Manual

Overview

Tools for analysis of RT-qPCR gene expression data using \(\Delta Ct\) and \(\Delta\Delta Ct\) methods, including t-tests and ANOVA models, and publication-ready visualizations. The package implements a general calculation method adopted from Ganger et al. (2017) and Taylor et al. (2019), covering both the Livak and Pfaffl methods. See the calculation method for details.

Functions

The rtpcr package gets efficiency (E) the Ct values of genes and performs different analyses using the following functions.

Function Description
ANOVA_DCt \(\Delta Ct\) ANOVA analysis
ANOVA_DDCt \(\Delta\Delta Ct\) ANOVA analysis
TTEST_DDCt \(\Delta\Delta Ct\) method t-test analysis
WILCOX_DDCt \(\Delta\Delta Ct\) method wilcox.test analysis
plotFactor Bar plot of gene expression for one-, two- or three-factor experiments
plotSingleGene Creates a bar plot of relative gene expression (fold change) values from single gene analysis showing all pairwise significances.
Means_DDCt Pairwise comparison of RE values for any user-specified effect
efficiency Amplification efficiency statistics and standard curves
meanTech Calculate mean of technical replicates
multiplot Combine multiple ggplot objects into a single layout
compute_wDCt Cleaning data and weighted delta Ct (wDCt) calculation
long_to_wide Converts a 4-column qPCR long data format to wide format

Quick start

Installing and loading

The rtpcr package can be installed by running the following code in R:

from CRAN:

# Installing from CRAN
install.packages("rtpcr")

# Loading the package
library(rtpcr)

Or from from GitHub (developing version):

devtools::install_github("mirzaghaderi/rtpcr", build_vignettes = TRUE)

Input data structure

For relative expression analysis (using TTEST_DDCt, WILCOX_DDCt, ANOVA_DCt, and ANOVA_DDCt functions), the amplification efficiency (E) and Ct or Cq values (the mean of technical replicates) is used for the input table. If the E values are not available you should use ‘2’ instead representing the complete primer amplification efficiency. The input data table should include the following columns from left to wright:

  1. Experimental condition columns (and one block if available NOTE 1)
  2. Replicates information (biological replicates or subjects; see NOTE 2, and NOTE 3)
  3. Target genes efficiency and Ct values (a pair column for each gene).
  4. Reference genes efficiency and Ct values (a pair column for each gene) NOTE 4.

The package supports one or more target or reference gene(s), supplied as efficiency–Ct column pairs. Reference gene columns must always appear last. Two sample input data sets are presented below.

Figure 1: A sample input data with one experimetal factor, replicate column and E/Ct information of target and reference genes
Figure 1: A sample input data with one experimetal factor, replicate column and E/Ct information of target and reference genes


If there is no blocking factor, the corresponding block columns should be omitted. However, a column for biological replicates (which may be named “Rep”, “id”, or similar) is always required.


Figure 2: A sample input data with two experimetal factors, blocking factor, replicate column and E/Ct information of target and reference genes
Figure 2: A sample input data with two experimetal factors, blocking factor, replicate column and E/Ct information of target and reference genes

NOTE 1

When a qPCR experiment is done in multiple qPCR plates, variation resulting from the plates may interfere with the actual amount of gene expression. One solution is to conduct each plate as a randomized block so that at least one replicate of each treatment and control is present on a plate. Block effect is usually considered as random and its interaction with any main effect is not considered.

NOTE 2

For TTEST_DDCt and WILCOX_DDCt (independent groups), ANOVA_DCt, and ANOVA_DDCt, each row may come from separate and unique biological replicates. For example, a dataframe with 12 rows has come from an experiment with 12 individuals. For experiments with repeated observations (e.g. time-course data) , a repeated measure model should be provided. In this case, the Replicate column should contain identifiers for each individual (id or subject). For example, all rows with a 1 at Rep column correspond to a single individual, all rows with a 2 correspond to another individual, and so on, which have been sampled at specific time points.

