badCt: Evaluation of the qPCR technical replicates

Description Usage Arguments Details Value Author(s) References Examples

Description

This function allows you to evaluate your qPCR technical replicates, you only need to define the threshold (according to Hellemans et al. (2007), 0.5 is a good threshold value), the dataset and the number of technical replicates you have done. I recommand you to use the gWidgets package to easily exclude the failed replicates.

Usage

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badCt(data, r, threshold, na.rm = FALSE)

Arguments

data

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

r

numeric, number of qPCR replicates.

threshold

numeric, the maximal variation between your qPCR replicates.

na.rm

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

Details

To facilitate the use of the function, I suggest you to use the gWidgets package as described in the vignette.

Value

This function returns the position (sample position and column position) where the variation between qPCR replicates is superior to the threshold value.

Author(s)

Sylvain Le pape <[email protected]>

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). <url:http://genomebiology.com/2007/8/2/R19>

Examples

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data(qPCR_run1)

badCt(data=qPCR_run1, r=3, threshold=0.3, na.rm=TRUE)

slepape/EasyqpcR documentation built on May 31, 2019, 12:13 p.m.