rtpcr (version 2.1.2)

ANOVA_DCt: Delta Ct ANOVA analysis

Description

Performs Delta Ct (dCt) analysis of the data from a 1-, 2-, or 3-factor experiment. Per-gene statistical grouping is also performed for all treatment (T) combinations.

Usage

ANOVA_DCt(
  x,
  numOfFactors,
  numberOfrefGenes,
  block,
  alpha = 0.05,
  p.adj = "none",
  analyseAllTarget = TRUE
)

Value

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

dCt expression table for all treatment combinations along with the per-gene statistical grouping

object$relativeExpression

ANOVA table for treatments

object$perGene$gene_name$ANOVA_T

ANOVA table factorial

object$perGene$gene_name$ANOVA_factorial

lm ANOVA for tratments

object$perGene$gene_name$lm_T

lm ANOVA factorial

object$perGene$gene_name$lm_factorial

Residuals

resid(object$perGene$gene_name$lm_T)

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. See "Input data structure" in vignettes for details about data structure.

numOfFactors

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

numberOfrefGenes

Integer. Number of reference genes. Each reference gene must be represented by two columns (E and Ct).

block

Character. Block column name or NULL. 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.

alpha

statistical level for comparisons

p.adj

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

analyseAllTarget

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

Details

The function returns analysis of variance components and the expression table which include these columns: gene (name of target genes), factor columns, dCt (mean weighted delta Ct for each treatment combination), RE (relative expression = 2^-dCt), log2FC (log(2) of relative expression), LCL (95% lower confidence level), UCL (95% upper confidence level), se (standard error of the mean calculated from the weighted delta Ct (wDCt) values of each treatment combination), 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), Upper.se.log2FC (The upper limit error bar for log2 RE), and sig (per-gene significance grouping letters).

Examples

data <- read.csv(system.file("extdata", "data_3factor.csv", package = "rtpcr"))
res <- ANOVA_DCt(
  data,
  numOfFactors = 3,
  numberOfrefGenes = 1,
  block = NULL)