Type: Package
Title: qPCR Data Analysis
Version: 2.1.1
Description: Tools for qPCR data analysis using Delta Ct and Delta Delta Ct methods, including t-tests, ANOVA, ANCOVA, repeated-measures models, and publication-ready visualizations. The package supports multiple target, and multiple reference genes, and uses a calculation framework adopted from Ganger et al. (2017) <doi:10.1186/s12859-017-1949-5> and Taylor et al. (2019) <doi:10.1016/j.tibtech.2018.12.002>, covering both the Livak and Pfaffl methods.
URL: https://mirzaghaderi.github.io/rtpcr/, https://github.com/mirzaghaderi/rtpcr
License: GPL-3
Imports: multcomp, ggplot2, lmerTest, purrr, reshape2, tidyr, dplyr, grid, emmeans
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.3
NeedsCompilation: no
Packaged: 2026-01-08 04:57:25 UTC; GM
Depends: R (≥ 3.5.0)
Suggests: knitr, rmarkdown, multcompView
VignetteBuilder: knitr
Author: Ghader Mirzaghaderi [aut, cre, cph]
Maintainer: Ghader Mirzaghaderi <gh.mirzaghaderi@uok.ac.ir>
Repository: CRAN
Date/Publication: 2026-01-08 05:20:07 UTC

Delta Ct ANOVA analysis

Description

Performs \Delta C_T analysis for target genes by applying \Delta C_T method to each target gene. Target genes must be provided as paired efficiency (E) and Ct columns followed by the the reference gene(s) columns. See "Input data structure and column arrangement" in vignettes for details about data structure.

Usage

ANOVA_DCt(
  x,
  numOfFactors,
  numberOfrefGenes,
  block,
  alpha = 0.05,
  p.adj = "none",
  analyseAllTarget = TRUE
)

Arguments

x

A data frame containing experimental design columns, target gene E/Ct column pairs, and reference gene E/Ct column pairs. Reference gene columns must be located at the end of the data frame.

numOfFactors

Integer. Number of experimental factor columns (excluding rep and optional block).

numberOfrefGenes

Integer. Number of reference genes. Each reference gene must be represented by two columns (E and Ct).

block

Character or NULL. Name of the blocking factor column. 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.

alpha

statistical level for comparisons

p.adj

Method for p-value adjustment. See p.adjust.

analyseAllTarget

Logical or character. If TRUE (default), all detected target genes are analysed. Alternatively, a character vector specifying the names (names of their Efficiency columns) of target genes to be analysed.

Value

An object containing expression table, lm models, ANOVA tables, residuals, raw data and ANOVA table for each gene.

\Delta C_T combined expression table

object$combinedResults

ANOVA table for treatments

object$perGene$gene_name$ANOVA_T

ANOVA table factorial

object$perGene$gene_name$ANOVA_factorial

lm ANOVA for tratments

object$perGene$gene_name$lm_T

lm ANOVA factorial

object$perGene$gene_name$lm_factorial

Residuals

resid(object$perGene$gene_name$lm_T)

Examples

data <- read.csv(system.file("extdata", "data_3factor.csv", package = "rtpcr"))
res <- ANOVA_DCt(
  data,
  numOfFactors = 3,
  numberOfrefGenes = 1,
  block = NULL)

Delta Delta Ct ANOVA analysis

Description

Apply \Delta \Delta C_T analysis to each target gene in the input data frame. Target and reference genes must be provided as paired efficiency (E) and Ct columns located after the experimental design columns. columns.

