oncoresolve 1.0.0

A High-Hygiene Explainable AI and Patient-Centric Uniqueness Framework for Breast Cancer Subtyping

OncoResolve: Reusable Python Bioinformatics Package

A High-Hygiene Explainable AI and Patient-Centric Uniqueness Framework for Breast Cancer Subtyping. This package allows you to clean custom transcriptomic matrices, run PAM50 predictions using pre-trained TCGA models, compute N-of-1 patient Uniqueness scores (CUS), and predict overall survival risk scores (CRS).

Installation

Local development install

From the root of this project folder:

pip install -e .

Direct install from GitHub

pip install git+https://github.com/shubhamkjha369/OncoResolve.git

Reusable Library Usage

Once installed, you can import the package using capital OncoResolve:

import OncoResolve as orr
import pandas as pd

# 1. Prepare and Harmonize your custom RNA-seq expression matrix (genes as columns)
# Pass in the path to the tcga_entrez_to_hugo.pkl mapping file
df_clean = orr.harmonize_namespaces(df_raw, "path/to/tcga_entrez_to_hugo.pkl")
df_scaled = orr.scale_cohort(df_clean)

# Load the required features list directly from the pre-trained model metadata 
# and align columns alphabetically (filling missing genes with 0.0)
required_features = sorted(orr.OncoPrognosis().model_.summary.index.tolist())
# Alternatively, use a custom list of consensus genes:
# required_features = list(pd.read_parquet("path/to/final_consensus_biomarkers.parquet")["gene"])

df_aligned = orr.align_features(df_scaled, required_features)

# 2. Run classification using pre-trained SVM or Logistic Regression models
# Note: Since models are bundled, you don't need to specify paths to load pre-trained models!
clf = orr.OncoClassifier(model_type="svm")
predictions = clf.predict(df_aligned)        # Returns PAM50 subtype strings
probabilities = clf.predict_proba(df_aligned)  # Returns class probabilities DataFrame

# 3. Compute Patient Uniqueness Scores (CUS)
df_cus = orr.compute_cus(df_aligned, barcodes=df_aligned.index, alpha=0.001)

# 4. Predict Overall Survival Risk Scores (Consensus Cox CRS)
# Note: Since survival models are bundled, no path specification is required!
prog = orr.OncoPrognosis()
risk_scores = prog.predict_risk(df_aligned)

Authors

• Shubham Jha ([e-mail])