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rtpcr: a package for statistical analysis of real-time PCR data in R"
In rtpcr: qPCR Data Analysis
options(tinytex.verbose = TRUE)
knitr::opts_chunk$set(echo = TRUE)
Overview
Real-time polymerase chain reaction (real-time PCR) is widely used in biological studies. Various analysis methods are employed on the real-time PCR data to measure the mRNA levels under different experimental conditions.
‘rtpcr’ package was developed for amplification efficiency calculation, statistical analysis and bar plot representation of real-time PCR data in R. By accounting for up to two reference genes and amplification efficiency values, a general calculation methodology described by Ganger et al. (2017)
and Taylor et al. (2019), matching both Livak and Schmittgen (2001) and Pfaffl et al. (2002) methods was used. Based on the experimental conditions, the functions of the ‘rtpcr’ package use t-test (for experiments with a two-level factor), analysis of variance, analysis of covariance (ANCOVA) or analysis of repeated measure data to calculate the fold change (FC, ${\Delta\Delta C_t}$ method) or relative expression (RE, ${\Delta C_t}$ method). The functions further provide standard errors and confidence interval for means, apply statistical mean comparisons and present significance. To facilitate function application, different data sets were used as examples and the outputs were explained. An outstanding feature of ‘rtpcr’ package is providing publication-ready bar plots with various controlling arguments for experiments with up to three different factors which are further editable by ggplot2 functions.
Calculation methods
The basic method for expression estimation of a gene between conditions relies on the calculation of fold differences by applying the PCR amplification efficiency (E) and the threshold cycle (syn. crossing point or Ct). Among the various approaches developed for data analysis in real-time PCR, the Livak approach, also known as the $2^{-\Delta\Delta C_t}$ method, stands out for its simplicity and widespread use where the fold change (FC) exoression $(2^{-\Delta\Delta C_t})$ in Treatment (Tr) compared to Control (Co) condition is calculated according to equation:
$$\begin{align}
\text{Fold change} & = 2^{-\Delta\Delta C_t} \
& = \frac{2^{-(C_{t_{\text{target}}}-C_{t_{\text{ref}}}){Tr}}}
{2^{-(C{t_{\text{target}}}-C_{t_{\text{ref}}}){Co}}} \
& =2^{-[(C{t_{\text{target}}}-C_{t_{\text{ref}}}){\text{Tr}}-
{(C{t_{\text{target}}}-C_{t_{\text{ref}}})}{\text{Co}}]} \
& = 2^{-[{(\Delta C_t){Tr} - (\Delta C_t)_{Co}}]}
\end{align}$$
Here, $\Delta C_t$ is the difference between target Ct and reference Ct values for a given sample. Livak method assumes that both the target and reference genes are amplified with efficiencies close to 100%, allowing for the relative quantification of gene expression levels.
On the other hand, the Pfaffl method offers a more flexible approach by accounting for differences in amplification efficiencies between the target and reference genes. This method adjusts the calculated expression ratio by incorporating the specific amplification efficiencies, thus providing a more accurate representation of the relative gene expression levels.
$$\text{Fold change} = \frac{E^{-(C_{t_{\text{Tr}}}-C_{t_{\text{Co}}}){target}}}
{E^{-(C{t_{\text{Tr}}}-C_{t_{\text{Co}}})_{ref}}}$$
A generalized calculation method
The rtpcr package was developed for the R environment in the major operating systems. The package functions are mainly based on the calculation of efficiency-weighted $\Delta C_t$ $(w\Delta C_t)$ values from target and reference gene Ct (equation 3). $w\Delta C_t$ values are weighted for the amplification efficiencies as described by Ganger et al. (2017) except that log2 is used instead of log10:
$$w\Delta Ct =\log_{2}(E_{target}).Ct_{target}-\log_{2}(E_{ref}).Ct_{ref}$$
The relative expression of the target gene normalized to that of reference gene(s) within the same sample or condition is called relative expression (RE). From the mean $w\Delta C_t$ values over biological replicates, RE of a target gene can be calculated for each condition according to the equation
$$\text{Relative Expression} = 2^{-\overline{w\Delta Ct}}$$
Relative expression is only calibrated for the reference gene(s) and not for a control condition. However, often one condition is considered as calibrator and the fold change (FC) expression in other conditions is calculated relative to the calibrator. Examples are Treatment versus Control where Control is served as the calibrator, or time 0 versus time 1 (e.g. after 1 hour) and time 2 (e.g. after 2 hours) where time 0 is served as the reference or calibrator level. So, calibrator is the reference level or sample that all others are compared to. The fold change (FC) expression of a target gene for the reference or calibrator level is 1 because it is not changed compared to itself. The fold change expression of a target gene due to the treatment can be calculated as follows:
$$\text{Fold Change due to Treatment}=2^{-(\overline{w\Delta Ct}{\text{Tr}}-{\overline{w\Delta Ct}{\text{Co}}})}$$
qpcrTTEST and qpcrTTESTplot functions calculate FC for multi-genes-two conditional cases, qpcrANOVAFC represents FC for single-gene-factorial (single- or multi-factor) experiments, and qpcrREPEATED calculates FC for the repeated measure data. If $w \Delta C_t$ values is calculated from the E values, the calculations match the formula of Pfaffl while if 2 (complete efficiency) be used instead, the result match the $2^{-\Delta\Delta C_t}$ method. In any case we called these as Fold Change in the outputs of rtpcr. Under factorial experiments where the calculation of the expression of the target gene relative to the reference gene (called Relative Expression) in each condition is desired, qpcrANOVARE, oneFACTORplot, twoFACTORplot and threeFACTORplot functions were developed for ANOVA analysis, and representing the plots from single, double or triple factor experiments, respectively. The last three functions generate ggplot2-derived graphs based on the output of the qpcrANOVARE function. If available, the blocking factor can also be handled by qpcrANOVARE, qpcrANOVAFC and qpcrREPEATED functions. Standard error of the FC and RE means is calculated according to Taylor et al. (2019) in rtpcr package.
Here, a brief methodology is presented but details about the $w\Delta C_t$ calculations and statistical analysis are available in Ganger et al. (2017). Importantly, because both the RE or FC gene expression values follow a lognormal distribution, a normal distribution is expected for the $w \Delta C_t$ values making it possible to apply t-test or analysis of variance to them. Following analysis, $w\Delta C_t$ values are statistically compared and standard deviations and confidence interval are calculated, but the transformation $y = 2^{-x}$ is applied in the final step in order to report the results.
Installing and loading
library(rtpcr)
library(multcomp)
library(dplyr)
library(reshape2)
library(tidyr)
library(ggplot2)
library(grid)
The rtpcr package can be installed and loaded using:
install.packages("rtpcr")
library(rtpcr)
Alternatively, the rtpcr with the latest changes can be installed by running the following code in your R software:
# install `rtpcr` from github (under development)
devtools::install_github("mirzaghaderi/rtpcr")
# I strongly recommend to install the package with the vignette as it contains information about how to use the 'rtpcr' package. Through the following code, Vignette is installed as well.
devtools::install_github("mirzaghaderi/rtpcr", build_vignettes = TRUE)
Data structure and column arrangement
To use the functions, input data should be prepared in the right format with appropriate column arrangement. The correct column arrangement is shown in Table 1 and Table 2. For qpcrANOVAFC or qpcrANOVARE analysis, ensure that each line in the data set belongs to a separate individual or biological replicate reflecting a non-repeated measure experiment.
