Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis.
Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis.
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DOI：
10.1093/bioinformatics/btad245
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发表时间：
2023-06-30
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--
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空间蛋白质组学数据已经被用来绘制细胞状态图，并提高我们对组织结构的理解。最近，这些方法已经扩展到研究这种组织对疾病进展和患者生存的影响。然而，到目前为止，大多数利用这些数据类型的监督学习方法并没有充分利用空间信息，影响了它们的性能和利用率。受生态学和流行病学的启发，我们开发了新的空间特征提取方法，用于空间蛋白质组数据。我们使用这些功能来学习癌症患者生存的预测模型。正如我们所展示的，与使用空间蛋白质组学数据用于相同任务的先前方法相比，使用空间特征导致了一致的改进。此外，特征重要性分析揭示了对有助于患者生存的细胞相互作用的新见解。这项工作的代码可以在gitLab.com/Enable-Medical-Public/spatsurv上找到。
Spatial proteomics data have been used to map cell states and improve our understanding of tissue organization. More recently, these methods have been extended to study the impact of such organization on disease progression and patient survival. However, to date, the majority of supervised learning methods utilizing these data types did not take full advantage of the spatial information, impacting their performance and utilization. Taking inspiration from ecology and epidemiology, we developed novel spatial feature extraction methods for use with spatial proteomics data. We used these features to learn prediction models for cancer patient survival. As we show, using the spatial features led to consistent improvement over prior methods that used the spatial proteomics data for the same task. In addition, feature importance analysis revealed new insights about the cell interactions that contribute to patient survival. The code for this work can be found at gitlab.com/enable-medicine-public/spatsurv.
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