Usage

ANOVA_DDCt(
  x,
  numOfFactors,
  numberOfrefGenes,
  mainFactor.column,
  block,
  analysisType = "anova",
  mainFactor.level.order = NULL,
  p.adj = "none",
  plot = FALSE,
  plotType = "RE",
  analyseAllTarget = TRUE
)

Arguments

x

A data frame containing experimental design columns, target gene E/Ct column pairs, and reference gene E/Ct column pairs. Reference gene columns must be located at the end of the data frame.

numOfFactors

Integer. Number of experimental factor columns (excluding rep and optional block).

numberOfrefGenes

Integer. Number of reference genes. Each reference gene must be represented by two columns (E and Ct).

mainFactor.column

Column index or name of the factor for which relative expression is calculated. When analysisType = "ancova", remaining factors are treated as covariates.

block

Character or NULL. Name of the blocking factor column. 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.

analysisType

Character string specifying the analysis type; one of "anova" (default) or "ancova".

mainFactor.level.order

Optional character vector specifying the order of levels for the main factor. If NULL, the first observed level is used as the calibrator. If provided, the first element of the vector is used as the calibrator level.

p.adj

Method for p-value adjustment. See p.adjust.

plot

Logical; if FALSE, per gene-plots are not generated.

plotType

Plot scale to use: "RE" for relative expression or "log2FC" for log2 fold change.

analyseAllTarget

Logical or character. If TRUE (default), all target genes are analysed. Alternatively, a character vector specifying the names (names of their Efficiency columns) of target genes to be analysed.

Details

\Delta \Delta C_T analysis is performed for the mainFactor.column based on a full model factorial experiment by default. However, if ancova, the analysisType argument, analysis of covariance is performed for the levels of the mainFactor.column and the other factors are treated as covariates. if the interaction between the main factor and the covariate is significant, ANCOVA is not appropriate. ANCOVA is basically used when a factor is affected by uncontrolled quantitative covariate(s). 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 interested to know 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 ANCOVA or using ANOVA based on a factorial experiment. ANCOVA is done even there is only one factor (without covariate or factor variable).

Value

An object containing expression table, lm models, residuals, raw data and ANOVA table for each gene.

\Delta \Delta C_T combined expression table

object$combinedFoldChange

ANOVA table

object$perGene$gene_name$ANOVA_table

lm ANOVA

object$perGene$gene_name$lm_ANOVA

lm ANCOVA

object$perGene$gene_name$lm_ANCOVA

Residuals

resid(object$perGene$gene_name$lm_ANOVA)

log2FC_Plot

object$perGene$gene_name$log2FC_Plot

RE_Plot

object$perGene$gene_name$RE_Plot

Examples

data1 <- read.csv(system.file("extdata", "data_2factorBlock3ref.csv", package = "rtpcr"))
ANOVA_DDCt(x = data1,
           numOfFactors = 2,
           numberOfrefGenes = 2,
           block = "block",
           mainFactor.column = 2,
           plot = FALSE,
           p.adj = "none")
           
data2 <- read.csv(system.file("extdata", "data_1factor_one_ref.csv", package = "rtpcr"))          
ANOVA_DDCt(
           x = data2,
           numOfFactors = 1,
           numberOfrefGenes = 1,
           block = NULL,
           mainFactor.column = 1,
           plot = FALSE,
           p.adj = "none")

Sample data (with technical replicates)

Description

A sample data for calculating biological replicated. Each line belongs to a separate individual (non-repeated measure experiment).

Usage

Lee_etal2020qPCR

Format

A data frame with 72 observations and 8 variables:

factor1

experimental factor

DS

DS

biolRep

biological replicate

techRep

technical replicates

E_APOE

Amplification efficiency of APOE gene

Ct_APOE

Ct of APOE gene

E_GAPDH

Amplification efficiency of GAPDH gene

Ct_GAPDH

Ct of GAPDH gene

Source

Lee et al, (2020) <doi:10.12688/f1000research.23580.2>

Delta Ct or Delta Delta Ct pairwise comparisons using a fitted model

Description

Performs relative expression (fold change) analysis based on the \Delta C_T or \Delta \Delta C_T methods using a fitted model object produced by ANOVA_DCt(), ANOVA_DDCt() or REPEATED_DDCt().

