Scale expression data to have zero mean and unit variance

This function applies z-score scaling to the expression data in an ExpressionSet. It calls scale, which operates column-wise, so scaling is performed per sample (each sample/column is scaled to zero mean and unit variance across genes). This is the "Scaling" normalisation strategy from the paper and matches the TACO reference application. Per-gene standardisation is handled separately inside the trained clock model (its StandardScaler step), using training-set statistics.

Usage

scale_eset(eset, verbose = TRUE)

Arguments

eset

An ExpressionSet object containing expression data.

verbose

Logical indicating whether to print progress messages and create density plots. Default is TRUE.

Value

An ExpressionSet object with scaled expression data.

Examples

# Load example data and create ExpressionSet
expr_data <- load_example_expression_data()
meta_data <- load_example_metadata()
eset <- make_ExpressionSet(expr_data, meta_data)
#> ✓ ExpressionSet created successfully
#>   - Number of genes: 57010 
#>   - Number of samples: 24 


# Scale expression data
scaled_eset <- scale_eset(eset, verbose = TRUE)
#> ✓ Scaling completed