伊斯卡齐姆:对零膨胀微生物组数据的综合统计相关性分析
Zhe Fan1,2, Jiali Lv1,2, Shuai Zhang1,2
1Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China.
Heliyon
|January 15, 2025
概括
分析肠道微生物组和代谢组数据是发现疾病生物标志物的关键. ISCAZIM改进了零膨胀微生物组数据的关联分析,增强了多omics集成和生物标志物发现.
科学领域:
- 微生物组研究的研究.
- 代谢学 代谢学 代谢学
- 计算生物学是一种计算生物学.
- 统计遗传学 统计遗传学
背景情况:
- 微生物组-代谢组关联分析对于识别慢性疾病中的微生物生物标志物至关重要.
- 微生物组数据的零通胀和过度分散特征挑战了准确的关联分析.
- 现有的统计方法可能无法充分解决这些独特的数据属性.
研究的目的:
- 评估现有的微生物组-代谢组关联分析的统计方法.
- 开发一个强大的计算框架,解决微生物组数据的复杂性.
- 提高多学科整合研究的准确性和可靠性.
主要方法:
- 开发了零膨胀微生物组数据的综合统计相关性分析 (ISCAZIM).
- 伊斯卡齐姆计算了零通胀率 (ZIR),分散和相关性模式.
- 基准Pearson,Spearman,ZINB模型,相互信息和最大信息系数,根据ZIR和相关性类型 (线性/非线性) 调整方法.
主要成果:
- 与单一方法相比,ISCAZIM在真实世界微生物组-代谢学数据中显示出更高的准确性.
- 该框架成功地确定了更多真正重要的协会对.
- ISCAZIM有效地处理复杂的微生物群数据特征.
结论:
- 在零膨胀微生物组数据中,ISCAZIM为关联分析提供了重大进展.
- 该框架为生物标志物发现提供了更可靠的多学科集成.
- 伊斯卡齐姆增强了确定肠道微生物群与代谢物相关性的信心.
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