Usage

Means_DDCt(model, specs, p.adj = "none")

Arguments

model

A fitted model object (typically an lmer or lm object) created by ANOVA_DCt(), ANOVA_DDCt() or REPEATED_DDCt().

specs

A character string or character vector specifying the predictors or combinations of predictors over which relative expression values are desired. This argument follows the specification syntax used by emmeans::emmeans() (e.g., "Factor", "Factor1 | Factor2").

p.adj

Character string specifying the method for adjusting p-values. See p.adjust for available options.

Details

The Means_DDCt function performs pairwise comparisons of relative expression values fo all combinations using estimated marginal means derived from a fitted model. For ANOVA models, relative expression values can be obtained for main effects, interactions, and sliced (simple) effects. For ANCOVA models returned by the rtpcr package, only simple effects are supported.

Internally, this function relies on the emmeans package to compute marginal means and contrasts, which are then back-transformed to fold change values using the \Delta \Delta C_T framework.

Value

A data frame containing estimated relative expression values, confidence intervals, p-values, and significance levels derived from the fitted model.

Author(s)

Ghader Mirzaghaderi

Examples


# Obtain a fitted model from ANOVA_DDCt
res <- ANOVA_DDCt(
  data_3factor,
  numOfFactors = 3,
  numberOfrefGenes = 1,
  mainFactor.column = 1,
  block = NULL)

# Relative expression values for Type main effect
Means_DDCt(res$perGene$E_PO$lm_ANOVA, specs = "Type")

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

# Relative expression values for Concentration sliced by Type
Means_DDCt(res$perGene$E_PO$lm_ANOVA, specs = "Conc | Type")

# Relative expression values for Concentration sliced by Type and SA
Means_DDCt(res$perGene$E_PO$lm_ANOVA, specs = "Conc | Type * SA")




data <- read.csv(system.file("extdata", "data_3factor.csv", package = "rtpcr"))
res <- ANOVA_DCt(
  data,
  numOfFactors = 3,
  numberOfrefGenes = 1,
  block = NULL)

# lm <- res$perGene$PO$lm_factorial
# Means_DDCt(lm, specs = "Type * Conc * SA", p.adj = "none")

Delta Delta Ct ANOVA analysis on repeated measure data

Description

REPEATED_DDCt function performs \Delta \Delta C_T method analysis of observations repeatedly taken over different time courses. Data may be obtained over time from a uni- or multi-factorial experiment. Target genes must be provided as paired efficiency (E) and Ct columns followed by the E/Ct column pairs of reference genes.

Usage

REPEATED_DDCt(
  x,
  numOfFactors,
  numberOfrefGenes,
  repeatedFactor,
  calibratorLevel,
  block,
  p.adj = "none",
  plot = FALSE,
  analyseAllTarget = TRUE
)

Arguments

x

input data frame in which the first column is id, followed by the factor column(s) which include at least time. The first level of time in data frame is used as calibrator or reference level. Additional factor(s) may also be present. Other columns are efficiency and Ct values of target and reference genes. In the id column, a unique number is assigned to each individual from which samples have been taken over time, for example see data_repeated_measure_1, all the three number 1 indicate one individual which has been sampled over three different time courses. See example data sets or refer vignettes, section "Input data structure" for details.

numOfFactors

Integer. Number of experimental factor columns (excluding optional block).

numberOfrefGenes

Integer. Number of reference genes. Each reference gene must be represented by two columns (E and Ct).

repeatedFactor

Character string specifying the factor for which fold changes are analysed (commonly "time").

calibratorLevel

A level of repeatedFactor to be used as the calibrator (reference level) which is the reference level or sample that all others are compared to. Examples are untreated or time 0.

block

Character or NULL. Name of the blocking factor column. 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.

p.adj

Method for p-value adjustment. See p.adjust.

plot

Logical; if FALSE, plots are not produced.

analyseAllTarget

Logical or character. If TRUE (default), all detected target genes are analysed. Alternatively, a character vector specifying the names (names of their Efficiency columns) of target genes to be analysed.

