test_peaks: Peak Test
In dpcR: Digital PCR Analysis

Description Arguments Details Value Author(s) References Examples

Detect, separate and count positive and negative peaks, as well as peak-like noise. Additionally, function calculates area of the peaks.

`x`	a vector containing the abscissa values (e.g., time, position) OR an object of class `adpcr`.
`y`	a vector of fluorescence value.
`threshold`	a value, which defines the peak heights not to consider as peak.
`noise_cut`	a numeric value between 0 and 1. All data between 0 and `noise_cut` quantile would be considered noise in the further analysis.
`savgol`	logical value. If `TRUE`, Savitzky-Golay smoothing filter is used.
`norm`	logical value. If `TRUE`, data is normalised.
`filter.q`	a vector of two numeric values. The first element represents the quantile of the noise and the second one is the quantile of the negative peaks.

The localization of peaks is determined by the findpeaks function. The area under the peak is calculated by integration of approximating spline.

A list of length 2. The first element is a data frame containing: peak number, peak group (noise, negative, positive), position of the peak maximum, area under the peak, peak width, peak height, position of the peak and time resolution.

The second element contains smoothed data.

Stefan Roediger, Michal Burdukiewicz.

Savitzky, A., Golay, M.J.E., 1964. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Anal. Chem. 36, 1627-1639.

data(many_peaks)
par(mfrow = c(3,1))
plot(many_peaks, type = "l", main = "Noisy raw data")
abline(h = 0.01, col = "red")

tmp.out <- test_peaks(many_peaks[, 1], many_peaks[, 2], threshold = 0.01, noise_cut = 0.1, 
                    savgol = TRUE)
plot(tmp.out[["data"]], type = "l", main = "Only smoothed")
abline(h = 0.01, col = "red")
abline(v = many_peaks[tmp.out[["peaks"]][, 3], 1], lty = "dashed")

tmp.out <- test_peaks(many_peaks[, 1], many_peaks[, 2], threshold = 0.01, noise_cut = 0.1, 
                    savgol = TRUE, norm = TRUE)
plot(tmp.out[["data"]], type = "l", main = "Smoothed and peaks detected")
abline(v = many_peaks[tmp.out[["peaks"]][, 3], 1], lty = "dashed")
for(i in 1:nrow(tmp.out$peaks)) {
  if(tmp.out$peaks[i, 2] == 1) {col = 1}
  if(tmp.out$peaks[i, 2] == 2) {col = 2}
  if(tmp.out$peaks[i, 2] == 3) {col = 3}
  points(tmp.out$peaks[i, 7], tmp.out$peaks[i, 6], col = col, pch = 19)
}

positive <- sum(tmp.out$peaks[, 2] == 3)
negative <- sum(tmp.out$peaks[, 2] == 2)
total <- positive + negative