TTEST_DDCt: Expression analysis of target genes using the DeltaDelta C_T...

In rtpcr: qPCR Data Analysis

Expression analysis of target genes using the \Delta\Delta C_T method and t-test

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 = "BH",
  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. See the package vignette for details on the required data structure.
numberOfrefGenes	Integer specifying the number of reference genes used for normalization (must be \ge 1).
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".
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

Fold change 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
data_1factor_one_ref

# Unpaired t-test
TTEST_DDCt(
  data_1factor_one_ref,
  paired = FALSE,
  var.equal = TRUE,
  numberOfrefGenes = 1
)

# With amplification efficiencies
data_1factor_one_ref_Eff

TTEST_DDCt(
  data_1factor_one_ref_Eff,
  paired = FALSE,
  var.equal = TRUE,
  numberOfrefGenes = 1
)

# Two reference genes
TTEST_DDCt(
  data_1factor_Two_ref,
  numberOfrefGenes = 2,
  var.equal = TRUE,
  p.adj = "BH"
)

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