drug-sensitivity-for-gene

To correlate gene expression with drug sensitivity, this analysis runs pairwise correlation between a specified gene vector and drug sensitivity profiles across cancer cell lines. It requires inputs like the gene symbol, omics type, and correlation method to generate a TSV of correlations and scatter plots.

Pharmacogenomic biomarker discovery using DepMap and PRISM data identifies drugs whose efficacy tracks gene activity and essentiality. By correlating gene expression or dependency vectors with drug sensitivity, it reveals pharmacogenomic associations for patient stratification and synthetic lethal interactions.

Yes, you can use pandas and scipy for synthetic lethal interaction analysis in cancer research. This approach leverages those dependencies to compute pairwise correlations between gene dependency and drug sensitivity vectors, outputting a summary report to interpret synthetic lethal strategies.

To generate publish-ready scatter plots for drug sensitivity correlations, run the analysis with the required gene and omics type parameters. It utilizes matplotlib to produce visualizations alongside a drug_correlation.tsv file and a concise summary report for interpretation.

Yes, this drug sensitivity analysis supports both gene expression and gene dependency vectors. You can specify the desired input type using the --omics-type argument to evaluate how either gene activity or essentiality tracks drug efficacy across the cell line panel.

A limitation of using correlation methods for cancer drug response prediction is that statistical association does not confirm causal mechanisms. Pairwise correlations between gene activity and drug sensitivity require careful interpretation to avoid false positives in biomarker discovery workflows.