EasyqpcR (version 1.14.0)

nrmData: Determination of the NF, RQ, NRQ, NRQ scaled to control and their SE and SD.

Description

This function determines the values of the normalization factors, the relative quantitues, the normalized relative quantities, the normalized relative quantities scaled to control and their respectives standard errors and standard deviations by the method described by Hellemans et al. (2007).

Usage

nrmData(data, r, E, Eerror, nSpl, nbRef, Refposcol, nCTL, CF, CalPos, 
	trace = FALSE, geo = FALSE, na.rm = na.rm)

Arguments

data

data.frame containing row datas (genes in columns, samples in rows, Cq values).

r

numeric, number of qPCR replicates.

E

numeric, amplification efficiency values for each gene (follow the same order of the genes).

Eerror

numeric, standard errors of amplification efficiencies for each gene (follow the same order of the genes).

nSpl

numeric, number of samples to analyzed.

nbRef

numeric, number of reference genes used.

Refposcol

column position of your reference gene(s).

nCTL

numeric, number of samples forming your control group.

CF

numeric (or object if you have used the calData function from this package), values of the calibration factors for each gene (follow the same order of the genes).

CalPos

numeric, sample number of your calibrator(s).

trace

logical, print additional information.

geo

logical, to scale to your control group, the function will use the geometrical mean if TRUE or the arithmetic mean if FALSE.

na.rm

a logical value indicating whether NA values should be stripped before the computation proceeds.

Value

NRQs normalized to control

Gives the normlized relative quantities scaled to your control group.

NRQs

Gives the normlized relative quantities.

NRQs of your calibrator for this run

Gives the normlized relative quantities of your calibrator(s).

Details

The algorithm used in this function is based on the article of Hellemans et al. (2007). This function calculates the expression value scaled to your control group and normalized to the calibration factor and the normalization factor. The limiting step is that you need to put the control samples on the top of the data frame otherwise, the algorithm will not work correctly. For more information for the way to use this function, please see the vignette.

References

Jan Hellemans, Geert Mortier, Anne De Paepe, Frank Speleman and Jo Vandesompele. qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biology 2007, 8:R19 (doi:10.1186/gb-2007-8-2-r19).

Examples

data(qPCR_run1,qPCR_run2,qPCR_run3)

nrmData(data = qPCR_run1 , r=3, E=c(2, 2, 2, 2), 
	      Eerror=c(0.02, 0.02, 0.02, 0.02), nSpl=5, 
	      nbRef=2, Refposcol=1:2, nCTL=2, 
	      CF=c(1, 1, 1, 1), CalPos=5, trace=TRUE, geo=TRUE, na.rm=TRUE)

nrmData(data = qPCR_run2 , r=3, E=c(2, 2, 2, 2), 
	      Eerror=c(0.02, 0.02, 0.02, 0.02), nSpl=5, 
	      nbRef=2, Refposcol=1:2, nCTL=2, 
	      CF=c(1, 1, 1, 1), CalPos=5, trace=TRUE, geo=TRUE, na.rm=TRUE)

nrmData(data = qPCR_run3 , r=3, E=c(2, 2, 2, 2), 
	      Eerror=c(0.02, 0.02, 0.02, 0.02), nSpl=5, 
	      nbRef=2, Refposcol=1:2, nCTL=2, 
	      CF=c(1, 1, 1, 1), CalPos=5, trace=TRUE, geo=TRUE, na.rm=TRUE)