R/plot.density.profile.smooth.R
In sebastian-gregoricchio/Rseb: An R-package for NGS data managing and visualization
Defines functions
plot.density.profile.smooth
Documented in
plot.density.profile.smooth
#' @title Plot of NGS density signal at specific regions from deepTools matrices (signal smoothing version).
#'
#' @description Plots the density profile of NGS data signals, using as input a score matrix computed by deeptools's computeMatrix function or by \link{computeMatrix.deeptools} and \link{density.matrix} functions from this package (signal smoothing version). The error on the line cannot be plotted in this case. See also \link{plot.density.profile}.
#'
#' @param matrix.file A single string indicating a full path to a matrix.gz file generated by \code{deepTools/computeMatrix} or by \link{computeMatrix.deeptools}, or a list generated by the function \link{read.computeMatrix.file} or \link{density.matrix}.
#' @param plot.by.group Logical value to define whether plot by group of regions or by sample. By default \code{TRUE}.
#' @param missing.data.as.zero Logical value to define whether treat missing data as 0. If set as \code{FALSE} missing data will be converted to \code{NA} and will be excluded from the computations of the signal. By default \code{TRUE}.
#' @param sample.names Samples names could be defined by a string vector. If set as \code{NULL} sample names will be get automatically by the matrix file. By default \code{NULL}. \cr Example: \code{c("sample1", "sample2", "sample3")}
#' @param region.names Region names could be defined by a string vector. If set as \code{NULL} sample names will be get automatically by the matrix file. By default \code{NULL}. \cr Example: \code{c("regionA", "regionB")}
#' @param signal.type String indicating the signal to be computed and plotted. Available parameters are "mean", "median" and "sum". By default "mean".
#' @param smooth.span Numerical value to indicate the span value for the loess function used to smooth bigWig signals. By default \code{0.1}.
#' @param title Title of each plot could be defined by a string vector. If set as \code{NULL} titles will be generated automatically. By default \code{NULL}. \cr Example: \code{c("Title1", "Title2")}
#' @param x.lab Single string or string vector to define the X-axis label for all the plots. By default \code{NULL}, the label will be defined automatically.
#' @param y.lab Single string or string vector to define the Y-axis label for all the plots. By default \code{NULL}, the label will be defined automatically.
#' @param line.type Vector to define each line type. Both numeric and string codes are accepted. If only one element is given this will be applied to all the lines. By default "solid". \cr Example 1: \code{c("solid", "dashed")}. \cr Example 2: \code{c(1, 2)}
#' @param line.width Numeric value to define the line width for all the plots. By default \code{0.5}.
#' @param x.lim List of numeric vectors with two elements each to define the range of the X-axis. To set only one side use NA for the side to leave automatic. If only one range is given this one will be applied to all the plots. By default \code{NULL}, the range will be defined automatically. \cr Example \code{list(c(0, 20), c(NA, 30), c(0, NA), c(NA, NA))}.,
#' @param y.lim List of numeric vectors with two elements each to define the range of the Y-axis. To set only one side use NA for the side to leave automatic. If only one range is given this one will be applied to all the plots. By default \code{NULL}, the range will be defined automatically. \cr Example \code{list(c(0, 20), c(NA, 30), c(0, NA), c(NA, NA))}.,
#' @param y.identical.auto Logical value to define whether use the same Y-axis range for all the plots automatically depending on the values. Not used when \code{y.lim} is not \code{NULL}. By default \code{TRUE}.
#' @param y.ticks.interval A number indicating the interval/bin spacing two ticks on the Y-axis. By default \code{NULL}: ticks are assigned automatically. Active only when \code{y.identical.auto = TRUE} and \code{y.lim != NULL}.
#' @param y.digits A numeric value to define the number of digits to use for the y.axis values. By default \code{1} (eg. 1.5).
#' @param axis.line.width Numeric value to define the axes and ticks line width for all plots. By default \code{0.5}.
#' @param text.size Numeric value to define the size of the text for the labels of all the plots. By default 12.
#' @param legend.position Any ggplot supported value for the legend position (eg. "none", top", "bottom", "left", "right", c(fraction.x, fraction.y)). By default \code{c(0.2, 0.85)}.
#' @param plot.vertical.lines Logical value to define whether to plot a dashed gray vertical line in correspondence of the reference points of each plot. By default \code{TRUE}.
#' @param write.reference.points Logical value to define whether to indicate the reference points on each plot. Applied only when \code{x.lim} is \code{NULL}. By default \code{TRUE}.
#' @param colors Vector to define the line and error area colors. If only one value is provided it will applied to all the samples/groups. If the number of values is lower than the the required one, a random set of colors will be generated. All standard R.colors values are accepted. By default \code{c("#00A5CF", "#F8766D", "#AC88FF", "#E08B00", "#00BA38", "#BB9D00", "#FF61C9", "gray30")}.
#' @param n.row.multiplot Numeric value to define the number of rows in the final multiplot.
#' @param multiplot.export.file If a string with the name of a PDF file is provided the multiplot will be exported. By default \code{NULL}.
#' @param real.width.single.plot Numeric value, in inches, to define the real width of each plot in the multiplot exported, if required. By default 2.9 inches.
#' @param real.height.single.plot Numeric value, in inches, to define the real height of each plot in the multiplot exported, if required. By default 3.5 inches.
#' @param by.row Logical value to define whether the plots should be arranged by row. By default \code{TRUE}.
#' @param print.multiplot Logical value to define whether to print the multiplot once created. By default \code{FALSE}.
#'
#' @return The function returns a list containing:
#' \itemize{
#' \item \code{data.table} with the computed values used for the plot;
#' \item \code{metadata} table with the information gotten from the matrix_file.gz;
#' \item \code{plot.list} with a plot for each list element;
#' \item \code{multiplot} with the image of all the plots together.
#' }
#'
#' @examples
#' plot.density.profile.smooth(
#' matrix.file = "/path.to/matrix.file.gz", plot.by.group = TRUE,
#' missing.data.as.zero = NULL, sample.names = NULL, region.names = NULL,
#' signal.type = "mean", error.type = "sem", plot.error = TRUE,
#' error.transparency = 0.125, title = NULL, x.lab = NULL, y.lab = NULL,
#' line.type = "solid", line.width = 0.5, x.lim = NULL, y.lim = NULL,
#' y.identical.auto = TRUE, y.ticks.number = 5, text.size = 12,
#' plot.vertical.lines = TRUE, colors = c("red", "blue", "#00BA38"),
#' n.row.multiplot = 1, multiplot.export.file = "/path.to/multiplot.pdf",
#' real.width.single.plot = 2.5, real.height.single.plot = 3,
#' print.multiplot = FALSE)
#'
#' @details To know more about the deepTools's function \code{computeMatrix} see the package manual at the following link: \cr \url{https://deeptools.readthedocs.io/en/develop/content/tools/computeMatrix.html}.
