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Deconvolve Overlapping Peaks

Source: R/peak-operations.R
step_measure_peaks_deconvolve.Rd

step_measure_peaks_deconvolve() creates a specification of a recipe step that resolves overlapping peaks using curve fitting. This step requires peaks to have been detected first using step_measure_peaks_detect().

Usage

step_measure_peaks_deconvolve(
  recipe,
  model = "gaussian",
  optimizer = "auto",
  max_iter = 500L,
  tol = 1e-06,
  n_starts = 5L,
  constrain_positions = TRUE,
  quality_threshold = 0.8,
  store_components = FALSE,
  smart_init = TRUE,
  peaks_col = ".peaks",
  measures_col = ".measures",
  role = NA,
  trained = FALSE,
  skip = FALSE,
  id = recipes::rand_id("measure_peaks_deconvolve")
)

Arguments

recipe

A recipe object.

model

Peak model to use. Either a character string naming a registered model ("gaussian", "emg", "bigaussian", "lorentzian"), or a peak_model object created directly. Use peak_models() to see all registered models. Default is "gaussian".

optimizer

Optimization method: "auto" (default), "lbfgsb", "multistart", or "nelder_mead". Auto-selection chooses based on problem complexity and signal-to-noise ratio.

max_iter

Maximum iterations for optimization. Default is 500.

tol

Convergence tolerance. Default is 1e-6.

n_starts

Number of random starts for optimizer = "multistart". Default is 5.

constrain_positions

Logical. If TRUE, enforce that peak centers maintain their relative ordering. Default is TRUE.

quality_threshold

Minimum R-squared to accept fit. Fits below this threshold trigger a warning. Default is 0.8.

store_components

Logical. If TRUE, store individual fitted peak curves in the output. Default is FALSE.

smart_init

Logical. If TRUE, use smart initialization based on peak properties. Default is TRUE.

peaks_col

Name of the peaks column. Default is ".peaks".

measures_col

Name of the measures column. Default is ".measures".

role

Not used.

trained

Logical indicating if the step has been trained.

skip

Logical. Should the step be skipped when baking?

id

Unique step identifier.

Value

An updated recipe with the new step added. The .peaks column will be updated with deconvolved peak parameters, fitted areas, and quality metrics.

Details

Peak deconvolution fits mathematical models to overlapping peaks to determine their individual contributions. This is essential for quantitative analysis when peaks are not baseline-resolved.

Peak Models:

Built-in models (use peak_models() to see all):

  • "gaussian": Symmetric Gaussian (3 params: height, center, width)

  • "emg": Exponentially Modified Gaussian (4 params, handles tailing)

  • "bigaussian": Bi-Gaussian (4 params, flexible asymmetry)

  • "lorentzian": Lorentzian/Cauchy peak (3 params, heavier tails)

Technique packs may register additional models.

Optimizers:

  • "auto": Selects based on problem complexity and SNR

  • "lbfgsb": L-BFGS-B (fast, local optimization)

  • "multistart": Multiple L-BFGS-B runs from perturbed starts (robust)

  • "nelder_mead": Derivative-free Nelder-Mead simplex

Quality Assessment:

Each fit is assessed for quality. The .peaks tibble gains columns:

  • fit_r_squared: R-squared of the overall fit

  • fit_quality: Quality grade (A/B/C/D/F)

  • purity: How much of signal at peak max comes from this peak

See also

optimize_deconvolution(), assess_deconv_quality(), peak_models(), gaussian_peak_model()

Other peak-operations: step_measure_peaks_detect(), step_measure_peaks_filter(), step_measure_peaks_integrate(), step_measure_peaks_to_table()

Examples

library(recipes)

# Create synthetic data with overlapping peaks
set.seed(42)
x <- seq(0, 20, by = 0.1)
y <- 1.5 * exp(-0.5 * ((x - 8) / 1)^2) +
  0.8 * exp(-0.5 * ((x - 12) / 1.5)^2) +
  rnorm(length(x), sd = 0.02)
df <- data.frame(id = "sample1", location = x, value = y)

# \donttest{
# Deconvolve overlapping peaks
rec <- recipe(~., data = df) |>
  update_role(id, new_role = "id") |>
  step_measure_input_long(value, location = vars(location)) |>
  step_measure_peaks_detect(min_height = 0.5, min_prominence = 0.3) |>
  step_measure_peaks_deconvolve(model = "gaussian") |>
  prep()

result <- bake(rec, new_data = NULL)
# Check fitted peaks
result$.peaks[[1]]
#> # A tibble: 2 × 10
#>   peak_id location height left_base right_base  area fit_r_squared fit_quality
#>     <int>    <dbl>  <dbl>     <dbl>      <dbl> <dbl>         <dbl> <chr>      
#> 1       1     8.00  1.50       6.49       9.50  3.77         0.998 A          
#> 2       2    12.0   0.792      9.74      14.3   2.99         0.998 A          
#> # ℹ 2 more variables: purity <dbl>, overlap_degree <dbl>
# }