Find peaks

Detects peaks in a chromatographic profile by identifying zero-crossings in the smoothed first derivative of the signal (y) that exceed a specified slope threshold (slope_thresh). Smoothing reduces spurious local extrema that do not represent true features. Peaks can be filtered using a minimum amplitude threshold (amp_thresh), which removes low-intensity peaks.

Usage

find_peaks(
  y,
  smooth_type = c("gaussian", "box", "savgol", "mva", "tmva", "none"),
  smooth_window = 0.001,
  slope_thresh = 0,
  amp_thresh = 0,
  bounds = TRUE
)

Arguments

y

A chromatographic signal (as a numeric vector).

smooth_type

Type of smoothing. Either gaussian kernel ("gaussian"), box kernel ("box"), Savitzky-Golay smoothing ("savgol"), moving average ("mva"), triangular moving average ("tmva"), or no smoothing ("none").

smooth_window

Smoothing window. Larger values of this parameter will exclude sharp, narrow features. If the supplied value is between 0 and 1, the window will be interpreted as a proportion of points to include. Otherwise, the window will be interpreted as the absolute number of points to include in the window. Defaults to 0.001.

slope_thresh

Minimum threshold for slope of the smoothed first derivative. This parameter filters peaks on the basis of their width, such that larger values will exclude broad peaks from the peak list. Defaults to 0.

amp_thresh

Minimum threshold for peak amplitude. This parameter filters on the basis of peak height, such that larger values will exclude small peaks from the peak list. Defaults to 0.

bounds

Logical. If TRUE (default), includes peak boundaries in data.frame.

Value

If bounds == TRUE, returns a data.frame containing the center, start, and end of each identified peak. Otherwise, returns a numeric vector of peak centers. All locations are expressed as indices.

Details

Available smoothing methods include Gaussian kernel ("gaussian"), box kernel ("box"), Savitzky–Golay ("savgol"), moving average ("mva"), triangular moving average ("tmva"), and no smoothing ("none").

Preprocessing with preprocess is recommended prior to peak detection. Overly high chromatographic resolution may sometimes cause peak splitting. In such cases, it is recommended to either increase the smooth_window or reduce the time-axis resolution by adjusting the dim1 argument during preprocessing.

Note

The find_peaks function is adapted from MATLAB code included in Prof. Tom O'Haver's Pragmatic Introduction to Signal Processing.

References

O'Haver, Tom. Pragmatic Introduction to Signal Processing: Applications in scientific measurement. https://terpconnect.umd.edu/~toh/spectrum/ (Accessed January, 2022).

See also

fit_peaks, get_peaks

Author

Ethan Bass

Examples

data(Sa_pr)
find_peaks(Sa_pr[[1]][,"220"])
#>    pos lower upper
#> 1   28    14    46
#> 2   49    46    52
#> 3   60    53    61
#> 4   69    61    78
#> 5   81    78    85
#> 6   89    85    93
#> 7  104    93   114
#> 8  115   114   118
#> 9  120   118   123
#> 10 131   123   139
#> 11 141   139   147
#> 12 155   149   161
#> 13 167   161   171
#> 14 182   171   188
#> 15 193   188   200
#> 16 215   200   228
#> 17 233   228   238
#> 18 243   238   254
#> 19 261   254   264
#> 20 272   264   279
#> 21 283   279   293
#> 22 297   293   303
#> 23 305   303   309
#> 24 319   315   323
#> 25 336   323   347
#> 26 351   347   363
#> 27 378   363   383
#> 28 388   383   390
#> 29 395   390   401
#> 30 408   401   411
#> 31 417   411   421