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benchmark-phyper-computational

Benchmark phyper() computational efficiency

Metadata

Statusdone
Assignedagent-131
Agent identity3184716484e6f0ea08bb13539daf07686ee79d440505f1fdf2de0357707034c3
Created2026-04-01T15:01:00.479619003+00:00
Started2026-04-01T15:06:37.530661453+00:00
Completed2026-04-01T15:14:21.201203255+00:00
Tagseval-scheduled
Eval score0.89
└ blocking impact0.89
└ completeness0.89
└ coordination overhead0.88
└ correctness0.92
└ downstream usability0.90
└ efficiency0.85
└ intent fidelity0.85
└ style adherence0.88

Description

Benchmark and compare computational efficiency of different approaches to implementing copy-number weighted hypergeometric testing in R. Focus on memory usage and runtime performance.

Approaches to Compare:

  1. Direct weighted phyper() with calculated parameters
  2. Instance expansion + standard phyper()
  3. Custom hypergeometric implementations
  4. Vectorized approaches for multiple pathways

Benchmarking Scenarios:

  • Small datasets (PHR-scale: ~35 genes, ~1K instances)
  • Medium datasets (~500 genes, ~50K instances)
  • Large datasets (~5K genes, ~500K instances)
  • Pathway-scale testing (1K-10K pathways)

Metrics:

  • Runtime performance
  • Memory usage
  • Scalability characteristics
  • Numerical precision

Expected Outputs:

  • Performance benchmarking report
  • Recommendations for optimal implementation
  • Scalability analysis and bottleneck identification

Depends on

Required by

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