NOTE 3

Your data table may also include a column of technical replicates (For example, using one target and one reference genes, if you want to have 4 biological and 3 technical replicates under Control and Treatment conditions, there would be a table of 24 rows containing both biological replicates and technical replicate columns in the data). In this case, the meanTech function should be applied first to calculate the mean of the technical replicates. The resulting collapsed table is then used as the input for expression analysis. To use the meanTech function correctly, the technical replicate column must appear immediately after the biological replicate column (see Mean of technical replicates for an example).

Figure 3: This schematic illustrates an experimental design with four biological replicates for both Control and Treatment conditions, assuming a single sampling time point, where cDNA samples were analyzed by qPCR. The diagram details the initial dataset containing technical replicates (three technical replicates shown for Biological Replicate 1 under Control, with example amplification efficiencies (E) and cycle threshold (Ct) values for both target and reference genes) and summarizes the data processing step where technical replicates are averaged using the command meanTech(df, groups = 1:2). The resulting condensed dataset, comprising eight rows (one per biological replicate for each condition), is the final data structure used for the downstream relative expression analysis, with example averaged Ct values for target and reference genes displayed for all four Control and four Treatment biological replicates.
Figure 3: This schematic illustrates an experimental design with four biological replicates for both Control and Treatment conditions, assuming a single sampling time point, where cDNA samples were analyzed by qPCR. The diagram details the initial dataset containing technical replicates (three technical replicates shown for Biological Replicate 1 under Control, with example amplification efficiencies (E) and cycle threshold (Ct) values for both target and reference genes) and summarizes the data processing step where technical replicates are averaged using the command meanTech(df, groups = 1:2). The resulting condensed dataset, comprising eight rows (one per biological replicate for each condition), is the final data structure used for the downstream relative expression analysis, with example averaged Ct values for target and reference genes displayed for all four Control and four Treatment biological replicates.

NOTE 4

Complete amplification efficiency (E) in the rtpcr package is denoted by 2. This means that 2 indicates 100%, and 1.85 and 1.70 indicate 0.85% and 0.70% amplification efficiencies.

Handling missing data

Missing Ct values for target genes is Handled using the set_missing_target_Ct_to_40 function. If TRUE, missing target gene Ct values become 40; if FALSE (default), they become NA. missing Ct values of reference genes are always converted to NA. If there are more than one reference gene, NA in the place of the E or the Ct value of cause skipping that gene and remaining references are geometrically averaged in that replicate.

Data Analysis

Amplification efficiency analysis

The efficiency function calculates the amplification efficiency (E), slope, and R² statistics for genes, and performs pairwise comparisons of slopes. It takes a data frame in which the first column contains the dilution ratios, followed by the Ct value columns for each gene.

# Applying the efficiency function
data <- read.csv(system.file("extdata", "data_efficiency1.csv", package = "rtpcr"))
data
dilutions   Gene1   Gene2   Gene3
1.00    25.58   24.25   22.61
1.00    25.54   24.13   22.68
1.00    25.50   24.04   22.63
0.50    26.71   25.56   23.67
0.50    26.73   25.43   23.65
0.50    26.87   26.01   23.70
0.20    28.17   27.37   25.11
0.20    28.07   26.94   25.12
0.20    28.11   27.14   25.11
0.10    29.20   28.05   26.17
0.10    29.49   28.89   26.15
0.10    29.07   28.32   26.15
0.05    30.17   29.50   27.12
0.05    30.14   29.93   27.14
0.05    30.12   29.71   27.16
0.02    31.35   30.69   28.52
0.02    31.35   30.54   28.57
0.02    31.35   30.04   28.53
0.01    32.55   31.12   29.49
0.01    32.45   31.29   29.48
0.01    32.28   31.15   29.26

# Analysis
efficiency(data)

$Efficiency
   Gene     Slope        R2        E
1 Gene1 -3.388094 0.9965504 1.973110
2 Gene2 -3.528125 0.9713914 1.920599
3 Gene3 -3.414551 0.9990278 1.962747

