Predict drug response at patient level

Aggregates clone-level drug response predictions to patient-level using various weighting strategies based on clone abundance. This function merges the former each_patient_viability (single drug) and each_patient_viabilityv2 (multi-drug) into a unified interface.

Usage

predict_patients(
  clone_pred,
  prepared_data,
  clone_counts = NULL,
  mode = "weighted_max",
  zscore = TRUE
)

Arguments

clone_pred

Matrix from predict_drugs(), with clones as rows and drugs as columns. Row names should match clone column names from prepare_data().

prepared_data

List from prepare_data(), containing $clone_viability_template and $clone_counts. Alternatively, you can pass clone_counts directly as a data frame (legacy mode).

clone_counts

Optional. Data frame with patients as rows and clone IDs as columns. Only needed if prepared_data is not a list from prepare_data().

mode

Character. Aggregation method:

"weighted_max"

Maximum of weighted viability across clones. Default.

"max"

Maximum viability across clones (most resistant clone)

"weighted_average"

Weighted average of clone viability by clone abundance

"min"

Minimum viability across clones (most sensitive clone)

"average"

Average of clone viability

zscore

Logical. Whether to z-score scale drug columns across patients before aggregation. Default = TRUE. Matches the original PERCEPTION pipeline.

Value

A data frame with patients as rows and drugs as columns, containing aggregated viability scores.

Examples

if (FALSE) { # \dontrun{
  # Simple workflow: pass prepare_data() output directly
  prepared <- prepare_data(patient_scRNA)
  clone_pred <- predict_drugs(models, prepared$clone_expression)
  patient_pred <- predict_patients(clone_pred, prepared)

  # Legacy workflow: manually build clone_viability_matrix
  clone_viability_df <- data.frame(
    patient = patient_ids,
    clone_id = clone_ids,
    clone_pred
  )
  patient_pred <- predict_patients(clone_viability_df, clone_counts)
} # }