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Delta Ct ANOVA analysis

Source: R/ANOVA_DCt.r
ANOVA_DCt.Rd

Performs \(\Delta C_T\) analysis for target genes by applying \(\Delta C_T\) method to each target gene. Target genes must be provided as paired efficiency (E) and Ct columns followed by the the reference gene(s) columns. See "Input data structure and column arrangement" in vignettes for details about data structure.

Usage

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

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).

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.

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.

Value

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

\(\Delta C_T\) combined expression table

object$combinedResults

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)

Examples

data <- read.csv(system.file("extdata", "data_3factor.csv", package = "rtpcr"))
res <- ANOVA_DCt(
  data,
  numOfFactors = 3,
  numberOfrefGenes = 1,
  block = NULL)
#> NULL
#> 
#> Relative expression (DCt method)
#>    Type Conc SA     RE  log2FC    LCL    UCL     se Lower.se.RE Upper.se.RE
#> 1     S    H A2 5.1934  2.3767 8.1197 3.3217 0.1309      4.7428      5.6867
#> 2     S    H A1 2.9690  1.5700 4.6420 1.8990 0.0551      2.8578      3.0846
#> 3     R    H A2 1.7371  0.7967 2.7159 1.1110 0.0837      1.6391      1.8409
#> 4     S    L A2 1.5333  0.6167 2.3973 0.9807 0.0865      1.4441      1.6280
#> 5     R    H A1 0.9885 -0.0167 1.5455 0.6323 0.0841      0.9325      1.0479
#> 6     S    L A1 0.7955 -0.3300 1.2438 0.5088 0.2128      0.6864      0.9220
#> 7     S    M A2 0.7955 -0.3300 1.2438 0.5088 0.2571      0.6657      0.9507
#> 8     R    M A1 0.6271 -0.6733 0.9804 0.4011 0.4388      0.4626      0.8500
#> 9     S    M A1 0.4147 -1.2700 0.6483 0.2652 0.2540      0.3477      0.4945
#> 10    R    M A2 0.3150 -1.6667 0.4925 0.2015 0.2890      0.2578      0.3848
#> 11    R    L A1 0.2852 -1.8100 0.4459 0.1824 0.0208      0.2811      0.2893
#> 12    R    L A2 0.0641 -3.9633 0.1002 0.0410 0.8228      0.0362      0.1134
#>    Lower.se.log2FC Upper.se.log2FC sig
#> 1           2.1705          2.6025   a
#> 2           1.5112          1.6311  ab
#> 3           0.7517          0.8443  bc
#> 4           0.5808          0.6548   c
#> 5          -0.0177         -0.0157  cd
#> 6          -0.3825         -0.2847   d
#> 7          -0.3944         -0.2761   d
#> 8          -0.9127         -0.4968  de
#> 9          -1.5145         -1.0650  ef
#> 10         -2.0363         -1.3641   f
#> 11         -1.8363         -1.7841   f
#> 12         -7.0103         -2.2407   g
#> 
#> Combined Expression Table (all genes)
#>    gene Type Conc SA     RE  log2FC    LCL    UCL     se Lower.se.RE
#> 1    PO    S    H A2 5.1934  2.3767 8.1197 3.3217 0.1309      4.7428
#> 2    PO    S    H A1 2.9690  1.5700 4.6420 1.8990 0.0551      2.8578
#> 3    PO    R    H A2 1.7371  0.7967 2.7159 1.1110 0.0837      1.6391
#> 4    PO    S    L A2 1.5333  0.6167 2.3973 0.9807 0.0865      1.4441
#> 5    PO    R    H A1 0.9885 -0.0167 1.5455 0.6323 0.0841      0.9325
#> 6    PO    S    L A1 0.7955 -0.3300 1.2438 0.5088 0.2128      0.6864
#> 7    PO    S    M A2 0.7955 -0.3300 1.2438 0.5088 0.2571      0.6657
#> 8    PO    R    M A1 0.6271 -0.6733 0.9804 0.4011 0.4388      0.4626
#> 9    PO    S    M A1 0.4147 -1.2700 0.6483 0.2652 0.2540      0.3477
#> 10   PO    R    M A2 0.3150 -1.6667 0.4925 0.2015 0.2890      0.2578
#> 11   PO    R    L A1 0.2852 -1.8100 0.4459 0.1824 0.0208      0.2811
#> 12   PO    R    L A2 0.0641 -3.9633 0.1002 0.0410 0.8228      0.0362
#>    Upper.se.RE Lower.se.log2FC Upper.se.log2FC sig
#> 1       5.6867          2.1705          2.6025   a
#> 2       3.0846          1.5112          1.6311  ab
#> 3       1.8409          0.7517          0.8443  bc
#> 4       1.6280          0.5808          0.6548   c
#> 5       1.0479         -0.0177         -0.0157  cd
#> 6       0.9220         -0.3825         -0.2847   d
#> 7       0.9507         -0.3944         -0.2761   d
#> 8       0.8500         -0.9127         -0.4968  de
#> 9       0.4945         -1.5145         -1.0650  ef
#> 10      0.3848         -2.0363         -1.3641   f
#> 11      0.2893         -1.8363         -1.7841   f
#> 12      0.1134         -7.0103         -2.2407   g