rtpcr (version 2.1.1)

ANOVA_DDCt: Delta Delta Ct ANOVA analysis

Description

Apply \(\Delta \Delta C_T\) analysis to each target gene in the input data frame. Target and reference genes must be provided as paired efficiency (E) and Ct columns located after the experimental design columns. columns.

Usage

ANOVA_DDCt(
  x,
  numOfFactors,
  numberOfrefGenes,
  mainFactor.column,
  block,
  analysisType = "anova",
  mainFactor.level.order = NULL,
  p.adj = "none",
  plot = FALSE,
  plotType = "RE",
  analyseAllTarget = TRUE
)

Value

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

\(\Delta \Delta C_T\) combined expression table

object$combinedFoldChange

ANOVA table

object$perGene$gene_name$ANOVA_table

lm ANOVA

object$perGene$gene_name$lm_ANOVA

lm ANCOVA

object$perGene$gene_name$lm_ANCOVA

Residuals

resid(object$perGene$gene_name$lm_ANOVA)

log2FC_Plot

object$perGene$gene_name$log2FC_Plot

RE_Plot

object$perGene$gene_name$RE_Plot

Arguments

x

A 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. Each reference gene must be represented by two columns (E and Ct).

mainFactor.column

Column index or name of the factor for which relative expression is calculated. When analysisType = "ancova", remaining factors are treated as covariates.

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.

analysisType

Character string specifying the analysis type; one of "anova" (default) or "ancova".

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.

plot

Logical; if FALSE, per gene-plots are not generated.

plotType

Plot scale to use: "RE" for relative expression or "log2FC" for log2 fold change.

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

\(\Delta \Delta C_T\) analysis is performed for the mainFactor.column based on a full model factorial experiment by default. However, if ancova, the analysisType argument, analysis of covariance is performed for the levels of the mainFactor.column and the other factors are treated as covariates. if the interaction between the main factor and the covariate is significant, ANCOVA is not appropriate. ANCOVA is basically used when a factor is affected by uncontrolled quantitative covariate(s). For example, suppose that wDCt of a target gene in a plant is affected by temperature. The gene may also be affected by drought. Since we already know that temperature affects the target gene, we are interested to know if the gene expression is also altered by the drought levels. We can design an experiment to understand the gene behavior at both temperature and drought levels at the same time. The drought is another factor (the covariate) that may affect the expression of our gene under the levels of the first factor i.e. temperature. The data of such an experiment can be analyzed by ANCOVA or using ANOVA based on a factorial experiment. ANCOVA is done even there is only one factor (without covariate or factor variable).

Examples

data1 <- read.csv(system.file("extdata", "data_2factorBlock3ref.csv", package = "rtpcr"))
ANOVA_DDCt(x = data1,
           numOfFactors = 2,
           numberOfrefGenes = 2,
           block = "block",
           mainFactor.column = 2,
           plot = FALSE,
           p.adj = "none")
           
data2 <- read.csv(system.file("extdata", "data_1factor_one_ref.csv", package = "rtpcr"))          
ANOVA_DDCt(
           x = data2,
           numOfFactors = 1,
           numberOfrefGenes = 1,
           block = NULL,
           mainFactor.column = 1,
           plot = FALSE,
           p.adj = "none")