质谱成像数据的拓分段
Maria M Derkach1, Anatoly A Sorokin1, Andrey A Kuzin1
1Laboratory for Molecular Medical Diagnostics, Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russian Federation.
Journal of mass spectrometry and advances in the clinical lab
|October 27, 2025
概括
本研究介绍了用于质谱成像的无监督拓分段方法. 这种方法可以准确地识别复杂的生物样本中的同质区域,从而保持癌症研究的光谱完整性.
科学领域:
- 生物医学数据分析
- 计算生物学是一种计算生物学.
- 质谱仪成像成像 质谱仪成像
背景情况:
- 图像细分对于质谱成像 (MSI) 数据处理至关重要.
- 现有的方法,如机器学习聚类,可能会改变光谱数据.
- 需要无监督的方法来保持数据完整性.
研究的目的:
- 为MSI数据开发一个无监督的拓细分方法.
- 为了保持质谱的物理和化学完整性.
- 为了能够准确地区分复杂组织中的光谱均区域.
主要方法:
- 利用等号相似性来识别和过异常值和混合来源的像素.
- 评估数据的多维度,以定义光谱均的区域.
- 应用拓细分对人类质瘤的MALDI-TOF MSI数据.
主要成果:
- 在人类质瘤中成功区分了侵略性的区域.
- 在平行组织段中展示了相关的区域分配.
- 展示了混合细胞含量的过对于异质瘤分析的重要性.
结论:
- 拓细分有效过过渡区,产生同质的数据集群.
- 确定的区域代表了特征性的分子组成,同时保持了自然的变异性.
- 这种方法对于识别瘤微环境和转移中的代谢变化至关重要.
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