(15, "mm"), # size of points
grid_height = grid::unit(15, "mm"),
grid_width = grid::unit(15, "mm"),
direction = "vertical")}
## 6.A3 - Assign the legend points
if(legend == TRUE && track_number >= 3){
legend1 <- ComplexHeatmap::Legend(at = c(track1_label, track2_label, track3_label),
labels_gp = grid::gpar(fontsize = 15),
ncol = 1,
border = NA, # color of legend borders, also for the ticks in the continuous legend
background = NA, # background colors
legend_gp = grid::gpar(col = c(discrete_palette[1], discrete_palette[2], discrete_palette[3])), # graphic parameters for the legend
type = "points", # type of legends, can be grid, points and lines
pch = 19, # type of points
size = grid::unit(15, "mm"), # size of points
grid_height = grid::unit(15, "mm"),
grid_width = grid::unit(15, "mm"),
direction = "vertical")}
## 6.B - Assign Pvalue legend point
if(legend == TRUE){
legend2 <- ComplexHeatmap::Legend(at = pvalue_label, # breaks, can be wither numeric or character
labels_gp = grid::gpar(fontsize = 15),
ncol = 1,
border = NA, # color of legend borders, also for the ticks in the continuous legend
background = NA, # background colors
legend_gp = grid::gpar(col = c("black")), # graphic parameters for the legend
type = "points", # type of legends, can be grid, points and lines
pch = 1, # type of points
size = grid::unit(15, "mm"), # size of points
grid_height = grid::unit(15, "mm"),
grid_width = grid::unit(15, "mm"),
direction = "vertical")
## 6.C - Assign legend section labelling
names <- levels(as.factor(data[[section_column]]))
names <- paste(1:nlevels(data[[section_column]]), names, sep=". ")
legend3 <- ComplexHeatmap::Legend(at = names,
labels_gp = grid::gpar(fontsize = 15),
nrow = 4,
ncol = 7,
border = NA, # color of legend borders, also for the ticks in the continuous legend
background = NA, # background colors
legend_gp = grid::gpar(col = c("black")), # graphic parameters for the legend
size = grid::unit(15, "mm"), # size of points
grid_height = grid::unit(15, "mm"),
grid_width = grid::unit(10, "mm"),
direction = "horizontal")
## 6.D - Pack lagend together
legend4 <- ComplexHeatmap::packLegend(legend1, legend2, direction = "vertical", gap = grid::unit(0, "mm"))
legend <- ComplexHeatmap::packLegend(legend4, legend3, direction = "horizontal", gap = grid::unit(0, "mm"))
legend_height <- legend@grob[["vp"]][["height"]]
legend_width <- legend@grob[["vp"]][["width"]]
# ## 6.E - Layer legend ontop of plot
grid::pushViewport(grid::viewport(x = grid::unit(0.5, "npc"),
y = grid::unit(0.08, "npc"),
width = legend_width,
height = legend_height,
just = c("center", "top")))
grid::grid.draw(legend)
grid::upViewport()}
}
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