使用聚合分子表型 (AMP) 评分来可视化质谱成像数据中的同时分子变化
Jessie R Chappel1, Mary E King2, Jonathon Fleming1
1Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA.
bioRxiv : the preprint server for biology
|June 19, 2023
概括
聚合分子表型 (AMP) 评分为使用质谱成像 (MSI) 的组织诊断提供了一种新的方法. 这种方法通过同时评估多个分子特征来提高诊断准确度,从而改善表型分类.
科学领域:
- 生物医学诊断 生物医学诊断
- 分析化学是一种分析化学.
- 分子成像学分子成像学
背景情况:
- 质谱成像 (MSI) 越来越多地用于组织诊断,可视化异质样本中的分子特征.
- 目前的MSI分析通常依赖于单个离子图像和机器学习进行分类分类.
- 在可视化多个分子特征和解释预测模型方面存在局限性.
研究的目的:
- 开发和评估一个聚合分子表型 (AMP) 评分系统,用于增强MSI数据分析.
- 通过整合多个分子特征来提高诊断准确性和解释性.
- 为了使组织样本中的表型差异可视化.
主要方法:
- 开发了一个使用整体机器学习和物流回归的AMP评分系统.
- 选择了区分特征,对它们进行加权,并将特征丰富性结合起来以生成AMP分数.
- 量化表型评估的AMP分数在0和1之间.
- 评估了AMP评分表现,使用了脱电喷电离 (DESI) MSI的代谢数据.
主要成果:
- AMP得分准确地区分了癌症和正常人体组织.
- 使用AMP评分,实现了高诊断准确度,灵敏度和特异性.
- 将AMP得分与空间坐标相结合,可以清楚地可视化组织段中的表型边界.
结论:
- AMP评分系统为MSI基础的诊断提供了一种强大且可解释的方法.
- AMP评分可以同时评估多个分子特征,从而改善表型分类.
- 这种方法通过提供准确和视觉直观的结果来增强MSI的诊断效用.
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