Package 'RQdeltaCT'

Title: Relative Quantification of Gene Expression using Delta Ct Methods
Description: The commonly used methods for relative quantification of gene expression levels obtained in real-time PCR (Polymerase Chain Reaction) experiments are the delta Ct methods, encompassing 2^-dCt and 2^-ddCt methods, originally proposed by Kenneth J. Livak and Thomas D. Schmittgen (2001) <doi:10.1006/meth.2001.1262>. The main idea is to normalise gene expression values using endogenous control gene, present gene expression levels in linear form by using the 2^-(value)^ transformation, and calculate differences in gene expression levels between groups of samples (or technical replicates of a single sample). The 'RQdeltaCT' package offers functions that cover both methods for comparison of either independent groups of samples or groups with paired samples, together with importing expression datasets, performing multi-step quality control of data, enabling numerous data visualisations, enrichment of the standard workflow with additional useful analyses (correlation analysis, Receiver Operating Characteristic analysis, logistic regression), and conveniently export obtained results in table and image formats. The package has been designed to be friendly to non-experts in R programming.
Authors: Daniel Zalewski [aut, cre]
Maintainer: Daniel Zalewski <[email protected]>
License: GPL-3
Version: 1.3.2
Built: 2025-03-13 06:20:48 UTC
Source: https://github.com/donadelnal/rqdeltact

Help Index

control_boxplot_gene

Description

This function creates boxplot that illustrate distribution of data in each gene.

Usage

control_boxplot_gene(
  data,
  sel.Gene = "all",
  pairwise.FCh = FALSE,
  coef = 1.5,
  by.group = TRUE,
  colors = c("#66c2a5", "#fc8d62"),
  axis.title.size = 11,
  axis.text.size = 12,
  x.axis.title = "Gene",
  y.axis.title = "value",
  legend.title = "Group",
  legend.title.size = 11,
  legend.text.size = 11,
  legend.position = "right",
  plot.title = "",
  plot.title.size = 14,
  save.to.tiff = FALSE,
  dpi = 600,
  width = 15,
  height = 15,
  name.tiff = "control_boxplot_genes"
)

Arguments

data

Object returned from make_Ct_ready() or delta_Ct() functions. Also, a data frame with fold change values returned from RQ_dCt() and RQ_ddCt() functions run in a pairwise mode can be used.
sel.Gene

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

Logical: If fold change values returned from RQ_dCt() and RQ_ddCt() functions in a pairwise approach are used as data, this parameter should be set to TRUE, otherwise to FALSE (default).
coef

Numeric: how many times of interquartile range should be used to determine range point for whiskers. Default to 1.5.
by.group

Logical: if TRUE, distributions will be drawn by compared groups of samples.
colors

Character vector containing colors for groups, length of one (when by.group = FALSE) or equal to the number of groups (when by.group = TRUE).
axis.title.size

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

Integer: font size of axis text. Default to 12.
x.axis.title

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

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

Character: title of legend. Default to "Group".
legend.title.size

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

Integer: font size of legend text. 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 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 "control_boxplot_genes".

Value

Object with boxplot illustrating distribution of data for each gene. Created plot is also displayed on the graphic device.

Examples

library(tidyverse)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                      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 = "GAPDH")
control.boxplot.gene <- control_boxplot_gene(data.dCt)

control_boxplot_sample

Description

Boxplot that illustrate distribution of data in each sample. This function is helpful to identify outlier samples.

Usage

control_boxplot_sample(
  data,
  sel.Sample = "all",
  pairwise.FCh = FALSE,
  coef = 1.5,
  colors = c("#66c2a5", "#fc8d62"),
  x.axis.title = "Sample",
  y.axis.title = "value",
  axis.title.size = 11,
  axis.text.size = 12,
  legend.title = "Group",
  legend.title.size = 11,
  legend.text.size = 11,
  legend.position = "right",
  plot.title = "",
  plot.title.size = 14,
  save.to.tiff = FALSE,
  dpi = 600,
  width = 15,
  height = 15,
  name.tiff = "control_boxplot_samples"
)

Arguments

data

Object returned from make_Ct_ready() or delta_Ct() functions. Also, a data frame with fold change values returned from RQ_dCt() and RQ_ddCt() functions run in a pairwise mode can be used.
sel.Sample

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

Logical: If fold change values returned from RQ_dCt() and RQ_ddCt() functions in a pairwise approach are used as data, this parameter should be set to TRUE, otherwise to FALSE (default).
coef

Numeric: how many times of interquartile range should be used to determine range point for whiskers. Default to 1.5.
colors

Character vector containing colors for compared groups. Numbers of colors must be equal to number of groups. Default to c("#66c2a5", "#fc8d62"). If pairwise.FCh parameter is set to TRUE, one color is required (data contain no groups).
x.axis.title

Character: title of x axis. Default to "Sample".
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 12.
legend.title

Character: title of legend. Default to "Group".
legend.title.size

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

Integer: font size of legend text. 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 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 "control_boxplot_samples".

Value

Object with a boxplot illustrating distribution of data in each sample. Created plot is also displayed on the graphic device.

Examples

library(tidyverse)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                      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 = "GAPDH")
control.boxplot.sample <- control_boxplot_sample(data.dCt)

control_cluster_gene

Description

This function performs hierarchical clustering of genes based on the data, useful to gain insight into similarity in expression of analyzed genes.

Usage

control_cluster_gene(
  data,
  method.dist = "euclidean",
  sel.Sample = "all",
  method.clust = "average",
  pairwise.FCh = FALSE,
  x.axis.title = "Genes",
  y.axis.title = "Height",
  label.size = 0.8,
  plot.title = "",
  save.to.tiff = FALSE,
  dpi = 600,
  width = 15,
  height = 15,
  name.tiff = "control_clust_genes"
)

Arguments

data

Object returned from make_Ct_ready() or delta_Ct() functions. Also, table with fold change values returned from RQ_dCt() and RQ_ddCt() functions can be used, but in such situation a pairwise.FCh parameter must be set to TRUE.
method.dist

Character: name of method used for calculation of distances, derived from stats::dist() function, must be one of "euclidean" (default) , "maximum", "manhattan", "canberra", "binary" or "minkowski".
sel.Sample

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

Character: name of used method for agglomeration, derived from stats::hclust() function, must be one of "ward.D", "ward.D2", "single", "complete", "average" (default), "mcquitty", "median" or "centroid".
pairwise.FCh

Logical: If fold change values returned from RQ_dCt() and RQ_ddCt() functions in a pairwise approach are used as data, this parameter should be set to TRUE, otherwise to FALSE (default).
x.axis.title

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

Character: title of y axis. Default to "Height".
label.size

Numeric: size of text labels. Default to 0.8.
plot.title

Character: title of plot. Default to "".
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 "control_clust_genes".

Value

Plot with hierarchical clustering of genes, displayed on the graphic device.

Examples

library(tidyverse)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                      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 = "GAPDH")
control_cluster_gene(data.dCt)

control_cluster_sample

Description

This function performs hierarchical clustering of samples based on the data, useful to identify outlier samples.

Usage

control_cluster_sample(
  data,
  sel.Gene = "all",
  method.dist = "euclidean",
  method.clust = "average",
  pairwise.FCh = FALSE,
  x.axis.title = "Samples",
  y.axis.title = "Height",
  label.size = 0.8,
  plot.title = "",
  save.to.tiff = FALSE,
  dpi = 600,
  width = 15,
  height = 15,
  name.tiff = "control_clust_samples"
)

Arguments

data

Object returned from make_Ct_ready() or delta_Ct() functions. Also, table with fold change values returned from RQ_dCt() and RQ_ddCt() functions can be used, but in such situation a pairwise.FCh parameter must be set to TRUE.
sel.Gene

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

Character: name of method used for calculation of distances, derived from stats::dist() function, must be one of "euclidean" (default) , "maximum", "manhattan", "canberra", "binary" or "minkowski".
method.clust

Character: name of used method for agglomeration, derived from stats::hclust() function, must be one of "ward.D", "ward.D2", "single", "complete", "average" (default), "mcquitty", "median" or "centroid".
pairwise.FCh

Logical: If fold change values returned from RQ_dCt() and RQ_ddCt() functions in a pairwise approach are used as data, this parameter should be set to TRUE, otherwise to FALSE (default).
x.axis.title

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

Character: title of y axis. Default to "Height".
label.size

Numeric: size of text labels. Default to 0.8.
plot.title

Character: title of plot. Default to "".
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 "control_clust_samples".

Value

Plot with hierarchical clustering of samples, displayed on the graphic device.

Examples

library(tidyverse)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                       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 = "GAPDH")
control_cluster_sample(data.dCt)

control_Ct_barplot_gene

Description

Gene-wide quality control of raw Ct values across groups by illustrating numbers of Ct values labelled as reliable or not by using reliability criteria (see function parameters).

