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Predict WSS Module Scores from Core Biomarkers

Source: R/predict_WSS.R
predict_WSS.Rd

Predicts module scores for a prebuilt reference set's joinet model (a two-layer elastic net stack) without needing to measure the full proteomic panel. For "core_AD_plasma_biomarkers", this predicts all 75 module scores from Age, Gender, and the 8 core plasma biomarkers used in the original analysis.

Usage

predict_WSS(newdata, prebuilt = "core_AD_plasma_biomarkers", type = "response")

Arguments

newdata

A data.frame or matrix with one row per sample and (at least) the columns required by the model; see x_cols in load_prebuilt_wss_model() for the exact set and order. For "core_AD_plasma_biomarkers" this is Age (raw years), Gender (numeric, 1 = male, 0 = not male), and the 8 core biomarkers (PlasmaPTau181, PlasmaAB142P, PlasmaAB140P, PlasmaABRatio, PlasmapTau217, PlasmapTau217_AB42Ratio, PlasmaGFAP, PlasmaNfL).

The biomarker columns must already be rank-based inverse-normal transformed before calling this function – see normalize_wss_biomarkers() to build them from raw biomarker values, including for a single new patient.

prebuilt

Name of a prebuilt reference set. See list_prebuilt_wss() for available options.

type

Prediction type passed to predict.joinet(): "response" (the default) or "link".

Value

A list with components base (first-layer-only predictions) and meta (final stacked predictions), each a matrix with one row per sample (row names taken from newdata, if present) and one column per module (named by the model's outcomes).

See also

normalize_wss_biomarkers(), load_prebuilt_wss_model(), calculate_WSS()

Examples

if (requireNamespace("glmnet", quietly = TRUE) &&
    requireNamespace("joinet", quietly = TRUE)) {
  # Simulated raw biomarkers; works for a single patient too
  raw_biomarkers <- wss_example_biomarkers[, -(1:2)]
  normalized <- normalize_wss_biomarkers(raw_biomarkers)

  newdata <- cbind(
    Age = wss_example_biomarkers$Age,
    Gender = wss_example_biomarkers$Gender,
    normalized
  )
  pred <- predict_WSS(newdata)
  pred$meta[1:3, 1:4]
}
#>           Merged.M17  Merged.M7  Merged.M4 PlasmaPTau181.M26
#> patient01  0.0699344 -0.1617110  0.1312342        -0.2210809
#> patient02  0.1919225  0.0521312 -0.6218288         0.2495096
#> patient03 -0.1928722  0.2029449  0.4536169         0.2990079