Details

Column layout requirements for x:

Value

An object containing expression table, lm models, residuals, raw data and ANOVA table for each gene.

\Delta \Delta C_T combined expression table

object$Relative_Expression_table

ANOVA table

object$perGene$gene_name$ANOVA_table

lm ANOVA

object$perGene$gene_name$lm

Residuals

resid(object$perGene$gene_name$lm)

log2FC_Plot

object$perGene$gene_name$log2FC_Plot

RE_Plot

object$perGene$gene_name$RE_Plot

Examples

data1 <- read.csv(system.file("extdata", "data_repeated_measure_1.csv", package = "rtpcr"))
REPEATED_DDCt(
  data1,
  numOfFactors = 1,
  numberOfrefGenes = 1,
  repeatedFactor = "time",
  calibratorLevel = "1",
  block = NULL)



data2 <- read.csv(system.file("extdata", "data_repeated_measure_2.csv", package = "rtpcr"))
REPEATED_DDCt(
  data2,
  numOfFactors = 2,
  numberOfrefGenes = 1,
  repeatedFactor = "time", 
  calibratorLevel = "1",
  block = NULL,
  p.adj = "none",
  plot = FALSE,
  analyseAllTarget = TRUE)

Delta Delta Ct method t-test analysis

Description

The TTEST_DDCt function performs fold change expression analysis based on the \Delta \Delta C_T method using Student's t-test. It supports analysis of one or more target genes evaluated under two experimental conditions (e.g. control vs treatment).

Usage

TTEST_DDCt(
  x,
  numberOfrefGenes,
  Factor.level.order = NULL,
  paired = FALSE,
  var.equal = TRUE,
  p.adj = "none",
  order = "none",
  plotType = "RE"
)

Arguments

x

A data frame containing experimental conditions, biological replicates, and amplification efficiency and Ct values for target and reference genes. The number of biological replicates must be equal across genes. If this is not true, or there are NA values use ANODA_DDCt function for independent samples or REPEATED_DDCt for paired samples. See the package vignette for details on the required data structure.

numberOfrefGenes

Integer specifying the number of reference genes used for normalization.

Factor.level.order

Optional character vector specifying the order of factor levels. If NULL, the first level of the factor column is used as the calibrator.

paired

Logical; if TRUE, a paired t-test is performed.

var.equal

Logical; if TRUE, equal variances are assumed and a pooled variance estimate is used. Otherwise, Welch's t-test is applied.

p.adj

Method for p-value adjustment. One of "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", or "none". See p.adjust.

order

Optional character vector specifying the order of genes in the output plot.

plotType

Plot scale to use: "RE" for relative expression or "log2FC" for log2 fold change.

Details

Relative expression values are computed using one or more reference genes for normalization. Both paired and unpaired experimental designs are supported.

Paired samples in quantitative PCR refer to measurements collected from the same individuals under two different conditions (e.g. before vs after treatment), whereas unpaired samples originate from different individuals in each condition. Paired designs allow within-individual comparisons and typically reduce inter-individual variability.

The function returns numerical summaries as well as bar plots based on either relative expression (RE) or log2 fold change (log2FC).

Value

A list with the following components:

Result

Table containing RE values, log2FC, p-values, significance codes, confidence intervals, standard errors, and lower/upper SE limits.

RE_Plot

Bar plot of relative expression values.

log2FC_Plot

Bar plot of log2 fold change values.

Author(s)

Ghader Mirzaghaderi

References

Livak, K. J. and Schmittgen, T. D. (2001). Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the Double Delta CT Method. Methods, 25(4), 402–408. doi:10.1006/meth.2001.1262

Ganger, M. T., Dietz, G. D., and Ewing, S. J. (2017). A common base method for analysis of qPCR data and the application of simple blocking in qPCR experiments. BMC Bioinformatics, 18, 1–11.