Usage

control_Ct_barplot_gene(
  data,
  flag.Ct = "Undetermined",
  maxCt = 35,
  flag = "Undetermined",
  colors = c("#66c2a5", "#fc8d62"),
  x.axis.title = "",
  y.axis.title = "Number",
  axis.title.size = 11,
  axis.text.size = 10,
  legend.title = "Reliable Ct value?",
  legend.title.size = 11,
  legend.text.size = 11,
  legend.position = "top",
  plot.title = "",
  plot.title.size = 14,
  save.to.tiff = FALSE,
  dpi = 600,
  width = 15,
  height = 15,
  name.tiff = "Ct_control_barplot_for_genes"
)

Arguments

data

Object returned from read_Ct_long() or read_Ct_wide() function, or data frame containing column named "Sample" with sample names, column named "Gene" with gene names, column named "Ct" with raw Ct values, column named "Group" with group names. Optionally, data frame can contain column named "Flag" with flag information (e.g. "Undetermined" and "OK"), which will be used for reliability assessment.
flag.Ct

Character of a flag used for undetermined Ct values. Default to "Undetermined".
maxCt

Numeric, a maximum of Ct value allowed. Default to 35.
flag

Character of a flag used in the Flag column for values which are unreliable. Default to "Undetermined".
colors

Character vector length of two, containing colors for Ct values which are labelled as reliable (first element of vector) or not (second element of vector).
x.axis.title

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

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

Character: title of legend. Default to "Reliable Ct value?".
legend.title.size

Integer: font size of legend title. Default to 12.
legend.text.size

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

Position of the legend, can be "top" (default), "right", "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 "Ct_control_barplot_for_genes".

Details

This function does not perform data filtering, but only counts Ct values labelled as reliable or not and presents them graphically. Could be useful to identify genes with low number of reliable Ct values.

Value

List containing plot and table with counts of reliable and non reliable Ct values in genes. Additional information about returned table is also printed to help the user to properly interpret returned table. Plot is also displayed on the graphic device.

Examples

library(tidyverse)
data(data.Ct)
gene.Ct.control <- control_Ct_barplot_gene(data.Ct)
gene.Ct.control[[2]]

control_Ct_barplot_sample

Description

Sample-wide quality control of raw Ct values by illustrating the numbers of Ct values labelled as reliable or not by using reliability criteria (see function parameters).

Usage

control_Ct_barplot_sample(
  data,
  flag.Ct = "Undetermined",
  maxCt = 35,
  flag = "Undetermined",
  colors = c("#66c2a5", "#fc8d62"),
  x.axis.title = "",
  y.axis.title = "Number",
  axis.title.size = 11,
  axis.text.size = 10,
  plot.title = "",
  plot.title.size = 14,
  legend.title = "Reliable Ct value?",
  legend.title.size = 11,
  legend.text.size = 11,
  legend.position = "top",
  save.to.tiff = FALSE,
  dpi = 600,
  width = 15,
  height = 15,
  name.tiff = "Ct_control_barplot_for_samples"
)

Arguments

data

Object returned from read_Ct_long() or read_Ct_wide() function, or data frame containing column named "Sample" with sample names, column named "Gene" with gene names, column named "Ct" with raw Ct values, and column named "Group" with group names. Optionally, data frame could contain column named "Flag" with flag information (e.g. "Undetermined" and "OK"), which will be used for reliability assessment.
flag.Ct

Character of a flag used for undetermined Ct values. Default to "Undetermined".
maxCt

Numeric, a maximum of Ct value allowed. Default to 35.
flag

Character of a flag used in the Flag column for values which are unreliable. Default to "Undetermined".
colors

Character vector length of two, containing colors for Ct values that were labelled as reliable (first element of vector) or not (second element of vector).
x.axis.title

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

Character: title of y axis. Default to "Number".
axis.title.size

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

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

Character: title of plot. Default to "".
plot.title.size

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

Character: title of legend. Default to "Reliable Ct value?".
legend.title.size

Integer: font size of legend title. Default to 12.
legend.text.size

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

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

Details

This function labels Ct values as reliable or not using given reliability criteria, counts them, and presents them graphically. Results are useful to identify samples with low numbers of reliable Ct values. This function does not perform data filtering.

Value

List containing plot and table with counts of reliable and no reliable Ct values in samples. Additional information about returned table is also printed to help the user to properly interpret returned table. Plot is also displayed on the graphic device.

Examples

library(tidyverse)
data(data.Ct)
sample.Ct.control <- control_Ct_barplot_sample(data.Ct)
sample.Ct.control[[2]]

control_heatmap

Description

This function creates heatmap with number of replicates for each gene across samples.

Usage

control_heatmap(
  data,
  sel.Gene = "all",
  display.numbers = TRUE,
  dist.row = "euclidean",
  dist.col = "euclidean",
  clust.method = "average",
  colors = c("#4575B4", "#FFFFBF", "#C32B23"),
  show.colnames = TRUE,
  show.rownames = TRUE,
  border.color = NA,
  fontsize = 10,
  fontsize.col = 10,
  fontsize.row = 10,
  angle.col = 0,
  cellwidth = NA,
  cellheight = NA,
  show.legend = FALSE,
  save.to.tiff = FALSE,
  dpi = 600,
  width = 15,
  height = 15,
  name.tiff = "heatmap_results"
)

Arguments

data

Object returned from read_Ct_long() or read_Ct_wide() function, or data frame containing at least column named "Sample" with sample names, column named "Gene" with gene names.
sel.Gene

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

Logical: if TRUE (default), numeric values will be printed to the cells.
dist.row, dist.col

Character: name of method used for calculation of distances between rows or columns, derived from stats::dist() function, must be one of "euclidean" (default) , "maximum", "manhattan", "canberra", "binary" or "minkowski".
clust.method

Character: name of used method for agglomeration, derived from stats::hclust() function, must be one of "ward.D", "ward.D2", "single", "complete", "average" (default), "mcquitty", "median" or "centroid".
colors

Vector with colors used to fill created heatmap.
show.colnames, show.rownames

Logical: of TRUE, names of columns (sample names) and rows (gene names) will be shown. Both default to TRUE.
border.color

Character: color of cell borders on heatmap. If set to NA (default) no border will be drawn.
fontsize

Numeric: global fontsize of heatmap. Default to 10.
fontsize.col, fontsize.row

Numeric: fontsize of colnames and rownames. Default to 10.
angle.col

Integer: angle of the column labels, one of the 0, 45, 90, 270, and 315.
cellwidth, cellheight

Numeric: width and height of individual cell. Both default to NA. These parameters are useful in situations where margins are too small and the plot is cropped (column names and annotation legend are sometimes partially hidden). Specification of this parameter allows to adjust size of the plot and solve this problem.
show.legend

Logical: if TRUE, legend will be shown. Default to FALSE.
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 "heatmap_results".

Value

Heatmap with hierarchical clustering, displayed on the graphic device (if save.to.tiff = FALSE) or saved to .tiff file (if save.to.tiff = TRUE).

Examples

library(tidyverse)
library(pheatmap)
data(data.Ct)
# Vector of colors to fill the heatmap can be specified to fit the user needings:
colors <- c("#4575B4","#FFFFBF","#C32B23")
control_heatmap(data.Ct,
                sel.Gene = "all",
                colors = colors,
                show.colnames = TRUE,
                show.rownames = TRUE,
                fontsize = 11,
                fontsize.row = 11)

control_pca_gene

Description

This function performs principal component analysis (PCA) for genes and generates plot illustrating spatial arrangement of genes using two first PCA components. This plot allows to gain insight into similarity in expression of analyzed genes. PCA analysis can not deal with missing values, thus all genes with at least one missing value are removed from data before analysis.

Usage

control_pca_gene(
  data,
  sel.Sample = "all",
  pairwise.FCh = FALSE,
  point.size = 4,
  point.shape = 19,
  alpha = 0.7,
  label.size = 3,
  hjust = 0.5,
  vjust = -1,
  color = "black",
  axis.title.size = 11,
  axis.text.size = 10,
  legend.text.size = 11,
  legend.title = "Group",
  legend.title.size = 11,
  legend.position = "right",
  plot.title = "",
  plot.title.size = 14,
  save.to.tiff = FALSE,
  dpi = 600,
  width = 15,
  height = 15,
  name.tiff = "control_pca_genes"
)

Arguments

data

Object returned from make_Ct_ready() or delta_Ct() functions. Also, table with fold change values returned from RQ_dCt() and RQ_ddCt() functions can be used, but in such situation a pairwise.FCh parameter must be set to TRUE.
sel.Sample

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

Logical: If fold change values returned from RQ_dCt() and RQ_ddCt() functions in a pairwise approach are used as data, this parameter should be set to TRUE, otherwise to FALSE (default).
point.size

Numeric: size of points. Default to 4.
point.shape

Integer: shape of points. Default to 19.
alpha

Numeric: transparency of points, a value between 0 and 1. Default to 0.7.
label.size

Numeric: size of points labels (names of samples). Default to 3.
hjust

Numeric: horizontal position of points labels. Default to 0.
vjust

Numeric: vertical position of points labels. Default to -1.
color

Character: color used for points.
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 "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 "control_pca_genes".