#'
#' @export plot.density.profile.smooth
#'
# @import dplyr
# @import ggplot2
# @importFrom data.table fread
# @importFrom stringr str_split
# @importFrom robustbase colMedians
# @importFrom matrixStats colSds
# @importFrom purrr reduce
# @importFrom cowplot plot_grid
# @importFrom tools toTitleCase
# @importFrom labeling extended
plot.density.profile.smooth = function(
matrix.file,
plot.by.group = T,
missing.data.as.zero = NULL,
sample.names = NULL,
region.names = NULL,
signal.type = "mean", # type of signal computation c("mean", "median", "sum")
smooth.span = 0.1,
title = NULL, # string vector
x.lab = NULL, # single string vector
y.lab = NULL, # string vector
line.type = "solid", # can be a vector for different lines
line.width = 0.5, # can be a vector for different lines
x.lim = NULL, # list of vectors c(min, max) or single vector
y.lim = NULL, # list of vectors c(min, max) or single vector
y.identical.auto = T, # subordinated to y.lim which has the priority
y.ticks.interval = NULL, # only for 'y.identical.auto'
y.digits = 1,
axis.line.width = 0.5,
text.size = 12,
legend.position = c(0.2, 0.85),
plot.vertical.lines = T,
write.reference.points = T,
colors = c("#00A5CF", "#F8766D", "#AC88FF", "#E08B00", "#00BA38", "#BB9D00", "#FF61C9", "gray30"),
n.row.multiplot = 1,
multiplot.export.file = NULL,
real.width.single.plot = 2.9, #inch
real.height.single.plot = 3.5, #inch
by.row = TRUE,
print.multiplot = F) {
##############################################################################
## ------------------------- BEGIN OF THE FUNCTION ------------------------ ##
##############################################################################
# Load all libraries
require(dplyr)
require(ggplot2)
# require(data.table)
# require(stringr)
# require(robustbase)
# require(matrixStats)
# require(purrr)
# require(cowplot)
# require(tools)
# require(labeling)
# Check if Rseb is up-to-date #
Rseb::actualize(update = F, verbose = F)
##############################################################################
# Define deprecated values
error.type = "sem"
plot.error = F
error.transparency = 0.125
## Convert and check x.lim and y.lim and title
### Check that the parameters are vectors or lists
if (!is.null(x.lim) & !(class(x.lim) %in% c("numeric", "list"))) {
return(warning("The 'x.lim' parameter must be a vector or list of numeric vectors c(min, max) or NULL. \nTo set just one boundary replace a number with NA."))
}
if (!is.null(y.lim) & !(class(y.lim) %in% c("numeric", "list"))) {
return(warning("The 'y.lim' parameter must be a vector or list of numeric vectors c(min, max) or NULL. \nTo set just one boundary replace a number with NA."))
}
### Check whether parameters are single vectors and convert them to 1-element lists eventually
if (!is.null(x.lim) & class(x.lim) == "numeric") {x.lim = list(x.lim)}
if (!is.null(y.lim) & class(y.lim) == "numeric") {y.lim = list(y.lim)}
### Check x.lab and y.lab class
if (!is.null(title) & class(title) != "character") {return(warning("The 'title' parameter must be a single string or a string vector."))}
if (!is.null(x.lab) & class(x.lab) != "character") {return(warning("The 'x.lab' parameter must be a single string or a string vector."))}
if (!is.null(y.lab) & class(y.lab) != "character") {return(warning("The 'y.lab' parameter must be a single string or a string vector."))}
### Check the size and type of each element in the lists
if (!is.null(x.lim)) {
for (i in 1:length(x.lim)) {
if (length(x.lim[[i]]) != 2 | class(x.lim[[i]]) != "numeric") {
return(warning("One ore more vectors of the 'x.lim' list do not have exactly 2 elements and/or are not numeric vectors."))}
}
}
if (!is.null(y.lim)) {
for (i in 1:length(y.lim)) {
if (length(y.lim[[i]]) != 2 | class(y.lim[[i]]) != "numeric") {
return(warning("One ore more vectors of the 'y.lim' list do not have exactly 2 elements and/or are not numeric vectors."))}
}
}
### Check title vector
if (!is.null(title) & class(title) != "character") {return(warning("The 'title' parameter must be a string vector or NULL."))}
### Check that the signal method is allowed
if (class(signal.type) != "character" | !(signal.type %in% c("mean", "median", "sum")) | length(signal.type) != 1) {return(warning("The 'siganl.type' parameter must be one among [mean, median, sum]."))}
#############################################################################
######### DEFINITION OF PLOTTING FUNCTION #########
#############################################################################
density_plot_smooth = function(
samples,
scores,
positions,
variance_scores,
xlab = "Distance from regions center [bp]",
ylab = "Average density signal",
line_type = "solid",
y_lim = NULL,
x_lim = NULL,
x_intercept = 0,
colors = c("blue", "red", "purple", "orange", "green"),
title = "Density profile",
text_size = 12,
variance = T,
print_plot = F,
line_width = 1,
variance_opacity = 0.25,
span = 0.1) { # BEGIN
# Create a matrix
matrix =
data.frame(sample = samples,
position = positions,
score = scores,
variance = variance_scores)
# Set to 0 the variance if variance == F
if (variance == F) {matrix$variance = 0}
# scaling size of parameters
n_samples = length(unique(matrix$sample))
# scaling number of colors/line.type to be equal to number samples
scaling = function(x, n_samples) {
if (length(x) < n_samples &
length(x) == 1) {
v = c(rep(x, n_samples))
} else if (length(x) > 1 &
length(x) < n_samples) {
v = c(rep(x[1], n_samples))
message("Number of colors and/or line.type lower than number of samples --> The first value is applied to all samples/groups.")
} else (v = as.vector(x))
return(v)
}
line_type = scaling(line_type, n_samples)
colors = scaling(colors, n_samples)
require(ggplot2)
# building the plot
plot =
ggplot(data = matrix,
aes(x = position,
y = score,
ymin = score-variance,
ymax = score+variance,
group = sample,
color = sample,
fill = sample,
linetype = sample)) +
geom_smooth(method = "loess", formula = y ~ x, span = span, size = line_width, se=F) +
scale_color_manual(values = as.vector(colors[1:n_samples])) +
scale_linetype_manual(values = as.vector(line_type[1:n_samples])) +
scale_fill_manual(values = as.vector(colors[1:n_samples])) +
xlab(xlab) +
ylab(ylab) +
ggtitle(title) +
geom_vline(xintercept = x_intercept,
linetype = "dashed",
col = "gray70") +
theme_classic() +
theme(text = element_text(size = text_size),
axis.text = element_text(color = "#000000"),
axis.title = element_text(color = "#000000"),
axis.line = element_line(color = "#000000"),
axis.ticks = element_line(color = "#000000"),
title = element_text(color = "#000000"),
legend.title = element_text(color = "#000000"),
legend.text = element_text(color = "#000000"))
if (!(is.null(y_lim))) plot = plot + ylim(y_lim)
if (!(is.null(x_lim))) plot = plot + xlim(x_lim)
if (print_plot == T) {
(print(plot))}
return(plot)
} # END
#############################################################################
##############################################################################
# Import/read the matrix.gz file
if (class(matrix.file) == "character" & length(matrix.file) == 1) {
m = Rseb::read.computeMatrix.file(matrix.file)
metadata = m$metadata
matrix.data = m$matrix.data
} else if (class(matrix.file) == "list" & length(matrix.file) == 3 & unique(names(matrix.file) == c("metadata", "matrix.data", "original.file.path"))) {
metadata = matrix.file$metadata
matrix.data = matrix.file$matrix.data
} else {
return(warning("The 'matrix.file' must be a single string indicating a full path to a matrix.gz file generated by deepTools/computeMatrix or by Rseb::computeMatrix.deeptools(), or a list generated by the function Rseb::read.computeMatrix.file."))
