Oligomeric Species Analysis for Protein SEC

step_sec_oligomer() creates a specification of a recipe step that identifies and quantifies individual oligomeric species (monomer, dimer, trimer, etc.) in protein SEC chromatograms.

Usage

step_sec_oligomer(
  recipe,
  measures = NULL,
  monomer_mw = NULL,
  peak_detection = c("auto", "manual"),
  peaks = NULL,
  species = c("monomer", "dimer", "trimer", "hmw", "lmw"),
  mw_tolerance = 0.15,
  mw_column = NULL,
  min_area_pct = 0.1,
  output_prefix = "oligo_",
  role = NA,
  trained = FALSE,
  skip = FALSE,
  id = recipes::rand_id("sec_oligomer")
)

Arguments

recipe

A recipe object.

measures

Character vector of measure columns to analyze. If NULL, analyzes all measure columns.

monomer_mw

Expected monomer molecular weight in Da. Required for species assignment. Typical range: 10,000 - 1,000,000 Da for proteins.

peak_detection

Method for peak detection:

"auto" (default): Automatic peak detection using derivative analysis
"manual": Use peaks specified in peaks argument

peaks

For manual mode, a list or data frame with peak definitions. Each peak should have start and end retention times.

species

Character vector of species to identify. Default includes "monomer", "dimer", "trimer", "hmw", "lmw".

mw_tolerance

Tolerance for MW-based species assignment as a fraction. Default is 0.15 (15%). A peak is assigned to "dimer" if its MW is within 15% of 2x the monomer MW.

mw_column

Optional name of a molecular weight measure column (from MALS or LALS). If provided, uses MW for species assignment; otherwise uses retention time patterns.

min_area_pct

Minimum peak area percentage to report. Peaks below this threshold are grouped into "other". Default is 0.1 (0.1%).

output_prefix

Prefix for output columns. Default is "oligo_".

role

Role for generated columns.

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 new columns:

oligo_monomer_pct: Percent monomer
oligo_dimer_pct: Percent dimer
oligo_trimer_pct: Percent trimer
oligo_hmw_pct: Percent high molecular weight (> trimer)
oligo_lmw_pct: Percent low molecular weight (fragments)
oligo_species_count: Number of detected species
oligo_monomer_mw: Observed monomer MW (if mw_column provided)
oligo_dimer_mw: Observed dimer MW (if mw_column provided)

Details

This step extends step_sec_aggregates() by providing detailed species identification rather than just HMWS/monomer/LMWS classification.

Species Assignment:

Monomer: MW within mw_tolerance of monomer_mw
Dimer: MW within mw_tolerance of 2x monomer_mw
Trimer: MW within mw_tolerance of 3x monomer_mw
HMW: MW > 3x monomer_mw
LMW: MW < monomer_mw (fragments, clips)

Peak Detection:

When using "auto" detection, peaks are identified using:

Signal derivative analysis
Local maxima identification
Valley detection for peak boundaries

Without MW Data:

If no MW column is available, species are assigned based on retention time:

Largest peak is assumed to be monomer
Earlier-eluting peaks are HMW/oligomers
Later-eluting peaks are LMW/fragments

Note

For best results:

Provide monomer_mw for accurate species assignment
Use with MALS/LALS data for MW-based assignment
Ensure good chromatographic resolution between species

Examples

if (FALSE) { # \dontrun{
library(recipes)
library(measure)

# Analyze IgG oligomers (monomer MW ~150 kDa)
rec <- recipe(~., data = igg_data) |>
  step_measure_input_long(uv280, location = vars(time), col_name = "uv") |>
  step_sec_baseline() |>
  step_sec_oligomer(monomer_mw = 150000) |>
  prep()

# With MALS-derived MW for accurate assignment
rec <- recipe(~., data = igg_mals_data) |>
  step_measure_input_long(uv280, location = vars(time), col_name = "uv") |>
  step_sec_mals() |>
  step_sec_oligomer(monomer_mw = 150000, mw_column = "mw_mals") |>
  prep()
} # }