Table 1. Data structure and column arrangement required for ‘rtpcr’ package. rep: technical replicate; targetE and refE: amplification efficiency columns for target and reference genes, respectively. targetCt and refCt: target gene and reference gene Ct columns, respectively. factors (up to three factors is allowed): experimental factors.
| Experiment type | Column arrangement of the input data | Example in the package |
|:---------------------|:---------------------------------------|:------------------------------------------|
|Amplification efficiency |Dilutions - geneCt ... | data_efficiency |
|t-test (accepts multiple genes) |condition (put the control level first) - gene (put reference gene(s) last.)- efficiency - Ct | data_ttest |
|Factorial (Up to three factors) |factor1 - rep - targetE - targetCt - refE - refCt | data_1factor |
| |factor1 - factor2 - rep - targetE - targetCt - refE - refCt | data_2factor |
| |factor1 - factor2 - factor3 - rep - targetE - targetCt - refE - refCt | data_3factor |
|Factorial with blocking |factor1 - block - rep - targetE - targetCt - refE - refCt | |
| |factor1 - factor2 - block - rep - targetE - targetCt - refE - refCt | data_2factorBlock |
| |factor1 - factor2 - factor3 - block - rep - targetE - targetCt - refE - refCt | |
|Two reference genes |. . . . . . rep - targetE - targetCt - ref1E - ref1Ct - ref2E - ref2Ct | |
|calculating biological replicated |. . . . . . biologicalRep - techcicalRep - Etarget - targetCt - Eref - refCt | data_withTechRep |
| |. . . . . . biologicalRep - techcicalRep - Etarget - targetCt - ref1E - ref1Ct - ref2E - ref2Ct | |
NOTE: For qpcrANOVAFC or qpcrANOVARE analysis, each line in the input data set belongs to a separate individual or biological replicate reflecting a non-repeated measure experiment.
Table 2. Repeated measure data structure and column arrangement required for the qpcrREPEATED function. targetE and refE: amplification efficiency columns for target and reference genes, respectively. targetCt and refCt: Ct columns for target and reference genes, respectively. In the "id" column, a unique number is assigned to each individual, e.g. all the three number 1 indicate a single individual.
| Experiment type | Column arrangement of the input data | Example in the package |
|:---------------------|:----------------------------------------|:------------------------------------------|
|Repeated measure | id - time - targetE - targetCt - ref1E - ref1Ct | data_repeated_measure_1 |
| | id - time - targetE - targetCt - ref1E - ref1Ct - ref2E - ref2Ct | |
|Repeated measure | id - treatment - time - targetE - targetCt - ref1E - ref1Ct | data_repeated_measure_2 |
| | id - treatment - time - targetE - targetCt - ref1E - ref1Ct - ref2E - ref2Ct | |
To see list of data in the rtpcr package run data(package = "rtpcr").
Example data sets can be presented by running the name of each data set. A description of the columns names in each data set is called by "?" followed by the names of the data set, for example ?data_1factor
functions usage
To simplify rtpcr usage, examples for using the functions are presented below.
Table 3. Functions and examples for using them.
| function | Analysis | Example (see package help for more arguments) |
|:---------------------|:-----------------------------------|:----------------------------------|
| efficiency | Efficiency, standard curves and related statistics | efficiency(data_efficiency) |
| meanTech | Calculating the mean of technical replicates | meanTech(data_withTechRep, groups = 1:4) |
| qpcrANOVAFC | FC and bar plot of the target gene (one or multi-factorial experiments) | qpcrANOVAFC(data_1factor, numberOfrefGenes = 1, mainFactor.column = 1, mainFactor.level.order = c("L1", "L2", "L3") |
| oneFACTORplot | Bar plot of the relative gene expression from a one-factor experiment | out <- qpcrANOVARE(data_1factor, numberOfrefGenes = 1)\$Result; oneFACTORplot(out, errorbar = "se") |
| qpcrANOVARE | Analysis of Variance of the qpcr data | qpcrANOVARE(data_3factor, numberOfrefGenes = 1) |
| qpcrTTEST | Computing the fold change and related statistics | qpcrTTEST(data_ttest, numberOfrefGenes = 1, paired = FALSE, var.equal = TRUE) |
| qpcrTTESTplot | Bar plot of the average fold change of the target genes | qpcrTTESTplot(data_ttest, numberOfrefGenes = 1, order = c("C2H2-01", "C2H2-12", "C2H2-26")) |
| threeFACTORplot | Bar plot of the relative gene expression from a three-factor experiment | res <- qpcrANOVARE(data_3factor, numberOfrefGenes = 1)\$Result; threeFACTORplot(res,
arrangement = c(3, 1, 2), errorbar = "se") |
| twoFACTORplot | Bar plot of the relative gene expression from a two-factor experiment | res <- qpcrANOVARE(data_2factor, numberOfrefGenes = 1)\$Result; twoFACTORplot(res, x.axis.factor = Genotype, group.factor = Drought, errorbar = "se") |
| qpcrREPEATED | Bar plot of the fold change expression for repeated measure observations (taken over time from each individual) | qpcrREPEATED(data_repeated_measure_2, numberOfrefGenes = 1), factor = "time" |
see package help for more arguments including the number of reference genes, levels arrangement, blocking, and arguments for adjusting the bar plots.
Amplification efficiency data analysis
Sample data of amplification efficiency
To calculate the amplification efficiencies of a target and a reference gene, a data frame should be prepared with 3 columns of dilutions, target gene Ct values, and reference gene Ct values, respectively, as shown below.
data_efficiency
Calculating amplification efficiency
Amplification efficiency in PCR can be either defined as percentage (from 0 to 1) or as time of PCR product increase per cycle (from 1 to 2). in the rtpcr package, the amplification efficiency (E) has been referred to times of PCR product increase (1 to 2). A complete efficiency is equal to 2. If dilutions and Ct values are available for a number of genes, the efficiency function calculates the amplification efficiency of genes and presents the related standard curves along with the Slope, Efficiency, and R2 statistics. The function also does pairwise comparisons of the slopes for the genes. For this, a regression line is fitted forst using the $\Delta C_t$ values of each pair of genes.
efficiency(data_efficiency)
Note: It is advised that the amplification efficiency be calculated for each cDNA sample because the amplification efficiency not only depends on the PCR mic and primer characteristics, but also varies among different cDNA samples.
Expression data analysis
Target genes in two conditions (t-test)
Example data
When a target gene is assessed under two different conditions (for example Control and treatment), it is possible to calculate the average fold change expression $({\Delta \Delta C_t}$ method) of the target gene in treatment relative to control conditions. For this, the data should be prepared according to the following data set consisting of 4 columns belonging to condition levels, E (efficiency), genes and Ct values, respectively. Each Ct value is the mean of technical replicates. Complete amplification efficiencies of 2 have been assumed here for all wells but the calculated efficiencies can be used instead.
data_ttest
Data analysis under two conditions
Here, the above data set was used for the Fold Change expression analysis of the target genes using the qpcrTTEST function. This function performs a t-test-based analysis of any number of genes that have been evaluated under control and treatment conditions. The output is a table of target gene names, fold changes confidence limits, and the t.test derived p-values. The qpcrTTEST function includes the var.equal argument. When set to FALSE, t.test is performed under the unequal variances hypothesis. Furthermore, the samples in qPCR may be unpaired or paired so the analysis can be done for unpaired or paired conditions. Paired samples refer to a situation where the measurements are made on the same set of subjects or individuals. This could occur if the data is acquired from the same set of individuals before and after a treatment. In such cases, the paired t-test is used for statistical comparisons by setting the t-test paired argument to TRUE.
qpcrTTEST(data_ttest,
numberOfrefGenes = 1,
paired = F,
var.equal = T)
Generating plot
The qpcrTTESTplot function generates a bar plot of fold changes (FC) and confidence intervals for the target genes. the qpcrTTESTplot function accepts any number of genes and any replicates. The qpcrTTESTplot function automatically puts appropriate signs of **, * on top of the plot columns based on the output p-values.
