Find peaks in chromatographic profile.
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
response (numerical 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, window will be interpreted as a proportion of points to include. Otherwise, the window will be the absolute number of points to include in the window. (Defaults to .001).
- slope_thresh
Minimum threshold for slope of the smoothed first derivative. This parameter filters on the basis of peak 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, includes peak boundaries in data.frame. (Defaults to
TRUE).
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
Find peaks by looking for zero-crossings in the smoothed first derivative of
the signal (y) that exceed the specified slope threshold
(slope_thresh). Additionally, peaks can be filtered by supplying a minimal
amplitude threshold (amp_thresh), filtering out peaks below the
specified height. Smoothing is intended to prevent the algorithm from
getting caught up on local minima and maxima that do not represent true
features. Several smoothing options are available, including gaussian,
box kernel (box), savitzky-golay smoothing (savgol),
moving average (mva), triangular moving average (tmva), or
no smoothing (none).
It is recommended to do pre-processing using the preprocess
function before peak detection. Overly high chromatographic resolution can
sometimes cause peaks to be split into multiple segments. In this case,
it is recommended to increase the smooth_window or reduce the
resolution along the time axis 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).
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