MFIaggr: Multiple comparison of the cycle dependent variance of the...

In chipPCR: Toolkit of Helper Functions to Pre-Process Amplification Data

Description

MFIaggr is used for a fast multiple comparison of the cycle dependent variance of the fluorescence.

Usage

## S4 method for signature 'numeric,numeric'
MFIaggr(x, y, cyc = 1, fluo = 2:ncol(x), 
		   RSD = FALSE, rob = FALSE, llul = c(1,10))
## S4 method for signature 'matrix,missing'
MFIaggr(x, y, cyc = 1, fluo = 2:ncol(x), 
		   RSD = FALSE, rob = FALSE, llul = c(1,10))
## S4 method for signature 'data.frame,missing'
MFIaggr(x, y, cyc = 1, fluo = 2:ncol(x), 
		   RSD = FALSE, rob = FALSE, llul = c(1,10))

Arguments

x	is the column of a data frame for the cycle or data.frame/matrix with whole data.
y	are multiple columns of fluorescence values from a data.frame (e.g., [, c(1:n)]).
cyc	is the index of column containing the cycle data. Used only if y is missing.
fluo	are the columns containing the fluorescence data. Used only if y is missing.
RSD	Setting the option RSD = TRUE shows the relative standard deviation (RSD) in percent.
rob	Using the option rob as TRUE the median and the median absolute deviation (MAD) are calculated instead of the mean and standard deviation.
llul	is a parameter to define the lower and upper data limit (cycle), aka region of interest (ROI) used for the density and quantile plot.

Value

An object of the class refMFI. refMFI means referenced Mean Fluorescence Intensity (Roediger et al. 2013).

Author(s)

Stefan Roediger, Michal Burdukiewicz

References

Roediger S, Boehm A, Schimke I. Surface Melting Curve Analysis with R. The R Journal 2013;5:37–53.

Examples

# First Example
# Cycle dependent variance of the refMFI using standard measures 
# (Mean, Standard Deviation (SD)).
# Use Standard Deviation (SD) in the plot

data(VIMCFX96_60)

MFIaggr(VIMCFX96_60[, 1], VIMCFX96_60[, 2:ncol(VIMCFX96_60)])

#alternative usage
MFIaggr(VIMCFX96_60)

#only second and forth column
plot(MFIaggr(VIMCFX96_60, fluo = c(2, 4)))

# Example
# Use of MFIaggr to test for heteroskedasticity using the Breusch-Pagan 
# test. The data were aggregated with the MFIaggr function and assigned to
# the object res. The standard deviation was transformed to the variance.
# The plot shows the cycle dependent variance.
# First cycles 1 to 10 of 96 qPCR replicate amplification curves were
# analyzed. Next the cycles 1 to 40 of the same amplification curve data 
# were analyzed. The Breusch-Pagan confirmed the heteroskedasticity in the
# amplification curve data.

default.par <- par(no.readonly = TRUE)
par(mfrow = c(1,2), bty = "n")
res <- MFIaggr(VIMCFX96_60[, 1], VIMCFX96_60[, 2:ncol(VIMCFX96_60)], 
	       llul = c(1,10))
head(res)
plot(res[, 1], res[, 3]^2, xlab = "Cycle", ylab = "Variance of refMFI", 
     xlim = c(1,10), main = "ROI from Cycle 1 to 10", pch = 19, type = "b")
abline(v = c(1,10), col = "grey", lty = 2, lwd = 2)
legend("top", paste0("Breusch-Pagan test p-value: \n", format(summary(res)[5], 
       digits = 2)), bty = "n")

res <- MFIaggr(VIMCFX96_60[, 1], VIMCFX96_60[, 2:ncol(VIMCFX96_60)], 
	       llul = c(1,40))
head(res)
plot(res[, 1], res[, 3]^2, xlab = "Cycle", ylab = "Variance of refMFI", 
     main = "ROI from Cycle 1 to 40", pch = 19, type = "b")
abline(v = c(1,40), col = "grey", lty = 2, lwd = 2)
legend("top", paste0("Breusch-Pagan test p-value: \n", format(summary(res)[5], 
       digits = 2)), bty = "n")
par(default.par)

chipPCR documentation built on March 5, 2021, 9:06 a.m.