ANOVA_DCt: Relative expression analysis using the Delta C_T method with...

In rtpcr: qPCR Data Analysis

Relative expression analysis using the \Delta C_T method with ANOVA

Description

The ANOVA_DCt function performs analysis of variance (ANOVA) on relative expression values calculated using the \Delta C_T method. Expression levels are normalized using one or more reference genes and analyzed across all combinations of experimental factor levels.

Usage

ANOVA_DCt(x, numberOfrefGenes, block, alpha = 0.05, adjust = "none")

Arguments

x	A data frame structured as described in the package vignette, containing experimental condition columns, 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 separate biological individual, reflecting a non-repeated-measures experimental design.
numberOfrefGenes	Integer specifying the number of reference genes used for normalization (must be \ge 1).
block	Character string or NULL. If provided, this specifies the column name in x corresponding to a blocking factor (e.g. qPCR plate). Blocking is used to reduce technical variation arising from experimental conditions such as plate-to-plate differences.
alpha	Significance level used for compact letter display (CLD); default is 0.05.
adjust	P-value adjustment method passed to emmeans and cld.

Details

Relative expression (RE) values are calculated using the \Delta C_T method, where Ct values of target genes are normalized to reference gene(s). The resulting weighted Delta Ct (wDCt) values are then analyzed using ANOVA.

The function supports uni- and multi-factorial experimental designs. Blocking factors (e.g. qPCR plates) can optionally be included to account for technical variation. Each row of the input data represents an independent biological individual, corresponding to a non-repeated-measures experiment.

Value

A list containing the following components:

Final_data

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

lm

Fitted linear model object, including ANOVA results.

ANOVA

ANOVA table based on a completely randomized design (CRD).

Result

Result table containing treatment and factor levels, relative expression (RE), log2 fold change (log2FC), confidence limits (LCL, UCL), compact letter display for pairwise comparisons, and standard errors with corresponding lower and upper limits.

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

# If the data include technical replicates, calculate means first:
# df <- meanTech(data_3factor, groups = 1:3)

# One-factor or multi-factor ANOVA without blocking
ANOVA_DCt(
  data_3factor,
  numberOfrefGenes = 1,
  block = NULL
)

# ANOVA with blocking factor
ANOVA_DCt(
  data_2factorBlock,
  numberOfrefGenes = 1,
  block = "Block"
)

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