Value

Object with plot. The plot is also displayed on the graphic device.

Examples

library(tidyverse)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                      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 = "GAPDH")
control_pca_gene(data.dCt)

control_pca_sample

Description

This function performs principal component analysis (PCA) for samples and generate plot that illustrate spatial arrangement of samples based on the two first components. This plot is useful to identify outlier samples. PCA analysis can not deal with missing values, thus all samples with at least one missing value are removed from data before analysis.

Usage

control_pca_sample(
  data,
  sel.Gene = "all",
  pairwise.FCh = FALSE,
  point.size = 4,
  point.shape = 19,
  alpha = 0.7,
  colors = c("#66c2a5", "#fc8d62"),
  label.size = 3,
  hjust = 0.5,
  vjust = -1,
  axis.title.size = 11,
  axis.text.size = 10,
  legend.text.size = 11,
  legend.title = "Group",
  legend.title.size = 11,
  legend.position = "right",
  plot.title = "",
  plot.title.size = 14,
  save.to.tiff = FALSE,
  dpi = 600,
  width = 15,
  height = 15,
  name.tiff = "control_pca_samples"
)

Arguments

data

Object returned from make_Ct_ready() or delta_Ct() functions. Also, table with fold change values returned from RQ_dCt() and RQ_ddCt() functions can be used, but in such situation a pairwise.FCh parameter must be set to TRUE.
sel.Gene

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

Logical: If fold change values returned from RQ_dCt() and RQ_ddCt() functions in a pairwise approach are used as data, this parameter should be set to TRUE, otherwise to FALSE (default).
point.size

Numeric: size of points. Default to 4.
point.shape

Integer: shape of points. Default to 19.
alpha

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

Character vector containing colors for compared groups. The number of colors must be equal to the number of groups. Default to c("#66c2a5", "#fc8d62").
label.size

Numeric: size of points labels (names of samples). Default to 3.
hjust

Numeric: horizontal position of points labels. Default to 0.
vjust

Numeric: vertical position of points labels. Default to -1.
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 "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 "control_pca_samples".

Value

Object with plot. The plot is also displayed on the graphic device.

Examples

library(tidyverse)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                      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 = "GAPDH")
control_pca_sample(data.dCt)

corr_gene

Description

This function performs correlation analysis of genes based on the data, useful to gain insight into relationships between analyzed genes.

Usage

corr_gene(
  data,
  sel.Gene = "all",
  type = "upper",
  method = "pearson",
  add.coef = "black",
  order = "hclust",
  hclust.method = "average",
  size = 0.6,
  coef.size = 0.6,
  p.adjust.method = "BH",
  save.to.tiff = FALSE,
  dpi = 600,
  width = 15,
  height = 15,
  name.tiff = "corr_genes",
  save.to.txt = FALSE,
  name.txt = "corr_genes"
)

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.
type

Character: type of displayed matrix, must be one of the 'full' (full matrix), 'upper' (upper triangular, default) or 'lower' (lower triangular).
method

Character: type of correlations to compute, can be "pearson" (default) for Pearson's correlation coefficients or "spearman" for Spearman's rank correlation coefficients.
add.coef

If correlation coefficients should be add to the plot, specify color of coefficients (default to "black"). If NULL, correlation coefficients will not be printed.
order

Character: method used for ordering the correlation matrix, inherited from corrplot::corrplot() function. Must be one of the "original" (original order), "AOE" (angular order of the eigenvectors), "FPC" (first principal component order), "hclust" (hierarchical clustering order, default), or "alphabet" (alphabetical order).
hclust.method

Character: name of method used for hclust agglomeration, must be one of "ward", ward.D", "ward.D2", "single", "complete", "average" (default), "mcquitty", "median" or "centroid".
size

Numeric: size of variable names and numbers in legend. Default to 0.6.
coef.size

Numeric: size of correlation coefficients. Default to 0.6.
p.adjust.method

Character: p value correction method for multiple testing, one of the "holm", "hochberg", "hommel", "bonferroni", "BH" (default), "BY","fdr", or "none". See documentation for stats::p.adjust() function for details.
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 "corr_genes".
save.to.txt

Logical: if TRUE, correlation results (sorted by absolute values of correlation coefficients in descending order) will be saved to .txt file. Default to FALSE.
name.txt

character: name of saved .txt file, without ".txt" name of extension.. Default to "corr_genes".

Value

Plot illustrating correlation matrix (displayed on the graphic device) and data frame with computed correlation coefficients and p values.

Examples

library(tidyverse)
library(Hmisc)
library(corrplot)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                      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 = "GAPDH")
corr.genes <- corr_gene(data.dCt)
head(corr.genes)

corr_sample

Description

This function performs correlation analysis of samples based on the data. Results are useful to gain insight into relationships between analyzed samples.

Usage

corr_sample(
  data,
  sel.Sample = "all",
  type = "upper",
  method = "pearson",
  add.coef = "black",
  order = "hclust",
  hclust.method = "average",
  size = 0.6,
  coef.size = 0.6,
  p.adjust.method = "BH",
  save.to.tiff = FALSE,
  dpi = 600,
  width = 15,
  height = 15,
  name.tiff = "corr_samples",
  save.to.txt = FALSE,
  name.txt = "corr_samples"
)

Arguments

data

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

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

Character: type of displayed matrix, must be one of the 'full' (full matrix), 'upper' (upper triangular, default) or 'lower' (lower triangular).
method

Character: type of correlations to compute, can be "pearson" (default) for Pearson's correlation coefficients or "spearman" for Spearman's rank correlation coefficients.
add.coef

If correlation coefficients should be add to the plot, specify color of coefficients (default to "black"). If NULL, correlation coefficients will not be printed.
order

Character: method used for ordering the correlation matrix, inherited from corrplot::corrplot() function. Must be one of the "original" (original order), "AOE" (angular order of the eigenvectors), "FPC" (first principal component order), "hclust" (hierarchical clustering order, default), or "alphabet" (alphabetical order).
hclust.method

Character: name of method used for hclust agglomeration, must be one of "ward", ward.D", "ward.D2", "single", "complete", "average" (default), "mcquitty", "median" or "centroid".
size

Numeric: size of variable names and numbers in legend. Default to 0.6.
coef.size

Numeric: size of correlation coefficients. Default to 0.6.
p.adjust.method

Character: p value correction method for multiple testing, one of the "holm", "hochberg", "hommel", "bonferroni", "BH" (default), "BY","fdr", or "none". See documentation for stats::p.adjust() function for details.
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 "corr_samples".
save.to.txt

Logical: if TRUE, correlation results (sorted by absolute values of correlation coefficients in descending order) will be saved to .txt file. Default to FALSE.
name.txt

character: name of saved .txt file, without ".txt" name of extension.. Default to "corr_samples".

Value

Plot illustrating correlation matrix (displayed on the graphic device) and data.frame with computed correlation coefficients and p values.

Examples

library(tidyverse)
library(Hmisc)
library(corrplot)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                      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 = "GAPDH")
corr.samples <- corr_sample(data.CtF.ready)
head(corr.samples)

Gene expression dataset for RQdeltaCT package.

Description

Dataset that contain table with gene expression data obtained for 20 genes and 64 samples using qPCR experiments with TaqMan assays. Samples are divided into two groups: AAA (40 samples from patients with abdominal aortic aneurysm) and Control (24 samples from healthy volunteers). This dataset is a part of data used in article: Zalewski, D.; Chmiel, P.; Kołodziej, P.; Borowski, G.; Feldo, M.; Kocki, J.; Bogucka-Kocka, A. Dysregulations of Key Regulators of Angiogenesis and Inflammation in Abdominal Aortic Aneurysm. Int. J. Mol. Sci. 2023, 24, 12087. https://doi.org/10.3390/ijms241512087

Usage

data.Ct

Format

A data frame with 1288 rows and 5 variables:

Sample

character COLUMN_DESCRIPTION

Gene

character COLUMN_DESCRIPTION

Ct

character COLUMN_DESCRIPTION

Group

character COLUMN_DESCRIPTION

Flag

character COLUMN_DESCRIPTION

Source

Dataset is attached to the 'RQdeltaCT' package and can be loaded using 'data(data.Ct)' command.