$Slope_compare
$contrasts
 contrast          estimate    SE df t.ratio p.value
 C2H2.26 - C2H2.01   0.1400 0.121 57   1.157  0.4837
 C2H2.26 - GAPDH     0.0265 0.121 57   0.219  0.9740
 C2H2.01 - GAPDH    -0.1136 0.121 57  -0.938  0.6186
Figure 4: Standard curve plot displaying the relationship between the logarithm of cDNA dilution factors (ranging from -2.0 to 0.0) and their corresponding qPCR cycle threshold (Ct) values for three genes: C2H2.26, C2H2.01, and GAPDH. The accompanying table provides the precise Ct measurements, which is used to determine the amplification efficiency for each gene
Figure 4: Standard curve plot displaying the relationship between the logarithm of cDNA dilution factors (ranging from -2.0 to 0.0) and their corresponding qPCR cycle threshold (Ct) values for three genes: C2H2.26, C2H2.01, and GAPDH. The accompanying table provides the precise Ct measurements, which is used to determine the amplification efficiency for each gene

Relative expression analysis

Single factor experiment with two levels (e.g. Control and Treatment): TTEST_DDCt() function is used for relative expression analysis in treatment condition compared to control condition. Both paired and unpaired experimental designs are supported. if the data doesn’t follow t.test assumptions, the WILCOX_DDCt() function can be used instead.

Single factor experiment with more than two levels, or multi-factor experiments: In these cases, ANOVA_DDCt() and ANOVA_DCt() functions are used for the analysis of qPCR data. By default, statistical analysis is performed based on uni- or multi-factorial Completely Randomized Design (CRD) or Randomized Complete Block Design (RCBD) design based on numOfFactors and the availability of block. However, optional custom model formula as a character string can be supplied to these functions. If provided, this overrides the default formula (uni- or multi-factorial CRD or RCBD design). The formula uses wDCt as the response variable (wDCt is automatically created by the function). For mixed models, include random effects using lmer syntax (e.g., wDCt ~ Treatment + (1 | id)). Below are a sample of most common models that can be used.

Example models may be used in ANOVA_DCt() or ANOVA_DDCt() functions Experimental design
wDCt ~ Condition Completely Randomized Design (CRD). Can also be used for t.test with two independent groups. (default)
wDCt ~ Factor1 * Factor2 * Factor3 Factorial under Completely Randomized Design (RCBD) (default)
wDCt ~ block + Factor1 * Factor2 Factorial under Randomized Complete Block Design (default)
wDCt ~ time + (1 | id) Repeated measure analysis: different time points. Also can be used for t.test with two paired groups.
wDCt ~ Condition * time + (1 | id) Repeated measure analysis: split-plot in time
wDCt ~ wDCt ~ Condition * time + (1 | block) + (1 | id) Repeated measure analysis: split-plot in time
wDCt ~ Type + Concentration Analysis of Covariance: Type is covariate
wDCt ~ block + Type + Concentration Analysis of Covariance with blocking factor: block and Type are covariates

NOTE

For CRD, RCBD, and factorial experiments arranged in either CRD or RCBD designs, you do not need to explicitly define a model. The package automatically selects an appropriate model based on the provided arguments. If no model is specified, the default model used is printed along with the output expression table.

NOTE

Sometime groups are independent or paired (Repeated measure experiments). Examples: 1) Analyzing gene expression in different time points, or before and after treatment in each biological replicate; 2) Analyzing gene expression between two tissue types within the same organism. For such analysis types, if there are only two time points, we can use the TTEST_DDCt with the argument paired = TRUE; or ANOVA_DDCt (if there are two or more time points) with a repeated measure model such as wDCt ~ Treatment + ( 1 | id) or wDCt ~ Treatment + ( 1 | Rep).