# Producing the plot
t1 <- qpcrTTESTplot(data_ttest,
numberOfrefGenes = 1,
fontsizePvalue = 4,
errorbar = "ci")
# Producing the plot: specifying gene order
t2 <- qpcrTTESTplot(data_ttest,
numberOfrefGenes = 1,
order = c("C2H2-01", "C2H2-12", "C2H2-26"),
paired = FALSE,
var.equal = TRUE,
width = 0.5,
fill = "palegreen",
y.axis.adjust = 3,
y.axis.by = 2,
ylab = "Average Fold Change",
xlab = "none",
fontsizePvalue = 4)
multiplot(t1, t2, cols = 2)
grid.text("A", x = 0.02, y = 1, just = c("right", "top"), gp=gpar(fontsize=16))
grid.text("B", x = 0.52, y = 1, just = c("right", "top"), gp=gpar(fontsize=16))
Uni- or multi-factorial experiments: Fold Change (FC) analysis
In the rtpcr package, the fold change analysis of a target gene is applied based on analysis of variance; ANOVA (or analysis of covariance; ANCOVA) using the qpcrANOVAFC function. the default argument is anova but can be changes to ancova as well. the ancova analysis is suitable when the levels of a factor are also affected by an uncontrolled quantitative covariate. For example, suppose that wDCt of a target gene in a plant is affected by temperature. The gene may also be affected by drought. since we already know that temperature affects the target gene, we are interesting now if the gene expression is also altered by the drought levels. We can design an experiment to understand the gene behavior at both temperature and drought levels at the same time. The drought is another factor (the covariate) that may affect the expression of our gene under the levels of the first factor i.e. temperature. The data of such an experiment can be analyzed by both ANCOVA or factorial ANOVA using qpcrANOVAFC function. The function also works for one factor experiment as well. Bar plot of fold changes (FC values) along with the 95\% confidence interval is also returned by the qpcrANOVAFC function. There is also a function called oneFACTORplot which returns FC values and related plot for a one-factor-experiment with more than two levels.
# See sample data
data_2factor
qpcrANOVAFC(data_2factor,
numberOfrefGenes = 1,
block = NULL,
analysisType = "ancova",
mainFactor.column = 2,
fontsizePvalue = 4,
x.axis.labels.rename = "none")
Uni- or multi-factorial experiments: Relative Expression (RE) analysis
the qpcrANOVARE function performs Relative Expression (RE) analysis for uni- or multi-factorial experiments in which all factor level combinations are used as treatments. The input data set should be prepared as shown in table 1. Factor columns should be presented first followed by blocking factor (if available), biological replicates and efficiency and Ct values of target and reference gene(s). The example data set below (data_3factor) represents amplification efficiency and Ct values for target and reference genes under three grouping factors (two different cultivars, three drought levels, and the presence or absence of bacteria). Here, the efficiency value of 2 has been used for all wells, but the calculated efficiencies can be used instead.
# See a sample dataset
data_3factor
Output table of the analysis
The qpcrANOVARE function produces the main analysis output including mean wDCt, LCL, UCL, grouping letters, and standard deviations for relative expression values. The standard deviation for each treatment is derived from the biological replicates of back-transformed wDCt data.
# If the data include technical replicates, means of technical replicates
# should be calculated first using meanTech function.
# Applying ANOVA analysis
res <- qpcrANOVARE(data_2factor,
numberOfrefGenes = 1,
block = NULL)
res$Result
res$Post_hoc_Test
# Before plotting, the result needs to be extracted as below:
out2 <- qpcrANOVARE(data_1factor, numberOfrefGenes = 1, block = NULL)$Result
f1 <- oneFACTORplot(out2,
width = 0.2,
fill = "skyblue",
y.axis.adjust = 0.5,
y.axis.by = 1,
errorbar = "ci",
show.letters = TRUE,
letter.position.adjust = 0.1,
ylab = "Relative Expression",
xlab = "Factor Levels",
fontsize = 12,
fontsizePvalue = 4)
addline_format <- function(x,...){
gsub('\\s','\n',x)
}
f2 <- qpcrANOVAFC(data_1factor,
numberOfrefGenes = 1,
mainFactor.column = 1,
block = NULL,
mainFactor.level.order = c("L1","L2","L3"),
width = 0.5,
fill = c("skyblue","#79CDCD"),
y.axis.by = 1,
letter.position.adjust = 0,
y.axis.adjust = 1,
ylab = "Fold Change",
fontsize = 12, plot = F,
x.axis.labels.rename = addline_format(c("Control",
"Treatment_1 vs Control",
"Treatment_2 vs Control")))
multiplot(f1, f2$FC_Plot_of_the_main_factor_levels, cols = 2)
grid.text("A", x = 0.02, y = 1, just = c("right", "top"), gp=gpar(fontsize=16))
grid.text("B", x = 0.52, y = 1, just = c("right", "top"), gp=gpar(fontsize=16))
Barplot with the (1-alpha)% confidence interval as error bars
# Before plotting, the result needs to be extracted as below:
res <- qpcrANOVARE(data_2factor, numberOfrefGenes = 1, block = NULL)$Result
Final_data <- qpcrANOVARE(data_2factor, numberOfrefGenes = 1, block = NULL)$Final_data
# Plot of the 'res' data with 'Genotype' as grouping factor
q1 <- twoFACTORplot(res,
x.axis.factor = Drought,
group.factor = Genotype,
errorbar = "se",
width = 0.5,
fill = "Greens",
y.axis.adjust = 0.5,
y.axis.by = 2,
ylab = "Relative Expression",
xlab = "Drought Levels",
legend.position = c(0.15, 0.8),
show.letters = TRUE,
fontsizePvalue = 4)
# Plotting the same data with 'Drought' as grouping factor
q2 <- twoFACTORplot(res,
x.axis.factor = Genotype,
group.factor = Drought,
errorbar = "se",
xlab = "Genotype",
fill = "Blues",
legend.position = c(0.15, 0.8),
show.letters = FALSE,
show.errorbars = T,
fontsizePvalue = 4)
multiplot(q1, q2, cols = 2)
grid.text("A", x = 0.02, y = 1, just = c("right", "top"), gp=gpar(fontsize=16))
grid.text("B", x = 0.52, y = 1, just = c("right", "top"), gp=gpar(fontsize=16))
A three-factorial experiment example
# Before plotting, the result needs to be extracted as below:
res <- qpcrANOVARE(data_3factor, numberOfrefGenes = 1, block = NULL)$Result
res
# releveling a factor levels first
res$Conc <- factor(res$Conc, levels = c("L","M","H"))
res$Type <- factor(res$Type, levels = c("S","R"))
# Arrange the first three colunms of the result table.
# This determines the columns order and shapes the plot output.
p1 <- threeFACTORplot(res,
arrangement = c(3, 1, 2),
errorbar = "se",
legend.position = c(0.2, 0.85),
xlab = "condition",
fontsizePvalue = 4)
# When using ci as error, increase y.axis.adjust to see the plot correctly!
p2 <- threeFACTORplot(res,
arrangement = c(2, 3, 1),
bar.width = 0.8,
fill = "Greens",
xlab = "Drought",
ylab = "Relative Expression",
errorbar = "ci",
y.axis.adjust = 2,
y.axis.by = 2,
letter.position.adjust = 0.6,
legend.title = "Genotype",
fontsize = 12,
legend.position = c(0.2, 0.8),
show.letters = TRUE,
fontsizePvalue = 4)
multiplot(p1, p2, cols = 2)
grid.text("A", x = 0.02, y = 1, just = c("right", "top"), gp=gpar(fontsize=16))
grid.text("B", x = 0.52, y = 1, just = c("right", "top"), gp=gpar(fontsize=16))
Repeated measure data in qpcr
Fold change (FC) analysis of observations repeatedly taken over time qpcrREPEATED function, for Repeated measure analysis of uni- or multi-factorial experiment data. The bar plot of the fold changes (FC) values along with the standard error (se) or confidence interval (ci) is also returned by the qpcrREPEATED function.
a <- qpcrREPEATED(data_repeated_measure_1,
numberOfrefGenes = 1,
block = NULL,
fill = c("#778899", "#BCD2EE"),
factor = "time",
axis.text.x.angle = 45,
axis.text.x.hjust = 1,
plot = F)
b <- qpcrREPEATED(data_repeated_measure_2,
numberOfrefGenes = 1,
factor = "time",
block = NULL,
axis.text.x.angle = 45,
axis.text.x.hjust = 1,
plot = F)
multiplot(a, b, cols = 2)
Fold change analysis using a model
The qpcrMeans performs fold change ${\Delta \Delta C_t}$ mwthid analysis using a model produced by the
qpcrANOVAFC or qpcrREPEATED. The values can be returned for any effects in the model including simple effects,
interactions and slicing if an ANOVA model is used, but ANCOVA models returned by rtpcr package only include simple effects.
