利用可操作分类单元(Operational Taxonomical Units,OTU)或扩增子序列变体(Amplicon Sequence Variants,ASV)推断下游信息时,现有扩增子测序数据分析可能丢失不同物种谱构建法的多模态信息,为此,详细分析了 4 种疾病的 OTU 和 ASV 数据集在肠道群落多样性和群落结构方面的差异,提出了一种有效整合 OTU与ASV用于疾病表征预测的方法:MDDMI(Microbiome-based Disease Detection with Multimodal Information).实验结果表明,MDDMI优于单模态数据分析法.
Multimodal Information Fusion of Gut Microbiome for Disease Detection Method
Current methods for analyzing amplicon sequencing data that utilize Operational Taxonomic U-nits(OTU)or Amplicon Sequence Variants(ASV)can lose multimodal information from various species spectrum construction methods.An analysis was conducted on the differences in community diversity and structure between OTU and ASV datasets across four diseases.An effective approach to integrate OTU and ASV for disease characterization prediction was proposed:MDDMI(Microbiome-based Disease Detec-tion with Multimodal Information).The results indicate that MDDMI is superior to the single-mode data analysis method.
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