Figure 5: Calculation of standard error (se) for ddCt–based relative expression in the ANOVA_DDCt() function of the rtpcr package. Standard errors in the ANOVA_DDCt() function are calculated from model-based residuals (modelBased_se = TRUE) by default. By setting modelBased_se = FALSE standard errors are calculated directly from the observed wDCt values within each treatment group according to the selected se.type (One of "paired.group", "two.group", or "single.group"). For single factor data, both methods are the same. It is recommended to use modelBased_se = TRUE (default). This figure illustrates how weighted dCt (wdCt) and weighted ddCt (wddCt) values are used under different experimental designs, and how the standard error is computed when modelBased_se = FALSE depending on the se.type argument. "paired.group" computes se from paired differences (used when a random id effect is present), "two.group" uses the unpaired two-group t-test standard error against the reference level, and "single.group" computes se within each level using a one-group t-test. For independent groups, ANOVA_DDCt() automatically uses se.type = "two.group", and if repeated‐measure or paired designs model is specified, ANOVA_DDCt() automatically selects se.type = "paired.group"
Figure 5: Calculation of standard error (se) for ddCt–based relative expression in the ANOVA_DDCt() function of the rtpcr package. Standard errors in the ANOVA_DDCt() function are calculated from model-based residuals (modelBased_se = TRUE) by default. By setting modelBased_se = FALSE standard errors are calculated directly from the observed wDCt values within each treatment group according to the selected se.type (One of "paired.group", "two.group", or "single.group"). For single factor data, both methods are the same. It is recommended to use modelBased_se = TRUE (default). This figure illustrates how weighted dCt (wdCt) and weighted ddCt (wddCt) values are used under different experimental designs, and how the standard error is computed when modelBased_se = FALSE depending on the se.type argument. "paired.group" computes se from paired differences (used when a random id effect is present), "two.group" uses the unpaired two-group t-test standard error against the reference level, and "single.group" computes se within each level using a one-group t-test. For independent groups, ANOVA_DDCt() automatically uses se.type = "two.group", and if repeated‐measure or paired designs model is specified, ANOVA_DDCt() automatically selects se.type = "paired.group"


Examples

Relative expression analysis can be done using \(\Delta\Delta Ct\) or \(\Delta Ct\) methods through different functions (i.e. TTEST_DDCt, WILCOX_DDCt, ANOVA_DDCt(), and ANOVA_DCt()). Below are some examples of expression analysis using \(\Delta\Delta Ct\) method.

data1 <- read.csv(system.file("extdata", "data_Yuan2006PMCBioinf.csv", package = "rtpcr"))
data1
      Con r target target_Ct Actin Actin_Ct
  control 1   1.88  21.13000  1.67 20.70333
  control 2   1.88  21.55667  1.67 20.35000
  control 3   1.88  21.33000  1.67 20.75333
treatment 1   1.88  22.09000  1.67 20.24333
treatment 2   1.88  22.69667  1.67 20.54000
treatment 3   1.88  23.05333  1.67 20.50000

# Anova analysis
ANOVA_DDCt(
  data1,
  mainFactor.column = 1,
  numOfFactors = 1,
  numberOfrefGenes = 1,
  block = NULL)


# An example of a properly arranged dataset from a repeated-measures experiment.
data2 <- read.csv(system.file("extdata", "data_repeated_measure_1.csv", package = "rtpcr"))
data2
time    id  E_Target    Ct_target   E_Ref      Ct_Ref
   1     1      2       18.92   2   32.77
   1     2      2       15.82   2   32.45
   1     3      2       19.84   2   31.62
   2     1      2       19.46   2   33.03
   2     2      2       17.56   2   33.24
   2     3      2       19.74   2   32.08
   3     1      2       15.73   2   32.95
   3     2      2       17.21   2   33.64
   3     3      2       18.09   2   33.40

# Repeated measure analysis
res <- ANOVA_DDCt(
  data2,
  numOfFactors = 1,
  numberOfrefGenes = 1,
  mainFactor.column = 1,
  block = NULL, model = wDCt ~ time + (1 | id))


# Paired t.test (equivalent to repeated measure analysis, but not always the same results, due to different calculation methods!)
TTEST_DDCt(
  data2[1:6,], 
  numberOfrefGenes = 1, 
  paired = T)