# Returning fold change values from a fitted model.
# Firstly, result of `qpcrANOVAFC` or `qpcrREPEATED` is
# acquired which includes a model object:
res <- qpcrANOVAFC(data_3factor, numberOfrefGenes = 1, mainFactor.column = 1, block = NULL)
# Returning fold change values of Conc levels from a fitted model:
qpcrMeans(res$lm_ANOVA, specs = "Conc")
# Returning fold change values of Conc levels sliced by Type*SA:
qpcrMeans(res$lm_ANOVA, specs = "Conc | (Type*SA)")
# Returning fold change values of Conc
qpcrMeans(res$lm_ANOVA, specs = "Conc * Type")
# Returning fold change values of Conc levels sliced by Type:
res2 <- qpcrMeans(res$lm_ANOVA, specs = "Conc | Type")
twoFACTORplot(res2, x.axis.factor = contrast, ylab = "Fold Change",
group.factor = Type, errorbar = "ci")
An example of showing point on the plot
library(ggplot2)
b <- qpcrANOVARE(data_3factor, numberOfrefGenes = 1, block = NULL)$Result
a <- qpcrANOVARE(data_3factor, numberOfrefGenes = 1, block = NULL)$Final_data
# Arrange factor levels to your desired order:
b$Conc <- factor(b$Conc, levels = c("L","M","H"))
a$Conc <- factor(a$Conc, levels = c("L","M","H"))
# Generating plot
ggplot(b, aes(x = Type, y = RE, fill = factor(Conc))) +
geom_bar(stat = "identity", position = "dodge") +
facet_wrap(~ SA) +
scale_fill_brewer(palette = "Reds") +
xlab("Type") +
ylab("Relative Expression") +
geom_point(data = a, aes(x = Type, y = (2^(-wDCt)), fill = factor(Conc)),
position = position_dodge(width = 0.9), color = "black") +
ylab("ylab") +
xlab("xlab") +
theme_bw() +
theme(axis.text.x = element_text(size = 12, color = "black", angle = 0, hjust = 0.5),
axis.text.y = element_text(size = 12, color = "black", angle = 0, hjust = 0.5),
axis.title = element_text(size = 12),
legend.text = element_text(size = 12)) +
theme(legend.position = c(0.2, 0.7)) +
theme(legend.title = element_text(size = 12, color = "black")) +
scale_y_continuous(breaks = seq(0, max(b$RE) + max(b$se) + 0.1, by = 5),
limits = c(0, max(b$RE) + max(b$se) + 0.1), expand = c(0, 0))
Checking normality of residuals
If the residuals from a t.test or an lm or and lmer object are not normally distributed, the significance results might be violated. In such cases, one could use non-parametric tests such as the Mann-Whitney test (also known as the Wilcoxon rank-sum test), wilcox.test(), which is an alternative to t.test, or the kruskal.test() test which alternative to one-way analysis of variance, to test the difference between medians of the populations using independent samples. However, the t.test function (along with the qpcrTTEST function described above) includes the var.equal argument. When set to FALSE, perform t.test under the unequal variances hypothesis. Residuals for lm (from qpcrANOVARE and qpcrANOVAFC functions) and lmer (from qpcrREPEATED function) objects can be extracted and plotted as follow:
residuals <- qpcrANOVARE(data_1factor, numberOfrefGenes = 1, block = NULL)$lmCRD$residuals
shapiro.test(residuals)
par(mfrow = c(1,2))
plot(residuals)
qqnorm(residuals)
qqline(residuals, col = "red")
For the repeated measure models, residulas can be extracted by residuals(a$lm) and plotted by plot(residuals(a$lm)) where 'a' is an object created by the qpcrREPEATED function.
a <- qpcrREPEATED(data_repeated_measure_2,
numberOfrefGenes = 1,
factor = "time",
block = NULL,
y.axis.adjust = 1.5)
residuals(a$lm)
plot(residuals(a$lm))
qqnorm(residuals(a$lm))
qqline(residuals(a$lm), col = "red")
Mean of technical replicates
Calculating the mean of technical replicates and getting an output table appropriate for subsequent ANOVA analysis can be done using the meanTech function. For this, the input data set should follow the column arrangement of the following example data. Grouping columns must be specified under the groups argument of the meanTech function.
# See example input data frame:
data_withTechRep
# Calculating mean of technical replicates
meanTech(data_withTechRep, groups = 1:4)
Combining FC results of different genes
qpcrANOVAFC and twoFACTORplot functions give FC results for one gene each time. you can combine FC tables of different genes and present their bar plot by twoFACTORplot function. An example has been shown bellow for two genes, however it works for any number of genes.
a <- qpcrREPEATED(data_repeated_measure_1,
numberOfrefGenes = 1,
factor = "time", block = NULL)
b <- qpcrREPEATED(data_repeated_measure_2,
factor = "time",
numberOfrefGenes = 1, block = NULL)
a1 <- a$FC_statistics_of_the_main_factor
b1 <- b$FC_statistics_of_the_main_factor
c <- rbind(a1, b1)
c$gene <- factor(c(1,1,1,2,2,2))
c
twoFACTORplot(c, x.axis.factor = contrast,
group.factor = gene, fill = 'Reds', errorbar = "se",
ylab = "FC", axis.text.x.angle = 45, y.axis.adjust = 1.5,
axis.text.x.hjust = 1, legend.position = c(0.2, 0.8))
How to edit ouptput graphs?
the rtpcr graphical functions create a list containing the ggplot object so for editing or adding new layers to the graph output, you need to extract the ggplot object first:
b <- qpcrANOVAFC(data_2factor,
numberOfrefGenes = 1,
mainFactor.column = 1,
block = NULL,
mainFactor.level.order = c("S", "R"),
fill = c("#CDC673", "#EEDD82"),
analysisType = "ancova",
fontsizePvalue = 7,
y.axis.adjust = 0.1, width = 0.35)
library(ggplot2)
p2 <- b$FC_Plot_of_the_main_factor_levels
p2 + theme_set(theme_classic(base_size = 20))
Citation
citation("rtpcr")
Contact
Email: gh.mirzaghaderi at uok.ac.ir
References
Livak, Kenneth J, and Thomas D Schmittgen. 2001. Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the Double Delta CT Method. Methods 25 (4). doi.org/10.1006/meth.2001.1262.
Ganger, MT, Dietz GD, Ewing SJ. 2017. A common base method for analysis of qPCR data and the application of simple blocking in qPCR experiments. BMC bioinformatics 18, 1-11. doi.org/10.1186/s12859-017-1949-5.
Pfaffl MW, Horgan GW, Dempfle L. 2002. Relative expression software tool (REST©) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic acids research 30, e36-e36. doi.org/10.1093/nar/30.9.e36.