Yuan, J. S., Reed, A., Chen, F., and Stewart, N. (2006). Statistical Analysis of Real-Time PCR Data. BMC Bioinformatics, 7, 85.

Examples

# Example data structure
data1 <- read.csv(system.file("extdata", "data_1factor_one_ref.csv", package = "rtpcr"))

# Unpaired t-test
TTEST_DDCt(
  data1,
  paired = FALSE,
  var.equal = TRUE,
  numberOfrefGenes = 1)

# With amplification efficiencies
data2 <- read.csv(system.file("extdata", "data_1factor_one_ref_Eff.csv", package = "rtpcr"))

TTEST_DDCt(
  data2,
  paired = FALSE,
  var.equal = TRUE,
  numberOfrefGenes = 1)

# Two reference genes
data3 <- read.csv(system.file("extdata", "data_1factor_Two_ref.csv", package = "rtpcr"))
TTEST_DDCt(
  data3,
  numberOfrefGenes = 2,
  var.equal = TRUE,
  p.adj = "BH")

Sample data (one factor three levels)

Description

A sample dataset. Each line belongs to a separate individual (non-repeated measure experiment).

Usage

data_1factor

Format

A data frame with 9 observations and 6 variables:

SA

An experimental factor here called SA

Rep

Biological replicates

E_PO

Mean amplification efficiency of PO gene

Ct_PO

Ct values of PO gene. Each is the mean of technical replicates

E_GAPDH

Mean amplification efficiency of GAPDH gene

Ct_GAPDH

Ct values of GAPDH gene. Each is the mean of technical replicates

Source

Not applicable

Sample qPCR data (one target and two reference genes under two different conditions)

Description

Sample qPCR data (one target and two reference genes under two different conditions)

Usage

data_1factor_Two_ref

Format

A data frame with 6 observations and 8 variables:

Condition

Experimental conditions

Rep

Biological replicates

E_DER5

amplification efficiency of DER5 gene

Ct_DER5

Ct values of DER5 gene. Each is the mean of technical replicates

E_Actin

Amplification efficiency of Actin gene

Ct_Actin

Ct values of Actin gene. Each is the mean of technical replicates

E_HPRT

Amplification efficiency of HPRT gene

Ct_HPRT

Ct values of HPRT gene

Source

Not applicable

Sample qPCR data (two different conditions)

Description

Sample qPCR data (two different conditions)

Usage

data_1factor_one_ref

Format

A data frame with 6 observations and 10 variables:

Condition

Experimental conditions

Rep

Biological replicates

C2H2_26_E

amplification efficiency of C2H2_26 gene

C2H2_26_Ct

Ct values of C2H2_26 gene. Each is the mean of technical replicates

C2H2_01_E

Amplification efficiency of C2H2_01 gene

C2H2_01_Ct

Ct values of C2H2_01 gene. Each is the mean of technical replicates

C2H2_12_E

Amplification efficiency of C2H2_12 gene

C2H2_12_Ct

Ct values of C2H2_12 gene

ref_E

Amplification efficiency of ref gene

ref_Ct

Ct values of ref gene

Source

University of Kurdistan

Sample qPCR data (two different conditions)

Description

Sample qPCR data (two different conditions)

Usage

data_1factor_one_ref_Eff

Format

A data frame with 6 observations and 6 variables:

Con

Experimental conditions

r

Biological replicates

target_E

Amplification efficiency of target gene

target_Ct

Ct values of target gene

Actin_E

Amplification efficiency of reference gene

Actin_Ct

Ct values of reference gene

Source

University of Kurdistan

Sample data (two factor)

Description

A sample dataset. Each line belongs to a separate individual (non-repeated measure experiment).