Gene expression dataset for RQdeltaCT package - a reduced version.

Description

Dataset that contain table with gene expression data obtained for 19 genes and 20 samples using qPCR experiments with TaqMan assays. Samples are divided into two groups: Disease (10 samples) and Control (10 samples). This dataset is a part of data used in article: Zalewski, D.; Chmiel, P.; Kołodziej, P.; Borowski, G.; Feldo, M.; Kocki, J.; Bogucka-Kocka, A. Dysregulations of Key Regulators of Angiogenesis and Inflammation in Abdominal Aortic Aneurysm. Int. J. Mol. Sci. 2023, 24, 12087. https://doi.org/10.3390/ijms241512087

Usage

data.Ct.10vs10

Format

A data frame with 410 rows and 5 variables:

Sample

character COLUMN_DESCRIPTION

Gene

character COLUMN_DESCRIPTION

Ct

character COLUMN_DESCRIPTION

Group

character COLUMN_DESCRIPTION

Flag

character COLUMN_DESCRIPTION

Source

Dataset is attached to the 'RQdeltaCT' package and can be loaded using 'data(data.Ct.10vs10)' command.

Gene expression dataset for RQdeltaCT package - variant with three groups.

Description

Dataset that contain table with gene expression data obtained for 19 genes and 104 samples using qPCR experiments with TaqMan assays. Samples are divided into three groups: Disease AAA (40 samples), Disease VV (40 samples), and Control (24 samples). This dataset is a part of data used in articles: Zalewski, D.; Chmiel, P.; Kołodziej, P.; Borowski, G.; Feldo, M.; Kocki, J.; Bogucka-Kocka, A. Dysregulations of Key Regulators of Angiogenesis and Inflammation in Abdominal Aortic Aneurysm. Int. J. Mol. Sci. 2023, 24, 12087. https://doi.org/10.3390/ijms241512087 and Zalewski, D.; Chmiel, P.; Kołodziej, P.; Kocki, M.; Feldo, M.; Kocki, J.; Bogucka-Kocka, A. Key Regulators of Angiogenesis and Inflammation Are Dysregulated in Patients with Varicose Veins. Int. J. Mol. Sci. 2024, 25, 6785. https://doi.org/10.3390/ijms25126785

Usage

data.Ct.3groups

Format

A data frame with 2048 rows and 5 variables:

Sample

character COLUMN_DESCRIPTION

Gene

character COLUMN_DESCRIPTION

Ct

character COLUMN_DESCRIPTION

Group

character COLUMN_DESCRIPTION

FLAG

character COLUMN_DESCRIPTION

Source

Dataset is attached to the 'RQdeltaCT' package and can be loaded using 'data(data.Ct.3groups)' command.

Gene expression dataset for RQdeltaCT package. Siutable for pairwise analysis.

Description

Dataset that contain table with gene expression data obtained for 18 genes and 42 samples using qPCR experiments with TaqMan assays. Samples are divided into two groups: Before (21 samples) and After (21 samples). This dataset is created based on the real data used in article: Zalewski, D.; Chmiel, P.; Kołodziej, P.; Borowski, G.; Feldo, M.; Kocki, J.; Bogucka-Kocka, A. Dysregulations of Key Regulators of Angiogenesis and Inflammation in Abdominal Aortic Aneurysm. Int. J. Mol. Sci. 2023, 24, 12087. https://doi.org/10.3390/ijms241512087

Usage

data.Ct.pairwise

Format

A data frame with 756 rows and 4 variables:

Sample

character COLUMN_DESCRIPTION

Gene

character COLUMN_DESCRIPTION

Ct

character COLUMN_DESCRIPTION

Group

character COLUMN_DESCRIPTION

Source

Dataset is attached to the 'RQdeltaCT' package and can be loaded using 'data(data.Ct.pairwise)' command.

delta_Ct

Description

This function calculates delta Ct (dCt) values by subtracting Ct values of reference gene or genes from Ct values of remaining genes. Obtained dCt values can be further transformed by using 2^-dCt formula (if transform == TRUE).

Usage

delta_Ct(
  data,
  ref,
  normalise = TRUE,
  transform = FALSE,
  save.to.txt = FALSE,
  name.txt = "data_dCt"
)

Arguments

data

Data object returned from make_Ct_ready function.
ref

Character vector with name of one or more reference genes.
normalise

Logical: if TRUE, data normalisation will be done using reference gene/genes (provided in ref parameter).
transform

Logical: if TRUE, calculated dCt values will be transformed using 2^-dCt formula. Default to FALSE.
save.to.txt

Logical: if TRUE, returned data will be saved to .txt file. Default to FALSE.
name.txt

Character: name of saved .txt file, without ".txt" name of extension. Default to "data_dCt".

Value

Data frame with dCt values.

Examples

library(tidyverse)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                      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 = "GAPDH")
head(data.dCt)
data.dCt.exp <- delta_Ct(data.CtF.ready, ref = "GAPDH", transform = TRUE)
head(data.dCt.exp)

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,
  signif.size = 3.88,
  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.
signif.size

Numeric: size of 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("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                      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 = "GAPDH")
data.dCtF <- filter_transformed_data(data.dCt, remove.Sample = c("Control11"))
results.ddCt <- RQ_ddCt(data.dCtF, "AAA", "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.show = TRUE,
                   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.show = TRUE,
                   signif.labels = signif.labels,
                   angle = 30)
head(FCh.plot[[2]])

filter_Ct

Description

This function filters Ct data according to the used filtering criteria (see parameters).

Usage

filter_Ct(
  data,
  flag.Ct = "Undetermined",
  maxCt = 35,
  flag = c("Undetermined"),
  remove.Gene = c(""),
  remove.Sample = c(""),
  remove.Group = c("")
)

Arguments

data

Object returned from read_Ct_long() or read_Ct_wide() function, or data frame containing column named "Sample" with sample names, column named "Gene" with gene names, column named "Ct" with raw Ct values, column named "Group" with group names. Optionally, data frame can contain column named "Flag" with flag information (e.g. "Undetermined" and "OK"), which will be used for filtering.
flag.Ct

Character of a flag used for undetermined Ct values, default to "Undetermined".
maxCt

Numeric, a maximum of Ct value allowed.
flag

Character: flag used in Flag column for values which should be filtered out, default to "Undetermined".
remove.Gene

Character: vector with names of genes to remove from data
remove.Sample

Character: vector with names of samples to remove from data
remove.Group

Character: vector with names of groups to remove from data

Value

Data frame with filtered data.

Examples

library(tidyverse)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                      remove.Sample = c("Control08","Control16","Control22"))

dim(data.Ct)
dim(data.CtF)

filter_transformed_data

Description

This function filters transformed Ct data (filtered Ct, delta Ct, and 2^-dCt data) according to the used filtering criteria (see parameters).

Usage

filter_transformed_data(
  data,
  remove.Gene = c(""),
  remove.Sample = c(""),
  remove.Group = c("")
)

Arguments

data

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

Character: vector with names of genes to remove from data.
remove.Sample

Character: vector with names of samples to remove from data.
remove.Group

Character: vector with names of groups to remove from data.

Value

Data frame with filtered data.

Examples

library(tidyverse)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                      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 = "GAPDH")
data.dCtF <- filter_transformed_data(data.dCt, remove.Sample = c("Control11"))

dim(data.dCt)
dim(data.dCtF)

find_ref_gene

Description

This function assess gene expression stability by calculation the following parameters: minimum, maximum, standard deviation, variance, and stability measures from NormFinder and geNorm algorithms. It also presents C values graphically on a line plot. This function is helpful to select the best reference gene for normalization of Ct values.

Usage

find_ref_gene(
  data,
  groups,
  candidates,
  colors,
  norm.finder.score = TRUE,
  genorm.score = TRUE,
  line.width = 1,
  angle = 0,
  x.axis.title = "",
  y.axis.title = "Ct",
  axis.title.size = 11,
  axis.text.size = 10,
  legend.title = "",
  legend.title.size = 11,
  legend.text.size = 11,
  legend.position = "top",
  plot.title = "",
  plot.title.size = 14,
  save.to.tiff = FALSE,
  dpi = 600,
  width = 15,
  height = 15,
  name.tiff = "Ct_reference_gene_selection",
  save.to.txt = FALSE,
  name.txt = "reference_gene_selection"
)

Arguments

data

Object returned from make_Ct_ready() functions.
groups

Character vector with names of groups used for analysis. If all groups should be included to the analysis, groups parameter should be set to "all".
candidates

Character: vector of names of genes - candidates for gene reference.
colors

Character: vector of colors for genes, the number of colors should be equal to the number of candidate genes.
norm.finder.score

Logical: if TRUE, NormFinder stability score will be calculated. Default to TRUE.
genorm.score

Logical: if TRUE, geNorm stability score will be calculated. Default to TRUE.
line.width

Numeric: width of lines drawn in the plot. Default to 1.
angle

Integer: value of angle in which names of genes are displayed. Default to 0.
x.axis.title

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

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

Character: title of legend. Default to "".
legend.title.size

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

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

Position of the legend, can be "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.
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 "Ct_reference_gene_selection".
save.to.txt

Logical: if TRUE, returned data will be saved to .txt file. Default to FALSE.
name.txt

Character: name of saved .txt file, without ".txt" name of extension. Default to "ref_gene_results".