Taylor SC, Nadeau K, Abbasi M, Lachance C, Nguyen M, Fenrich, J. 2019. The ultimate qPCR experiment: producing publication quality, reproducible data the first time. Trends in Biotechnology, 37(7), 761-774doi.org/10.1016/j.tibtech.2018.12.002.
Yuan, JS, Ann Reed, Feng Chen, and Neal Stewart. 2006. Statistical Analysis of Real-Time PCR Data. BMC Bioinformatics 7 (85). doi.org/10.1186/1471-2105-7-85.
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Overview
Real-time polymerase chain reaction (real-time PCR) is widely used in biological studies. Various analysis methods are employed on the real-time PCR data to measure the mRNA levels under different experimental conditions. ‘rtpcr’ package was developed for amplification efficiency calculation, statistical analysis and bar plot representation of real-time PCR data in R. By accounting for up to two reference genes and amplification efficiency values, a general calculation methodology described by Ganger et al. (2017) and Taylor et al. (2019), matching both Livak and Schmittgen (2001) and Pfaffl et al. (2002) methods was used. Based on the experimental conditions, the functions of the ‘rtpcr’ package use t-test (for experiments with a two-level factor), analysis of variance, analysis of covariance (ANCOVA) or analysis of repeated measure data to calculate the fold change (FC, ${\Delta\Delta C_t}$ method) or relative expression (RE, ${\Delta C_t}$ method). The functions further provide standard errors and confidence interval for means, apply statistical mean comparisons and present significance. To facilitate function application, different data sets were used as examples and the outputs were explained. An outstanding feature of ‘rtpcr’ package is providing publication-ready bar plots with various controlling arguments for experiments with up to three different factors which are further editable by ggplot2 functions.
Calculation methods
The basic method for expression estimation of a gene between conditions relies on the calculation of fold differences by applying the PCR amplification efficiency (E) and the threshold cycle (syn. crossing point or Ct). Among the various approaches developed for data analysis in real-time PCR, the Livak approach, also known as the $2^{-\Delta\Delta C_t}$ method, stands out for its simplicity and widespread use where the fold change (FC) exoression $(2^{-\Delta\Delta C_t})$ in Treatment (Tr) compared to Control (Co) condition is calculated according to equation:
$$\begin{align} \text{Fold change} & = 2^{-\Delta\Delta C_t} \ & = \frac{2^{-(C_{t_{\text{target}}}-C_{t_{\text{ref}}}){Tr}}} {2^{-(C{t_{\text{target}}}-C_{t_{\text{ref}}}){Co}}} \ & =2^{-[(C{t_{\text{target}}}-C_{t_{\text{ref}}}){\text{Tr}}- {(C{t_{\text{target}}}-C_{t_{\text{ref}}})}{\text{Co}}]} \ & = 2^{-[{(\Delta C_t){Tr} - (\Delta C_t)_{Co}}]} \end{align}$$
Here, $\Delta C_t$ is the difference between target Ct and reference Ct values for a given sample. Livak method assumes that both the target and reference genes are amplified with efficiencies close to 100%, allowing for the relative quantification of gene expression levels.
On the other hand, the Pfaffl method offers a more flexible approach by accounting for differences in amplification efficiencies between the target and reference genes. This method adjusts the calculated expression ratio by incorporating the specific amplification efficiencies, thus providing a more accurate representation of the relative gene expression levels.
$$\text{Fold change} = \frac{E^{-(C_{t_{\text{Tr}}}-C_{t_{\text{Co}}}){target}}} {E^{-(C{t_{\text{Tr}}}-C_{t_{\text{Co}}})_{ref}}}$$
A generalized calculation method
The rtpcr package was developed for the R environment in the major operating systems. The package functions are mainly based on the calculation of efficiency-weighted $\Delta C_t$ $(w\Delta C_t)$ values from target and reference gene Ct (equation 3). $w\Delta C_t$ values are weighted for the amplification efficiencies as described by Ganger et al. (2017) except that log2 is used instead of log10:
$$w\Delta Ct =\log_{2}(E_{target}).Ct_{target}-\log_{2}(E_{ref}).Ct_{ref}$$
The relative expression of the target gene normalized to that of reference gene(s) within the same sample or condition is called relative expression (RE). From the mean $w\Delta C_t$ values over biological replicates, RE of a target gene can be calculated for each condition according to the equation
$$\text{Relative Expression} = 2^{-\overline{w\Delta Ct}}$$ Relative expression is only calibrated for the reference gene(s) and not for a control condition. However, often one condition is considered as calibrator and the fold change (FC) expression in other conditions is calculated relative to the calibrator. Examples are Treatment versus Control where Control is served as the calibrator, or time 0 versus time 1 (e.g. after 1 hour) and time 2 (e.g. after 2 hours) where time 0 is served as the reference or calibrator level. So, calibrator is the reference level or sample that all others are compared to. The fold change (FC) expression of a target gene for the reference or calibrator level is 1 because it is not changed compared to itself. The fold change expression of a target gene due to the treatment can be calculated as follows:
$$\text{Fold Change due to Treatment}=2^{-(\overline{w\Delta Ct}{\text{Tr}}-{\overline{w\Delta Ct}{\text{Co}}})}$$
qpcrTTEST and qpcrTTESTplot functions calculate FC for multi-genes-two conditional cases, qpcrANOVAFC represents FC for single-gene-factorial (single- or multi-factor) experiments, and qpcrREPEATED calculates FC for the repeated measure data. If $w \Delta C_t$ values is calculated from the E values, the calculations match the formula of Pfaffl while if 2 (complete efficiency) be used instead, the result match the $2^{-\Delta\Delta C_t}$ method. In any case we called these as Fold Change in the outputs of rtpcr. Under factorial experiments where the calculation of the expression of the target gene relative to the reference gene (called Relative Expression) in each condition is desired, qpcrANOVARE, oneFACTORplot, twoFACTORplot and threeFACTORplot functions were developed for ANOVA analysis, and representing the plots from single, double or triple factor experiments, respectively. The last three functions generate ggplot2-derived graphs based on the output of the qpcrANOVARE function. If available, the blocking factor can also be handled by qpcrANOVARE, qpcrANOVAFC and qpcrREPEATED functions. Standard error of the FC and RE means is calculated according to Taylor et al. (2019) in rtpcr package.
Here, a brief methodology is presented but details about the $w\Delta C_t$ calculations and statistical analysis are available in Ganger et al. (2017). Importantly, because both the RE or FC gene expression values follow a lognormal distribution, a normal distribution is expected for the $w \Delta C_t$ values making it possible to apply t-test or analysis of variance to them. Following analysis, $w\Delta C_t$ values are statistically compared and standard deviations and confidence interval are calculated, but the transformation $y = 2^{-x}$ is applied in the final step in order to report the results.
Installing and loading
library(rtpcr) library(multcomp) library(dplyr) library(reshape2) library(tidyr) library(ggplot2) library(grid)
The rtpcr package can be installed and loaded using:
install.packages("rtpcr") library(rtpcr)
Alternatively, the rtpcr with the latest changes can be installed by running the following code in your R software:
# install `rtpcr` from github (under development) devtools::install_github("mirzaghaderi/rtpcr") # I strongly recommend to install the package with the vignette as it contains information about how to use the 'rtpcr' package. Through the following code, Vignette is installed as well. devtools::install_github("mirzaghaderi/rtpcr", build_vignettes = TRUE)
Data structure and column arrangement
To use the functions, input data should be prepared in the right format with appropriate column arrangement. The correct column arrangement is shown in Table 1 and Table 2. For qpcrANOVAFC or qpcrANOVARE analysis, ensure that each line in the data set belongs to a separate individual or biological replicate reflecting a non-repeated measure experiment.