}
# Generate metadata variables
sample_start_column = as.numeric(as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "sample_boundaries")$values, pattern = ",")[[1]]))
sample_names = as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "sample_labels")$values, pattern = ",")[[1]])
group_start_row = as.numeric(as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "group_boundaries")$values, pattern = ",")[[1]]))
group_names = as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "group_labels")$values, pattern = ",")[[1]])
##############################################################################
# Get the number at which the matrix data starts (after 'chr' 'start' 'end' 'strand' etc.)
number.info.columns = ncol(matrix.data) - sample_start_column[length(sample_start_column)]
# Remove NaN values and substitute them with 0 if missing.data.as.zero = TRUE or NA if FALSE
missing.data.as.zero.automatic = as.logical(toupper(dplyr::filter(.data = metadata, parameters == "missing_data_as_zero")$values))
if (is.null(missing.data.as.zero)) {
missing.data.as.zero = missing.data.as.zero.automatic
} else if (is.logical(missing.data.as.zero)) {
if (missing.data.as.zero != missing.data.as.zero.automatic) {
message(paste("The parameter 'missing.data.as.zero' set in this function as ", as.character(missing.data.as.zero),
", differs from the automatic obtained from the matrix file which is set as ", as.character(missing.data.as.zero.automatic),
".\nMissing data will be treated as set in this function: 'missing.data.as.zero = ", as.character(missing.data.as.zero), "'.", sep = ""))}
} else {return(warning("The parameter 'missing.data.as.zero' must be a logical value or NULL."))}
is.nan.data.frame = function(data.frame) do.call(cbind, lapply(data.frame, is.nan))
matrix.data[is.nan(matrix.data)] = ifelse(test = missing.data.as.zero,
yes = 0,
no = NA)
message(ifelse(test = missing.data.as.zero,
yes = "Missing data have been converted to 0 since the parameter 'missing.data.as.zero' is TRUE.",
no = "Missing data have been converted to NA since the parameter 'missing.data.as.zero' is FALSE. \nNA values will be excluded from the statistical computations."))
##############################################################################
# Generate a list with a table per sample
samples.table.list = list()
# Change sample and group names by custom ones
if (!is.null(sample.names)) {
if (length(unique(sample.names)) == length(sample_names)) {
sample_names = sample.names
} else {message("Default sample.names have been used because custom valus are not unique and/or incomplete")}
}
if (!is.null(region.names)) {
if (length(unique(region.names)) == length(group_names)) {
group_names = region.names
} else {message("Default region.names have been used because custom valus are not unique and/or incomplete")}
}
for (i in 1:length(sample_names)) {
# define the limits of the sample keeping the first lines
start.col = sample_start_column[i] + number.info.columns + 1
end.col = sample_start_column[i+1] + number.info.columns
sample.table =
matrix.data %>%
dplyr::select(c(1:number.info.columns, start.col:end.col)) %>%
# adding a column with the groups names
mutate(group = rep(group_names,
times = c(group_start_row[-1] - group_start_row[-length(group_start_row)])))
# Add the single table to a list
samples.table.list[[i]] = sample.table
}
names(samples.table.list) = sample_names
##############################################################################
# Generate the statistical tables
sample.stat.table.list = list()
for (s in 1:length(sample_names)) {
group.stats.table.list = list()
# Computations of the stats per group in order to then merge the different group tables
for (g in 1:length(group_names)) {
group.table =
samples.table.list[[s]] %>%
filter(group == group_names[g]) %>%
dplyr::select(-c(1:number.info.columns), -group)
group.mean = colMeans(group.table, na.rm = T)
group.median = robustbase::colMedians(as.matrix(group.table), na.rm = T)
group.sum = colSums(group.table, na.rm = T)
group.sd = matrixStats::colSds(as.matrix(group.table), na.rm = T)
group.sem = group.sd / sqrt(nrow(group.table))
group.stats.table.list[[g]] =
data.frame(group = group_names[g],
mean = group.mean,
median = group.median,
sum = group.sum,
sd = group.sd,
sem = group.sem)
} # 'g' For end
# x-axes values, distance from center
upstream = as.numeric(as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "upstream")$values, pattern = ",")[[1]]))[s]
downstream = as.numeric(as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "downstream")$values, pattern = ",")[[1]]))[s]
body = as.numeric(as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "body")$values, pattern = ",")[[1]]))[s]
bin = as.numeric(as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "bin_size")$values, pattern = ",")[[1]]))[s]
distance.range = seq(from = -upstream, to = (body + downstream), by = bin)
distance.range = distance.range[distance.range != 0]
sample.stat.table.list[[s]] =
purrr::reduce(.x = group.stats.table.list, .f = rbind) %>%
mutate(distance = rep(distance.range, length(group_names)),
sample = sample_names[s])
} # 's' For end
# Merge of all the tables in a single one
full.stat.table =
purrr::reduce(.x = sample.stat.table.list, .f = rbind) %>%
dplyr::mutate(sample = factor(sample, levels = sample_names),
group = factor(group, levels = group_names))
##############################################################################
# Generation of the plots
plot.list = list()
# Remove error plotting option when using the 'sum' mode, since it means nothing for a sum
if (signal.type == "sum") {
plot.error = F
message("The error will not be plotted since mode 'sum' is used.")
}
# Function to check the length of x.lim, y.lim, title
check.parameter.length =
function(parameter, parameter.name, plots.number) {
if (!is.null(parameter) & length(parameter) != plots.number & length(parameter) > 1) {
message(paste("The parameter", parameter.name, "has been set as NULL since the number of elements was different from the number of plots required."))
return(NULL)}
else {return(parameter)}
}
###########
### Generate a plot by group (each plot is a group, each signal/line is a sample)
if (plot.by.group == T) {
# Define the colors for the samples
if ((length(colors) > 1 & length(colors) < length(sample_names)) | length(colors) == 0) {colors = rainbow(length(sample_names))}
for (i in 1:length(group_names)) {
# Check if plot the center line
regionEnd = as.numeric(as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "body")$values, pattern = ",")[[1]]))
if (length(regionEnd) == 1) {regionEnd = rep(regionEnd, length(group_names))}
if (plot.vertical.lines == T) {
center.line.value = unique(c(0, regionEnd[i]))} else {
center.line.value = NULL}
# Repeat the value of x.lim, y.lim, title parameters if their length is 1
if (!is.null(x.lim) & length(x.lim) == 1) {x.lim = lapply(1:length(group_names), function(j) x.lim[[1]])}
if (!is.null(y.lim) & length(y.lim) == 1) {y.lim = lapply(1:length(group_names), function(j) y.lim[[1]])}
if (!is.null(title) & length(title) == 1) {title = rep(title, length(group_names))}
if (!is.null(x.lab) & length(x.lab) == 1) {x.lab = rep(x.lab, length(group_names))}
if (!is.null(y.lab) & length(y.lab) == 1) {y.lab = rep(y.lab, length(group_names))}
# Check the length of the x.lim, y.lim, title parameters
x.lim = check.parameter.length(parameter = x.lim, parameter.name = "'x.lim'", plots.number = length(group_names))
y.lim = check.parameter.length(parameter = y.lim, parameter.name = "'y.lim'", plots.number = length(group_names))
title = check.parameter.length(parameter = title, parameter.name = "'title'", plots.number = length(group_names))
x.lab = check.parameter.length(parameter = x.lab, parameter.name = "'x.lab'", plots.number = length(group_names))
y.lab = check.parameter.length(parameter = y.lab, parameter.name = "'y.lab'", plots.number = length(group_names))
# Select only current group subtable
current.table = full.stat.table %>% filter(group == group_names[i])
# Define which value to use for the scores
if (signal.type == "mean") {
score = current.table$mean} else if (signal.type == "median") {
score = current.table$median} else if (signal.type == "sum") {
score = current.table$sum} else {
return(warning("The signal.type must be one among 'mean', 'median', 'sum'."))
}
# Define which value to use for error
if (error.type == "sem") {
error = current.table$sem} else if (error.type == "sd") {
error = current.table$sd} else {
return(warning("The error.type must be one between 'sem' or 'sd'."))