Usage

data_2factor

Format

A data frame with 18 observations and 7 variables:

Genotype

First experimental factor

Drought

Second experimental factor

Rep

Biological replicates

E_PO

Mean amplification efficiency of PO gene

Ct_PO

Ct values of PO gene. Each is the mean of technical replicates

E_GAPDH

Mean amplification efficiency of GAPDH gene

Ct_GAPDH

Ct values of GAPDH gene. Each is the mean of technical replicates

Source

Not applicable

Sample data in (two factor with blocking factor)

Description

A sample qPCR data set with blocking factor. Each line belongs to a separate individual (non-repeated measure experiment).

Usage

data_2factor3ref

Format

A data frame with 18 observations and 8 variables:

factor1

First experimental factor

factor2

Second experimental factor

Rep

Biological replicates

PO

Mean amplification efficiency of PO gene

Ct_PO

Ct values of PO gene. Each is the mean of technical replicates

GAPDH

Mean amplification efficiency of GAPDH gene

Ct_GAPDH

Ct values of GAPDH gene. Each is the mean of technical replicates

ref2

Mean amplification efficiency of ref2 gene

Ct_ref2

Ct values of ref2 gene. Each is the mean of technical replicates

ref3

Mean amplification efficiency of ref3 gene

Ct_ref3

Ct values of GAPDH gene. Each is the mean of technical replicates

Source

Not applicable

Sample data in (two factor with blocking factor)

Description

A sample qPCR data set with blocking factor. Each line belongs to a separate individual (non-repeated measure experiment).

Usage

data_2factorBlock

Format

A data frame with 18 observations and 8 variables:

factor1

First experimental factor

factor2

Second experimental factor

block

Second experimental factor

Rep

Biological replicates

E_PO

Mean amplification efficiency of PO gene

Ct_PO

Ct values of PO gene. Each is the mean of technical replicates

E_GAPDH

Mean amplification efficiency of GAPDH gene

Ct_GAPDH

Ct values of GAPDH gene. Each is the mean of technical replicates

Source

Not applicable

Sample data in (two factor with blocking factor and 3 reference genes)

Description

A sample qPCR data set with blocking factor and 3 reference genes. Each line belongs to a separate individual (non-repeated measure experiment).

Usage

data_2factorBlock3ref

Format

A data frame with 18 observations and 8 variables:

factor1

First experimental factor

factor2

Second experimental factor

block

blocking factor

Rep

Biological replicates

E_PO

Mean amplification efficiency of PO gene

Ct_PO

Ct values of PO gene. Each is the mean of technical replicates

E_GAPDH

Mean amplification efficiency of GAPDH gene

Ct_GAPDH

Ct values of GAPDH gene. Each is the mean of technical replicates

ref2E

Mean amplification efficiency of ref2 gene

ref2Ct

Ct values of ref2 gene. Each is the mean of technical replicates

ref3E

Mean amplification efficiency of ref3 gene

ref3Ct

Ct values of GAPDH gene. Each is the mean of technical replicates

Source

Not applicable

Sample data (three factor)

Description

A sample dataset. Each line belongs to a separate individual (non-repeated measure experiment).

Usage

data_3factor

Format

A data frame with 36 observations and 8 variables:

Type

First experimental factor

Conc

Second experimental factor

SA

Third experimental factor

Replicate

Biological replicates

E_PO

Mean amplification efficiency of PO gene

Ct_PO

Ct values of PO gene. Each is the mean of technical replicates

E_GAPDH

Mean amplification efficiency of GAPDH gene

Ct_GAPDH

Ct values of GAPDH gene. Each is the mean of technical replicates

Source

Not applicable

Sample qPCR data: amplification efficiency

Description

A sample qPCR dataset for demonstrating efficiency calculation.

