Compute Composite Exposome Scores

Calculates a summary exposome score per sample using one of several methods including mean, sum, median, PCA (first principal component), IRT (Item Response Theory), quantile binning, or row-wise variance. The resulting score is added to the colData of the MultiAssayExperiment object.

Usage

run_exposome_score(
  expomicset,
  score_type,
  exposure_cols = NULL,
  scale = TRUE,
  score_column_name = NULL
)

Arguments

expomicset

A MultiAssayExperiment object containing exposure data in its colData.

score_type

Character. The method used to compute the score. Options are: "mean", "sum", "median", "pca", "irt", "quantile", "var".

exposure_cols

Optional character vector. Specific exposure column names to include. If NULL, all numeric columns are used.

scale

Logical. Whether to scale the exposures before computing the score. Default is TRUE.

score_column_name

Optional name for the resulting score column. If NULL, an automatic name is used (e.g., "exposome_score_pca").

Value

A MultiAssayExperiment object with the exposome score added to colData().

Details

• "pca" uses the first principal component from prcomp().

• "irt" uses the mirt package to fit a graded response model to discretized exposures.

• "quantile" assigns decile bins (1–10) to each variable and sums them row-wise.

• "var" computes the row-wise variance across exposures.

Examples

# create the example data
mae <- make_example_data(
    n_samples = 10,
    return_mae = TRUE
)
#> Ensuring all omics datasets are matrices with column names.
#> Creating SummarizedExperiment objects.
#> Creating MultiAssayExperiment object.
#> MultiAssayExperiment created successfully.

# create the air pollution score
mae <- run_exposome_score(
    mae,
    score_type = "pca",
    exposure_cols = c("exposure_pm25", "exposure_no2"),
    scale = TRUE,
    score_column_name = "air_pollution_score"
)
#> Extracting exposure data...
#> Calculating PCA exposure scores...