MIAMI: mutual information-based analysis of multiplex imaging data.
MIAMI: mutual information-based analysis of multiplex imaging data.
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迈阿密：基于互信息的多路成像数据分析。
DOI：
10.1093/bioinformatics/btac414
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发表时间：
2022-08-02
期刊：
影响因子：
5.8
通讯作者：
Ghosh, Debashis
中科院分区：
文献类型：
--
作者：
Seal, Souvik;Ghosh, Debashis
研究癌症患者肿瘤微环境中蛋白质或标志物的相互作用或共同表达对于评估死亡或复发等风险至关重要。在传统的方法中，细胞需要根据其强度宣布为阳性或阴性标记。对于多个标记，需要为所有标记设置手动阈值，这可能会变得很麻烦。后续分析的性能严重依赖于这一步，因此受到主观性和缺乏鲁棒性的影响。我们提出了一种新的方法，将不同的标记强度视为相关的随机变量，并将它们之间的互信息（MI）视为共表达的度量。传统形式的MI所要求的关节密度估计，随着标记物数量的增加，变得越来越具有挑战性。我们考虑了一种替代的MI公式，它在概念上相似，但具有有效的估计技术，我们为此开发了一种新的推广方法。利用提出的方法，我们分析了肺癌数据集，发现HLA-DR和CK标记物的共同表达与生存相关。我们还分析了一个三阴性乳腺癌数据集，发现免疫调节蛋白PD1、PD-L1、Lag3和IDO的共表达与疾病复发有关。通过不同的仿真研究证明了该方法的鲁棒性。相关的R包可以在这里找到，https://github.com/sealx017/MIAMI。补充数据可在生物信息学网站获得。
Studying the interaction or co-expression of the proteins or markers in the tumor microenvironment of cancer subjects can be crucial in the assessment of risks, such as death or recurrence. In the conventional approach, the cells need to be declared positive or negative for a marker based on its intensity. For multiple markers, manual thresholds are required for all the markers, which can become cumbersome. The performance of the subsequent analysis relies heavily on this step and thus suffers from subjectivity and lacks robustness. We present a new method where different marker intensities are viewed as dependent random variables, and the mutual information (MI) between them is considered to be a metric of co-expression. Estimation of the joint density, as required in the traditional form of MI, becomes increasingly challenging as the number of markers increases. We consider an alternative formulation of MI which is conceptually similar but has an efficient estimation technique for which we develop a new generalization. With the proposed method, we analyzed a lung cancer dataset finding the co-expression of the markers, HLA-DR and CK to be associated with survival. We also analyzed a triple negative breast cancer dataset finding the co-expression of the immuno-regulatory proteins, PD1, PD-L1, Lag3 and IDO, to be associated with disease recurrence. We demonstrated the robustness of our method through different simulation studies. The associated R package can be found here, https://github.com/sealx017/MIAMI. Supplementary data are available at Bioinformatics online.
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