Apply Delta Delta Ct (ddCt) analysis to each target gene and performs per-gene statistical analysis.
ANCOVA_DDCt(
x,
numOfFactors,
numberOfrefGenes,
mainFactor.column,
block,
mainFactor.level.order = NULL,
p.adj = "none",
analyseAllTarget = TRUE
)An object containing expression table, lm model, residuals, raw data and ANOVA table for each gene:
object$relativeExpression
object$perGene$gene_name$ANOVA_table
object$perGene$gene_name$lm
object$perGene$gene_name$lm_formula
resid(object$perGene$gene_name$lm)
The input data frame containing experimental design columns, replicates (integer), 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.
Integer. Number of experimental factor columns
(excluding rep and optional block).
Integer. Number of reference genes.
Integer. Column index of the factor for which the relative expression analysis is applied. The remaining factors are treated as covariate(s).
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.
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.
Method for p-value adjustment. See p.adjust.
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.
ddCt analysis of covariance (ANCOVA) 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).
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)
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.
data1 <- read.csv(system.file("extdata", "data_2factorBlock3ref.csv", package = "rtpcr"))
ANCOVA_DDCt(x = data1,
numOfFactors = 2,
numberOfrefGenes = 2,
block = "block",
mainFactor.column = 2,
p.adj = "none")
data2 <- read.csv(system.file("extdata", "data_1factor_one_ref.csv", package = "rtpcr"))
ANCOVA_DDCt(x = data2,
numOfFactors = 1,
numberOfrefGenes = 1,
block = NULL,
mainFactor.column = 1,
p.adj = "none")
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