Value

List containing an object with plot and a table with calculated parameters. Created plot is also displayed on the graphic device.

Examples

library(ctrlGene)
library(tidyverse)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                       remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                       remove.Sample = c("Control08","Control16","Control22"))
data.CtF.ready <- make_Ct_ready(data.CtF, imput.by.mean.within.groups = TRUE)
ref <- find_ref_gene(data.CtF.ready,
                     groups = c("AAA","Control"),
                     candidates = c("CCL5", "IL1B","GAPDH","TGFB","TNF", "VEGFA"),
                     col = c("#66c2a5", "#fc8d62","#6A6599", "#D62728", "#1F77B4", "black"),
                     norm.finder.score = TRUE,
                     genorm.score = TRUE)
ref[[2]]

log_reg

Description

This function performs logistic regression analysis, computes odd ratio values, and presents them graphically.

Usage

log_reg(
  data,
  sel.Gene = "all",
  group.study,
  group.ref,
  increment,
  centerline = 1,
  ci = 0.95,
  p.adjust = FALSE,
  p.adjust.method = "BH",
  log.axis = FALSE,
  x.axis.title = "Odds ratio",
  y.axis.title = "",
  axis.title.size = 11,
  axis.text.size = 10,
  legend.title = "p value",
  legend.text.size = 11,
  legend.title.size = 11,
  legend.position = "right",
  plot.title = "",
  plot.title.size = 14,
  save.to.tiff = FALSE,
  dpi = 600,
  width = 15,
  height = 15,
  name.tiff = "OR_plot",
  save.to.txt = FALSE,
  name.txt = "OR_results"
)

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.
group.study

Character: name of study group (group of interest).
group.ref

Character: name of reference group.
increment

Numeric or "mean": the change of expression for which odds ratio values are calculated. If increment = 1, odds ratio values regards for a one-unit increase in gene expression (more suitable where data are not transformed using 2^-value formula). If increment = "mean", odds ratio values are calculated for the situation where gene expression increases by mean of gene expression levels in all samples (more suitable where data were transformed using 2^-value formula).
centerline

Numeric: position of vertical centerline on the plot. Default to 1.
ci

Numeric: confidence level used for computation of confidence interval. Default to 0.95.
p.adjust

Logical: if TRUE, p values adjusted using a method provided in p.adjust.method parameter will be shown on the plot. Default to FALSE.
p.adjust.method

Character: p value correction method for multiple testing, one of the "holm", "hochberg", "hommel", "bonferroni", "BH" (default), "BY","fdr", or "none". See documentation for stats::p.adjust() function for details.
log.axis

Logical: if TRUE, axis with odds ratio values will be in log10 scale. Default to FALSE.
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.title

Character: title of legend. Default to "Group".
legend.text.size

Integer: font size of legend text. Default to 11.
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.
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 "OR_plot".
save.to.txt

Logical: if TRUE, returned table with results will be saved to .txt file. Default to FALSE.
name.txt

Character: name of saved .txt file, without ".txt" name of extension. Default to "OR_results".

Value

A list that contains an object with plot and data frame with calculated results. Created plot is also displayed on the graphic device.

Examples

library(tidyverse)
library(oddsratio)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                       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 = "GAPDH")
log.reg.results <- log_reg(data.dCt,
                           sel.Gene = c("ANGPT1","IL8", "VEGFB"),
                           group.study = "AAA",
                           group.ref = "Control",
                           increment = 1)

make_Ct_ready

Description

This function collapses technical replicates (if present in data) by means and (optionally) imputes missing data by means calculated separately for each group. This function also prepares Ct data for further steps of analysis.

Usage

make_Ct_ready(
  data,
  imput.by.mean.within.groups,
  save.to.txt = FALSE,
  name.txt = "Ct_ready"
)

Arguments

data

Data object returned from read_Ct_long(), read_Ct_wide() or filter_Ct() function, or data frame containing column named "Sample" with sample names, column named "Gene" with gene names, column named "Ct" with raw Ct values (must be numeric), column named "Group" with group names. Presence of any other columns is allowed, but they will not be used by this function.
imput.by.mean.within.groups

Logical: if TRUE, missing values will be imputed by means calculated separately for each group. This parameter can influence results, thus to draw more user attention on this parameter, no default value was set.
save.to.txt

Logical: if TRUE, returned data will be saved to .txt file. Default to FALSE.
name.txt

Character: name of saved .txt file, without ".txt" name of extension. Default to "Ct_ready".

Value

Data frame with prepared data. Information about number and percentage of missing values is also printed.

Examples

library(tidyverse)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                      remove.Sample = c("Control08","Control16","Control22"))
data.CtF.ready <- make_Ct_ready(data.CtF, imput.by.mean.within.groups = TRUE)
head(data.CtF.ready)

norm_finder

Description

This function calculates stability scores using NormFinder algorithm (https://www.moma.dk/software/normfinder) published in https://aacrjournals.org/cancerres/article/64/15/5245/511517/Normalization-of-Real-Time-Quantitative-Reverse. This function is internally used by other RQdeltaCT package function, find_ref_gene(); therefore norm_finder() function does not need to be used separately.

Usage

norm_finder(
  data,
  candidates,
  save.to.txt = FALSE,
  name.txt = "NormFinder_results"
)

Arguments

data

Object returned from make_Ct_ready() functions.
candidates

Character: vector of names of genes - candidates for reference gene.
save.to.txt

Logical: if TRUE, returned table with results will be saved to .txt file. Default to FALSE.
name.txt

Character: name of saved .txt file, without ".txt" name of extension. Default to "norm_finder_results".

Value

Table with calculated stability score; the lowest value the best candidate for reference gene.

Examples

library(tidyverse)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                      remove.Sample = c("Control08","Control16","Control22"))
data.CtF.ready <- make_Ct_ready(data.CtF, imput.by.mean.within.groups = TRUE)
reference.stability.nF <- norm_finder(data.CtF.ready,
                                      candidates = c("CCL5", "IL1B","GAPDH","TGFB","TNF", "VEGFA"))

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"))

pca_kmeans

Description

This function performs principal component analysis (PCA) together with k means analysis for samples, and generate plot that illustrate spatial arrangement of samples based on the two first components and with assignment to k means clusters. PCA analysis can not deal with missing values, thus all samples with at least one missing value are removed from data before analysis.

Usage

pca_kmeans(
  data,
  do.k.means = TRUE,
  k.clust = 2,
  clust.names = c("Cluster1", "Cluster2"),
  sel.Gene = "all",
  point.size = 4,
  point.shape = c(19, 17),
  alpha = 0.7,
  point.color = c("#66c2a5", "#fc8d62"),
  add.sample.labels = FALSE,
  label.size = 3,
  hjust = 0,
  vjust = -1,
  axis.title.size = 11,
  axis.text.size = 10,
  legend.text.size = 11,
  legend.title.group = "Group",
  legend.title.cluster = "Cluster",
  legend.title.size = 11,
  legend.position = "right",
  plot.title = "",
  plot.title.size = 14,
  save.to.tiff = FALSE,
  dpi = 600,
  width = 15,
  height = 15,
  name.tiff = "pca_and_kmeans"
)

Arguments

data

Object returned from make_Ct_ready() or delta_Ct() functions.
do.k.means

Logical: if TRUE (default), k means analysis will be performed.
k.clust

Integer: number of clusters for k means analysis. Default to 2.
clust.names

Character vector with names of clusters, must be equal to the number of clusters specified in the k.clust parameter.
sel.Gene

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

Numeric: size of points. Default to 4.
point.shape

Integer: shape of points. If do.k.means = TRUE, the number of provided values must be equal to the number of cluster (k.clust). Default to c(19, 17).
alpha

Numeric: transparency of points, a value between 0 and 1. Default to 0.7.
point.color

Character vector containing colors for compared groups. The number of colors must be equal to the number of groups. Default to c("#66c2a5", "#fc8d62").
add.sample.labels

Logical: if TRUE, points labels (names of samples) will be added. Default to FALSE.
label.size

Numeric: size of points labels (names of samples). Default to 3.
hjust

Numeric: horizontal position of points labels. Default to 0.
vjust

Numeric: vertical position of points labels. Default to -1.
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.group

Character: title of legend for groups. Default to "Group".
legend.title.cluster

Character: title of legend for k means clusters. Default to "Clusters".
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

A list containing object with plot and, if do.k.means = TRUE, a confusion matrix that show classification performance of k means method. Created plot is also displayed on the graphic device.

