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Normalize Raw Biomarker Values for predict_WSS()

Source: R/normalize_wss_biomarkers.R
normalize_wss_biomarkers.Rd

Builds the rank-based inverse-normal-transformed biomarker columns predict_WSS() requires, from raw biomarker values, using one of two methods. Output columns keep the same names as the input columns (e.g. PlasmaPTau181 stays PlasmaPTau181) – predict_WSS() reads whatever column names its underlying model actually expects.

Usage

normalize_wss_biomarkers(
  raw_biomarkers,
  prebuilt = "core_AD_plasma_biomarkers",
  method = c("project", "self")
)

Arguments

raw_biomarkers

A data.frame or matrix, one row per sample, with columns named by the raw (unsuffixed) biomarker names (e.g. PlasmaPTau181, not PlasmaPTau181_norm).

prebuilt

Name of a prebuilt reference set to use when method = "project". See list_prebuilt_wss() for available options. Ignored when method = "self".

method

Normalization method:

"project"

(default) Project each raw value onto a bundled reference distribution via project_rank_norm(). Works for a single sample or a small/differently-distributed cohort, since each value is scored independently against the fixed reference.

"self"

Rank-normalize (via RNOmni::RankNorm()) within raw_biomarkers itself. Appropriate if you have a sizeable cohort of your own with a distribution you're comfortable normalizing against directly, instead of the bundled reference. Requires at least two samples, since a rank transform needs multiple values to rank against.

Value

A data.frame with one normalized column per input biomarker column (same names as raw_biomarkers), row names preserved. Combine with Age/Gender columns and pass to predict_WSS().

See also

predict_WSS(), project_rank_norm(), load_prebuilt_biomarker_reference()

Examples

raw_biomarkers <- data.frame(PlasmaPTau181 = 1.5, PlasmaNfL = 20)
normalize_wss_biomarkers(raw_biomarkers)
#>   PlasmaPTau181    PlasmaNfL
#> 1    -0.2168629 -0.009634851