In the analysis of diverse omics data, a common and important preliminary step involves computing low-dimensional embeddings using techniques such as PCA, UMAP, t-SNE, or variational autoencoders. These embeddings provide a global overview of sample distributions and their relationships, often serving as the basis for formulating biological hypotheses. To facilitate rapid and intuitive exploration of such low-dimensional embeddings, we developed Yomix, a interactive omics-agnostic visualisation and data exploration tool. Yomix enables users to flexibly define subsets of interest using a lasso selection tool, instantly compute their feature signatures, and compare their distributions. Yomix is a fast and efficient tool for interactive exploration of diverse omics datasets.
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Nisma Amjad
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Nisma Amjad is a Research Engineer at ISIR, Sorbonne University, working with Institut Curie on AI-driven oncology research. She specialises in digital pathology, medical imaging, and multi-omics, and is passionate about advancing cancer diagnostics through data-centric AI. A dedicated open-source contributor, she developed Yomix, a Python toolkit for interactive multi-omics analysis, and actively supports community-driven scientific software. With an Erasmus Mundus MSc in Medical Imaging & Applications and a strong background in biomedical engineering, she is committed to developing impactful, accessible AI tools that accelerate cancer research and clinical insights.
Bio from: PyLadies Paris Python Talks #21
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