Aligns simulated pseudobulk training data with real bulk expression data, filters genes by variance, applies log transformation and per-sample scaling, and returns matrices ready for OmicsTweezer training and prediction.
Usage
omics_process(
simudata,
real_bulk,
variance_threshold = 0.98,
scaler = c("ss", "mms", "none")
)Arguments
- simudata
A list returned by [omics_simulate()]. Must contain `X` and `obs`.
- real_bulk
Numeric matrix or `data.frame` with genes in rows and bulk samples in columns.
- variance_threshold
Numeric scalar. Fraction of genes used to define the variance cutoff separately in simulated and real bulk data.
- scaler
Character scalar specifying the per-sample scaling method. One of `"ss"` for standard scaling, `"mms"` for min-max scaling, or `"none"`.
Value
A named list with components:
- train_x
Processed training matrix with samples in rows and genes in columns.
- train_y
Training target matrix with samples in rows and cell types in columns.
- test_x
Processed real bulk matrix with samples in rows and genes in columns.
- genename
Character vector of retained shared genes.
- celltypes
Character vector of cell-type names.
- samplename
Character vector of real bulk sample names.