| qPCR {PMCMRplus} | R Documentation |
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.