Examples

library(tidyverse)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                      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 = "GAPDH")
pca_kmeans(data.dCt, sel.Gene = c("ANGPT1","IL8","VEGFB"))

read_Ct_long

Description

Imports a long-format table with Ct values.

Usage

read_Ct_long(
  path,
  sep,
  dec,
  skip = 0,
  column.Sample,
  column.Gene,
  column.Ct,
  column.Group,
  add.column.Flag = FALSE,
  column.Flag
)

Arguments

path

Path to a .txt or csv. file with long-type table with Ct values. This table must contain at least 4 columns, separately for sample names, gene names, Ct values and group names (these columns will be imported by this function). Imported table could also contain a column with a flag information, which could be optionally imported (see add.col.Flag and col.Flag parameters).
sep

Character of a field separator in imported file.
dec

Character used for decimal points in Ct values.
skip

Integer: number of lines of the data file to skip before beginning to read data. Default to 0.
column.Sample

Integer: number of column with sample names.
column.Gene

Integer: number of column with gene names.
column.Ct

Integer: number of column with Ct values.
column.Group

Integer: number of column with group names.
add.column.Flag

Logical: if data contains a column with flag information which should also be imported, this parameter should be set to TRUE. Default to FALSE.
column.Flag

Integer: number of column with flag information. Should be specified if add.col.Flag = TRUE. This column should contain a character-type values (e.g. "Undetermined" and "OK"), however, other types of values are allowed (e.g. numeric), but must be converted to character or factor after importing data (see examples).

Value

Data.frame in long format ready for analysis.

Examples

path <- system.file("extdata",
                    "data_Ct_long.txt",
                    package = "RQdeltaCT")

library(tidyverse)
data.Ct <- read_Ct_long(path = path,
                        sep = "\t",
                        dec = ".",
                        skip = 0,
                        add.column.Flag = TRUE,
                        column.Sample = 1,
                        column.Gene = 2,
                        column.Ct = 5,
                        column.Group = 9,
                        column.Flag = 4)
str(data.Ct)

data.Ct <- mutate(data.Ct,
                  Flag = ifelse(Flag < 1, "Undetermined", "OK"))
str(data.Ct)

read_Ct_wide

Description

This function imports Ct data in a wide-format table with sample names given in columns.

Usage

read_Ct_wide(path.Ct.file, path.design.file, sep, dec)

Arguments

path.Ct.file

Path to wide-format table in .txt or csv. format with Ct values. This table must contain gene names in the first column, and sample names in the first row (genes by rows and samples by columns).
path.design.file

Path to table in .txt or csv. file that contains two columns: column named "Sample" with names of samples and column named "Group" with names of groups assigned to samples. The names of samples in this file must correspond to the names of columns in the file with Ct values.
sep

Character of a field separator in both imported files.
dec

Character used for decimal points in Ct values.

Details

This function needs two files to import: a wide-format table with Ct values and an additional file with group names (see parameters path.Ct.file and path.design.file for further details on tables structure). Both files are merged to return a long-format table ready for analysis. All parameters must be specified; there are no default values.

Value

Data frame in long format ready to analysis.

Examples

path.Ct.file <- system.file("extdata",
                            "data_Ct_wide.txt",
                            package = "RQdeltaCT")
path.design.file <- system.file("extdata",
                                "data_design.txt",
                                package = "RQdeltaCT")

library(tidyverse)
data.Ct <- read_Ct_wide(path.Ct.file = path.Ct.file,
                        path.design.file = path.design.file,
                        sep ="\t",
                        dec = ".")
str(data.Ct)

results_barplot

Description

This function creates a barplot that illustrate mean and sd values of genes. Faceting and adding custom labels of statistical significance are available. This function is useful to present results for finally selected genes.

Usage

results_barplot(
  data,
  sel.Gene = "all",
  bar.width = 0.8,
  signif.show = FALSE,
  signif.labels,
  signif.length = 0.2,
  signif.dist = 0.2,
  signif.size = 3.88,
  faceting = FALSE,
  facet.row,
  facet.col,
  y.exp.low = 0.1,
  y.exp.up = 0.2,
  angle = 0,
  rotate = FALSE,
  colors = c("#66c2a5", "#fc8d62"),
  x.axis.title = "",
  y.axis.title = "value",
  axis.title.size = 11,
  axis.text.size = 10,
  legend.text.size = 11,
  legend.title = "Group",
  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 = "results_barplot"
)

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.
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. Can be in y axis units (if faceting = FALSE) or fraction of y axis value reached by errorbar (mean + sd value) (if faceting = TRUE).
signif.size

Numeric: size of significance labels.
faceting

Logical: if TRUE (default), plot will be drawn with facets with free scales.
facet.row, facet.col

Integer: number of rows and columns to arrange facets.
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, boxplots will be arranged horizontally. Default to FALSE.
colors

Character vector length of one (when by.group = FALSE) or two (when by.group = TRUE), containing colors for groups. The numbers of colors must be equal to the number of groups. Default to c("#66c2a5", "#fc8d62").
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 "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.
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 "results_barplot".

Value

Object with plot. Created plot will be also displayed on graphic device.

Examples

library(ggsignif)
library(tidyverse)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                      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 = "GAPDH")
data.dCtF <- filter_transformed_data(data.dCt, remove.Sample = c("Control11"))
results_barplot(data.dCtF,
                sel.Gene = c("ANGPT1","IL8", "VEGFB"),
                signif.labels = c("****","**","***"),
                signif.show = TRUE,
                angle = 30,
                signif.dist = 1.05,
                facet.row = 1,
                facet.col = 4,
                y.exp.up = 0.1,
                y.axis.title = bquote(~2^-dCt))

results_boxplot

Description

This function creates boxplot that illustrate distribution of data for selected genes. It is similar to control_boxplot_gene() function; however, some new options are added, including gene selection, faceting, adding mean labels to boxes, and adding statistical significance labels. This function can be used to present results for finally selected genes.

Usage

results_boxplot(
  data,
  coef = 1.5,
  sel.Gene = "all",
  by.group = TRUE,
  signif.show = FALSE,
  signif.labels,
  signif.length = 0.2,
  signif.dist = 0.2,
  signif.size = 3.88,
  faceting = TRUE,
  facet.row,
  facet.col,
  y.exp.low = 0.1,
  y.exp.up = 0.2,
  angle = 0,
  rotate = FALSE,
  add.mean = TRUE,
  add.mean.size = 2,
  add.mean.color = "black",
  colors = c("#66c2a5", "#fc8d62"),
  x.axis.title = "",
  y.axis.title = "value",
  axis.title.size = 11,
  axis.text.size = 10,
  legend.text.size = 11,
  legend.title = "Group",
  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 = "results_boxplot"
)

Arguments

data

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

Numeric: how many times of interquartile range should be used to determine range point for whiskers. Default to 1.5.
sel.Gene

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

Logical: if TRUE (default), distributions will be drawn by compared groups of samples.
signif.show

Logical: if TRUE, labels for statistical significance will be added to the plot (default to FALSE). It should be used if faceting parameter is set to TRUE. If faceting is not used, the solution to properly show significance labels is provide in vignette.
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 the highest value and statistical significance labels. Can be in y axis units (if faceting = FALSE) or fraction of y axis value reached by the highest value (if faceting = TRUE).
signif.size

Numeric: size of significance labels.
faceting

Logical: if TRUE (default), plot will be drawn with facets with free scales.
facet.row, facet.col

Integer: number of rows and columns to arrange facets.
y.exp.low, y.exp.up

Numeric: space between data on the plot and lower and 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, boxplots will be arranged horizontally. Default to FALSE.
add.mean

Logical: if TRUE, mean points will be added to boxes as squares. Default to TRUE.
add.mean.size

Numeric: size of squares indicating means. Default to 2.
add.mean.color

Character: color of squares indicating means. Default to "black".
colors

Character vector length of one (when by.group = FALSE) or more (when by.group = TRUE), containing colors for groups. The number of colors must be equal to the number of groups. Default to c("#66c2a5", "#fc8d62").
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 "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.
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 "results_boxplot".

Value

Object with plot. Created plot will be also displayed on the graphic device.