}
# Plot saving in the list
plot.list[[i]] =
density_plot_smooth(
samples = current.table$sample,
scores = score,
span = smooth.span,
positions = current.table$distance,
variance_scores = error,
ylab = ifelse(test = is.null(y.lab),
yes = ifelse(test = plot.error == T,
yes = paste(tools::toTitleCase(signal.type), "\u00b1", toupper(error.type), "of density signal"),
no = paste(tools::toTitleCase(signal.type), "of density signal")),
no = y.lab[i]),
xlab = ifelse(test = is.null(x.lab),
yes = "Distance from reference point [bp]",
no = x.lab[i]),
line_type = line.type,
x_lim = x.lim[[i]],
y_lim = y.lim[[i]],
x_intercept = center.line.value,
colors = colors,
title = ifelse(test = is.null(title),
yes = group_names[i],
no = title[i]),
text_size = text.size,
variance = plot.error,
print_plot = F,
line_width = line.width,
variance_opacity = error.transparency) +
theme(legend.position = legend.position,
legend.background = element_blank(),
axis.line = element_line(size = axis.line.width),
axis.ticks = element_line(size = axis.line.width))
names(plot.list)[i] = group_names[i]
} # for loop end
########
### Generate a plot by sample (each plot is a sample, each signal/line is a group)
} else { # if end
# Define the colors for each group
if ((length(colors) > 1 & length(colors) < length(group_names)) | length(colors) == 0) {colors = rainbow(length(group_names))}
for (i in 1:length(sample_names)) {
# Check if plot the center line
regionEnd = as.numeric(as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "body")$values, pattern = ",")[[1]]))
if (length(regionEnd) == 1) {regionEnd = rep(regionEnd, length(sample_names))}
if (plot.vertical.lines == T) {
center.line.value = unique(c(0, regionEnd[i]))} else {
center.line.value = NULL}
# Repeat the value of x.lim, y.lim, title parameters if their length is 1
if (!is.null(x.lim) & length(x.lim) == 1) {x.lim = lapply(1:length(sample_names), function(j) x.lim[[1]])}
if (!is.null(y.lim) & length(y.lim) == 1) {y.lim = lapply(1:length(sample_names), function(j) y.lim[[1]])}
if (!is.null(title) & length(title) == 1) {title = rep(title, length(sample_names))}
if (!is.null(x.lab) & length(x.lab) == 1) {x.lab = rep(x.lab, length(sample_names))}
if (!is.null(y.lab) & length(y.lab) == 1) {y.lab = rep(y.lab, length(sample_names))}
# Check the length of the x.lim, y.lim, title parameters
x.lim = check.parameter.length(parameter = x.lim, parameter.name = "'x.lim'", plots.number = length(sample_names))
y.lim = check.parameter.length(parameter = y.lim, parameter.name = "'y.lim'", plots.number = length(sample_names))
title = check.parameter.length(parameter = title, parameter.name = "'title'", plots.number = length(sample_names))
x.lab = check.parameter.length(parameter = x.lab, parameter.name = "'x.lab'", plots.number = length(sample_names))
y.lab = check.parameter.length(parameter = y.lab, parameter.name = "'y.lab'", plots.number = length(sample_names))
# Select only current group subtable
current.table = full.stat.table %>% filter(sample == sample_names[i])
# Define which value to use for the scores
if (signal.type == "mean") {
score = current.table$mean} else if (signal.type == "median") {
score = current.table$median} else if (signal.type == "sum") {
score = current.table$sum} else {
return(warning("The signal.type must be one among 'mean', 'median', 'sum'"))
}
# Define which value to use for error
if (error.type == "sem") {
error = current.table$sem} else if (error.type == "sd") {
error = current.table$sd} else {
return(warning("The error.type must be one between 'sem' or 'sd'"))
}
# Plot saving in the list
plot.list[[i]] =
density_plot_smooth(
samples = current.table$group,
scores = score,
span = smooth.span,
positions = current.table$distance,
variance_scores = error,
ylab = ifelse(test = is.null(y.lab),
yes = ifelse(test = plot.error == T,
yes = paste(tools::toTitleCase(signal.type), "\u00b1", toupper(error.type), "of density signal"),
no = paste(tools::toTitleCase(signal.type), "of density signal")),
no = y.lab[i]),
xlab = ifelse(test = is.null(x.lab),
yes = "Distance from reference point [bp]",
no = x.lab[i]),
line_type = line.type,
x_lim = x.lim[[i]],
y_lim = y.lim[[i]],
x_intercept = center.line.value,
colors = colors,
title = ifelse(test = is.null(title),
yes = sample_names[i],
no = title[i]),
text_size = text.size,
variance = plot.error,
print_plot = F,
line_width = line.width,
variance_opacity = error.transparency) +
labs(color = "regions group",
fill = "regions group",
linetype = "regions group",
group = "regions group") +
theme(legend.position = legend.position,
legend.background = element_blank(),
axis.line = element_line(size = axis.line.width),
axis.ticks = element_line(size = axis.line.width))
# Add a name to the plot, element in the list
names(plot.list)[i] = sample_names[i]
} # for end
} # else end
# Set the same Y-axes for all the plots, if required by 'y.identical.auto = T'
if (y.identical.auto == T & is.null(y.lim) & length(plot.list) > 1) {
plot.list = Rseb::uniform.y.axis(plot.list = plot.list, ticks.each = y.ticks.interval, digits = y.digits)
}
# if (y.identical.auto == T & is.null(y.lim) & length(plot.list) > 1) {
# y.min_list = c()
# y.max_list = c()
#
# for (i in 1:length(plot.list)) {
# y.min_list = c(y.min_list, ggplot_build(plot.list[[i]])$layout$panel_params[[1]]$y$limits[1])
# y.max_list = c(y.max_list, ggplot_build(plot.list[[i]])$layout$panel_params[[1]]$y$limits[2])
# }
#
# breaks = labeling::extended(dmin = floor(min(y.min_list)),
# dmax = ceiling(max(y.max_list)),
# m = y.ticks.number)
#
# for (i in 1:length(plot.list)) {
# plot.list[[i]] =
# plot.list[[i]] +
# scale_y_continuous(breaks = breaks,
# limits = c(min(y.min_list), max(y.max_list)))
# }
# }
# Reshape the X-axis to include the reference points, if required by 'write.reference.points = T'
if (write.reference.points == T & is.null(x.lim)) {
reference_point = as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "ref_point")$values, pattern = ",")[[1]])
body = as.numeric(as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "body")$values, pattern = ",")[[1]]))
if (length(reference_point) < length(plot.list)) {reference_point = rep(reference_point[1], length(plot.list))}
if (length(body) < length(plot.list)) {body = rep(body[1], length(plot.list))}
for (i in 1:length(plot.list)) {
labels = ggplot_build(plot.list[[i]])$layout$panel_params[[1]]$x$get_labels()
# Substitute the 0 value
labels = gsub(pattern = "^0$",
replacement = ifelse(test = reference_point[i] == "null",
yes = "start",
no = reference_point[i]),
x = labels)
# Substitute the 'end region' value when the plot is in scale-regions mode
if (reference_point[i] == "null") {
for (k in 1:length(labels)) {
# check if the current label could be a number
if (grepl("[-]?[0-9]+[.]?[0-9]*|[-]?[0-9]+[L]?|[-]?[0-9]+[.]?[0-9]*[eE][0-9]+", labels[k])) {
if (as.numeric(labels[k]) >= body[i]) {labels[k] = as.character(as.numeric(labels[k]) - body[i])}
}
}
labels = gsub(pattern = "^0$", replacement = "end", x = labels)
}
plot.list[[i]] = plot.list[[i]] + scale_x_continuous(labels = labels)
} # end For
} # end If
##############################################################################
# generate the multiplot
multiplot = cowplot::plot_grid(plotlist = plot.list, nrow = n.row.multiplot, byrow = by.row, align = "hv")
# Print multiplot if required
if (print.multiplot == T) {print(multiplot)}
# Export the file if required
if (!is.null(multiplot.export.file)) {
pdf(file = multiplot.export.file,
height = real.height.single.plot * n.row.multiplot,
width = real.width.single.plot * ceiling(length(plot.list)/n.row.multiplot))
print(multiplot)
dev.off()
message("Multiplot exported as: ", multiplot.export.file)
}
##############################################################################
# RETURN a list of data
return(list(data.table = full.stat.table,
metadata = metadata,
plot.list = plot.list,
multiplot = multiplot))
} # End function
################################################################################
#----------------------------- END OF FUNCTION --------------------------------#
################################################################################
sebastian-gregoricchio/Rseb documentation built on May 20, 2024, 9:01 a.m.