# Anova analysis
data3 <- read.csv(system.file("extdata", "data_2factorBlock3ref.csv", package = "rtpcr"))

res <- ANOVA_DDCt(
  x = data3,
  mainFactor.column = 2,
  numOfFactors = 2,
  numberOfrefGenes = 3,
  block = "block",
  analyseAllTarget = TRUE)

Output

Data output

All the functions for relative expression analysis (including TTEST_DDCt, WILCOX_DDCt, ANOVA_DDCt(), and ANOVA_DCt()) return the relative expression table which include fold change and corresponding statistics. The output of ANOVA_DDCt(), and ANOVA_DCt() also include default or the user defined lm models, residuals, raw data and ANOVA table for each gene. These outputs can be obtained as follow:

Per_gene Output Code
expression table res$relativeExpression
ANOVA table res$perGene$gene_name$ANOVA_table
ANOVA lm res$perGene$gene_name$lm
ANOVA lm formula res$perGene$gene_name$lm_formula
Residuals resid(res$perGene$gene_name$lm) 
# Relative expression table for the specified column in the input data:
data3 <- read.csv(system.file("extdata", "data_2factorBlock3ref.csv", package = "rtpcr"))

res <- ANOVA_DDCt(
  x = data3,
  mainFactor.column = 2,
  numOfFactors = 2,
  numberOfrefGenes = 3,
  block = "block",
  analyseAllTarget = TRUE)

# Relative Expression
#   gene contrast     ddCt      RE   log2FC     LCL     UCL      se Lower.se.RE Upper.se.RE Lower.se.log2FC Upper.se.log2FC  pvalue sig
# 1   PO       L1  0.00000 1.00000  0.00000 0.00000 0.00000 0.13940     0.90790     1.10144         0.00000         0.00000 1.00000    
# 2   PO L2 vs L1 -0.94610 1.92666  0.94610 1.25860 2.94934 0.14499     1.74245     2.13036         0.85564         1.04613 0.00116  **
# 3   PO L3 vs L1 -2.19198 4.56931  2.19198 3.08069 6.77724 0.29402     3.72685     5.60221         1.78783         2.68748 0.00000 ***
# 4  NLM       L1  0.00000 1.00000  0.00000 0.00000 0.00000 0.91809     0.52921     1.88962         0.00000         0.00000 1.00000    
# 5  NLM L2 vs L1  0.86568 0.54879 -0.86568 0.39830 0.75614 0.36616     0.42577     0.70734        -1.11579        -0.67163 0.00018 ***
# 6  NLM L3 vs L1 -1.44341 2.71964  1.44341 1.94670 3.79946 0.17132     2.41511     3.06256         1.28179         1.62542 0.00000 ***
# 
# The L1 level was used as calibrator.
# Note: Using default model for statistical analysis: wDCt ~ block + Concentration * Type 


ANOVA_table <- res$perGene$PO$ANOVA_table
ANOVA_table

lm <- res$perGene$PO$lm
lm

lm_formula <- res$perGene$gene_name$lm_formula
lm_formula

residuals <- resid(res$perGene$gene_name$lm)
residuals

Graphical presentation

A single function of plotFactor is used to produce barplots for one- to three-factor expression tables.

Plot output: example 1

data <- read.csv(system.file("extdata", "data_3factor.csv", package = "rtpcr"))
#Perform analysis first
res <- ANOVA_DCt(
  data,
  numOfFactors = 3,
  numberOfrefGenes = 1,
  block = NULL)
  
df <- res$relativeExpression
df
# Generate three-factor bar plot
plotFactor(
  df,
  x_col = "SA",       
  y_col = "log2FC",       
  group_col = "Type",   
  facet_col = "Conc",    
  Lower.se_col = "Lower.se.log2FC",
  Upper.se_col = "Upper.se.log2FC",
  letters_col = "sig",
  letters_d = 0.3,
  col_width = 0.7, 
  dodge_width = 0.7,
  fill_colors = c("palegreen3", "skyblue"),
  color = "black",
  base_size = 14, 
  alpha = 1,
  legend_position = c(0.1, 0.2))

