fixNA: Impute missing values into a column of amplification data
In chipPCR: Toolkit of Helper Functions to Pre-Process Amplification Data

Description Usage Arguments Details Author(s) References See Also Examples

fixNA imputes missing values in a single column of data. The imputation is based on a linear approximation by default. However, the data can also be estimated from an approximation by splines.

## S4 method for signature 'numeric,numeric'
fixNA(x, y, spline = TRUE, verbose = FALSE)
## S4 method for signature 'matrix,missing'
fixNA(x, y, spline = TRUE, verbose = FALSE)
## S4 method for signature 'data.frame,missing'
fixNA(x, y, spline = TRUE, verbose = FALSE)

`x`	`x` is a vector containing the time or cycle values or data frame/matrix where second column contains missing data.
`y`	`y` is a vector containing missing values. Used only if `x` is also a vector.
`spline`	`spline` if `TRUE` missing values are imputed by spline approximation as per `spline`. The default `FAlSE` is linear a approximation as per `approx`.
`verbose`	`verbose` defines if the number of missing values should be reported.

Amplification data of experimental systems may contain missing values (NA). The NAs may be caused by detector problems, acquisition error or other assorted problems. There are different ways to handle missing values. One approach is to ignore NAs which is generally acceptable. However, in case of further calculation it is often necessary to handle cases of missing values in a way that the next calculation steps can be performed. Missing values can be eliminated by a imputation. Imputation encompasses various approaches. This includes to calculate a location parameter (e.g., mean, median) or other significant values (e.g., minimum, maximum, modus) of a data column. However, in non-linear processes such as amplification processes its is better to estimate the missing values from a trend. fixNA was empirically tested and relies on a linear trend estimation based on the approx function. This approach is useful but may be problematic on the phases other then background or plateau phases of an amplification reaction. The parameter spline on fixNA enables a trend estimation on splines and may be more appropriate in most scenarios. Other smoothing functions such as the Savitzky-Golay smoothing filter have the intrinsic capability to remove missing values [Savitzky and Golay 1964, Eilers 2003].

Stefan Roediger, Michal Burdukiewicz

Eilers, P. H. C. Anal. Chem. 2003, 75, 3631–3636. Savitzky, A.; Golay, M. J. E. Anal. Chem. 1964, 36, 1627–1639

approx, spline.

# Simulate a qPCR reaction with AmpSim with for 40 cycles
res <- AmpSim(cyc = c(1:40))
# Introduce a missing value in the transition between 
# the background and the exponential phase
res.NA <- res
res.NA[18, 2] <- NA

default.par <- par(no.readonly = TRUE)

par(mfrow = c(2,2))
plot(res, xlab = "Cycles", ylab = "Fluorescence", type = "b", pch = 20, 
     main = "Simulation without missing value")
     abline(v = c(17.5, 18.5), col = "grey")
     abline(h = c(0.09, 0.14), col = "grey")

res.NA.linear <- fixNA(res.NA[, 1], res.NA[, 2], spline = FALSE, 
		       verbose = FALSE)
plot(res.NA.linear, xlab = "Cycles", ylab = "Fluorescence", type = "b", 
     pch = 20, main = "Simulation with linear imputed\n NA 
     value during transition")
     abline(v = c(17.5, 18.5), col = "grey")
     abline(h = c(0.09, 0.14), col = "grey")

plot(res.NA, xlab = "Cycles", ylab = "Fluorescence", type = "b", pch = 20, 
     main = "Simulation with missing\n value during transition")
     abline(v = c(17.5, 18.5), col = "grey")
     abline(h = c(0.09, 0.14), col = "grey")
res.NA.spline <- fixNA(res.NA[, 1], res.NA[, 2], spline = TRUE, 
		       verbose = FALSE)
plot(res.NA.spline, xlab = "Cycles", ylab = "Fluorescence", type = "b", 
      pch = 20, main = "Simulation with spline imputed\n NA value 
      during transition")
     abline(v = c(17.5, 18.5), col = "grey")
     abline(h = c(0.09, 0.14), col = "grey")     
par(default.par)