Statistical Enrichment Analysis of Samples: A General-Purpose Tool to Annotate Metadata Neighborhoods of Biological Samples.
Statistical Enrichment Analysis of Samples: A General-Purpose Tool to Annotate Metadata Neighborhoods of Biological Samples.
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DOI：
10.3389/fdata.2021.725276
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发表时间：
2021
影响因子：
3.1
通讯作者：
Chen JY
中科院分区：
文献类型：
--
作者：
Nguyen TM;Bharti S;Yue Z;Willey CD;Chen JY
无监督学习技术，如聚类和嵌入，已经越来越受欢迎的聚类生物医学样本从高维生物医学数据。提取给定生物状况的生物医学样本之间共享的临床数据或样本元数据仍然是一个重大挑战。在这里，我们描述了一个强大的分析方法，称为统计富集分析的样本（SEAS）解释聚类或嵌入式样本数据的组学研究。该方法通过关注样本集来获得其功效，即，为各种目的构建的生物样品组，例如，共享特定特征的样本的人工管理或通过嵌入来自多维组学空间的样本组学概况而生成的自动聚类。样本集中的样本具有共同的临床测量值，我们将其称为“临床型”，例如年龄组、性别、治疗状态或生存天数。我们演示了如何SEAS产生的见解，使用胶质母细胞瘤（GBM）样本的生物数据集。值得注意的是，当分析癌症基因组图谱（TCGA）-患者来源的异种移植物（PDX）数据时，SEAS允许近似放射治疗的PDX样本的不同临床结果，这是其他工具无法解决的。结果表明，SEAS可以为临床决策提供支持。SEAS工具作为一个免费软件包在https://aimed-lab.shinyapps.io/SEAS/上公开提供。
Unsupervised learning techniques, such as clustering and embedding, have been increasingly popular to cluster biomedical samples from high-dimensional biomedical data. Extracting clinical data or sample meta-data shared in common among biomedical samples of a given biological condition remains a major challenge. Here, we describe a powerful analytical method called Statistical Enrichment Analysis of Samples (SEAS) for interpreting clustered or embedded sample data from omics studies. The method derives its power by focusing on sample sets, i.e., groups of biological samples that were constructed for various purposes, e.g., manual curation of samples sharing specific characteristics or automated clusters generated by embedding sample omic profiles from multi-dimensional omics space. The samples in the sample set share common clinical measurements, which we refer to as “clinotypes,” such as age group, gender, treatment status, or survival days. We demonstrate how SEAS yields insights into biological data sets using glioblastoma (GBM) samples. Notably, when analyzing the combined The Cancer Genome Atlas (TCGA)—patient-derived xenograft (PDX) data, SEAS allows approximating the different clinical outcomes of radiotherapy-treated PDX samples, which has not been solved by other tools. The result shows that SEAS may support the clinical decision. The SEAS tool is publicly available as a freely available software package at https://aimed-lab.shinyapps.io/SEAS/.