Usage

data_efficiency

Format

A data frame with 21 observations and 4 variables:

dilutions

Dilution factor

C2H2.26

Target gene 1

C2H2.01

Target gene 2

GAPDH

Reference gene

Source

Where the data comes from (if applicable)

Repeated measure sample data

Description

A repeated measure sample data in which 3 individuals have been analysed. In the "id" column, a unique number is assigned to each individual, e.g. all the three number 1 indicate one individual. samples are taken or measurements are scored over different time points (time column) from each individual.

Usage

data_repeated_measure_1

Format

A data frame with 9 observations and 6 variables:

time

time course levels

id

experimental factor

Target

Amplification efficiency of target gene

Ct_Target

Ct of target gene

Ref

Amplification efficiency of reference gene

Ct_Ref

Ct of reference gene

Source

NA

Repeated measure sample data

Description

A repeated measure sample data in which 6 individuals have been analysed. In the "id" column, a unique number is assigned to each individual, e.g. all the three number 1 indicate one individual. samples are taken or measurements are scored over different time points (time column) from each individual.

Usage

data_repeated_measure_2

Format

A data frame with 18 observations and 7 variables:

treatment

treatment

time

time course levels

id

experimental factor

Target

Amplification efficiency of target gene

Target_Ct

Ct of target gene

Ref

Amplification efficiency of reference gene

Ref_Ct

Ct of reference gene

Source

NA

Sample data (with technical replicates)

Description

A sample data for calculating biological replicated. Each line belongs to a separate individual (non-repeated measure experiment).

Usage

data_withTechRep

Format

A data frame with 18 observations and 9 variables:

factor1

experimental factor

factor2

experimental factor

factor3

experimental factor

biolrep

biological replicate

techrep

technical replicates

E_target

Amplification efficiency of target gene

Ct_target

Ct of target gene

E_ref

Amplification efficiency of reference gene

Ct_ref

Ct of reference gene

Source

Not applicable

Amplification efficiency statistics and standard curves

Description

The efficiency function calculates amplification efficiency (E) and related statistics, including slope and coefficient of determination (R^2), and generates standard curves for qPCR assays.

Usage

efficiency(df)

Arguments

df

A data frame containing dilution series and corresponding Ct values. The first column should represent dilution levels, and the remaining columns should contain Ct values for different genes.

Details

Amplification efficiency is estimated from standard curves generated by regressing Ct values against the logarithm of template dilution. For each gene, the function reports the slope of the standard curve, amplification efficiency (E), and R^2 as a measure of goodness of fit. The function also provides graphical visualization of the standard curves.

Value

A list with the following components:

efficiency

A data frame containing slope, amplification efficiency (E), and R^2 statistics for each gene.

Slope_compare

A table comparing slopes between genes.

plot

A ggplot2 object showing standard curves for all genes.

Author(s)

Ghader Mirzaghaderi

Examples


# Load example efficiency data
data <- read.csv(system.file("extdata", "data_efficiency.csv", package = "rtpcr"))

# Calculate amplification efficiency and generate standard curves
efficiency(data)

Internal global variables

Description

These objects are declared to avoid R CMD check notes about non-standard evaluation (e.g., in ggplot2).

Calculate mean of technical replicates

Description

Computes the arithmetic mean of technical replicates for each sample or group. This is often performed before ANOVA or other statistical analyses to simplify comparisons between experimental groups.

Usage

meanTech(x, groups)

Arguments

x

A raw data frame containing technical replicates.

groups

An integer vector or character vector specifying the column(s) to group by before calculating the mean of technical replicates.

Details

The meanTech function calculates the mean of technical replicates based on one or more grouping columns. This reduces the dataset to a single representative value per group, facilitating downstream analysis such as fold change calculation or ANOVA.

Value

A data frame with the mean of technical replicates for each group.

Author(s)

Ghader Mirzaghaderi

Examples


# 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 = 1:4)

# Another example using different dataset and grouping columns
data2 <- read.csv(system.file("extdata", "Lee_etal2020qPCR.csv", package = "rtpcr"))
meanTech(data2, groups = 1:3)

Combine multiple ggplot objects into a single layout

Description

The multiplot function arranges multiple ggplot2 objects into a single plotting layout with a specified number of columns.

