监督的拓数据分析用于MALDI质谱成像应用程序
Gideon Klaila1, Vladimir Vutov2, Anastasios Stefanou3
1Institute for Algebra, Geometry, Topology and their Applications (ALTA), University of Bremen, 28359, Bremen, Germany. klailag@uni-bremen.de.
BMC bioinformatics
|July 10, 2023
概括
一个新的代数拓框架增强了用于肺癌亚型的矩阵辅助激光消化/电离质谱成像 (MALDI MSI). 这种方法改善了信号噪声比和数据压缩,有助于准确分类腺癌和状细胞癌.
科学领域:
- 计算生物学是一种计算生物学.
- 数据科学是数据科学.
- 质谱仪成像成像 质谱仪成像
背景情况:
- 矩阵辅助激光消耗/电离质谱成像 (MALDI MSI) 对癌症研究有前景,特别是在瘤类型和亚型方面.
- 精确区分肺癌亚型,如腺癌 (ADC) 和状细胞癌 (SqCC),对于治疗决策至关重要.
- 肺癌仍然是全球癌症相关死亡的主要原因.
研究的目的:
- 为分析MALDI MSI数据引入一种新的代数拓框架.
- 为了利用拓持久性来改善MALDI数据集中的信号噪声比和数据压缩.
- 使用MALDI MSI数据开发一种用于肺癌的自动瘤亚型化方法.
主要方法:
- 开发一个代数拓框架,从MALDI数据中提取内在信息和拓持久性.
- 实现了一种高效的算法,具有单一的调参数,用于无声化和数据压缩.
- 在提取的持久性特征上应用后勤回归和随机森林分类器,用于自动瘤亚型化.
- 在真实世界MALDI数据集上使用交叉验证进行验证,并在合成噪音图像上进行评估.
主要成果:
- 拟议的框架有效地将MALDI数据中的信号与噪声区分开来.
- 拓持久性特征有助于压缩MALDI数据,优化计算时间和存储.
- 自动分类在区分肺癌亚型方面取得了具有竞争力的结果.
- 在 MALDI 图像中,无声化参数在处理不同级别的噪音方面表现出有效性.
结论:
- 代数拓框架成功地利用来自MALDI数据的内在光谱信息来进行竞争性肺癌亚型分类.
- 该框架的微调无线化能力提高了其用于MALDI数据分析的多功能性.
- 这种方法为改善肺癌研究中的诊断准确性和患者管理提供了有前途的工具.
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