mitoomics-gpu 0.1.1

GPU-accelerated pipeline to compute a Mitochondrial Health Index (MHI)

MitoOmics-GPU [Work in Progress]

GPU-accelerated multi-omics pipeline to quantify and visualize the Mitochondrial Health Index (MHI) by integrating extracellular vesicle/mitochondrial-derived vesicle (EV/MDV) proteomics with single-cell RNA-seq.

Hackathon project by Team Go Getters at the NVIDIA Accelerate Omics Hackathon (8-25 Sept 2025).

👥 Team Go Getters

• Sayane Shome, PhD (AI in Healthcare, Stanford)[Team Lead]
• Seema Parte, PhD (Ophthalmology, Stanford)
• Hirenkumar Patel, PhD (Ophthalmology, Stanford)
• Ankit Maisuriya (PhD candidate, Quantum Photonics, Northeastern)
• Medha Bhattacharya (CS undergrad, UC Irvine)

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🚀 Project Objective

• Develop a GPU-accelerated pipeline for mitochondrial health analysis.

• Link blood-derived EV/MDV proteomics with mitochondrial DNA copy-number proxies from scRNA-seq.

• Provide interpretable measures:

  • Biogenesis (capacity to grow new mitochondria)
  • Fusion/Fission (structural remodeling)
  • Mitophagy (repair/recycling)
  • Heterogeneity (variation across cells).

• Output: a unified Mitochondrial Health Index (MHI) summarizing mitochondrial resilience, fitness, and disease risk.

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🖥️ GPU Acceleration

• Optimized with RAPIDS + GPU backends.
• Clear CPU vs GPU speedups for large datasets.
• Open-source, designed for integration with scverse/rapids-singlecell.

📊 Key Insights

• Unified mitochondrial health scoring (MHI).
• Patient-level and cell-type–level insights.
• Supports biomarker discovery, disease progression prediction, and drug response stratification.

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🔮 Future Directions

• Add modalities: scATAC, metabolomics, spatial transcriptomics.
• Deploy web-server / pip package for biologist-friendly use.
• Clinical validation with partners & cohorts.
• ML upgrades for pattern discovery & prediction on MHI.

📬 Contact

📧 [e-mail]