Examples

library(ggsignif)
library(tidyverse)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                      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 = "GAPDH")
data.dCtF <- filter_transformed_data(data.dCt, remove.Sample = c("Control11"))
results_boxplot(data.dCtF,
                sel.Gene = c("ANGPT1","IL8", "VEGFB"),
                signif.labels = c("****","**","****"),
                angle = 30,
                signif.show = TRUE,
                signif.dist = 1.05,
                facet.row = 1,
                facet.col = 4,
                y.exp.up = 0.1,
                y.axis.title = bquote(~2^-dCt))

results_heatmap

Description

This function creatse heatmap with hierarchical clustering.

Usage

results_heatmap(
  data,
  sel.Gene = "all",
  dist.row = "euclidean",
  dist.col = "euclidean",
  clust.method = "average",
  col.groups,
  colors = c("navy", "#313695", "#4575B4", "#74ADD1", "#ABD9E9", "#E0F3F8", "#FFFFBF",
    "#FEE090", "#FDAE61", "#F46D43", "#D73027", "#C32B23", "#A50026", "#8B0000",
    "#7E0202", "#000000"),
  show.colnames = TRUE,
  show.rownames = TRUE,
  border.color = NA,
  fontsize = 10,
  fontsize.col = 10,
  fontsize.row = 10,
  angle.col = 0,
  cellwidth = NA,
  cellheight = NA,
  save.to.tiff = FALSE,
  dpi = 600,
  width = 15,
  height = 15,
  name.tiff = "heatmap_results"
)

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.
dist.row, dist.col

Character: name of method used for calculation of distances between rows or columns, derived from stats::dist() function, must be one of "euclidean" (default) , "maximum", "manhattan", "canberra", "binary" or "minkowski".
clust.method

Character: name of used method for agglomeration, derived from stats::hclust() function, must be one of "ward.D", "ward.D2", "single", "complete", "average" (default), "mcquitty", "median" or "centroid".
col.groups

A named list with colors for groups annotation (see example).
colors

Vector with colors used to fill created heatmap.
show.colnames, show.rownames

Logical: of TRUE, names of columns (sample names) and rows (gene names) will be shown. Both default to TRUE.
border.color

Character: color of cell borders on heatmap. If set to NA (default) no border will be drawn.
fontsize

Numeric: global fontsize of heatmap. Default to 10.
fontsize.col, fontsize.row

Numeric: fontsize of colnames and rownames. Default to 10.
angle.col

Integer: angle of the column labels, one of the 0, 45, 90, 270, and 315.
cellwidth, cellheight

Numeric: width and height of individual cell. Both default to NA. These parameters are useful in situations where margins are too small and the plot is cropped (column names and annotation legend are sometimes partially hidden). Specification of this parameter allows to adjust size of the plot and solve this problem.
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 "heatmap_results".

Value

Heatmap with hierarchical clustering, displayed on the graphic device (if save.to.tiff = FALSE) or saved to .tiff file (if save.to.tiff = TRUE).

Examples

library(tidyverse)
library(pheatmap)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                       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 = "GAPDH")
# Remember to firstly create named list with colors for groups annotation:
colors.for.groups = list("Group" = c("AAA"="firebrick1","Control"="green3"))
# Vector of colors to fill the heatmap can be also specified to fit the user needs:
colors <- c("navy","navy","#313695","#4575B4","#74ADD1","#ABD9E9",
            "#E0F3F8","#FFFFBF","#FEE090","#FDAE61","#F46D43",
            "#D73027","#C32B23","#A50026","#8B0000",
            "#7E0202","#000000")
results_heatmap(data.dCt,
                sel.Gene = "all",
                col.groups = colors.for.groups,
                colors = colors,
                show.colnames = FALSE,
                show.rownames = TRUE,
                fontsize = 11,
                fontsize.row = 11)

results_volcano

Description

This function creates volcano plot that illustrate the arrangement of genes based on fold change values and p values. Significant genes can be pointed out using specified p value and fold change thresholds, and highlighted on the plot by color and (optionally) isolated by thresholds lines.

Usage

results_volcano(
  data,
  mode,
  p.threshold = 0.05,
  FCh.threshold = 2,
  sel.Gene = "all",
  point.size = 4,
  point.shape = 19,
  alpha = 0.7,
  colors = c("#66c2a5", "#fc8d62"),
  add.thr.lines = TRUE,
  linewidth = 0.25,
  linetype = "dashed",
  x.axis.title = "log2(fold change)",
  y.axis.title = "-log10(p value)",
  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 = "results_volcano"
)

Arguments

data

Object returned from RQ_dCt() or RQ_ddCt() functions.
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.
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.
sel.Gene

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

Numeric: size of points. Default to 4.
point.shape

Integer: shape of points. Default to 19..
alpha

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

Character vector length of two, containing colors for significant and no significant genes.
add.thr.lines

Logical, if TRUE (default), threshold lines will be added to the plot.
linewidth

Numeric: width of the added threshold lines. Default to 0.25.
linetype

Character: type of the added threshold lines. One of the "solid", "dashed" (default), "dotted", "dotdash", "longdash", "twodash", and "blank".
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(tidyverse)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                       remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                       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 = "GAPDH")
data.dCtF <- filter_transformed_data(data.dCt, remove.Sample = c("Control11"))
results.dCt <- RQ_dCt(data.dCtF, "AAA", "Control")

RQ.volcano <- results_volcano(data = results.dCt,
                         mode = "depends",
                         p.threshold = 0.05,
                         FCh.threshold = 2)
head(RQ.volcano[[2]])

ROCh

Description

This function is designed to perform Receiver Operating Characteristic (ROC) analysis based on the gene expression data. This kind of analysis is useful to further examine performance of samples classification into two groups.

Usage

ROCh(
  data,
  sel.Gene = "all",
  groups,
  panels.row,
  panels.col,
  text.size = 1.1,
  print.auc = TRUE,
  print.auc.size = 0.8,
  save.to.tiff = FALSE,
  dpi = 600,
  width = 15,
  height = 15,
  name.tiff = "ROC_plot",
  save.to.txt = FALSE,
  name.txt = "ROC_results"
)

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.
groups

Character vector length of two with names of two compared groups.
panels.row, panels.col

Integer: number of rows and columns to arrange panels with plots.
text.size

Numeric: size of text on the plot. Default to 1.1.
print.auc

Logical: if TRUE, AUC values with confidence interval will be added to the plot. Default to TRUE.
print.auc.size

Numeric: size of AUC text on the plot. Default to 0.8.
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 "ROC_plot".
save.to.txt

Logical: if TRUE, returned table with results will be saved to .txt file. Default to FALSE.
name.txt

Character: name of saved .txt file, without ".txt" name of extension. Default to "ROC_results".

Value

Data frame with ROC parameters including AUC, threshold, specificity, sensitivity, accuracy, positive predictive value, negative predictive value, and Youden's J statistic. Plot with ROC curves can be saved to .tiff file and opened from the working directory (will not be displayed on the graphic device).

Examples

library(tidyverse)
library(pROC)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                      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 = "GAPDH")
data.dCtF <- filter_transformed_data(data.dCt, remove.Sample = c("Control11"))
roc_parameters <- ROCh(data.dCtF, sel.Gene = c("ANGPT1","IL8", "VEGFB"),
                        groups = c("AAA","Control"),
                        panels.row = 2,
                        panels.col = 2)

RQ_dCt

Description

This function performs relative quantification of gene expression using 2^-dCt method.

Usage

RQ_dCt(
  data,
  group.study,
  group.ref,
  do.tests = TRUE,
  pairwise = FALSE,
  alternative = "two.sided",
  p.adjust.method = "BH",
  save.to.txt = FALSE,
  name.txt = "results_dCt"
)

Arguments

data

Data object returned from delta_Ct() or filter_transformed_data() function.
group.study

Character: name of study group (group of interest).
group.ref

Character: name of reference group.
do.tests

Logical: if TRUE, statistical significance of differences between compared groups will be calculated using Student's t test and Mann-Whitney U test. Default to TRUE.
pairwise

Logical: if TRUE, a pairwise analysis will be performed (see details). Default to FALSE.
alternative

Character: alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".
p.adjust.method

Character: p value correction method for multiple testing, one of the "holm", "hochberg", "hommel", "bonferroni", "BH" (default), "BY","fdr", or "none". See documentation for stats::p.adjust() function for details.
save.to.txt

Logical: if TRUE, returned table with results will be saved to .txt file. Default to FALSE.
name.txt

Character: name of saved .txt file, without ".txt" name of extension. Default to "results_dCt".

