Multiscale geometric and topological analyses for characterizing and predicting immune responses from single cell data.

Single cell genomics has revolutionized our ability to map immune heterogeneity and responses. In this review, we highlight single cell methods and principles for learning geometric and topological properties of data at multiple scales, discussing their contributions to immunology.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 With the influx of large-scale data sets from diverse modalities, the resolution achieved has supported the long-held notion that immune cells are naturally organized into hierarchical relationships, characterized at multiple levels.
• 2 Such a multigranular structure corresponds to key geometric and topological features.
• 3 Given that differences between an effective and ineffective immunological response may not be found at one level, there is vested interest in characterizing and predicting outcomes from such features.

Understanding this could lead to better treatments, improved diagnostics, or a deeper grasp of how the human body works — benefiting patient care globally.

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