Table 1. Data structure and column arrangement required for ‘rtpcr’ package. rep: technical replicate; targetE and refE: amplification efficiency columns for target and reference genes, respectively. targetCt and refCt: target gene and reference gene Ct columns, respectively. factors (up to three factors is allowed): experimental factors.
| Experiment type | Column arrangement of the input data | Example in the package | |:---------------------|:---------------------------------------|:------------------------------------------| |Amplification efficiency |Dilutions - geneCt ... | data_efficiency | |t-test (accepts multiple genes) |condition (put the control level first) - gene (put reference gene(s) last.)- efficiency - Ct | data_ttest | |Factorial (Up to three factors) |factor1 - rep - targetE - targetCt - refE - refCt | data_1factor | | |factor1 - factor2 - rep - targetE - targetCt - refE - refCt | data_2factor | | |factor1 - factor2 - factor3 - rep - targetE - targetCt - refE - refCt | data_3factor | |Factorial with blocking |factor1 - block - rep - targetE - targetCt - refE - refCt | | | |factor1 - factor2 - block - rep - targetE - targetCt - refE - refCt | data_2factorBlock | | |factor1 - factor2 - factor3 - block - rep - targetE - targetCt - refE - refCt | | |Two reference genes |. . . . . . rep - targetE - targetCt - ref1E - ref1Ct - ref2E - ref2Ct | | |calculating biological replicated |. . . . . . biologicalRep - techcicalRep - Etarget - targetCt - Eref - refCt | data_withTechRep | | |. . . . . . biologicalRep - techcicalRep - Etarget - targetCt - ref1E - ref1Ct - ref2E - ref2Ct | |
NOTE: For qpcrANOVAFC or qpcrANOVARE analysis, each line in the input data set belongs to a separate individual or biological replicate reflecting a non-repeated measure experiment.
Table 2. Repeated measure data structure and column arrangement required for the qpcrREPEATED function. targetE and refE: amplification efficiency columns for target and reference genes, respectively. targetCt and refCt: Ct columns for target and reference genes, respectively. In the "id" column, a unique number is assigned to each individual, e.g. all the three number 1 indicate a single individual.
| Experiment type | Column arrangement of the input data | Example in the package | |:---------------------|:----------------------------------------|:------------------------------------------| |Repeated measure | id - time - targetE - targetCt - ref1E - ref1Ct | data_repeated_measure_1 | | | id - time - targetE - targetCt - ref1E - ref1Ct - ref2E - ref2Ct | | |Repeated measure | id - treatment - time - targetE - targetCt - ref1E - ref1Ct | data_repeated_measure_2 | | | id - treatment - time - targetE - targetCt - ref1E - ref1Ct - ref2E - ref2Ct | |
To see list of data in the rtpcr package run data(package = "rtpcr").
Example data sets can be presented by running the name of each data set. A description of the columns names in each data set is called by "?" followed by the names of the data set, for example ?data_1factor
functions usage
To simplify rtpcr usage, examples for using the functions are presented below.
Table 3. Functions and examples for using them.
| function | Analysis | Example (see package help for more arguments) | |:---------------------|:-----------------------------------|:----------------------------------| | efficiency | Efficiency, standard curves and related statistics | efficiency(data_efficiency) | | meanTech | Calculating the mean of technical replicates | meanTech(data_withTechRep, groups = 1:4) | | qpcrANOVAFC | FC and bar plot of the target gene (one or multi-factorial experiments) | qpcrANOVAFC(data_1factor, numberOfrefGenes = 1, mainFactor.column = 1, mainFactor.level.order = c("L1", "L2", "L3") | | oneFACTORplot | Bar plot of the relative gene expression from a one-factor experiment | out <- qpcrANOVARE(data_1factor, numberOfrefGenes = 1)\$Result; oneFACTORplot(out, errorbar = "se") | | qpcrANOVARE | Analysis of Variance of the qpcr data | qpcrANOVARE(data_3factor, numberOfrefGenes = 1) | | qpcrTTEST | Computing the fold change and related statistics | qpcrTTEST(data_ttest, numberOfrefGenes = 1, paired = FALSE, var.equal = TRUE) | | qpcrTTESTplot | Bar plot of the average fold change of the target genes | qpcrTTESTplot(data_ttest, numberOfrefGenes = 1, order = c("C2H2-01", "C2H2-12", "C2H2-26")) | | threeFACTORplot | Bar plot of the relative gene expression from a three-factor experiment | res <- qpcrANOVARE(data_3factor, numberOfrefGenes = 1)\$Result; threeFACTORplot(res, arrangement = c(3, 1, 2), errorbar = "se") | | twoFACTORplot | Bar plot of the relative gene expression from a two-factor experiment | res <- qpcrANOVARE(data_2factor, numberOfrefGenes = 1)\$Result; twoFACTORplot(res, x.axis.factor = Genotype, group.factor = Drought, errorbar = "se") | | qpcrREPEATED | Bar plot of the fold change expression for repeated measure observations (taken over time from each individual) | qpcrREPEATED(data_repeated_measure_2, numberOfrefGenes = 1), factor = "time" |
see package help for more arguments including the number of reference genes, levels arrangement, blocking, and arguments for adjusting the bar plots.
Amplification efficiency data analysis
Sample data of amplification efficiency
To calculate the amplification efficiencies of a target and a reference gene, a data frame should be prepared with 3 columns of dilutions, target gene Ct values, and reference gene Ct values, respectively, as shown below.
data_efficiency
Calculating amplification efficiency
Amplification efficiency in PCR can be either defined as percentage (from 0 to 1) or as time of PCR product increase per cycle (from 1 to 2). in the rtpcr package, the amplification efficiency (E) has been referred to times of PCR product increase (1 to 2). A complete efficiency is equal to 2. If dilutions and Ct values are available for a number of genes, the efficiency function calculates the amplification efficiency of genes and presents the related standard curves along with the Slope, Efficiency, and R2 statistics. The function also does pairwise comparisons of the slopes for the genes. For this, a regression line is fitted forst using the $\Delta C_t$ values of each pair of genes.
efficiency(data_efficiency)
Note: It is advised that the amplification efficiency be calculated for each cDNA sample because the amplification efficiency not only depends on the PCR mic and primer characteristics, but also varies among different cDNA samples.
Expression data analysis
Target genes in two conditions (t-test)
Example data
When a target gene is assessed under two different conditions (for example Control and treatment), it is possible to calculate the average fold change expression $({\Delta \Delta C_t}$ method) of the target gene in treatment relative to control conditions. For this, the data should be prepared according to the following data set consisting of 4 columns belonging to condition levels, E (efficiency), genes and Ct values, respectively. Each Ct value is the mean of technical replicates. Complete amplification efficiencies of 2 have been assumed here for all wells but the calculated efficiencies can be used instead.
data_ttest
Data analysis under two conditions
Here, the above data set was used for the Fold Change expression analysis of the target genes using the qpcrTTEST function. This function performs a t-test-based analysis of any number of genes that have been evaluated under control and treatment conditions. The output is a table of target gene names, fold changes confidence limits, and the t.test derived p-values. The qpcrTTEST function includes the var.equal argument. When set to FALSE, t.test is performed under the unequal variances hypothesis. Furthermore, the samples in qPCR may be unpaired or paired so the analysis can be done for unpaired or paired conditions. Paired samples refer to a situation where the measurements are made on the same set of subjects or individuals. This could occur if the data is acquired from the same set of individuals before and after a treatment. In such cases, the paired t-test is used for statistical comparisons by setting the t-test paired argument to TRUE.
qpcrTTEST(data_ttest, numberOfrefGenes = 1, paired = F, var.equal = T)
Generating plot
The qpcrTTESTplot function generates a bar plot of fold changes (FC) and confidence intervals for the target genes. the qpcrTTESTplot function accepts any number of genes and any replicates. The qpcrTTESTplot function automatically puts appropriate signs of **, * on top of the plot columns based on the output p-values.
