Utilizing Aggregated Molecular Phenotype (AMP) Scores to Visualize Simultaneous Molecular Changes in Mass Spectrometry Imaging Data.
Utilizing Aggregated Molecular Phenotype (AMP) Scores to Visualize Simultaneous Molecular Changes in Mass Spectrometry Imaging Data.
复制标题
利用聚合分子表型 (AMP) 评分可视化质谱成像数据中的同时分子变化。
DOI：
10.1101/2023.06.01.543306
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Baker,ErinS
中科院分区：
文献类型：
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作者：
Chappel,JessieR;King,MaryE;Fleming,Jonathon;Eberlin,LiviaS;Reif,DavidM;Baker,ErinS
质谱成像（MSI）由于其能够识别和可视化异质样品中不同表型所特有的分子特征而在基于组织的诊断中越来越受欢迎。来自MSI实验的数据通常使用单离子图像进行可视化，并使用机器学习和多变量统计进行进一步分析，以识别感兴趣的m/z特征并创建用于表型分类的预测模型。然而，通常每个离子图像仅可视化单个分子或m/z特征，并且主要从预测模型提供分类分类。作为一种替代方法，我们开发了一种聚集分子表型（AMP）评分系统。使用集成机器学习方法来生成AMP分数，以首先选择区分表型的特征，使用逻辑回归对特征进行加权，并将权重和特征丰度联合收割机组合。然后将AMP评分在0和1之间缩放，较低的值通常对应于1类表型（通常为对照），较高的评分与2类表型相关。因此，AMP评分允许同时评估多个特征，并显示这些特征与各种表型相关的程度，从而提高诊断准确性和预测模型的可解释性。在此，使用从解吸电喷雾电离（DESI）MSI收集的代谢组学数据评价AMP评分性能。癌性人体组织与正常或良性组织的初步比较表明，AMP评分以高准确性、灵敏度和特异性区分表型。此外，当与空间坐标相结合时，AMP评分允许在一个具有区分的表型边界的图中可视化组织切片，突出其诊断实用性。
Mass spectrometry imaging (MSI) has gained increasing popularity for tissue-based diagnostics due to its ability to identify and visualize molecular characteristics unique to different phenotypes within heterogeneous samples. Data from MSI experiments are often visualized using single ion images and further analyzed using machine learning and multivariate statistics to identify m/z features of interest and create predictive models for phenotypic classification. However, often only a single molecule or m/z feature is visualized per ion image, and mainly categorical classifications are provided from the predictive models. As an alternative approach, we developed an aggregated molecular phenotype (AMP) scoring system. AMP scores are generated using an ensemble machine learning approach to first select features differentiating phenotypes, weight the features using logistic regression, and combine the weights and feature abundances. AMP scores are then scaled between 0 and 1, with lower values generally corresponding to class 1 phenotypes (typically control) and higher scores relating to class 2 phenotypes. AMP scores therefore allow the evaluation of multiple features simultaneously and showcase the degree to which these features correlate with various phenotypes, leading to high diagnostic accuracy and interpretability of predictive models. Here, AMP score performance was evaluated using metabolomic data collected from desorption electrospray ionization (DESI) MSI. Initial comparisons of cancerous human tissues to normal or benign counterparts illustrated that AMP scores distinguished phenotypes with high accuracy, sensitivity, and specificity. Furthermore, when combined with spatial coordinates, AMP scores allow visualization of tissue sections in one map with distinguished phenotypic borders, highlighting their diagnostic utility.