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Adam Streck^1,2, Roland F Schwarz^1,2,3

¹Institute for Computational Cancer Biology (ICCB), Center for Integrated Oncology (CIO), Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, 50931, Cologne, Germany.

|September 19, 2025

概括

CNSistent 是一个新的 Python 软件包,它集成和处理来自不同癌症数据集的体质副本数量改变 (SCNA) 配置文件. 它有助于分析SCNA数据,以改善癌症基因的发现和分类.

科学领域:

基因组学就是基因组学.
生物信息学是一种生物信息学.
癌症研究癌症研究

背景情况:

身体拷贝数变化 (SCNA) 在癌症的发展和进展中至关重要.
由于数据异质性,在样本和队列中分析SCNA资料具有挑战性.
缺乏用于集成SCNA分析的现有工具包.

研究的目的:

开发一个全面的Python包,用于处理和分析癌症SCNA资料.
为了实现一致的细分,特征提取和SCNA数据的可视化.
为了促进跨队列分析和改善癌症分类.

主要方法:

开发了CNSistent,这是一个用于SCNA数据归算,过,细分和可视化的Python包.
应用于公开数据集的CNSistent,包括癌症基因组图谱和全癌症全基因组分析.
评估了癌症分类任务的预处理,细分和聚合策略.

主要成果:

证明了 CNSistent 在处理异质 SCNA 数据集中的实用性.
对比不同的分析策略及其对癌症类型和亚型分类的影响.
引入了基于细分的得分,以调查样本内部和样本与癌症类型之间的关系.
确定SOX2放大作为非小细胞肺癌中的关键SCNA.

结论:

CNSistent提供了一个通用工具包,用于集成的SCNA配置文件处理.
该包支持跨多个患者和队列的分析.
CNSistent可用于研究使用,并提供研究资源标识符.

关键词:

这就是SCNA SCNA.癌症癌症癌症癌症癌症癌症分类癌症分类癌症分类处理数据的数据处理.深度学习是一种深度学习.

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