| FCh_plot {RQdeltaCT} | R Documentation |
FCh_plot
Description
This function creates barplot that illustrate fold change values with indicating of significance by different colors of bars.
Usage
FCh_plot(
data,
use.p = TRUE,
mode,
p.threshold = 0.05,
use.FCh = FALSE,
FCh.threshold = 2,
use.sd = FALSE,
sel.Gene = "all",
bar.width = 0.8,
signif.show = FALSE,
signif.labels,
signif.length = 0.2,
signif.dist = 0.5,
y.exp.low = 0.1,
y.exp.up = 0.1,
angle = 0,
rotate = FALSE,
colors = c("#66c2a5", "#fc8d62"),
border.color = "black",
x.axis.title = "",
y.axis.title = "log10(Fold change)",
axis.title.size = 11,
axis.text.size = 10,
legend.text.size = 11,
legend.title = "Selected as significant?",
legend.title.size = 11,
legend.position = "top",
plot.title = "",
plot.title.size = 14,
dpi = 600,
width = 15,
height = 15,
save.to.tiff = FALSE,
name.tiff = "FCh_plot"
)
Arguments
data |
Object returned from RQ_dCt() or RQ_ddCt() functions. |
use.p |
Logical: if TRUE, p value threshold will be used to label gene as significant. |
mode |
Character: which p value should be used? One of the "t" (p values from Student's t test), "t.adj" (adjusted p values from Student's t test), "mw" (p values from Mann-Whitney U test),"mw.adj" (adjusted p values from Mann-Whitney U test), "depends" (if data in both compared groups were considered as derived from normal distribution (p value from Shapiro_Wilk test > 0.05) - p values from Student's t test will be used for significance assignment, otherwise p values from Mann-Whitney U test will be used), "depends.adj" (if data in both compared groups were considered as derived from normal distribution (p value from Shapiro_Wilk test > 0.05) - adjusted p values from Student's t test will be used for significance assignment, otherwise adjusted p values from Mann-Whitney U test will be used), and "user" that can be used the user intend to use another p values, e.g. obtained from other statistical test. In such situation, before run FCh_plot function, the user should prepare data frame object named "user" that contains two columns, the first of them with Gene names and the second with p values. The order of columns must be kept as described. |
p.threshold |
Numeric: threshold of p values for statistical significance. Default to 0.05. |
use.FCh |
Logical: if TRUE, the criterion of fold change will be also used for significance assignment of genes. |
FCh.threshold |
Numeric: threshold of fold change values used for significance assignment of genes. If is set to 2 (default), genes with 2-fold changed expression (increased or decreased) between groups will be assigned as significant. |
use.sd |
Logical: if TRUE, errorbars with standard deviations will be added to the plot. |
sel.Gene |
Character vector with names of genes to include, or "all" (default) to use all names of genes. |
bar.width |
numeric: width of bars. |
signif.show |
Logical: if TRUE, labels for statistical significance will be added to the plot. Default to FALSE. |
signif.labels |
Character vector with statistical significance labels (e.g. "ns.","***", etc.). The number of elements must be equal to the number of genes used for plotting. All elements in the vector must be different; therefore, symmetrically white spaces to repeated labels must be add to the same labels, e.g. "ns.", " ns. ", " ns. ". |
signif.length |
Numeric: length of horizontal bars under statistical significance labels, values from 0 to 1. |
signif.dist |
Numeric: distance between errorbar and statistical significance labels. |
y.exp.low, y.exp.up |
Numeric: space between data on the plot and lower or upper axis. Useful to add extra space for statistical significance labels when faceting = TRUE. |
angle |
Integer: value of angle in which names of genes are displayed. Default to 0. |
rotate |
Logical: if TRUE, bars will be arranged horizontally. Deafault to FALSE. |
colors |
Character vector length of one (when use.p = FALSE) or two (when use.p = TRUE), containing colors for significant and no significant genes. |
border.color |
Character: color for borders of bars. |
x.axis.title |
Character: title of x axis. Default to "Gene". |
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. Default to "Group". |
legend.title.size |
Integer: font size of legend title. Default to 11. |
legend.position |
Position of the legend, can be one of "top" (default), "right", "bottom", "left", or "none" (no legend). See description for legend.position in ggplot2::theme() function. |
plot.title |
Character: title of plot. Default to "". |
plot.title.size |
Integer: font size of plot title. Default to 14. |
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. |
save.to.tiff |
Logical: if TRUE, plot will be saved as .tiff file. Default to FALSE. |
name.tiff |
Character: name of saved .tiff file, without ".tiff" name of extension. Default to "FCh_plot". |
Value
List containing object with barplot and data frame with results. Created plot is also displayed on graphic device.
Examples
library(ggsignif)
library(tidyverse)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
remove.Gene = c("Gene2","Gene5","Gene6","Gene9","Gene11"),
remove.Sample = c("Control08","Control16","Control22"))
data.CtF.ready <- make_Ct_ready(data.CtF, imput.by.mean.within.groups = TRUE)
data.dCt <- delta_Ct(data.CtF.ready, ref = "Gene8")
data.dCtF <- filter_transformed_data(data.dCt, remove.Sample = c("Control11"))
results.ddCt <- RQ_ddCt(data.dCtF, "Disease", "Control")
signif.labels <- c("****",
"**",
"ns.",
" ns. ",
" ns. ",
" ns. ",
" ns. ",
" ns. ",
" ns. ",
" ns. ",
" ns. ",
" ns. ",
" ns. ",
"***")
FCh.plot <- FCh_plot(results.ddCt,
mode = "depends",
use.FCh = TRUE,
FCh.threshold = 2.5,
signif.labels = signif.labels,
angle = 30)
head(FCh.plot[[2]])
# with user p values - calculated using stats::wilcox.test() function:
user <- data.dCt %>%
pivot_longer(cols = -c(Group, Sample), names_to = "Gene", values_to = "dCt") %>%
group_by(Gene) %>%
summarise(MW_test_p = wilcox.test(dCt ~ Group)$p.value, .groups = "keep")
FCh.plot <- FCh_plot(results.ddCt,
mode = "user",
use.FCh = TRUE,
FCh.threshold = 2,
signif.labels = signif.labels,
angle = 30)
head(FCh.plot[[2]])