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Achievement: Starstruckx2Achievement: Pair ExtraordinaireAchievement: QuickdrawAchievement: YOLOAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx2

Achievements

Achievement: Starstruckx2Achievement: Pair ExtraordinaireAchievement: QuickdrawAchievement: YOLOAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx2

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quentinblampey/README.md

Hi, I'm Quentin 👋

Last-year PhD student at the MICS Laboratory (Paris-Saclay University) and the Gustave Roussy Institute

I'm developing deep learning tools on multi-omics data for precision medicine in oncology. I have a particular focus on spatial omics data with single-cell resolution.

Main packages

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  1. gustaveroussy/sopa gustaveroussy/sopa Public

    Technology-invariant pipeline for spatial omics analysis that scales to millions of cells (Xenium / Visium HD / MERSCOPE / CosMx / PhenoCycler / MACSima / etc)

    Python 137 15

  2. MICS-Lab/novae MICS-Lab/novae Public

    Graph-based foundation model for spatial transcriptomics data. Zero-shot spatial domain inference, batch-effect correction, and many other features.

    Python 23

  3. scverse/spatialdata scverse/spatialdata Public

    An open and interoperable data framework for spatial omics data

    Python 235 43

  4. MICS-Lab/scyan MICS-Lab/scyan Public

    Biology-driven deep generative model for cell-type annotation in cytometry. Scyan is an interpretable model that also corrects batch-effect and can be used for debarcoding or population discovery.

    Python 33 1