使用DeepInsight进行零膨胀的高维组合数据.
1Department of Statistics, Kyungpook National University, Daegu, South Korea.
PloS one
|April 16, 2025
概括
这项研究引入了一种新的方法来分析复杂的微生物组数据,解决零通货膨胀和高维度问题. 这种方法可以提高儿童炎症性肠病 (IBD) 等疾病的诊断准确度.
科学领域:
- 微生物组研究 微生物组研究
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 人类微生物组研究因下一代测序 (NGS) 和高通量测序 (HTS) 等先进的测序技术而扩大.
- 微生物组数据分析经常面临零通胀和高维度的挑战,复杂化了构成性解释.
- 操作分类单位 (OTU) 和扩展序列变异 (ASV) 是微生物组研究中常用的数值代理.
研究的目的:
- 开发一种分析零膨胀和高维微生物组数据的方法,并进行组合解释.
- 解决现有方法在准确分析复杂微生物组数据集方面的局限性.
主要方法:
- 将平方根转换应用于组合微生物组数据,以解决零通胀问题,将其映射到超球上.
- 修改了使用卷积神经网络 (CNN) 的DeepInsight图像生成方法,以适应高维数据的超球空间.
- 引入了一种策略,通过添加一个小值来区分零膨胀数据中的真零和假零.
主要成果:
- 提出的方法在分析儿科炎性肠病 (IBD) 便样本数据方面表现出有效性.
- 实现了0.847的曲线下面积 (AUC),超过了之前研究的0.83.3的AUC.
结论:
- 开发的方法成功地处理零膨胀和高维微生物组数据.
- 这种方法通过增强微生物组数据分析,为IBD等疾病提供了更好的诊断潜力.
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