rtpcr (version 2.1.2)

REPEATED_DDCt: Delta Delta Ct repeated measure analysis

Description

REPEATED_DDCt function performs Delta Delta Ct (ddCt) method analysis of observations repeatedly taken over different time courses. Data may be obtained over time from a uni- or multi-factorial experiment.

Usage

REPEATED_DDCt(
  x,
  numOfFactors,
  numberOfrefGenes,
  mainFactor.column,
  block,
  mainFactor.level.order = NULL,
  p.adj = "none",
  analyseAllTarget = TRUE
)

Value

An object containing expression table, lm model, residuals, raw data and ANOVA table for each gene:

ddCt expression table along with per-gene statistical comparison outputs

object$relativeExpression

ANOVA table

object$perGene$gene_name$ANOVA_table

lm ANOVA

object$perGene$gene_name$lm

lm_formula

object$perGene$gene_name$lm_formula

Residuals

resid(object$perGene$gene_name$lm)

Arguments

x

The input data frame containing experimental design columns, target gene E/Ct column pairs, and reference gene E/Ct column pairs. Reference gene columns must be located at the end of the data frame.

numOfFactors

Integer. Number of experimental factor columns (excluding rep and optional block).

numberOfrefGenes

Integer. Number of reference genes.

mainFactor.column

Integer. Column index of the factor (commonly "time") for which relative expression is calculated.

block

Character or NULL. Name of the blocking factor column. 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.

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.

p.adj

Method for p-value adjustment. See p.adjust.

analyseAllTarget

Logical or character. If TRUE (default), all target genes are analysed. Alternatively, a character vector specifying the names (names of their Efficiency columns) of target genes to be analysed.

Details

ddCt analysis of repeated measure data is performed for the mainFactor.column based on a full model factorial experiment.

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), ddCt (mean of weighted delta delta Ct values), RE (relative expression or fold change = 2^-ddCt), 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

data1 <- read.csv(system.file("extdata", "data_repeated_measure_1.csv", package = "rtpcr"))
REPEATED_DDCt(
  data1,
  numOfFactors = 1,
  numberOfrefGenes = 1,
  mainFactor.column = 1,
  block = NULL)



data2 <- read.csv(system.file("extdata", "data_repeated_measure_2.csv", package = "rtpcr"))
REPEATED_DDCt(
  data2,
  numOfFactors = 2,
  numberOfrefGenes = 1,
  mainFactor.column = 2, 
  block = NULL,
  p.adj = "none")