inst/shinyapp/README.md

In rtpcr: qPCR Data Analysis

This is the shiny version of the rtpcr package, a web application developed using R/Shiny for interactive analysis of qPCR data

rtpcr uses dCt and ddCt methods, including t-tests and ANOVA, repeated-measures 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. The rtpcr package gets efficiency (E) the Ct values of genes and performs different analyses using the following functions.

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 block column 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 is from a separate and unique biological replicate. For example, a data frame with 12 rows has come from an experiment with 12 individuals. The repeated measure models are intended for experiments with repeated observations (e.g. time-course data). In repeated measure experiments the Replicate column contains 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).

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.

Example of data format for amplification Efficiency

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.

# 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

For further details please visit: https://github.com/mirzaghaderi/rtpcr

Contact

Email: gh.mirzaghaderi at uok.ac.ir

Citation

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},
  }

rtpcr documentation built on Feb. 24, 2026, 5:08 p.m.