bioconverge 0.1.2

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bioconverge

Multi-layer biological score integration and hypothesis validation tool for cancer research.

Version history

0.1.2

• Fixed convergence index clipping bug (values now bounded to [-1, 1])
• Added benchmarking against MOFA+, PCA, NMF, Hierarchical, GMM
• Added GBM validation cohort (488 patients, ARI 0.851)
• Added sensitivity analysis for all key parameters

0.1.1

• Added long description for PyPI

0.1.0

• Initial release

What it does

bioconverge takes multiple biological scores per patient (transcriptomic, genomic, imaging, clinical) and runs them through four layers:

• Layer 1 — finds which scores agree and which conflict per patient, assigns convergence strata and discordance archetypes
• Layer 2 — computes per-patient biological fragility using model gradient perturbation
• Layer 3 — generates ranked hypotheses for each archetype by querying Reactome, Enrichr, STRING, GWAS Catalog, and PubMed
• Layer 4 — validates each hypothesis across four independent sources (survival, replication cohort, known-biology benchmark, literature) and assigns Tier A / B / C confidence

Install

pip install bioconverge

Quick start

import bioconverge as bc

result = bc.integrate(
    scores="my_scores.csv",
    patient_col="patient_id",
    score_metadata={
        "immune_score": {"process": "T-cell exhaustion", "modality": "transcriptomic", "genes": ["CD8A", "CD8B", "GZMA", "PRF1"]},
        "genomic_score": {"process": "DNA repair instability", "modality": "genomic", "genes": ["BRCA1", "BRCA2", "ATM"]}
    },
    outcome="survival.csv",
    time_col="OS_days",
    event_col="OS_event"
)

result.concordance()
result.hypotheses()
result.report("output/")

TNBC demonstration

The full demonstration on 116 TCGA-BRCA triple-negative breast cancer patients is in notebooks/01_TNBC_demo.ipynb.

Datasets required:

Dataset	Source
TCGA-BRCA clinical	https://portal.gdc.cancer.gov/projects/TCGA-BRCA
TCGA-BRCA mutations	http://gdac.broadinstitute.org
TCGA-BRCA RNA-seq	http://gdac.broadinstitute.org
METABRIC clinical	https://www.cbioportal.org/study/summary?id=brca_metabric
Lehmann subtypes	https://www.cbioportal.org/study/summary?id=brca_tcga
MSigDB Hallmark	https://www.gsea-msigdb.org/gsea/msigdb

Output

Running result.report("output/") produces:

• summary.html — interactive concordance matrix and patient strata
• per_patient_scores.csv — convergence index, archetype, fragility per patient
• hypotheses_ranked.csv — full hypothesis table with Tier A/B/C labels
• reproducibility_log.txt — all API queries with timestamps
• kaplan_meier/ — survival plots per archetype
• fragility_topology.png — UMAP of patient fragility clusters

Confidence tiers

Tier	Meaning	Validation sources passed
A	finding	3 or 4
B	supported hypothesis	2
C	exploratory	0 or 1

Requirements

numpy, pandas, scipy, scikit-learn, matplotlib, seaborn,
lifelines, umap-learn, hdbscan, requests, plotly, torch,
tensorflow, jupyter

License

MIT