# Producing the plot t1 <- qpcrTTESTplot(data_ttest, numberOfrefGenes = 1, fontsizePvalue = 4, errorbar = "ci") # Producing the plot: specifying gene order t2 <- qpcrTTESTplot(data_ttest, numberOfrefGenes = 1, order = c("C2H2-01", "C2H2-12", "C2H2-26"), paired = FALSE, var.equal = TRUE, width = 0.5, fill = "palegreen", y.axis.adjust = 3, y.axis.by = 2, ylab = "Average Fold Change", xlab = "none", fontsizePvalue = 4) multiplot(t1, t2, cols = 2) grid.text("A", x = 0.02, y = 1, just = c("right", "top"), gp=gpar(fontsize=16)) grid.text("B", x = 0.52, y = 1, just = c("right", "top"), gp=gpar(fontsize=16))
Uni- or multi-factorial experiments: Fold Change (FC) analysis
In the rtpcr package, the fold change analysis of a target gene is applied based on analysis of variance; ANOVA (or analysis of covariance; ANCOVA) using the qpcrANOVAFC function. the default argument is anova but can be changes to ancova as well. the ancova analysis is suitable when the levels of a factor are also affected by an uncontrolled quantitative covariate. For example, suppose that wDCt of a target gene in a plant is affected by temperature. The gene may also be affected by drought. since we already know that temperature affects the target gene, we are interesting now if the gene expression is also altered by the drought levels. We can design an experiment to understand the gene behavior at both temperature and drought levels at the same time. The drought is another factor (the covariate) that may affect the expression of our gene under the levels of the first factor i.e. temperature. The data of such an experiment can be analyzed by both ANCOVA or factorial ANOVA using qpcrANOVAFC function. The function also works for one factor experiment as well. Bar plot of fold changes (FC values) along with the 95\% confidence interval is also returned by the qpcrANOVAFC function. There is also a function called oneFACTORplot which returns FC values and related plot for a one-factor-experiment with more than two levels.
# See sample data data_2factor qpcrANOVAFC(data_2factor, numberOfrefGenes = 1, block = NULL, analysisType = "ancova", mainFactor.column = 2, fontsizePvalue = 4, x.axis.labels.rename = "none")
Uni- or multi-factorial experiments: Relative Expression (RE) analysis
the qpcrANOVARE function performs Relative Expression (RE) analysis for uni- or multi-factorial experiments in which all factor level combinations are used as treatments. The input data set should be prepared as shown in table 1. Factor columns should be presented first followed by blocking factor (if available), biological replicates and efficiency and Ct values of target and reference gene(s). The example data set below (data_3factor) represents amplification efficiency and Ct values for target and reference genes under three grouping factors (two different cultivars, three drought levels, and the presence or absence of bacteria). Here, the efficiency value of 2 has been used for all wells, but the calculated efficiencies can be used instead.
# See a sample dataset
data_3factor
Output table of the analysis
The qpcrANOVARE function produces the main analysis output including mean wDCt, LCL, UCL, grouping letters, and standard deviations for relative expression values. The standard deviation for each treatment is derived from the biological replicates of back-transformed wDCt data.
# If the data include technical replicates, means of technical replicates # should be calculated first using meanTech function. # Applying ANOVA analysis res <- qpcrANOVARE(data_2factor, numberOfrefGenes = 1, block = NULL) res$Result res$Post_hoc_Test
# Before plotting, the result needs to be extracted as below: out2 <- qpcrANOVARE(data_1factor, numberOfrefGenes = 1, block = NULL)$Result f1 <- oneFACTORplot(out2, width = 0.2, fill = "skyblue", y.axis.adjust = 0.5, y.axis.by = 1, errorbar = "ci", show.letters = TRUE, letter.position.adjust = 0.1, ylab = "Relative Expression", xlab = "Factor Levels", fontsize = 12, fontsizePvalue = 4) addline_format <- function(x,...){ gsub('\\s','\n',x) } f2 <- qpcrANOVAFC(data_1factor, numberOfrefGenes = 1, mainFactor.column = 1, block = NULL, mainFactor.level.order = c("L1","L2","L3"), width = 0.5, fill = c("skyblue","#79CDCD"), y.axis.by = 1, letter.position.adjust = 0, y.axis.adjust = 1, ylab = "Fold Change", fontsize = 12, plot = F, x.axis.labels.rename = addline_format(c("Control", "Treatment_1 vs Control", "Treatment_2 vs Control"))) multiplot(f1, f2$FC_Plot_of_the_main_factor_levels, cols = 2) grid.text("A", x = 0.02, y = 1, just = c("right", "top"), gp=gpar(fontsize=16)) grid.text("B", x = 0.52, y = 1, just = c("right", "top"), gp=gpar(fontsize=16))
Barplot with the (1-alpha)% confidence interval as error bars
# Before plotting, the result needs to be extracted as below: res <- qpcrANOVARE(data_2factor, numberOfrefGenes = 1, block = NULL)$Result Final_data <- qpcrANOVARE(data_2factor, numberOfrefGenes = 1, block = NULL)$Final_data # Plot of the 'res' data with 'Genotype' as grouping factor q1 <- twoFACTORplot(res, x.axis.factor = Drought, group.factor = Genotype, errorbar = "se", width = 0.5, fill = "Greens", y.axis.adjust = 0.5, y.axis.by = 2, ylab = "Relative Expression", xlab = "Drought Levels", legend.position = c(0.15, 0.8), show.letters = TRUE, fontsizePvalue = 4) # Plotting the same data with 'Drought' as grouping factor q2 <- twoFACTORplot(res, x.axis.factor = Genotype, group.factor = Drought, errorbar = "se", xlab = "Genotype", fill = "Blues", legend.position = c(0.15, 0.8), show.letters = FALSE, show.errorbars = T, fontsizePvalue = 4) multiplot(q1, q2, cols = 2) grid.text("A", x = 0.02, y = 1, just = c("right", "top"), gp=gpar(fontsize=16)) grid.text("B", x = 0.52, y = 1, just = c("right", "top"), gp=gpar(fontsize=16))
A three-factorial experiment example
# Before plotting, the result needs to be extracted as below: res <- qpcrANOVARE(data_3factor, numberOfrefGenes = 1, block = NULL)$Result res # releveling a factor levels first res$Conc <- factor(res$Conc, levels = c("L","M","H")) res$Type <- factor(res$Type, levels = c("S","R")) # Arrange the first three colunms of the result table. # This determines the columns order and shapes the plot output. p1 <- threeFACTORplot(res, arrangement = c(3, 1, 2), errorbar = "se", legend.position = c(0.2, 0.85), xlab = "condition", fontsizePvalue = 4) # When using ci as error, increase y.axis.adjust to see the plot correctly! p2 <- threeFACTORplot(res, arrangement = c(2, 3, 1), bar.width = 0.8, fill = "Greens", xlab = "Drought", ylab = "Relative Expression", errorbar = "ci", y.axis.adjust = 2, y.axis.by = 2, letter.position.adjust = 0.6, legend.title = "Genotype", fontsize = 12, legend.position = c(0.2, 0.8), show.letters = TRUE, fontsizePvalue = 4) multiplot(p1, p2, cols = 2) grid.text("A", x = 0.02, y = 1, just = c("right", "top"), gp=gpar(fontsize=16)) grid.text("B", x = 0.52, y = 1, just = c("right", "top"), gp=gpar(fontsize=16))
Repeated measure data in qpcr
Fold change (FC) analysis of observations repeatedly taken over time qpcrREPEATED function, for Repeated measure analysis of uni- or multi-factorial experiment data. The bar plot of the fold changes (FC) values along with the standard error (se) or confidence interval (ci) is also returned by the qpcrREPEATED function.
a <- qpcrREPEATED(data_repeated_measure_1, numberOfrefGenes = 1, block = NULL, fill = c("#778899", "#BCD2EE"), factor = "time", axis.text.x.angle = 45, axis.text.x.hjust = 1, plot = F) b <- qpcrREPEATED(data_repeated_measure_2, numberOfrefGenes = 1, factor = "time", block = NULL, axis.text.x.angle = 45, axis.text.x.hjust = 1, plot = F) multiplot(a, b, cols = 2)
Fold change analysis using a model
The qpcrMeans performs fold change ${\Delta \Delta C_t}$ mwthid analysis using a model produced by the
qpcrANOVAFC or qpcrREPEATED. The values can be returned for any effects in the model including simple effects,
interactions and slicing if an ANOVA model is used, but ANCOVA models returned by rtpcr package only include simple effects.
