An LLM multi-agent framework (SpaCellAgent) automates end-to-end trajectory inference on single-cell and spatial transcriptomics data, achieving expert-aligned accuracy with 41.2% faster analysis time.
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scFM learns bidirectional velocity fields from entropically regularized OT couplings between snapshots, with added alignment and regularization to reduce drift in long-horizon predictions of single-cell trajectories.
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SpaCellAgent: A Self-Evolving LLM-Based Multi-Agent Framework for Trajectory Analysis
An LLM multi-agent framework (SpaCellAgent) automates end-to-end trajectory inference on single-cell and spatial transcriptomics data, achieving expert-aligned accuracy with 41.2% faster analysis time.
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From Snapshots to Trajectories: Learning Single-Cell Gene Expression Dynamics via Conditional Flow Matching
scFM learns bidirectional velocity fields from entropically regularized OT couplings between snapshots, with added alignment and regularization to reduce drift in long-horizon predictions of single-cell trajectories.