qPCR: qPCR Curve Analysis Methods

In PMCMRplus: Calculate Pairwise Multiple Comparisons of Mean Rank Sums Extended

qPCR Curve Analysis Methods

Description

The data set contains 4 classifiers (blocks), i.e. bias, linearity, precision and resolution, for 11 different qPCR analysis methods. The null hypothesis is that there is no preferred ranking of the method results per gene for the performance parameters analyzed. The rank scores were obtained by averaging results across a large set of 69 genes in a biomarker data file.

Format

A data frame with 4 observations on the following 11 variables.

Cy0

a numeric vector

LinRegPCR

a numeric vector

Standard_Cq

a numeric vector

PCR_Miner

a numeric vector

MAK2

a numeric vector

LRE_E100

a numeric vector

5PSM

a numeric vector

DART

a numeric vector

FPLM

a numeric vector

LRE_Emax

a numeric vector

FPK_PCR

a numeric vector

Source

Data were taken from Table 2 of Ruijter et al. (2013, p. 38). See also Eisinga et al. (2017, pp. 14–15).

References

Eisinga, R., Heskes, T., Pelzer, B., Te Grotenhuis, M. (2017) Exact p-values for pairwise comparison of Friedman rank sums, with application to comparing classifiers. BMC Bioinformatics, 18:68.

Ruijter, J. M. et al. (2013) Evaluation of qPCR curve analysis methods for reliable biomarker discovery: Bias, resolution, precision, and implications, Methods 59, 32–46.

PMCMRplus documentation built on Nov. 27, 2023, 1:08 a.m.