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Defines functions plot.density.profile.smooth
Documented in plot.density.profile.smooth
#' @title Plot of NGS density signal at specific regions from deepTools matrices (signal smoothing version).
#'
#' @description Plots the density profile of NGS data signals, using as input a score matrix computed by deeptools's computeMatrix function or by \link{computeMatrix.deeptools} and \link{density.matrix} functions from this package (signal smoothing version). The error on the line cannot be plotted in this case. See also \link{plot.density.profile}.
#'
#' @param matrix.file A single string indicating a full path to a matrix.gz file generated by \code{deepTools/computeMatrix} or by \link{computeMatrix.deeptools}, or a list generated by the function \link{read.computeMatrix.file} or \link{density.matrix}.
#' @param plot.by.group Logical value to define whether plot by group of regions or by sample. By default \code{TRUE}.
#' @param missing.data.as.zero Logical value to define whether treat missing data as 0. If set as \code{FALSE} missing data will be converted to \code{NA} and will be excluded from the computations of the signal. By default \code{TRUE}.
#' @param sample.names Samples names could be defined by a string vector. If set as \code{NULL} sample names will be get automatically by the matrix file. By default \code{NULL}. \cr Example: \code{c("sample1", "sample2", "sample3")}
#' @param region.names Region names could be defined by a string vector. If set as \code{NULL} sample names will be get automatically by the matrix file. By default \code{NULL}. \cr Example: \code{c("regionA", "regionB")}
#' @param signal.type String indicating the signal to be computed and plotted. Available parameters are "mean", "median" and "sum". By default "mean".
#' @param smooth.span Numerical value to indicate the span value for the loess function used to smooth bigWig signals. By default \code{0.1}.
#' @param title Title of each plot could be defined by a string vector. If set as \code{NULL} titles will be generated automatically. By default \code{NULL}. \cr Example: \code{c("Title1", "Title2")}
#' @param x.lab Single string or string vector to define the X-axis label for all the plots. By default \code{NULL}, the label will be defined automatically.
#' @param y.lab Single string or string vector to define the Y-axis label for all the plots. By default \code{NULL}, the label will be defined automatically.
#' @param line.type Vector to define each line type. Both numeric and string codes are accepted. If only one element is given this will be applied to all the lines. By default "solid". \cr Example 1: \code{c("solid", "dashed")}. \cr Example 2: \code{c(1, 2)}
#' @param line.width Numeric value to define the line width for all the plots. By default \code{0.5}.
#' @param x.lim List of numeric vectors with two elements each to define the range of the X-axis. To set only one side use NA for the side to leave automatic. If only one range is given this one will be applied to all the plots. By default \code{NULL}, the range will be defined automatically. \cr Example \code{list(c(0, 20), c(NA, 30), c(0, NA), c(NA, NA))}.,
#' @param y.lim List of numeric vectors with two elements each to define the range of the Y-axis. To set only one side use NA for the side to leave automatic. If only one range is given this one will be applied to all the plots. By default \code{NULL}, the range will be defined automatically. \cr Example \code{list(c(0, 20), c(NA, 30), c(0, NA), c(NA, NA))}.,
#' @param y.identical.auto Logical value to define whether use the same Y-axis range for all the plots automatically depending on the values. Not used when \code{y.lim} is not \code{NULL}. By default \code{TRUE}.
#' @param y.ticks.interval A number indicating the interval/bin spacing two ticks on the Y-axis. By default \code{NULL}: ticks are assigned automatically. Active only when \code{y.identical.auto = TRUE} and \code{y.lim != NULL}.
#' @param y.digits A numeric value to define the number of digits to use for the y.axis values. By default \code{1} (eg. 1.5).
#' @param axis.line.width Numeric value to define the axes and ticks line width for all plots. By default \code{0.5}.
#' @param text.size Numeric value to define the size of the text for the labels of all the plots. By default 12.
#' @param legend.position Any ggplot supported value for the legend position (eg. "none", top", "bottom", "left", "right", c(fraction.x, fraction.y)). By default \code{c(0.2, 0.85)}.
#' @param plot.vertical.lines Logical value to define whether to plot a dashed gray vertical line in correspondence of the reference points of each plot. By default \code{TRUE}.
#' @param write.reference.points Logical value to define whether to indicate the reference points on each plot. Applied only when \code{x.lim} is \code{NULL}. By default \code{TRUE}.
#' @param colors Vector to define the line and error area colors. If only one value is provided it will applied to all the samples/groups. If the number of values is lower than the the required one, a random set of colors will be generated. All standard R.colors values are accepted. By default \code{c("#00A5CF", "#F8766D", "#AC88FF", "#E08B00", "#00BA38", "#BB9D00", "#FF61C9", "gray30")}.
#' @param n.row.multiplot Numeric value to define the number of rows in the final multiplot.
#' @param multiplot.export.file If a string with the name of a PDF file is provided the multiplot will be exported. By default \code{NULL}.
#' @param real.width.single.plot Numeric value, in inches, to define the real width of each plot in the multiplot exported, if required. By default 2.9 inches.
#' @param real.height.single.plot Numeric value, in inches, to define the real height of each plot in the multiplot exported, if required. By default 3.5 inches.
#' @param by.row Logical value to define whether the plots should be arranged by row. By default \code{TRUE}.
#' @param print.multiplot Logical value to define whether to print the multiplot once created. By default \code{FALSE}.
#'
#' @return The function returns a list containing:
#' \itemize{
#' \item \code{data.table} with the computed values used for the plot;
#' \item \code{metadata} table with the information gotten from the matrix_file.gz;
#' \item \code{plot.list} with a plot for each list element;
#' \item \code{multiplot} with the image of all the plots together.
#' }
#'
#' @examples
#' plot.density.profile.smooth(
#' matrix.file = "/path.to/matrix.file.gz", plot.by.group = TRUE,
#' missing.data.as.zero = NULL, sample.names = NULL, region.names = NULL,
#' signal.type = "mean", error.type = "sem", plot.error = TRUE,
#' error.transparency = 0.125, title = NULL, x.lab = NULL, y.lab = NULL,
#' line.type = "solid", line.width = 0.5, x.lim = NULL, y.lim = NULL,
#' y.identical.auto = TRUE, y.ticks.number = 5, text.size = 12,
#' plot.vertical.lines = TRUE, colors = c("red", "blue", "#00BA38"),
#' n.row.multiplot = 1, multiplot.export.file = "/path.to/multiplot.pdf",
#' real.width.single.plot = 2.5, real.height.single.plot = 3,
#' print.multiplot = FALSE)
#'
#' @details To know more about the deepTools's function \code{computeMatrix} see the package manual at the following link: \cr \url{https://deeptools.readthedocs.io/en/develop/content/tools/computeMatrix.html}.
