# 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.

rtpcr is now available as shiny_rtpcr, a web application developed using R/Shiny for interactive analysis of qPCR data at https://mirzaghaderi.shinyapps.io/rtpcr/

# 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
`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

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

#### 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.

#### 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

## 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 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: A) Basic structure of independent group- or paired group-experiments. Examples of paired groups ( or repeated measure experiments): 1) Analysing gene expression before and after treatment in the sampe biological replicates; 2) Analysing gene expression between two tissue types within the same organism. For such analysis types we can use the TTEST_DDCt with the argument paired = TRUE; or ANOVA_DDCt with a repeated measure model such as wDCt ~ Treatment + ( 1 | id) or wDCt ~ Treatment + ( 1 | Rep). B) Calculation of standard error (se) and confidense interval (CI) for ddCt–based relative expression in the `ANOVA_DDCt()` function of the rtpcr package. Standard errors and confidense interval are calculated from model-based residuals by default (`modelBased_se = TRUE`) according to the selected `se.type` (One of `"paired.group"`, `"two.group"`, or `"single.group"`). By setting `modelBased_se = FALSE` standard errors are calculated directly from the observed wDCt values within each group, although for the single factor experiments the two cases are the same. Method 4 is based on the pooled error from which the confidence interval is calculated and can be used in any conditions as it is derived from a fitted model. se: group standard error, SE: pooled standard error. The 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(
      data,
      specs = "condition",
      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(
      data,
      numOfFactors = 1,
      numberOfrefGenes = 1,
      specs = "time",
      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,
      specs = "Type | Concentration",
      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,
      specs = "Concentration",
      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

Figure 6: 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 lm model, residuals, raw data and ANOVA table for each gene.

# 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,
      specs = "Treatment",
      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.

# 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,
      specs = "Conc",
      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 `lm`and plotted as follow:

    data <- read.csv(system.file("extdata", "data_repeated_measure_1.csv", package = "rtpcr"))
    res3 <- ANOVA_DDCt(
      data,
      numOfFactors = 1,
      numberOfrefGenes = 1,
      specs = "time",
      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

- If you encounter a clear bug, please file a minimal reproducible example on github

# 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.

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