ANOVA_DDCt: Relative expression analysis using the DeltaDelta C_T method...

In rtpcr: qPCR Data Analysis

Relative expression analysis using the \Delta\Delta C_T method with ANOVA and ANCOVA

Description

The ANOVA_DDCt function performs relative expression (RE) analysis based on the \Delta\Delta C_T method using analysis of variance (ANOVA) or analysis of covariance (ANCOVA). It supports uni- and multi-factorial qPCR experimental designs.

Bar plots of relative expression (RE) or log2 fold change (log2FC), together with standard errors and confidence intervals, are optionally returned.

Usage

ANOVA_DDCt(
  x,
  numberOfrefGenes = 1,
  mainFactor.column = 1,
  analysisType = "anova",
  mainFactor.level.order = NULL,
  block = NULL,
  x.axis.labels.rename = "none",
  p.adj = "none",
  plot = TRUE,
  plotType = "RE"
)

Arguments

x	A data frame containing experimental conditions, biological replicates, amplification efficiency (E), and Ct values for target and reference genes. Each Ct value should represent the mean of technical replicates. NOTE: Each row corresponds to a different biological individual, reflecting a non-repeated-measures experimental design. See the package vignette for details on data structure and column arrangement.
numberOfrefGenes	Integer specifying the number of reference genes used for normalization (must be \ge 1).
mainFactor.column	Column index or name of the factor for which relative expression is calculated. When analysisType = "ancova", remaining factors are treated as covariates.
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.
block	Optional column name specifying a blocking factor. Blocking is commonly used to account for variation between qPCR plates. Block effects are treated as random, and interactions with main effects are not considered.
x.axis.labels.rename	Optional character vector used to relabel the x-axis in bar plots.
p.adj	Method for p-value adjustment.
plot	Logical; if FALSE, plots are not generated.
plotType	Plot scale to use: "RE" for relative expression or "log2FC" for log2 fold change.

Details

The function calculates weighted Delta Ct (wDCt) values using as many specified reference genes and then performs statistical analysis on the resulting relative expression values.

For multi-factorial experiments, relative expression is calculated for the levels of the factor specified by mainFactor.column.

If analysisType = "anova", a full factorial ANOVA model is fitted by default.

If analysisType = "ancova", relative expression is calculated for the levels of mainFactor.column, while the remaining factor(s), if any, are treated as covariates. In such cases, the ANCOVA table should be examined carefully: a significant interaction between the main factor and a covariate indicates that ANCOVA assumptions are violated and the model may be inappropriate.

ANCOVA is typically used when gene expression is influenced by one or more uncontrolled quantitative variables. For example, gene expression may depend on temperature while the primary interest is in treatment or stress effects.

The function also supports single-factor experiments, in which case ANOVA reduces to a one-way analysis.

Value

A list containing the following components:

Final_data

Input data frame augmented with weighted Delta Ct (wDCt) values.

lm_ANOVA

Linear model object for ANOVA analysis (if applicable).

lm_ANCOVA

Linear model object for ANCOVA analysis (if applicable).

ANOVA_table

ANOVA table.

ANCOVA_table

ANCOVA table.

Expression_Table

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

RE_Plot

Bar plot of relative expression values for main factor levels.

log2FC_Plot

Bar plot of log2 fold change values for main factor levels.

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

ANOVA_DDCt(data_1factor,
  numberOfrefGenes = 1,
  mainFactor.column = 1,
  block = NULL
)

ANOVA_DDCt(data_2factor,
  numberOfrefGenes = 1,
  mainFactor.column = 2,
  analysisType = "ancova",
  block = NULL
)

df <- meanTech(Lee_etal2020qPCR, groups = 1:3)

ANOVA_DDCt(df,
  numberOfrefGenes = 1,
  analysisType = "ancova",
  mainFactor.column = 2,
  plotType = "log2FC",
  block = NULL
)

rtpcr documentation built on Dec. 19, 2025, 5:07 p.m.