PCRedux (version 1.1-2)

qPCR2fdata: A helper function to convert amplification curve data to the fdata format.

Description

qPCR2fdata is a helper function to convert qPCR data to the functional fdata class as proposed by Febrero-Bande & de la Fuente (2012). This function prepares the data for further analysis with the fda.usc package, which includes utilities for functional data analysis (e.g., Hausdorff distance).

Usage

qPCR2fdata(data, preprocess = FALSE)

Arguments

data

is a data set containing the amplification cycles (1. column) and the fluorescence (subsequent columns).

preprocess

is a logical parameter (default FALSE). If TRUE, the CPP function from the chipPCR package (Roediger et al. 2015) is used to pre-process the data (e.g., imputation of missing values). and the fluorescence (subsequent columns).

Value

gives an fdata object (S3 class, type of list) as output for a converted amplification curve.

References

M. Febrero-Bande, M.O. de la Fuente, others, Statistical computing in functional data analysis: The R package fda.usc, Journal of Statistical Software. 51 (2012) 1--28. http://www.jstatsoft.org/v51/i04/

S. Roediger, M. Burdukiewicz, P. Schierack, chipPCR: an R package to pre-process raw data of amplification curves, Bioinformatics. 31 (2015) 2900--2902. doi:10.1093/bioinformatics/btv205.

Examples

# NOT RUN {
default.par <- par(no.readonly = TRUE)
# Calculate slope and intercept on noise (negative) amplification curve data
# for the last eight cycles.
library(qpcR)
library(fda.usc)

# Convert the qPCR data set to the fdata format
res_fdata <- qPCR2fdata(testdat)

# Extract column names and create rainbow color to label the data
res_fdata_colnames <- colnames(testdat[-1])
data_colors <- rainbow(length(res_fdata_colnames), alpha=0.5)

# Plot the converted qPCR data
par(mfrow=c(1,2))
plot(res_fdata, xlab="cycles", ylab="RFU", main="testdat", type="l",
                   lty=1, lwd=2, col=data_colors)
legend("topleft", as.character(res_fdata_colnames), pch=19,
         col=data_colors, bty="n", ncol=2)

# Calculate the Hausdorff distance (fda.usc) package and plot the distances
# as clustered data.

res_fdata_hclust <- metric.hausdorff(res_fdata)
plot(hclust(as.dist(res_fdata_hclust)), main="Clusters of the amplification\n
   curves as calculated by the Hausdorff distance")
par(default.par)
# }