#'
#' @export plot.density.profile.smooth
#'
# @import dplyr
# @import ggplot2
# @importFrom data.table fread
# @importFrom stringr str_split
# @importFrom robustbase colMedians
# @importFrom matrixStats colSds
# @importFrom purrr reduce
# @importFrom cowplot plot_grid
# @importFrom tools toTitleCase
# @importFrom labeling extended
plot.density.profile.smooth = function(
matrix.file,
plot.by.group = T,
missing.data.as.zero = NULL,
sample.names = NULL,
region.names = NULL,
signal.type = "mean", # type of signal computation c("mean", "median", "sum")
smooth.span = 0.1,
title = NULL, # string vector
x.lab = NULL, # single string vector
y.lab = NULL, # string vector
line.type = "solid", # can be a vector for different lines
line.width = 0.5, # can be a vector for different lines
x.lim = NULL, # list of vectors c(min, max) or single vector
y.lim = NULL, # list of vectors c(min, max) or single vector
y.identical.auto = T, # subordinated to y.lim which has the priority
y.ticks.interval = NULL, # only for 'y.identical.auto'
y.digits = 1,
axis.line.width = 0.5,
text.size = 12,
legend.position = c(0.2, 0.85),
plot.vertical.lines = T,
write.reference.points = T,
colors = c("#00A5CF", "#F8766D", "#AC88FF", "#E08B00", "#00BA38", "#BB9D00", "#FF61C9", "gray30"),
n.row.multiplot = 1,
multiplot.export.file = NULL,
real.width.single.plot = 2.9, #inch
real.height.single.plot = 3.5, #inch
by.row = TRUE,
print.multiplot = F) {
##############################################################################
## ------------------------- BEGIN OF THE FUNCTION ------------------------ ##
##############################################################################
# Load all libraries
require(dplyr)
require(ggplot2)
# require(data.table)
# require(stringr)
# require(robustbase)
# require(matrixStats)
# require(purrr)
# require(cowplot)
# require(tools)
# require(labeling)
# Check if Rseb is up-to-date #
Rseb::actualize(update = F, verbose = F)
##############################################################################
# Define deprecated values
error.type = "sem"
plot.error = F
error.transparency = 0.125
## Convert and check x.lim and y.lim and title
### Check that the parameters are vectors or lists
if (!is.null(x.lim) & !(class(x.lim) %in% c("numeric", "list"))) {
return(warning("The 'x.lim' parameter must be a vector or list of numeric vectors c(min, max) or NULL. \nTo set just one boundary replace a number with NA."))
}
if (!is.null(y.lim) & !(class(y.lim) %in% c("numeric", "list"))) {
return(warning("The 'y.lim' parameter must be a vector or list of numeric vectors c(min, max) or NULL. \nTo set just one boundary replace a number with NA."))
}
### Check whether parameters are single vectors and convert them to 1-element lists eventually
if (!is.null(x.lim) & class(x.lim) == "numeric") {x.lim = list(x.lim)}
if (!is.null(y.lim) & class(y.lim) == "numeric") {y.lim = list(y.lim)}
### Check x.lab and y.lab class
if (!is.null(title) & class(title) != "character") {return(warning("The 'title' parameter must be a single string or a string vector."))}
if (!is.null(x.lab) & class(x.lab) != "character") {return(warning("The 'x.lab' parameter must be a single string or a string vector."))}
if (!is.null(y.lab) & class(y.lab) != "character") {return(warning("The 'y.lab' parameter must be a single string or a string vector."))}
### Check the size and type of each element in the lists
if (!is.null(x.lim)) {
for (i in 1:length(x.lim)) {
if (length(x.lim[[i]]) != 2 | class(x.lim[[i]]) != "numeric") {
return(warning("One ore more vectors of the 'x.lim' list do not have exactly 2 elements and/or are not numeric vectors."))}
}
}
if (!is.null(y.lim)) {
for (i in 1:length(y.lim)) {
if (length(y.lim[[i]]) != 2 | class(y.lim[[i]]) != "numeric") {
return(warning("One ore more vectors of the 'y.lim' list do not have exactly 2 elements and/or are not numeric vectors."))}
}
}
### Check title vector
if (!is.null(title) & class(title) != "character") {return(warning("The 'title' parameter must be a string vector or NULL."))}
### Check that the signal method is allowed
if (class(signal.type) != "character" | !(signal.type %in% c("mean", "median", "sum")) | length(signal.type) != 1) {return(warning("The 'siganl.type' parameter must be one among [mean, median, sum]."))}
#############################################################################
######### DEFINITION OF PLOTTING FUNCTION #########
#############################################################################
density_plot_smooth = function(
samples,
scores,
positions,
variance_scores,
xlab = "Distance from regions center [bp]",
ylab = "Average density signal",
line_type = "solid",
y_lim = NULL,
x_lim = NULL,
x_intercept = 0,
colors = c("blue", "red", "purple", "orange", "green"),
title = "Density profile",
text_size = 12,
variance = T,
print_plot = F,
line_width = 1,
variance_opacity = 0.25,
span = 0.1) { # BEGIN
# Create a matrix
matrix =
data.frame(sample = samples,
position = positions,
score = scores,
variance = variance_scores)
# Set to 0 the variance if variance == F
if (variance == F) {matrix$variance = 0}
# scaling size of parameters
n_samples = length(unique(matrix$sample))
# scaling number of colors/line.type to be equal to number samples
scaling = function(x, n_samples) {
if (length(x) < n_samples &
length(x) == 1) {
v = c(rep(x, n_samples))
} else if (length(x) > 1 &
length(x) < n_samples) {
v = c(rep(x[1], n_samples))
message("Number of colors and/or line.type lower than number of samples --> The first value is applied to all samples/groups.")
} else (v = as.vector(x))
return(v)
}
line_type = scaling(line_type, n_samples)
colors = scaling(colors, n_samples)
require(ggplot2)
# building the plot
plot =
ggplot(data = matrix,
aes(x = position,
y = score,
ymin = score-variance,
ymax = score+variance,
group = sample,
color = sample,
fill = sample,
linetype = sample)) +
geom_smooth(method = "loess", formula = y ~ x, span = span, size = line_width, se=F) +
scale_color_manual(values = as.vector(colors[1:n_samples])) +
scale_linetype_manual(values = as.vector(line_type[1:n_samples])) +
scale_fill_manual(values = as.vector(colors[1:n_samples])) +
xlab(xlab) +
ylab(ylab) +
ggtitle(title) +
geom_vline(xintercept = x_intercept,
linetype = "dashed",
col = "gray70") +
theme_classic() +
theme(text = element_text(size = text_size),
axis.text = element_text(color = "#000000"),
axis.title = element_text(color = "#000000"),
axis.line = element_line(color = "#000000"),
axis.ticks = element_line(color = "#000000"),
title = element_text(color = "#000000"),
legend.title = element_text(color = "#000000"),
legend.text = element_text(color = "#000000"))
if (!(is.null(y_lim))) plot = plot + ylim(y_lim)
if (!(is.null(x_lim))) plot = plot + xlim(x_lim)
if (print_plot == T) {
(print(plot))}
return(plot)
} # END
#############################################################################
##############################################################################
# Import/read the matrix.gz file
if (class(matrix.file) == "character" & length(matrix.file) == 1) {
m = Rseb::read.computeMatrix.file(matrix.file)
metadata = m$metadata
matrix.data = m$matrix.data
} else if (class(matrix.file) == "list" & length(matrix.file) == 3 & unique(names(matrix.file) == c("metadata", "matrix.data", "original.file.path"))) {
metadata = matrix.file$metadata
matrix.data = matrix.file$matrix.data
} else {
return(warning("The 'matrix.file' must be a single string indicating a full path to a matrix.gz file generated by deepTools/computeMatrix or by Rseb::computeMatrix.deeptools(), or a list generated by the function Rseb::read.computeMatrix.file."))
}
# Generate metadata variables
sample_start_column = as.numeric(as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "sample_boundaries")$values, pattern = ",")[[1]]))
sample_names = as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "sample_labels")$values, pattern = ",")[[1]])
group_start_row = as.numeric(as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "group_boundaries")$values, pattern = ",")[[1]]))
group_names = as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "group_labels")$values, pattern = ",")[[1]])
##############################################################################
# Get the number at which the matrix data starts (after 'chr' 'start' 'end' 'strand' etc.)
number.info.columns = ncol(matrix.data) - sample_start_column[length(sample_start_column)]
# Remove NaN values and substitute them with 0 if missing.data.as.zero = TRUE or NA if FALSE
missing.data.as.zero.automatic = as.logical(toupper(dplyr::filter(.data = metadata, parameters == "missing_data_as_zero")$values))
if (is.null(missing.data.as.zero)) {
missing.data.as.zero = missing.data.as.zero.automatic
} else if (is.logical(missing.data.as.zero)) {
if (missing.data.as.zero != missing.data.as.zero.automatic) {
message(paste("The parameter 'missing.data.as.zero' set in this function as ", as.character(missing.data.as.zero),
", differs from the automatic obtained from the matrix file which is set as ", as.character(missing.data.as.zero.automatic),
".\nMissing data will be treated as set in this function: 'missing.data.as.zero = ", as.character(missing.data.as.zero), "'.", sep = ""))}
} else {return(warning("The parameter 'missing.data.as.zero' must be a logical value or NULL."))}
is.nan.data.frame = function(data.frame) do.call(cbind, lapply(data.frame, is.nan))
matrix.data[is.nan(matrix.data)] = ifelse(test = missing.data.as.zero,
yes = 0,
no = NA)
message(ifelse(test = missing.data.as.zero,
yes = "Missing data have been converted to 0 since the parameter 'missing.data.as.zero' is TRUE.",
no = "Missing data have been converted to NA since the parameter 'missing.data.as.zero' is FALSE. \nNA values will be excluded from the statistical computations."))
