kreview 0.0.23

Evaluate cfDNA fragmentomics features for ctDNA detection

kreview

Advanced cfDNA Fragmentomics Core Evaluation Engine

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🧬 Overview

kreview is a production-grade, notebook-first (nbdev) evaluation engine designed for high-throughput cancer liquid biopsy fragmentomics feature analysis. Developed at Memorial Sloan Kettering (MSKCC), it processes cohorts containing tens of thousands of samples using an embedded DuckDB query engine with chunked I/O and automatic retry logic.

📖 Full Documentation

🚀 Features

• 5-Tier ctDNA Taxonomy: MSK-IMPACT paired-inference to label True ctDNA+, Possible ctDNA+, Possible ctDNA−, Healthy Normal, and Insufficient Data. Optional CH hotspot demotion via --ch-hotspot-maf.
• DuckDB Dynamic Data Lake: In-memory read_parquet bindings with chunked I/O and exponential backoff retry. Builds a merged SQL-queryable kreview_lake.duckdb on demand.
• Multi-Model Evaluation: Logistic Regression, Random Forest, and XGBoost (CPU) plus TabPFN and TabICL (GPU) with Stratified K-Fold CV, SHAP explainability, and subgroup analysis.
• Nested CV Feature Ablation: Automated feature group subset selection via inner-loop cross-validation, eliminating non-informative feature groups before final evaluation. Uses sensitivity_at_100spec_healthy as the optimization metric.
• Feature Selection: mRMR (Minimum Redundancy Maximum Relevance) as default strategy — iteratively selects features maximizing target relevance while minimizing inter-feature redundancy. Legacy hybrid_union (AUC ∪ MI) also available.
• Multimodal Stacking: Cross-evaluator fusion via super-matrix with Mutual Information or Boruta-SHAP selection, followed by stacking ensemble + ablation analysis.
• Interactive Dashboards: Plotly-native HTML reports with ROC curves, violin plots, SHAP beeswarm/waterfall, mRMR scatter plots, per-cancer-type sensitivity tables, and Decision Curve Analysis.
• Nextflow HPC Integration: Decomposed multistage DAG for SLURM-based HPC execution with per-evaluator parallelism, GPU scheduling, and automatic retry logic.
• 26 Built-In Evaluators: Modular extractors covering fragment sizes (FSC, FSD, FSR), nucleosome protection (WPS, TFBS), cleavage motifs (EndMotif, BreakPointMotif), chromatin accessibility (ATAC), motif divergence (MDS), and orientation (OCF).

🏗️ Pipeline Architecture

graph LR
    A[Label] --> B["Extract ×N"]
    B --> C[Select]
    C --> D["Ablate (opt)"]
    D --> E["Eval CPU"]
    D --> F["Eval GPU"]
    C --> E
    C --> F
    C --> G[Fuse]
    E --> H[Scoreboard]
    F --> H
    E --> I["Eval Multimodal"]
    F --> I
    G --> I
    H --> J[Report]
    I --> K["Report Multimodal"]

The pipeline supports two modes:

Mode	Command	Use Case
Monolithic	kreview run	Single-machine, sequential execution
Multistage	nextflow run ... -profile iris	HPC parallelism, per-evaluator scatter

⚙️ Quick Start

Installation

[!IMPORTANT] Quarto is strictly required for programmatic dashboard generation. Because quarto-cli wrapper packages are unreliable across Python environments, kreview assumes the Quarto executable is installed dynamically on your OS or container.

Option 1: Docker (Recommended "Batteries-Included" Method)

The easiest way to run kreview without managing external dependencies is to use our pre-built Docker containers (hosted on GHCR). They ship with Python 3.12, all ML libraries, and quarto:

# CPU image (~1.5 GB) — for all standard pipeline processes
docker pull ghcr.io/msk-access/kreview:latest

# GPU image (~8-10 GB) — adds PyTorch, TabPFN, TabICL (requires NVIDIA drivers)
docker pull ghcr.io/msk-access/kreview:latest-gpu

# Run
docker run -v /your/data:/data ghcr.io/msk-access/kreview:latest \
  kreview run --cancer-samplesheet /data/cancer.csv ...

Option 2: Local Install (Pip)

If you install via pip, you must separately install Quarto via your OS manager:

1. Install Quarto: Follow the official Quarto Installation Guide (e.g. brew install quarto on macOS).
2. Install kreview:

git clone https://github.com/msk-access/kreview.git
cd kreview
pip install -e .          # CPU models only
pip install -e ".[gpu]"   # + TabPFN, TabICL (requires CUDA)

Running the Pipeline

Local (Single Machine)

kreview run \
  --cancer-samplesheet "/path/to/cancer/samplesheet.csv" \
  --healthy-xs1-samplesheet "/path/to/healthy/xs1/samplesheet.csv" \
  --healthy-xs2-samplesheet "/path/to/healthy/xs2/samplesheet.csv" \
  --cbioportal-dir "/path/to/cBioPortal_MAF_CNA_SV/" \
  --krewlyzer-dir "/path/to/unified_krewlyzer_results" \
  --output output/ \
  --strategy mrmr \
  --top-percentile 10 \
  --compute-univariate-auc \
  --ch-hotspot-maf "/path/to/ch_hotspots.maf" \
  --export-duckdb

HPC (Nextflow + SLURM)

nextflow run /path/to/kreview/nextflow/main.nf \
  --cancer_samplesheet /path/to/cancer.csv \
  --healthy_xs1_samplesheet /path/to/healthy_xs1.csv \
  --healthy_xs2_samplesheet /path/to/healthy_xs2.csv \
  --cbioportal_dir /path/to/cbioportal/ \
  --krewlyzer_dir /path/to/manifest.txt \
  --outdir /path/to/output/ \
  --pipeline_mode multistage \
  --run_gpu_eval true \
  --gpu_models "tabpfn,tabicl" \
  --run_ablation true \
  --run_multimodal_eval true \
  -profile iris

Dashboard Access

Once finished, open the generated HTML reports:

open output/reports/ATAC_dashboard.html

🧪 Feature Selection

Strategy	Scope	Method	Default
mrmr	Single-evaluator	F-statistic relevance + Pearson redundancy penalty	✅
hybrid_union	Single-evaluator	Top-X% AUC ∪ Top-X% MI	Legacy
Nested CV ablation	Single-evaluator	Inner CV on feature group subsets → best subset per model	Optional (--run-ablation)
mi	Multimodal	Mutual Information top-K ranking	✅
boruta_shap	Multimodal	SHAP importance vs shadow variables (50 trials)	Optional

See Statistical Evaluation for full documentation.

📓 nbdev Architecture

This project operates as an nbdev repo. Do not edit .py scripts manually in kreview/. Build natively inside Jupyter notebooks within nbs/ and trigger:

nbdev_export

📚 Resources

• Documentation — Full user and developer guide
• Contributing — How to contribute
• Changelog — Version history