How to edit ouptput plots?

the rtpcr plotFactor function creates ggplot objects for one to three factor tables. The plot can further be edited by adding new layers:

Task Example Code
Change y-axis label p + ylab("Relative expression ($\Delta\Delta Ct$ method)")
Add a horizontal reference line p + geom_hline(yintercept = 0, linetype = "dashed")
Change y-axis limits p + scale_y_continuous(expand = expansion(mult = c(0, 0.1)))
Relabel x-axis p + scale_x_discrete(labels = c("A" = "Control", "B" = "Treatment"))
Change fill colors p + scale_fill_brewer(palette = "Set2")

Plot output: example 2

data <- read.csv(system.file("extdata", "data_2factorBlock.csv", package = "rtpcr"))
res <- ANOVA_DCt(data, 
      numOfFactors = 2,
      block = "block",
      numberOfrefGenes = 1)

df <- res$relativeExpression

plotFactor(
  data = df,
  x_col = "factor2",
  y_col = "RE",
  group_col = "factor1",
  Lower.se_col = "Lower.se.RE",
  Upper.se_col = "Upper.se.RE",
  letters_col = "sig",
  letters_d = 0.2,
  fill_colors = c("aquamarine4", "gold2"),
  color = "black",
  alpha = 1,
  col_width = 0.7,
  dodge_width = 0.7,
  base_size = 16, 
  legend_position = c(0.8, 0.8))

Plot output: example 3

# Heffer et al., 2020, PlosOne
library(dplyr)
df <- read.csv(system.file("extdata", "data_Heffer2020PlosOne.csv", package = "rtpcr"))

res <- ANOVA_DDCt(
  df,
  numOfFactors = 1,
  mainFactor.column = 1,
  numberOfrefGenes = 1,
  block = NULL)

data <- res$relativeExpression

# Selecting only the first words in 'contrast' column to be used as the x-axis labels.
data$contrast <- sub(" .*", "", data$contrast)

plotFactor(
  data = data,
  x_col = "contrast",
  y_col = "RE",
  group_col = "contrast",
  facet_col = "gene",
  Lower.se_col = "Lower.se.RE",
  Upper.se_col = "Upper.se.RE",
  letters_col = "sig",
  legend_position = "none") # more controlling arguments are available.

Plot output: example 4

The function plotSingleGene() creates a bar plot of relative gene expression (fold change) values from single gene analysis showing all pairwise significances.

res <- ANOVA_DDCt(
  data_Heffer2020PlosOne,
  numOfFactors = 1,
  mainFactor.column = 1,
  numberOfrefGenes = 1,
  block = NULL,
  analyseAllTarget = "Tnfa") 

plotSingleGene(res, fill = "cyan4", color = "black", base_size = 12)

Post-hoc analysis

Although all the expression analysis functions perform statistical comparisons for the levels of the analysed factor, further post-hoc analysis is still possible. The Means_DDCt function performs post-hoc comparisons using a fitted model object produced by ANOVA_DCt and ANOVA_DDCt. It applies pairwise statistical comparisons of relative expression (RE) values for user-specified effects via the specs argument. Supported effects include simple effects, interactions, and slicing, provided the underlying model is an ANOVA. For ANCOVA models returned by this package, the Means_DDCt output is limited to simple effects only.

res <- ANOVA_DDCt(
  data_3factor,
  numOfFactors = 3,
  numberOfrefGenes = 1,
  mainFactor.column = 1,
  block = NULL)

model <- res$perGene$E_PO$lm
# Relative expression values for Concentration main effect
Means_DDCt(model, specs = "Conc")

# contrast        RE        SE df       LCL       UCL p.value sig
# L vs H   0.1703610 0.2208988 24 0.1242014 0.2336757 <0.0001 ***
# M vs H   0.2227247 0.2208988 24 0.1623772 0.3055004 <0.0001 ***
# M vs L   1.3073692 0.2208988 24 0.9531359 1.7932535  0.0928 .  
#
#Results are averaged over the levels of: Type, SA 
#Confidence level used: 0.95 