Details

This function calculates: 1. Means (returned in columns with the "_mean" pattern) and standard deviations (returned in columns with the "_sd" pattern) of 2^-dCt transformed dCt values for each analyzed gene across compared groups. 2. P values of normality test (Shapiro_Wilk test) performed on 2^-dCt values across compared groups (returned in columns with the "_norm_p" pattern). 3. Fold change values (returned in "FCh" column) calculated for each gene by dividing mean of 2^-dCt values in study group by mean of 2^-dCt values in reference group. 4. Statistics (returned in column with the "_test_stat" pattern) and p values (returned in column with "_test_p" pattern) of differences in 2^-dCt values between study group and reference group using Student's t test and Mann-Whitney U test. 5. P values adjusted for multiple testing using a selected method.

Value

Data frame with results (if pairwise = FALSE) or, if pairwise = TRUE, the list object with two elements: a table with the results and the second table with fold change values calculated individually for each sample.

Examples

library(tidyverse)
library(coin)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                     remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                     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 = "GAPDH", transform = TRUE)
results.dCt <- RQ_dCt(data.dCt,
                           group.study = "AAA",
                           group.ref = "Control")
head(results.dCt)

RQ_ddCt

Description

This function performs relative quantification of gene expression using 2^-ddCt method.

Usage

RQ_ddCt(
  data,
  group.study,
  group.ref,
  do.tests = TRUE,
  pairwise = FALSE,
  alternative = "two.sided",
  p.adjust.method = "BH",
  save.to.txt = FALSE,
  name.txt = "ddCt_RQ_results"
)

Arguments

data

Data object returned from delta_Ct() function.
group.study

Character: name of study group (group of interest).
group.ref

Character: name of reference group.
do.tests

Logical: if TRUE, statistical significance of delta delta Ct values between compared groups is calculated using Student's t test and Mann-Whitney U test. Default to TRUE.
pairwise

Logical: set to TRUE if a pairwise analysis should be done.
alternative

Character: alternative hypothesis, must be one of "two.sided" (default), "greater" or "less".
p.adjust.method

Character: p value correction method for multiple testing, one of the "holm", "hochberg", "hommel", "bonferroni", "BH" (default), "BY","fdr", or "none". See documentation for stats::p.adjust() function for details.
save.to.txt

Logical: if TRUE, returned table with results is saved to .txt file. Default to FALSE.
name.txt

Character: name of saved .txt file, without ".txt" name of extension. Default to "results_ddCt".

Details

This function performs: 1. Calculation of means (returned in columns with the "_mean" pattern) and standard deviations (returned in columns with the "_sd" pattern) of delta Ct values for analyzed genes across compared groups. 2. Normality tests (Shapiro_Wilk test) of delta Ct values of analyzed genes across compared groups and returned p values are stored in columns with the "_norm_p" pattern. 3. Calculation of differences in mean delta Ct values of genes between compared groups, obtaining delta delta Ct values (returned in"ddCt" column). 4. Calculation of fold change values (returned in "FCh" column) for each gene by exponentiation of ddCt values using 2^-ddCt formula. 5. Statistical testing of differences between study group and reference group using Student's t test and Mann-Whitney U test. Resulted statistics (in column with "_test_stat" pattern) and p values (in column with "_test_p" pattern) are returned. 6. P values adjusted for multiple testing using a selected method.

Value

Data frame with relative quantification results.

Examples

library(tidyverse)
library(coin)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                      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 = "GAPDH")
results.ddCt <- RQ_ddCt(data.dCt, "AAA", "Control")
head(results.ddCt)

single_pair_gene

Description

This function generates scatter plot with linear regression line for two specified genes.

Usage

single_pair_gene(
  data,
  x,
  y,
  by.group = TRUE,
  point.size = 4,
  point.shape = 19,
  point.alpha = 0.7,
  colors = c("#66c2a5", "#fc8d62"),
  axis.title.size = 11,
  axis.text.size = 10,
  legend.title = "Group",
  legend.title.size = 11,
  legend.text.size = 11,
  legend.position = "right",
  plot.title = "",
  plot.title.size = 14,
  labels = TRUE,
  label = c("eq", "R2", "p"),
  label.position.x = c(1, 1),
  label.position.y = c(1, 0.95),
  small.p = FALSE,
  small.r = FALSE,
  p.digits = 3,
  rr.digits = 2,
  save.to.tiff = FALSE,
  dpi = 600,
  width = 15,
  height = 15,
  name.tiff = "_single_pair_plot"
)

Arguments

data

Object returned from make_Ct_ready() or delta_Ct() functions.
x, y

Characters: names of genes to use.
by.group

Logical: if TRUE (default), relationships will be shown separately for compared groups.
point.size

Numeric: size of points. Default to 4.
point.shape

Integer: shape of points. Default to 19.
point.alpha

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

Character vector containing colors for compared groups. The number of colors must be equal to the number of groups. Default to c("#66c2a5", "#fc8d62").
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.title

Character: title of legend. Default to "Group".
legend.title.size

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

Integer: font size of legend text. 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.
labels

Logical: if TRUE (default), a regression statistics will be added to the plot using ggpimsc::stat_poly_eq() function.
label

Character: specifies regression statistics to add, names specified by ggpimsc::stat_poly_eq() function must be used. Default to c("eq", "R2", "p") for regression equation, coefficient of determination and p value, respectively.
label.position.x, label.position.y

Numeric: coordinates for position of regression statistics. If by.group = TRUE, a vector length of groups number can be provided to avoid overlapping. See description of label.x and label.y parameters from ggpimsc::stat_poly_eq() function.
small.p, small.r

Logical, if TRUE, p character in p value label and r character in coefficient of determination label will be lowercase. Default to FALSE.
rr.digits, p.digits

Integer: number of digits after the decimal point in coefficient of determination and p value in labels. Default to 2 for rr.digits and 3 for p.digits.
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 "Gene1_Gene2_single_pair_plot".

Value

Object with plot. Created plot is also displayed on the graphic device.

Examples

library(tidyverse)
library(ggpmisc)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                      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 = "GAPDH")
single_pair_gene(data.dCt, "PDGFB", "TGFB")

single_pair_sample

Description

This function generates scatter plot with linear regression line for two specified samples.

Usage

single_pair_sample(
  data,
  x,
  y,
  pairwise.data = FALSE,
  by.group = FALSE,
  point.size = 4,
  point.shape = 19,
  point.alpha = 0.7,
  colors = c("#66c2a5", "#fc8d62"),
  axis.title.size = 11,
  axis.text.size = 10,
  legend.title = "Group",
  legend.title.size = 11,
  legend.text.size = 11,
  legend.position = "right",
  plot.title = "",
  plot.title.size = 14,
  labels = TRUE,
  label = c("eq", "R2", "p"),
  label.position.x = 1,
  label.position.y = 1,
  small.p = FALSE,
  small.r = FALSE,
  p.digits = 3,
  rr.digits = 2,
  save.to.tiff = FALSE,
  dpi = 600,
  width = 15,
  height = 15,
  name.tiff = "samples_single_pair_plot"
)

Arguments

data

Object returned from make_Ct_ready() or delta_Ct() functions.
x, y

Characters: names of samples to use.
pairwise.data

Logical: set to TRUE if a pairwise data is used, default to FALSE.
by.group

Logical: If TRUE (only for pairwise analysis), relationships will be shown separately for groups. Default to FALSE.
point.size

Numeric: size of points. Default to 4.
point.shape

Integer: shape of points. Default to 19.
point.alpha

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

Character vector containing colors for compared groups. Use only if pairwise.data = TRUE. The number of colors must be equal to the number of groups. Default to c("#66c2a5", "#fc8d62").
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.title

Character: title of legend. Default to "Group".
legend.title.size

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

Integer: font size of legend text. 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.
labels

Logical: if TRUE (default), a regression statistics will be added to the plot using ggpimsc::stat_poly_eq() function.
label

Character: specifies regression statistics to add, names specified by ggpimsc::stat_poly_eq() function must be used. Default to c("eq", "R2", "p") for regression equation, coefficient of determination and p value, respectively.
label.position.x, label.position.y

Numeric: coordinates for position of regression statistics. See description of label.x and label.y parameters from ggpimsc::stat_poly_eq() function.
small.p, small.r

Logical, if TRUE, p character in p value label and r character in coefficient of determination label will be lowercase. Default to FALSE.
rr.digits, p.digits

Integer: number of digits after the decimal point in coefficient of determination and p value in labels. Default to 2 for rr.digits and 3 for p.digits.
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 "Sample1_Sample2_single_pair_plot".

Value

Object with plot. Created plot is also displayed on the graphic device.

Examples

library(tidyverse)
library(ggpmisc)
data(data.Ct)
data.CtF <- filter_Ct(data.Ct,
                      remove.Gene = c("FGF23","ANGPT2","IL1A","CSF2","IL6"),
                      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 = "GAPDH")
single_pair_sample(data.dCt, "AAA6", "Control17")