depmap-analysis-for-gene

Analyze gene essentiality and expression across DepMap cancer cell lines by running a multi-module analysis script that processes expression, mutation, copy number, and essentiality datasets, outputting clear summaries and publication-ready plots.

Coessentiality analysis identifies functionally related genes by correlating essentiality scores across DepMap cancer cell lines, complementing coexpression analysis which correlates expression patterns, with both visualizable through standalone network and barplot scripts.

Generate coexpression network plots for a single gene using the standalone depmap_coexpression.py script, which processes DepMap expression data to produce barplots and network visualizations for richer biological interpretation.

You can use user-provided input data for DepMap gene analysis, as the tool supports flexible data handling and will automatically download missing DepMap datasets only when required inputs are absent.

Yes, you need to install numpy, pandas, matplotlib, and requests, as these are the core dependencies required to process DepMap datasets and generate visualization outputs for the gene analysis scripts.

The best way to visualize gene mutation and copy number alterations across cancer cell lines is using an integrated analysis pipeline that processes DepMap data modules and automatically generates organized plots and summaries for publication.