SPARTA:对微生物组进行可解释的功能分类并检测隐藏的累积效应。

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS PLoS Computational Biology Pub Date : 2024-11-18 DOI:10.1371/journal.pcbi.1012577
Baptiste Ruiz, Arnaud Belcour, Samuel Blanquart, Sylvie Buffet-Bataillon, Isabelle Le Huërou-Luron, Anne Siegel, Yann Le Cunff
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引用次数: 0

摘要

肠道微生物群的组成是各种疾病的一个已知因素,已被证明是疾病状态自动分类的坚实基础。自此以后,人们提出了在功能尺度上更好地理解微生物群数据的需求,因为这将提高这些方法的生物学可解释性。在本文中,我们开发了一个计算管道,用于将肠道微生物群的功能注释整合到自动分类过程中,并促进对其结果的下游解释。该流程将分类组成数据作为输入,这些数据可通过 16S 或全基因组测序建立,并通过查询 UniProt 数据库将每个组成成分与其功能注释联系起来。在此基础上建立肠道微生物群的功能档案。微生物和功能这两个图谱都用于训练随机森林分类器,以区分不健康样本和对照样本。SPARTA 从三个方面扩展了最先进的方法,从而确保了充分的可重复性和对固有变异性的探索:增加训练随机森林的数量、通过迭代过程选择重要变量、从不同种子重复完整的选择过程。这一过程表明,在 6 个数据集中的 5 个数据集上,将微生物群转化为功能剖面图与微生物剖面图相比,其性能没有显著差异。不过,这种方法的主要贡献源于其可解释性而非性能:通过重复,它还输出了一个稳健的判别变量子集。与最先进的方法相比,这些选择的一致性更高,其内容也通过人工文献研究得到了验证。此外,还分析了所选分类群与功能注释之间的相互联系,发现重要的注释来自于非所选分类群的累积影响。
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SPARTA: Interpretable functional classification of microbiomes and detection of hidden cumulative effects.

The composition of the gut microbiota is a known factor in various diseases and has proven to be a strong basis for automatic classification of disease state. A need for a better understanding of microbiota data on the functional scale has since been voiced, as it would enhance these approaches' biological interpretability. In this paper, we have developed a computational pipeline for integrating the functional annotation of the gut microbiota into an automatic classification process and facilitating downstream interpretation of its results. The process takes as input taxonomic composition data, which can be built from 16S or whole genome sequencing, and links each component to its functional annotations through interrogation of the UniProt database. A functional profile of the gut microbiota is built from this basis. Both profiles, microbial and functional, are used to train Random Forest classifiers to discern unhealthy from control samples. SPARTA ensures full reproducibility and exploration of inherent variability by extending state-of-the-art methods in three dimensions: increased number of trained random forests, selection of important variables with an iterative process, repetition of full selection process from different seeds. This process shows that the translation of the microbiota into functional profiles gives non-significantly different performances when compared to microbial profiles on 5 of 6 datasets. This approach's main contribution however stems from its interpretability rather than its performance: through repetition, it also outputs a robust subset of discriminant variables. These selections were shown to be more consistent than those obtained by a state-of-the-art method, and their contents were validated through a manual bibliographic research. The interconnections between selected taxa and functional annotations were also analyzed and revealed that important annotations emerge from the cumulated influence of non-selected taxa.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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