rtpcr (version 2.1.3)

TTEST_DDCt: Delta Delta Ct method t-test analysis

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 = "none",
  set_missing_target_Ct_to_40 = FALSE
)

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.

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. If this is not true, or there are NA values use ANODA_DDCt function for independent samples or REPEATED_DDCt for paired samples. See the package vignette for details on the required data structure.

numberOfrefGenes

Integer specifying the number of reference genes used for normalization.

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". See p.adjust.

set_missing_target_Ct_to_40

If TRUE, missing target gene Ct values become 40; if FALSE (default), they become NA.

Author

Ghader Mirzaghaderi

Details

Relative expression 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).

All the functions for relative expression analysis (including `TTEST_DDCt()`, `WILCOX_DDCt()`, `ANOVA_DDCt()`, `ANCOVA_DDCt()`, `REPEATED_DDCt()`, and `ANOVA_DCt()`) return the relative expression table which include fold change and corresponding statistics. The output of `ANOVA_DDCt()`, `ANCOVA_DDCt()`, `ANCOVA_DDCt()`, `REPEATED_DDCt()`, and `ANOVA_DCt()` also include lm models, residuals, raw data and ANOVA table for each gene.

The expression table returned by `TTEST_DDCt()`, `WILCOX_DDCt()`, `ANOVA_DDCt()`, `ANCOVA_DDCt()`, and `REPEATED_DDCt()` functions include these columns: gene (name of target genes), contrast (calibrator level and contrasts for which the relative expression is computed), RE (relative expression or fold change), log2FC (log(2) of relative expression or fold change), pvalue, sig (per-gene significance), LCL (95% lower confidence level), UCL (95% upper confidence level), se (standard error of mean calculated from the weighted delta Ct values of each of the main factor levels), Lower.se.RE (The lower limit error bar for RE which is 2^(log2(RE) - se)), Upper.se.RE (The upper limit error bar for RE which is 2^(log2(RE) + se)), Lower.se.log2FC (The lower limit error bar for log2 RE), and Upper.se.log2FC (The upper limit error bar for log2 RE)

References

LivakKJ, Schmittgen TD (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 MT, Dietz GD, and 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.

Taylor SC, Nadeau K, Abbasi M, Lachance C, Nguyen M, Fenrich, J. (2019). The ultimate qPCR experiment: producing publication quality, reproducible data the first time. Trends in Biotechnology, 37, 761-774.

Yuan JS, Reed A, Chen F, Stewart N (2006). Statistical Analysis of Real-Time PCR Data. BMC Bioinformatics, 7, 85.

Examples

# Example data structure
data1 <- read.csv(system.file("extdata", "data_ttest18genes.csv", package = "rtpcr"))

# Unpaired t-test
TTEST_DDCt(
  data1,
  paired = FALSE,
  var.equal = TRUE,
  numberOfrefGenes = 1)

# With amplification efficiencies
data2 <- read.csv(system.file("extdata", "data_1factor_one_ref_Eff.csv", package = "rtpcr"))

TTEST_DDCt(
  data2,
  numberOfrefGenes = 1)

# Two reference genes
data3 <- read.csv(system.file("extdata", "data_1factor_Two_ref.csv", package = "rtpcr"))
TTEST_DDCt(
  data3,
  numberOfrefGenes = 2)