omicverse.single.Drug_Response

omicverse.single.Drug_Response#

class omicverse.single.Drug_Response(adata, scriptpath, modelpath, output='./', model='GDSC', clusters='All', cell='A549', cpus=4, n_drugs=10)[source]#

Predict drug sensitivity from single-cell transcriptomes using CaDRReS models.

Parameters:

  • adata (AnnData) – Query single-cell AnnData.

  • scriptpath (str) – Path to CaDRReS-Sc scripts.

  • modelpath (str) – Path to pretrained pharmacogenomic model/data resources.

  • output (str, optional) – Output directory for prediction tables and plots.

  • model ({'GDSC', 'PRISM'}, optional) – Pharmacogenomic reference model.

  • clusters (str, optional) – Cluster subset to analyze ('All' uses all cells).

  • cell (str, optional) – Cell-line context used by the model.

  • cpus (int, optional) – CPU threads used by downstream steps.

  • n_drugs (int, optional) – Number of top drugs to report/plot.

Returns:

Initializes drug-response prediction workflow state.

Return type:

None

Examples

>>> job = ov.single.Drug_Response(adata, scriptpath="CaDRReS-Sc")
__init__(adata, scriptpath, modelpath, output='./', model='GDSC', clusters='All', cell='A549', cpus=4, n_drugs=10)[source]#

Initialize the Drug_Response class.

Parameters:

  • adata (anndata.AnnData) – Input AnnData used for single-cell drug-response prediction.

  • scriptpath (str) – Path to cloned CaDRReS-Sc script directory.

  • modelpath (str) – Path containing pretrained CaDRReS model/data files.

  • output (str) – Output directory for prediction tables and figures.

  • model (str) – Pharmacogenomic reference model, typically 'GDSC' or 'PRISM'.

  • clusters (str) – Comma-separated louvain cluster IDs, or 'All'.

  • cell (str) – Cell-line context label used by CaDRReS.

  • cpus (int) – Number of CPUs used by downstream routines.

  • n_drugs (int) – Number of top drugs displayed in output figures.

Returns:

None

Methods

__init__(adata, scriptpath, modelpath[, ...])

Initialize the Drug_Response class.

bulk_exp()

extract the bulk gene expression data.

cell_death_proportion()

Predict cell death proportion and cell death percentage at the ref_type dosage

draw_plot(df[, n_drug, name, figsize])

plot heatmap of drug response prediction

drug_info()

read the drug information.

figure_output()

plot figures

kernel_feature_preparartion()

kernel feature preparation

load_model()

Load the pre-trained model.

output_result()

Export predicted drug response tables to CSV files.

sc_exp()

Load cluster-specific gene expression profile

sensitivity_prediction()

Predict drug sensitivity
Contents