threeFACTORplot {rtpcr}

Bar plot of the relative gene expression (\Delta C_T method) from the qpcrANOVARE output of a a three-factorial experiment data

Description

Bar plot of the relative expression (\Delta C_T method) of a gene along with the confidence interval and significance

Usage

threeFACTORplot(
  res,
  arrangement = c(1, 2, 3),
  bar.width = 0.5,
  fill = "Reds",
  xlab = "none",
  ylab = "Relative Expression",
  errorbar = "se",
  y.axis.adjust = 0.5,
  y.axis.by = 2,
  letter.position.adjust = 0.3,
  legend.title = "Legend Title",
  legend.position = c(0.4, 0.8),
  fontsize = 12,
  fontsizePvalue = 5,
  show.letters = TRUE,
  axis.text.x.angle = 0,
  axis.text.x.hjust = 0.5
)

Arguments

res

the FC data frame created by qpcrANOVARE(x)$Result function on a three factorial data such as data_3factor example data frame.

arrangement

order based on the columns in the output table (e.g. c(2,3,1) or c(1,3,2)) affecting factor arrangement of the output graph.

bar.width

a positive number determining bar width.

fill

a color vector specifying the fill color for the columns of the bar plot. One of the palettes in display.brewer.all (e.g. "Reds" or "Blues", ...) can be applied.

xlab

the title of the x axis

ylab

the title of the y axis

errorbar

Type of error bar, can be se or ci.

y.axis.adjust

a negative or positive number for reducing or increasing the length of the y axis.

y.axis.by

determines y axis step length

letter.position.adjust

adjust the distance between the grouping letters to the error bars

legend.title

legend title

legend.position

a two digit vector specifying the legend position.

fontsize

all fonts size of the plot

fontsizePvalue

font size of the pvalue labels

show.letters

a logical variable. If TRUE, mean grouping letters are added to the bars.

axis.text.x.angle

angle of x axis text

axis.text.x.hjust

horizontal justification of x axis text

Details

The threeFACTORplot function generates the bar plot of the average fold change for target genes along with the significance, standard error (se) and the 95% confidence interval (ci).

Value

Bar plot of the average fold change for target genes along with the significance and the 95% confidence interval as error bars.

Author(s)

Ghader Mirzaghaderi

Examples

#' # See a sample data frame
data_3factor

# Before plotting, the result needs to be extracted as below:
res <- qpcrANOVARE(data_3factor, numberOfrefGenes = 1)$Result
res

# Arrange the first three colunms of the result table.
# This determines the columns order and shapes the plot output.
threeFACTORplot(res,
    arrangement = c(3, 1, 2),
    xlab = "condition")


threeFACTORplot(res, arrangement = c(1, 2, 3), bar.width = 0.5, fill = "Greys", 
xlab = "Genotype", ylab = "Relative Expression")


# Reordering factor levels to a desired order.
res$Conc <- factor(res$Conc, levels = c("L","M","H"))
res$Type <- factor(res$Type, levels = c("S","R"))

# Producing the plot
threeFACTORplot(res, arrangement = c(2, 3, 1), bar.width = 0.5, 
fill = "Reds", xlab = "Drought", ylab = "Relative Expression", 
errorbar = "se", legend.title = "Genotype", legend.position = c(0.2, 0.8))


# When using ci as error, increase the 
# y.axis.adjust value to see the plot correctly!
threeFACTORplot(res, arrangement = c(2, 3, 1), bar.width = 0.8, fill = "Greens", 
xlab = "Drought", ylab = "Relative Expression", errorbar = "ci", 
y.axis.adjust = 1, y.axis.by = 2, letter.position.adjust = 0.6, 
legend.title = "Genotype", fontsize = 12, legend.position = c(0.2, 0.8), 
show.letters = TRUE)