# Returning fold change values from a fitted model. # Firstly, result of `qpcrANOVAFC` or `qpcrREPEATED` is # acquired which includes a model object: res <- qpcrANOVAFC(data_3factor, numberOfrefGenes = 1, mainFactor.column = 1, block = NULL) # Returning fold change values of Conc levels from a fitted model: qpcrMeans(res$lm_ANOVA, specs = "Conc") # Returning fold change values of Conc levels sliced by Type*SA: qpcrMeans(res$lm_ANOVA, specs = "Conc | (Type*SA)") # Returning fold change values of Conc qpcrMeans(res$lm_ANOVA, specs = "Conc * Type") # Returning fold change values of Conc levels sliced by Type: res2 <- qpcrMeans(res$lm_ANOVA, specs = "Conc | Type") twoFACTORplot(res2, x.axis.factor = contrast, ylab = "Fold Change", group.factor = Type, errorbar = "ci")
An example of showing point on the plot
library(ggplot2) b <- qpcrANOVARE(data_3factor, numberOfrefGenes = 1, block = NULL)$Result a <- qpcrANOVARE(data_3factor, numberOfrefGenes = 1, block = NULL)$Final_data # Arrange factor levels to your desired order: b$Conc <- factor(b$Conc, levels = c("L","M","H")) a$Conc <- factor(a$Conc, levels = c("L","M","H")) # Generating plot ggplot(b, aes(x = Type, y = RE, fill = factor(Conc))) + geom_bar(stat = "identity", position = "dodge") + facet_wrap(~ SA) + scale_fill_brewer(palette = "Reds") + xlab("Type") + ylab("Relative Expression") + geom_point(data = a, aes(x = Type, y = (2^(-wDCt)), fill = factor(Conc)), position = position_dodge(width = 0.9), color = "black") + ylab("ylab") + xlab("xlab") + theme_bw() + theme(axis.text.x = element_text(size = 12, color = "black", angle = 0, hjust = 0.5), axis.text.y = element_text(size = 12, color = "black", angle = 0, hjust = 0.5), axis.title = element_text(size = 12), legend.text = element_text(size = 12)) + theme(legend.position = c(0.2, 0.7)) + theme(legend.title = element_text(size = 12, color = "black")) + scale_y_continuous(breaks = seq(0, max(b$RE) + max(b$se) + 0.1, by = 5), limits = c(0, max(b$RE) + max(b$se) + 0.1), expand = c(0, 0))
Checking normality of residuals
If the residuals from a t.test or an lm or and lmer object are not normally distributed, the significance results might be violated. In such cases, one could use non-parametric tests such as the Mann-Whitney test (also known as the Wilcoxon rank-sum test), wilcox.test(), which is an alternative to t.test, or the kruskal.test() test which alternative to one-way analysis of variance, to test the difference between medians of the populations using independent samples. However, the t.test function (along with the qpcrTTEST function described above) includes the var.equal argument. When set to FALSE, perform t.test under the unequal variances hypothesis. Residuals for lm (from qpcrANOVARE and qpcrANOVAFC functions) and lmer (from qpcrREPEATED function) objects can be extracted and plotted as follow:
residuals <- qpcrANOVARE(data_1factor, numberOfrefGenes = 1, block = NULL)$lmCRD$residuals shapiro.test(residuals) par(mfrow = c(1,2)) plot(residuals) qqnorm(residuals) qqline(residuals, col = "red")
For the repeated measure models, residulas can be extracted by residuals(a$lm) and plotted by plot(residuals(a$lm)) where 'a' is an object created by the qpcrREPEATED function.
a <- qpcrREPEATED(data_repeated_measure_2, numberOfrefGenes = 1, factor = "time", block = NULL, y.axis.adjust = 1.5) residuals(a$lm) plot(residuals(a$lm)) qqnorm(residuals(a$lm)) qqline(residuals(a$lm), col = "red")
Mean of technical replicates
Calculating the mean of technical replicates and getting an output table appropriate for subsequent ANOVA analysis can be done using the meanTech function. For this, the input data set should follow the column arrangement of the following example data. Grouping columns must be specified under the groups argument of the meanTech function.
# See example input data frame: data_withTechRep # Calculating mean of technical replicates meanTech(data_withTechRep, groups = 1:4)
Combining FC results of different genes
qpcrANOVAFC and twoFACTORplot functions give FC results for one gene each time. you can combine FC tables of different genes and present their bar plot by twoFACTORplot function. An example has been shown bellow for two genes, however it works for any number of genes.
a <- qpcrREPEATED(data_repeated_measure_1, numberOfrefGenes = 1, factor = "time", block = NULL) b <- qpcrREPEATED(data_repeated_measure_2, factor = "time", numberOfrefGenes = 1, block = NULL) a1 <- a$FC_statistics_of_the_main_factor b1 <- b$FC_statistics_of_the_main_factor c <- rbind(a1, b1) c$gene <- factor(c(1,1,1,2,2,2)) c twoFACTORplot(c, x.axis.factor = contrast, group.factor = gene, fill = 'Reds', errorbar = "se", ylab = "FC", axis.text.x.angle = 45, y.axis.adjust = 1.5, axis.text.x.hjust = 1, legend.position = c(0.2, 0.8))
How to edit ouptput graphs?
the rtpcr graphical functions create a list containing the ggplot object so for editing or adding new layers to the graph output, you need to extract the ggplot object first:
b <- qpcrANOVAFC(data_2factor, numberOfrefGenes = 1, mainFactor.column = 1, block = NULL, mainFactor.level.order = c("S", "R"), fill = c("#CDC673", "#EEDD82"), analysisType = "ancova", fontsizePvalue = 7, y.axis.adjust = 0.1, width = 0.35) library(ggplot2) p2 <- b$FC_Plot_of_the_main_factor_levels p2 + theme_set(theme_classic(base_size = 20))
Citation
citation("rtpcr")
Contact
Email: gh.mirzaghaderi at uok.ac.ir
References
Livak, Kenneth J, and Thomas D Schmittgen. 2001. Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the Double Delta CT Method. Methods 25 (4). doi.org/10.1006/meth.2001.1262.
Ganger, MT, Dietz GD, Ewing SJ. 2017. A common base method for analysis of qPCR data and the application of simple blocking in qPCR experiments. BMC bioinformatics 18, 1-11. doi.org/10.1186/s12859-017-1949-5.
Pfaffl MW, Horgan GW, Dempfle L. 2002. Relative expression software tool (REST©) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic acids research 30, e36-e36. doi.org/10.1093/nar/30.9.e36.
Taylor SC, Nadeau K, Abbasi M, Lachance C, Nguyen M, Fenrich, J. 2019. The ultimate qPCR experiment: producing publication quality, reproducible data the first time. Trends in Biotechnology, 37(7), 761-774doi.org/10.1016/j.tibtech.2018.12.002.
Yuan, JS, Ann Reed, Feng Chen, and Neal Stewart. 2006. Statistical Analysis of Real-Time PCR Data. BMC Bioinformatics 7 (85). doi.org/10.1186/1471-2105-7-85.
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