##############################################################################
# Generate a list with a table per sample
samples.table.list = list()
# Change sample and group names by custom ones
if (!is.null(sample.names)) {
if (length(unique(sample.names)) == length(sample_names)) {
sample_names = sample.names
} else {message("Default sample.names have been used because custom valus are not unique and/or incomplete")}
}
if (!is.null(region.names)) {
if (length(unique(region.names)) == length(group_names)) {
group_names = region.names
} else {message("Default region.names have been used because custom valus are not unique and/or incomplete")}
}
for (i in 1:length(sample_names)) {
# define the limits of the sample keeping the first lines
start.col = sample_start_column[i] + number.info.columns + 1
end.col = sample_start_column[i+1] + number.info.columns
sample.table =
matrix.data %>%
dplyr::select(c(1:number.info.columns, start.col:end.col)) %>%
# adding a column with the groups names
mutate(group = rep(group_names,
times = c(group_start_row[-1] - group_start_row[-length(group_start_row)])))
# Add the single table to a list
samples.table.list[[i]] = sample.table
}
names(samples.table.list) = sample_names
##############################################################################
# Generate the statistical tables
sample.stat.table.list = list()
for (s in 1:length(sample_names)) {
group.stats.table.list = list()
# Computations of the stats per group in order to then merge the different group tables
for (g in 1:length(group_names)) {
group.table =
samples.table.list[[s]] %>%
filter(group == group_names[g]) %>%
dplyr::select(-c(1:number.info.columns), -group)
group.mean = colMeans(group.table, na.rm = T)
group.median = robustbase::colMedians(as.matrix(group.table), na.rm = T)
group.sum = colSums(group.table, na.rm = T)
group.sd = matrixStats::colSds(as.matrix(group.table), na.rm = T)
group.sem = group.sd / sqrt(nrow(group.table))
group.stats.table.list[[g]] =
data.frame(group = group_names[g],
mean = group.mean,
median = group.median,
sum = group.sum,
sd = group.sd,
sem = group.sem)
} # 'g' For end
# x-axes values, distance from center
upstream = as.numeric(as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "upstream")$values, pattern = ",")[[1]]))[s]
downstream = as.numeric(as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "downstream")$values, pattern = ",")[[1]]))[s]
body = as.numeric(as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "body")$values, pattern = ",")[[1]]))[s]
bin = as.numeric(as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "bin_size")$values, pattern = ",")[[1]]))[s]
distance.range = seq(from = -upstream, to = (body + downstream), by = bin)
distance.range = distance.range[distance.range != 0]
sample.stat.table.list[[s]] =
purrr::reduce(.x = group.stats.table.list, .f = rbind) %>%
mutate(distance = rep(distance.range, length(group_names)),
sample = sample_names[s])
} # 's' For end
# Merge of all the tables in a single one
full.stat.table =
purrr::reduce(.x = sample.stat.table.list, .f = rbind) %>%
dplyr::mutate(sample = factor(sample, levels = sample_names),
group = factor(group, levels = group_names))
##############################################################################
# Generation of the plots
plot.list = list()
# Remove error plotting option when using the 'sum' mode, since it means nothing for a sum
if (signal.type == "sum") {
plot.error = F
message("The error will not be plotted since mode 'sum' is used.")
}
# Function to check the length of x.lim, y.lim, title
check.parameter.length =
function(parameter, parameter.name, plots.number) {
if (!is.null(parameter) & length(parameter) != plots.number & length(parameter) > 1) {
message(paste("The parameter", parameter.name, "has been set as NULL since the number of elements was different from the number of plots required."))
return(NULL)}
else {return(parameter)}
}
###########
### Generate a plot by group (each plot is a group, each signal/line is a sample)
if (plot.by.group == T) {
# Define the colors for the samples
if ((length(colors) > 1 & length(colors) < length(sample_names)) | length(colors) == 0) {colors = rainbow(length(sample_names))}
for (i in 1:length(group_names)) {
# Check if plot the center line
regionEnd = as.numeric(as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "body")$values, pattern = ",")[[1]]))
if (length(regionEnd) == 1) {regionEnd = rep(regionEnd, length(group_names))}
if (plot.vertical.lines == T) {
center.line.value = unique(c(0, regionEnd[i]))} else {
center.line.value = NULL}
# Repeat the value of x.lim, y.lim, title parameters if their length is 1
if (!is.null(x.lim) & length(x.lim) == 1) {x.lim = lapply(1:length(group_names), function(j) x.lim[[1]])}
if (!is.null(y.lim) & length(y.lim) == 1) {y.lim = lapply(1:length(group_names), function(j) y.lim[[1]])}
if (!is.null(title) & length(title) == 1) {title = rep(title, length(group_names))}
if (!is.null(x.lab) & length(x.lab) == 1) {x.lab = rep(x.lab, length(group_names))}
if (!is.null(y.lab) & length(y.lab) == 1) {y.lab = rep(y.lab, length(group_names))}
# Check the length of the x.lim, y.lim, title parameters
x.lim = check.parameter.length(parameter = x.lim, parameter.name = "'x.lim'", plots.number = length(group_names))
y.lim = check.parameter.length(parameter = y.lim, parameter.name = "'y.lim'", plots.number = length(group_names))
title = check.parameter.length(parameter = title, parameter.name = "'title'", plots.number = length(group_names))
x.lab = check.parameter.length(parameter = x.lab, parameter.name = "'x.lab'", plots.number = length(group_names))
y.lab = check.parameter.length(parameter = y.lab, parameter.name = "'y.lab'", plots.number = length(group_names))
# Select only current group subtable
current.table = full.stat.table %>% filter(group == group_names[i])
# Define which value to use for the scores
if (signal.type == "mean") {
score = current.table$mean} else if (signal.type == "median") {
score = current.table$median} else if (signal.type == "sum") {
score = current.table$sum} else {
return(warning("The signal.type must be one among 'mean', 'median', 'sum'."))
}
# Define which value to use for error
if (error.type == "sem") {
error = current.table$sem} else if (error.type == "sd") {
error = current.table$sd} else {
return(warning("The error.type must be one between 'sem' or 'sd'."))
