dpcR (version 0.1.2-1)

test_peaks-methods: Peak Test

Description

Detect, separate and count positive peaks, negative peaks and peak-like noise. In addition, function calculates area of the peaks.

Usage

## S3 method for class 'numeric':
test_peaks(x, y, threshold = 0.05, noise_cut = 0.05, savgol = TRUE, 
norm = FALSE, filter.q = c(0.7, 0.8))
## S3 method for class 'adpcr':
test_peaks(x, threshold = 0.05, noise_cut = 0.05, savgol = TRUE, 
norm = FALSE, filter.q = c(0.7, 0.8))

Arguments

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.

Value

• 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.

Details

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

References

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

Examples

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