parallel_plot {RQdeltaCT}

parallel_plot

Description

This function illustrates expression values in a pairwise samples as series of lines connected across each axis. This function can be used only for a pairwise data.

Usage

parallel_plot(
  data,
  sel.Gene = "all",
  scale = "globalminmax",
  alpha = 0.7,
  custom.colors = FALSE,
  order = "anyClass",
  colors,
  linewidth = 1,
  show.points = TRUE,
  x.axis.title = "",
  y.axis.title = "value",
  axis.title.size = 11,
  axis.text.size = 10,
  legend.text.size = 11,
  legend.title = "Gene",
  legend.title.size = 11,
  legend.position = "top",
  plot.title = "",
  plot.title.size = 14,
  save.to.tiff = FALSE,
  dpi = 600,
  width = 15,
  height = 15,
  name.tiff = "parallel_plot"
)

Arguments

data

Object returned from make_Ct_ready() or delta_Ct() functions.

sel.Gene

Character vector with names of genes to include, or "all" (default) to use all genes.

scale

Character: a scale used for data presentation, one of the passed to ggparcoord() function. Generally, scaling is not required since variables are the same units. Default to "globalminmax (no scaling).

alpha

Numeric: transparency of lines, a value between 0 and 1. Default to 0.7.

custom.colors

Logical: if custom vector colors for genes is provided (and passed to the colors parameter), it should be set to TRUE. For default colors, use custom.colors = FALSE (default).

order

Character: method for groups ordering, one of the used in the ggparcoord() function. Default to 'anyClass'. Must either be a vector of column indices (obligatory if only one gene is plotted), starting from 3 (e.g., for two groups it can be c(3,4) or c(4,3)), or one of 'skewness', 'allClass', 'anyClass' (default), as well as scagnostic measures available in the 'scagnostics' package (must be loaded): 'Outlying', ‘Skewed’, 'Clumpy', 'Sparse', 'Striated', 'Convex', 'Skinny', 'Stringy', 'Monotonic'.

colors

Character vector containing custom colors for genes. The number of colors must be equal to the number of presented genes. Must be provided if custom.colors = TRUE.

linewidth

Numeric: width of lines. Default to 1.

show.points

Logical: if TRUE (default), points will be also shown.

x.axis.title

Character: title of x axis. Default to "".

y.axis.title

Character: title of y axis. Default to "value".

axis.title.size

Integer: font size of axis titles. Default to 11.

axis.text.size

Integer: font size of axis text. Default to 10.

legend.text.size

Integer: font size of legend text. Default to 11.

legend.title

Character: title of legend for groups. Default to "Gene".

legend.title.size

Integer: font size of legend title. Default to 11.

legend.position

Position of the legend, can be "top", "right" (default), "bottom", "left", or "none" (no legend). See description for legend.position parameter in ggplot2::theme() function.

plot.title

Character: title of plot. Default to "".

plot.title.size

Integer: font size of plot title. Default to 14.

save.to.tiff

Logical: if TRUE, plot will be saved as .tiff file. Default to FALSE.

dpi

Integer: resolution of saved .tiff file. Default to 600.

width

Numeric: width (in cm) of saved .tiff file. Default to 15.

height

Numeric: height (in cm) of saved .tiff file. Default to 15.

name.tiff

character: name of saved .tiff file, without ".tiff" name of extension. Default to "pca_and_kmeans".

Value

An object with plot. Created plot is also displayed on the graphic device.

Examples

library(tidyverse)
library(GGally)
data(data.Ct.pairwise)
data.CtF.pairwise <- filter_Ct(data = data.Ct.pairwise,
                               flag.Ct = "Undetermined",
                               maxCt = 35,
                               flag = c("Undetermined"),
                               remove.Gene = c("Gene9", "Gene2","Gene5", "Gene11","Gene1"))
data.CtF.ready.pairwise <- make_Ct_ready(data = data.CtF.pairwise,
                                         imput.by.mean.within.groups = TRUE)
data.dCt.pairwise <- delta_Ct(data = data.CtF.ready.pairwise,
                              ref = "Gene4")
parallel.plot <- parallel_plot(data = data.dCt.pairwise,
                               sel.Gene = c("Gene8","Gene19"))