Personalized Drug Response Prediction from Single-Cell Transcriptomics

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Documentation for package ‘PERCEPTIONx’ version 0.3.0

• DESCRIPTION file
• User guides, package vignettes and other documentation
• Package NEWS
• Package README

Help Pages

add_mirrors	Add custom download mirrors
annotate_clones	Annotate cells with clone IDs via Seurat clustering
build_clone_counts	Build clone abundance table from cell-clone mapping
build_clone_key	Build a clone key from patient and clone id
build_on_BULK_v2	Build PERCEPTIONx model on bulk expression data
clone_mean_expression	Compute clone-level mean expression from single-cell data
compare_performance	Compare performance of multiple trained models
cor.test_trimmed_v0	Fast correlation test (generic)
each_patient_pseudo_bulk	Compute pseudo-bulk expression for a patient
export_plot_cairo	Export plot to file via Cairo device
fdrcorr	FDR correction for multiple testing
feature_ranking_bulk	Feature ranking for a single drug using bulk expression
get_cellLine_list	Determine training and test cell-lines for a given drug
get_depmap	Get the DepMap dataset
get_mirrors	Get current download mirrors
get_performance	Load performance metrics from a saved model file
get_response_matrix	Get specific drug response data for cell-lines
get_significant_models	Get best performing models
hypergeometric_test_for_twolists	Hypergeometric test for gene list overlap
list_mirrors	List current download mirrors
load_depmap	Download filtered DepMap data
load_model	Load pre-built model of provided drugs
parse_clone_keys	Parse clone keys in "Patient@Clone" format
plot_clone_distribution	Plot clone distribution as stacked bar
plot_clone_umap	Plot UMAP colored by clone identity
plot_clone_viability	Plot clone-level viability (lollipop plot)
plot_model_performance	Plot model performance across datasets
plot_model_roc	Plot validation ROC curves for trained models
plot_patient_response_panel	Complete patient response visualization pipeline
plot_response_boxplot	Plot predicted vs observed response boxplot
plot_roc_curve	Plot ROC curve with AUC annotation
plot_seurat_clustering	Run Seurat clustering and plot 2D embedding
plot_tsne_biomarker_viability	Plot UMAP side-by-side for biomarker and viability
plot_tsne_response	Plot UMAP with drug response overlay
predict_drugs	Predict drug response for cells or clones
predict_patients	Predict drug response at patient level
prepare_data	Prepare patient data for PERCEPTIONx prediction
range01	Change range to 0-1
rank_normalization_mat	Rank-normalize each column of a matrix
reset_mirrors	Reset mirrors to default
run_parallel_feature_ranking_bulk	Parallel feature ranking for multiple drugs
run_perception_app	Launch PERCEPTIONx Shiny Dashboard
train_models	Train PERCEPTIONx models for multiple drugs
zscore_viability	Z-score scale viability values across patients