}
# Plot saving in the list
plot.list[[i]] =
density_plot_smooth(
samples = current.table$sample,
scores = score,
span = smooth.span,
positions = current.table$distance,
variance_scores = error,
ylab = ifelse(test = is.null(y.lab),
yes = ifelse(test = plot.error == T,
yes = paste(tools::toTitleCase(signal.type), "\u00b1", toupper(error.type), "of density signal"),
no = paste(tools::toTitleCase(signal.type), "of density signal")),
no = y.lab[i]),
xlab = ifelse(test = is.null(x.lab),
yes = "Distance from reference point [bp]",
no = x.lab[i]),
line_type = line.type,
x_lim = x.lim[[i]],
y_lim = y.lim[[i]],
x_intercept = center.line.value,
colors = colors,
title = ifelse(test = is.null(title),
yes = group_names[i],
no = title[i]),
text_size = text.size,
variance = plot.error,
print_plot = F,
line_width = line.width,
variance_opacity = error.transparency) +
theme(legend.position = legend.position,
legend.background = element_blank(),
axis.line = element_line(size = axis.line.width),
axis.ticks = element_line(size = axis.line.width))
names(plot.list)[i] = group_names[i]
} # for loop end
########
### Generate a plot by sample (each plot is a sample, each signal/line is a group)
} else { # if end
# Define the colors for each group
if ((length(colors) > 1 & length(colors) < length(group_names)) | length(colors) == 0) {colors = rainbow(length(group_names))}
for (i in 1:length(sample_names)) {
# Check if plot the center line
regionEnd = as.numeric(as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "body")$values, pattern = ",")[[1]]))
if (length(regionEnd) == 1) {regionEnd = rep(regionEnd, length(sample_names))}
if (plot.vertical.lines == T) {
center.line.value = unique(c(0, regionEnd[i]))} else {
center.line.value = NULL}
# Repeat the value of x.lim, y.lim, title parameters if their length is 1
if (!is.null(x.lim) & length(x.lim) == 1) {x.lim = lapply(1:length(sample_names), function(j) x.lim[[1]])}
if (!is.null(y.lim) & length(y.lim) == 1) {y.lim = lapply(1:length(sample_names), function(j) y.lim[[1]])}
if (!is.null(title) & length(title) == 1) {title = rep(title, length(sample_names))}
if (!is.null(x.lab) & length(x.lab) == 1) {x.lab = rep(x.lab, length(sample_names))}
if (!is.null(y.lab) & length(y.lab) == 1) {y.lab = rep(y.lab, length(sample_names))}
# Check the length of the x.lim, y.lim, title parameters
x.lim = check.parameter.length(parameter = x.lim, parameter.name = "'x.lim'", plots.number = length(sample_names))
y.lim = check.parameter.length(parameter = y.lim, parameter.name = "'y.lim'", plots.number = length(sample_names))
title = check.parameter.length(parameter = title, parameter.name = "'title'", plots.number = length(sample_names))
x.lab = check.parameter.length(parameter = x.lab, parameter.name = "'x.lab'", plots.number = length(sample_names))
y.lab = check.parameter.length(parameter = y.lab, parameter.name = "'y.lab'", plots.number = length(sample_names))
# Select only current group subtable
current.table = full.stat.table %>% filter(sample == sample_names[i])
# Define which value to use for the scores
if (signal.type == "mean") {
score = current.table$mean} else if (signal.type == "median") {
score = current.table$median} else if (signal.type == "sum") {
score = current.table$sum} else {
return(warning("The signal.type must be one among 'mean', 'median', 'sum'"))
}
# Define which value to use for error
if (error.type == "sem") {
error = current.table$sem} else if (error.type == "sd") {
error = current.table$sd} else {
return(warning("The error.type must be one between 'sem' or 'sd'"))
}
# Plot saving in the list
plot.list[[i]] =
density_plot_smooth(
samples = current.table$group,
scores = score,
span = smooth.span,
positions = current.table$distance,
variance_scores = error,
ylab = ifelse(test = is.null(y.lab),
yes = ifelse(test = plot.error == T,
yes = paste(tools::toTitleCase(signal.type), "\u00b1", toupper(error.type), "of density signal"),
no = paste(tools::toTitleCase(signal.type), "of density signal")),
no = y.lab[i]),
xlab = ifelse(test = is.null(x.lab),
yes = "Distance from reference point [bp]",
no = x.lab[i]),
line_type = line.type,
x_lim = x.lim[[i]],
y_lim = y.lim[[i]],
x_intercept = center.line.value,
colors = colors,
title = ifelse(test = is.null(title),
yes = sample_names[i],
no = title[i]),
text_size = text.size,
variance = plot.error,
print_plot = F,
line_width = line.width,
variance_opacity = error.transparency) +
labs(color = "regions group",
fill = "regions group",
linetype = "regions group",
group = "regions group") +
theme(legend.position = legend.position,
legend.background = element_blank(),
axis.line = element_line(size = axis.line.width),
axis.ticks = element_line(size = axis.line.width))
# Add a name to the plot, element in the list
names(plot.list)[i] = sample_names[i]
} # for end
} # else end
# Set the same Y-axes for all the plots, if required by 'y.identical.auto = T'
if (y.identical.auto == T & is.null(y.lim) & length(plot.list) > 1) {
plot.list = Rseb::uniform.y.axis(plot.list = plot.list, ticks.each = y.ticks.interval, digits = y.digits)
}
# if (y.identical.auto == T & is.null(y.lim) & length(plot.list) > 1) {
# y.min_list = c()
# y.max_list = c()
#
# for (i in 1:length(plot.list)) {
# y.min_list = c(y.min_list, ggplot_build(plot.list[[i]])$layout$panel_params[[1]]$y$limits[1])
# y.max_list = c(y.max_list, ggplot_build(plot.list[[i]])$layout$panel_params[[1]]$y$limits[2])
# }
#
# breaks = labeling::extended(dmin = floor(min(y.min_list)),
# dmax = ceiling(max(y.max_list)),
# m = y.ticks.number)
#
# for (i in 1:length(plot.list)) {
# plot.list[[i]] =
# plot.list[[i]] +
# scale_y_continuous(breaks = breaks,
# limits = c(min(y.min_list), max(y.max_list)))
# }
# }
# Reshape the X-axis to include the reference points, if required by 'write.reference.points = T'
if (write.reference.points == T & is.null(x.lim)) {
reference_point = as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "ref_point")$values, pattern = ",")[[1]])
body = as.numeric(as.vector(stringr::str_split(string = dplyr::filter(.data = metadata, parameters == "body")$values, pattern = ",")[[1]]))
if (length(reference_point) < length(plot.list)) {reference_point = rep(reference_point[1], length(plot.list))}
if (length(body) < length(plot.list)) {body = rep(body[1], length(plot.list))}
for (i in 1:length(plot.list)) {
labels = ggplot_build(plot.list[[i]])$layout$panel_params[[1]]$x$get_labels()
# Substitute the 0 value
labels = gsub(pattern = "^0$",
replacement = ifelse(test = reference_point[i] == "null",
yes = "start",
no = reference_point[i]),
x = labels)
# Substitute the 'end region' value when the plot is in scale-regions mode
if (reference_point[i] == "null") {
for (k in 1:length(labels)) {
# check if the current label could be a number
if (grepl("[-]?[0-9]+[.]?[0-9]*|[-]?[0-9]+[L]?|[-]?[0-9]+[.]?[0-9]*[eE][0-9]+", labels[k])) {
if (as.numeric(labels[k]) >= body[i]) {labels[k] = as.character(as.numeric(labels[k]) - body[i])}
}
}
labels = gsub(pattern = "^0$", replacement = "end", x = labels)
}
plot.list[[i]] = plot.list[[i]] + scale_x_continuous(labels = labels)
} # end For
} # end If
##############################################################################
# generate the multiplot
multiplot = cowplot::plot_grid(plotlist = plot.list, nrow = n.row.multiplot, byrow = by.row, align = "hv")
# Print multiplot if required
if (print.multiplot == T) {print(multiplot)}
# Export the file if required
if (!is.null(multiplot.export.file)) {
pdf(file = multiplot.export.file,
height = real.height.single.plot * n.row.multiplot,
width = real.width.single.plot * ceiling(length(plot.list)/n.row.multiplot))
print(multiplot)
dev.off()
message("Multiplot exported as: ", multiplot.export.file)
}
##############################################################################
# RETURN a list of data
return(list(data.table = full.stat.table,
metadata = metadata,
plot.list = plot.list,
multiplot = multiplot))
} # End function
################################################################################
#----------------------------- END OF FUNCTION --------------------------------#
################################################################################
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