# Relative expression values for Concentration sliced by Type
Means_DDCt(model, specs = "Conc | Type")

#Type = R:
# contrast       RE        SE df       LCL      UCL p.value sig
# L vs H   0.103187 0.3123981 24 0.0659984 0.161331 <0.0001 ***
# M vs H   0.339151 0.3123981 24 0.2169210 0.530255 <0.0001 ***
# M vs L   3.286761 0.3123981 24 2.1022126 5.138776 <0.0001 ***
#
#Type = S:
# contrast       RE        SE df       LCL      UCL p.value sig
# L vs H   0.281265 0.3123981 24 0.1798969 0.439751 <0.0001 ***
# M vs H   0.146266 0.3123981 24 0.0935518 0.228684 <0.0001 ***
# M vs L   0.520030 0.3123981 24 0.3326112 0.813055  0.0059 ** 
#
#Results are averaged over the levels of: SA 
#Confidence level used: 0.95 

# Relative expression values for Concentration sliced by Type and SA
Means_DDCt(model, specs = "Conc | Type * SA")

Checking normality of residuals

If the residuals from a t.test or an lm object are not normally distributed, the significance results might be violated. In such cases, non-parametric tests can be used. For example, the Mann–Whitney test - also known as the Wilcoxon rank-sum test, (implemented via WILCOX_DDCt() in the rtpcr package), is an alternative to t.test, and kruskal.test() is an alternative to one-way analysis of variance. These tests assess differences between population medians using independent groups. However, the t.test function (also the TTEST_DDCt function described above) includes the var.equal argument. When set to FALSE, performs t.test under the unequal variances hypothesis. Residuals from ANOVA_DCt and ANOVA_DDCt functions objects can be extracted from lmand plotted as follow:

data <- read.csv(system.file("extdata", "data_repeated_measure_1.csv", package = "rtpcr"))
res3 <- ANOVA_DDCt(
  data,
  numOfFactors = 1,
  numberOfrefGenes = 1,
  mainFactor.column = 1,
  block = NULL,
  model = wDCt ~ time + (1 | id)
)

residuals <- resid(res3$perGene$Target$lm)
shapiro.test(residuals) 
par(mfrow = c(1,2))
plot(residuals)
qqnorm(residuals)
qqline(residuals, col = "red")

Mean of technical replicates

Calculating the mean of technical replicates and generating an output table suitable for subsequent ANOVA analysis can be accomplished using the meanTech function. The input dataset must follow the column structure illustrated in the example data below. Columns used for grouping should be explicitly specified via the groups argument of the meanTech function.

# Example input data frame with technical replicates
data1 <- read.csv(system.file("extdata", "data_withTechRep.csv", package = "rtpcr"))

# Calculate mean of technical replicates using first four columns as groups
meanTech(data1,
         groups = 1:2,
         numOfFactors = 1,
         block = NULL)

Contact

Email: gh.mirzaghaderi at uok.ac.ir

Citation

citation("rtpcr")

To cite the package ‘rtpcr’ in publications, please use:

  Ghader Mirzaghaderi (2025). rtpcr: a package for statistical analysis and graphical
  presentation of qPCR data in R. PeerJ 13:e20185. https://doi.org/10.7717/peerj.20185

A BibTeX entry for LaTeX users is

  @Article{,
    title = {rtpcr: A package for statistical analysis and graphical presentation of qPCR data in R},
    author = {Ghader Mirzaghaderi},
    journal = {PeerJ},
    volume = {13},
    pages = {e20185},
    year = {2025},
    doi = {10.7717/peerj.20185},
  }

Getting help

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.

Mirzaghaderi G. 2025. rtpcr: a package for statistical analysis and graphical presentation of qPCR data in R. PeerJ 13, e20185. doi.org/10.7717/peerj.20185.

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), 761-774. doi.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.