rtpcr (version 2.1.3)

compute_wDCt: Cleaning data and weighted delta Ct (wDCt) calculation

Description

The compute_wDCt function cleans the data and computes wDCt. This function is automatically applied to the expression analysis functions like ANOVA_DDCt, TTEST_DDCt, etc. So it should not be applied in advance of expression analysis functions.

Usage

compute_wDCt(
  x,
  numOfFactors,
  numberOfrefGenes,
  block,
  set_missing_target_Ct_to_40 = FALSE
)

Value

The original data frame along with the weighted delta Ct column.

Arguments

x

A 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.

numOfFactors

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

numberOfrefGenes

Integer. Number of reference genes.

block

Character or NULL. Name of the blocking factor column. When a qPCR experiment is done in multiple qPCR plates, each plate is considered as a random block so that at least one replicate of each treatment and control is present on a plate.

set_missing_target_Ct_to_40

If TRUE, missing target gene Ct values become 40; if FALSE (default), they become NA.

Details

The compute_wDCt function computes weighted delta Ct (wDCt) for the input data. Missing data can be denoted by NA in the input data frame. Values such as '0' and 'undetermined' (for any E and Ct) are automatically converted to NA. For target genes, NA for E or Ct measurements cause returning NA for the corresponding delta Ct for that replicate (row). If there are more than one reference gene, NA in the place of the E or the Ct value cause skipping that gene and remaining references are geometrically averaged. The compute_wDCt function is automatically applied to the expression analysis functions.

Examples

           
data <- read.csv(system.file("extdata", "data_2factorBlock3ref.csv", package = "rtpcr"))
data
compute_wDCt(x = data,
             numOfFactors = 2,
             numberOfrefGenes = 3,
             block = "block")