bioconverge 0.1.3

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bioconverge

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

What it does

When you have multiple biological scores per patient — from transcriptomics, genomics, imaging, or clinical data — they often disagree. One patient might have high immune activity but low genomic instability. Another might show the opposite. bioconverge asks: what biology explains these conflicts, and how confident can we be?

It runs 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 and assigns Tier A / B / C confidence

Install

pip install bioconverge

What data you need

A CSV file with patients as rows and scores as columns:

patient_id, immune_score, genomic_score, proliferation_score
TCGA-A1-001, 0.82, 0.31, 0.74
TCGA-A1-002, 0.45, 0.88, 0.22
TCGA-A1-003, 0.61, 0.55, 0.60

bioconverge works with any domain where multiple scores exist per sample:

Domain	Example scores
Cancer multi-omics	immune infiltration, TMB, CNA burden, methylation, proliferation
Drug response	IC50 from multiple assays on same cell lines
Clinical trial	PET scan score, blood biomarker, genomic risk score
Single cell	pathway activity scores across multiple pathways per cell

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", "TP53"]
        },
        "proliferation_score": {
            "process": "cell cycle proliferation",
            "modality": "transcriptomic",
            "genes": ["MKI67", "PCNA", "TOP2A", "CDK1"]
        }
    }
)

result.report("output/")

With survival data

result = bc.integrate(
    scores="my_scores.csv",
    patient_col="patient_id",
    score_metadata={...},
    outcome="survival.csv",
    time_col="OS_days",
    event_col="OS_event"
)

result.survival_analysis()

What you get back

result.concordance()       # pairwise score agreement with confidence intervals
result.convergence()       # per-patient convergence index and strata
result.discordance()       # patients with conflicting scores
result.archetypes()        # patient archetype assignments
result.hypotheses()        # ranked hypotheses with Tier A/B/C labels
result.survival_analysis() # KM curves per archetype (if survival data provided)
result.compare_layers()    # patients flagged by both Layer 1 and Layer 2
result.report("output/")   # full HTML report and CSV outputs

Confidence tiers

Each hypothesis is validated across four independent sources:

1. Survival consistency — does this archetype show a survival difference?
2. Replication — does the same hypothesis appear in an independent cohort?
3. Known-biology benchmark — does it match established pathway signatures?
4. Literature — how many PubMed records support this process?

Tier	Validation sources passed	Interpretation
A	3 or 4	finding — state as result
B	2	supported hypothesis — state as supported
C	0 or 1	exploratory — requires experimental follow-up

Output files

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

File	Contents
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 and database links
reproducibility_log.txt	all API queries with timestamps for reproducibility
kaplan_meier/	survival plots per archetype as PNG
fragility_topology.png	UMAP of patient fragility clusters

TNBC demonstration

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

Scores used:

Score	Source	Genes
proliferation_score	MKI67 RNA-seq expression	MKI67
immune_score	cytotoxic T-cell signature	CD8A, CD8B, GZMA, PRF1
emt_score	EMT hallmark gene set mean	200 genes
mutation_score	TMB from MAF files	—
genomic_score	Fraction Genome Altered	—
size_score	Longest Dimension (clinical)	—

Results: 6 Tier A findings, 9 Tier B supported hypotheses, 3 Tier C exploratory.

Validated on GBM (488 patients, ARI 0.851): 8 Tier A findings with significant survival separation (archetype 0 p=0.034, archetype 2 p=0.008).

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

Benchmarking

bioconverge was compared against five clustering methods on the TNBC cohort:

Method	ARI	Silhouette
bioconverge (Layer 1)	0.398	0.197
PCA + KMeans	0.401	0.203
NMF + KMeans	0.491	0.140
Hierarchical Ward	0.210	0.286
Gaussian Mixture	0.223	0.159
MOFA+	0.789	0.128

bioconverge is the only method that combines clustering with concordance analysis, database-driven hypothesis generation, and tiered validation.

Parameters

Parameter	Required	Description
scores	yes	path to CSV with patient scores
patient_col	yes	name of patient ID column
score_metadata	yes	dict with process, modality, genes per score
models	no	trained sklearn/PyTorch/TensorFlow models for Layer 2
feature_matrix	no	feature matrix X for Layer 2 fragility
pathway_constraints	no	"hallmark" to use MSigDB Hallmark gene sets
outcome	no	path to survival CSV
time_col	no	name of time column in survival CSV
event_col	no	name of event column in survival CSV

If a parameter is not provided, that layer is skipped gracefully and noted in the report.

Requirements

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

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

License

MIT

Citation

If you use bioconverge in your research, please cite:

Koushik, C. (2026). bioconverge: Multi-layer biological score integration
and hypothesis validation for cancer research.
https://github.com/chykoushik/bioconverge