Drug mass spectrometry imaging (MSI) data contain knowledge about drug and several other molecular ions present in a biological sample. However, a proper approach to fully explore the potential of such type of data is still missing. Therefore, a computational pipeline that combines different spatial and non-spatial methods is proposed to link the observed drug distribution profile with tumor heterogeneity in solid tumor. Our data analysis steps include pre-processing of MSI data, cluster analysis, drug local indicators of spatial association (LISA) map, and ions selection.

中文翻译：

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一种将肿瘤异质性与质谱成像数据中的药物分布特征相关联的方法学方法

药物质谱成像 (MSI) 数据包含有关药物和生物样品中存在的其他几种分子离子的知识。但是，仍然缺少一种充分探索此类数据潜力的适当方法。因此，提出了一种结合不同空间和非空间方法的计算管道，将观察到的药物分布特征与实体瘤中的肿瘤异质性联系起来。我们的数据分析步骤包括 MSI 数据的预处理、聚类分析、药物局部空间关联指标 (LISA) 地图和离子选择。