CellScout: 用于细胞状态发现中生物标志物挖掘的可视化分析
CellScout: Visual Analytics for Mining Biomarkers in Cell State Discovery
AI总结：
CellScout通过混合专家技术识别细胞群体与生物标志物的关联，帮助生物学家更有效地发现新细胞状态。
摘要：
细胞状态发现对于理解生物系统和提高医疗成果至关重要。这一过程中的关键方面是识别定义特定细胞状态的生物标志物。然而，细胞状态和生物标志物的共同发现过程存在困难：生物学家通常使用降维技术将细胞可视化为二维空间中的点。然后他们通常将视觉上聚类的细胞解释为不同的状态，从而寻找独特的生物标志物。然而，由于聚类内部的一致性问题，这种假设往往不成立，使整个过程变得试错性和高度不确定。因此，生物学家迫切需要有效的工具来帮助揭示不同细胞群体及其潜在生物标志物之间的隐藏关联关系。为了解决这个问题，我们首先设计了一种基于混合专家（MoE）技术的机器学习算法，以识别细胞群体和生物标志物之间的有意义关联。我们进一步与生物学家合作，开发了可视化分析系统CellScout，以帮助他们探索和优化这些关联关系，从而推进细胞状态发现。我们通过专家访谈验证了我们的系统，从中进一步选择了具有代表性的案例，以展示其在发现新细胞状态方面的有效性。
英文摘要：
Cell state discovery is crucial for understanding biological systems and enhancing medical outcomes. A key aspect of this process is identifying distinct biomarkers that define specific cell states. However, difficulties arise from the co-discovery process of cell states and biomarkers: biologists often use dimensionality reduction to visualize cells in a two-dimensional space. Then they usually interpret visually clustered cells as distinct states, from which they seek to identify unique biomarkers. However, this assumption is often invalid due to internal inconsistencies in a cluster, making the process trial-and-error and highly uncertain. Therefore, biologists urgently need effective tools to help uncover the hidden association relationships between different cell populations and their potential biomarkers. To address this problem, we first designed a machine-learning algorithm based on the Mixture-of-Experts (MoE) technique to identify meaningful associations between cell populations and biomarkers. We further developed a visual analytics system, CellScout, in collaboration with biologists, to help them explore and refine these association relationships to advance cell state discovery. We validated our system through expert interviews, from which we further selected a representative case to demonstrate its effectiveness in discovering new cell states.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
