Learning representations of microbe–metabolite interactions文献总结


Learning representations of microbe–metabolite interactions



这篇文章中的interactions across omics就是指在统计学意义上的两个变量的关系,如相关性.

microbe-metabolite relationship就是指微生物和metabolite的相关性.

1 Abstract

Previous work has been able to predict metabolite abundance profiles from microbe abundance profiles1 2. However, because conventional correlation techniques have unacceptably high false-discovery rates, finding meaningful relationships between genes within complex microbiomes and their products in the metabolome is challenging.

传统的correlation计算方法有着非常大的错误发现率,因此发现microbe和他们的product metabolite还是非常具有挑战性的.

Relative abundances of thousands of microbes and metabolites can be measured using sequencing technology and mass spectrometry, respectively, resulting in the generation of high-dimensional microbiome and metabolomics datasets.

microbiome得到的定量信息也是relative abundance.对于metabolome数据,能够测到的metabolite依赖于提取的方法以及分析方法,因此其实我们得到的metabolome是a partial snapshot of the metabolome.并不完全的完整的.

Quantifying microbe–metabolite interactions from these abundances requires estimating a distribution across all possible microbe–metabolite interactions.

从相对定量的数据中得到microbe-metabolite interactions需要得到所有的microbe–metabolite interactions的分布.是否可以理解为需要由NULL分布,然后才能知道某个interaction是不是真的interaction?

Pearson’s and Spearman’s correlations assume independence between interactions.



Supplementary Figure 1

Supplementary Figure 1




scale invariance什么意思?标度不变.

An alternative approach is to consider co-occurrence probabilities instead of correlations.


Here, co-occurrence probabilities refer to the conditional probability of observing a metabolite given that a microbe was observed, thereby allowing us to identify the most likely microbe–metabolite interactions.

共出现概率是指当一个microbe(细菌)在的时候,观察到某个代谢物的条件概率.从而能够鉴定到真正的microbe-metabolite interaction.

To do this, we propose ‘mmvec’, (microbe–metabolite vecors), a neural network that predicts an entire metabolite abundance profile from a single microbe sequence.

因此作者提出了mmvec(microbe–metabolite vecors)概念.利用neural network从一个single microbe的sequence来预测整个的metabolite abundance.

Figure 1

Figure 1

这幅图是指如何使用microbe sequence去预测metabolite abundance.

Through iterative training, mmvec can learn the co-occurrence probabilities between microbes and metabolites. The microbe–metabolite interactions can be ranked and visualized through standard dimensionality reduction interfaces, enabling interpretable findings.

其实就是给定microbe的中的一个sequnce之后,预测所有metabolite abundance.

2 Results

3 Reference

  1. Noecker, C. et al. Metabolic model-based integration of microbiome taxonomic and metabolomic profiles elucidates mechanistic links between ecological and metabolic variation. MSystems 1, e00013–e00015 (2016).

  2. Mallick, H. et al. Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences. Nat. Commun. 10, 3136 (2019).


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