PCA-Plus: 增强主要组件分析,并对批量效应及其量化进行说明性应用
Nianxiang Zhang1, Tod D Casasent1, Anna K Casasent1
1Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
bioRxiv : the preprint server for biology
|January 23, 2024
概括
我们增强了主要组件分析 (PCA) 以更好地可视化和量化多原子数据中的批量效应. 我们的PCA-Plus工具改善了对生物样本差异的检测和分析.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 基因组学就是基因组学.
背景情况:
- 主要组件分析 (PCA) 是分析大型生物医学数据集的标准.
- 传统的PCA在多组数据 (例如TCGA) 中存在中度的批量/趋势效应.
- 对批量效应的客观量化在标准PCA中具有挑战性.
研究的目的:
- 增强PCA以改善多原子数据中群体差异的检测,诊断和量化.
- 开发一种更具信息性的PCA方法,用于复杂的生物数据集的质量控制.
- 为了解决可视化和量化批量和趋势效应的局限性.
主要方法:
- 开发了算法和一个工具箱 (PCA-Plus) 来增强传统的PCA.
- 实现的特征:计算群中心体,样本分散射线,差异色调,趋势轨迹.
- 引入了一种新的分离指数 (DSC) 用于定量组比较.
主要成果:
- PCA-Plus显著改善了批量和趋势效应的分析和可视化.
- 改进允许客观量化样本组之间的差异.
- PCA-Plus有效地处理各种多原子数据类型 (mRNA,microRNA,甲基化,复制号).
结论:
- PCA-Plus是分析,可视化和量化分子分析数据效应的宝贵工具.
- 该软件已成功应用于众多由NCI资助的项目 (TCGA,泛癌图谱).
- PCA-Plus算法是通用的,可以作为一个免费的R包.
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