Usage

multiplot(..., cols = 1)

Arguments

...

One or more ggplot2 objects.

cols

Integer specifying the number of columns in the layout.

Details

Multiple ggplot2 objects can be provided either as separate arguments via .... The function uses the grid package to control the layout.

Value

A grid object displaying multiple plots arranged in the specified layout.

Author(s)

Pedro J. (adapted from https://gist.github.com/pedroj/ffe89c67282f82c1813d)

Examples


# Example using output from TTEST_DDCt
data1 <- read.csv(system.file("extdata", "data_1factor_one_ref.csv", package = "rtpcr"))
a <- TTEST_DDCt(
  data1,
  numberOfrefGenes = 1,
  plotType = "log2FC")
p1 <- a$plot

# Example using output from ANOVA_DCt
data2 <- read.csv(system.file("extdata", "data_1factor.csv", package = "rtpcr"))
out2 <- ANOVA_DCt(
  data2,
  numOfFactors = 1,
  numberOfrefGenes = 1,
  block = NULL)
  
df <- out2$combinedResults

p2 <- plotFactor(
  df,
  x_col = "SA",
  y_col = "RE",
  Lower.se_col = "Lower.se.RE",
  Upper.se_col = "Upper.se.RE",
  letters_col = "sig",
  letters_d = 0.1,
  col_width = 0.7,
  err_width = 0.15,
  fill_colors = "skyblue",
  alpha = 1,
  base_size = 16)

# Combine plots into a single layout
multiplot(p1, p2, cols = 2)

multiplot(p1, p2, cols = 1)

Bar plot of gene expression for 1-, 2-, or 3-factor experiments

Description

Creates a bar plot of relative gene expression (fold change) values from 1-, 2-, or 3-factor experiments, including error bars and statistical significance annotations.

Usage

plotFactor(
  data,
  x_col,
  y_col,
  Lower.se_col,
  Upper.se_col,
  group_col = NULL,
  facet_col = NULL,
  letters_col = NULL,
  letters_d = 0.2,
  col_width = 0.8,
  err_width = 0.15,
  dodge_width = 0.8,
  fill_colors = NULL,
  alpha = 1,
  base_size = 12,
  legend_position = "right",
  ...
)

Arguments

data

Data frame containing expression results

x_col

Character. Column name for x-axis

y_col

Character. Column name for bar height

Lower.se_col

Character. Column name for lower SE

Upper.se_col

Character. Column name for upper SE

group_col

Character. Column name for grouping bars (optional)

facet_col

Character. Column name for faceting (optional)

letters_col

Character. Column name for significance letters (optional)

letters_d

Numeric. Vertical offset for letters (default 0.2)

col_width

Numeric. Width of bars (default 0.8)

err_width

Numeric. Width of error bars (default 0.15)

dodge_width

Numeric. Width of dodge for grouped bars (default 0.8)

fill_colors

Optional vector of fill colors

alpha

Numeric. Transparency of bars (default 1)

base_size

Numeric. Base font size for theme (default 12)

legend_position

Character or numeric vector. Legend position (default right)

...

Additional ggplot2 layer arguments

Value

ggplot2 plot object

Author(s)

Ghader Mirzaghaderi

Examples

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

p1 <- 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"),
  alpha = 1,
  col_width = 0.7,
  dodge_width = 0.7,
  base_size = 16, 
  legend_position = c(0.2, 0.8))
  
p1



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$combinedResults
 df
 # Generate three-factor bar plot
 p <- 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("blue", "brown"),
  base_size = 14, 
  alpha = 1,
  legend_position = c(0.1, 0.2))
p
library(ggplot2)
p + theme(
  panel.border = element_rect(color = "black", linewidth = 0.5))