By creating networks of biochemical pathways, communities of micro-organisms are able to modulate the properties of their environment and even the metabolic processes within their hosts. Next-generation high-throughput sequencing has led to a new frontier in microbial ecology, promising the ability to leverage the microbiome to make crucial advancements in the environmental and biomedical sciences. However, this is challenging, as genomic data are high-dimensional, sparse, and noisy. Much of this noise reflects the exact conditions under which sequencing took place, and is so significant that it limits consensus-based validation of study results. We propose an ensemble approach for cross-study exploratory analyses of microbial abundance data in which we first estimate the variance-covariance matrix of the underlying abundances from each dataset on the log scale assuming Poisson sampling, and subsequently model these covariances jointly so as to find a shared low-dimensional subspace of the feature space. By viewing the projection of the latent true abundances onto this common structure, the variation is pared down to that which is shared among all datasets, and is likely to reflect more generalizable biological signal than can be inferred from individual datasets. We investigate several ways of achieving this, and demonstrate that they work well on simulated and real metagenomic data in terms of signal retention and interpretability.

翻译：通过构建生化通路网络，微生物群落能够调节其环境属性甚至宿主体内的代谢过程。新一代高通量测序技术为微生物生态学开辟了新前沿，有望通过利用微生物组在环境与生物医学领域取得关键突破。然而，由于基因组数据具有高维性、稀疏性和噪声干扰特征，这一目标面临挑战。大量噪声反映测序进行的特定条件，其影响显著以至于限制了研究结果的共识性验证。我们提出了一种集成方法用于微生物丰度数据的跨研究探索性分析：首先假设泊松采样，在对数尺度上估计每个数据集中潜在丰度的方差-协方差矩阵，随后联合建模这些协方差矩阵以寻找特征空间的共享低维子空间。通过将潜在真实丰度投影至这一公共结构，变异被缩减至所有数据集共有的部分，这比从单个数据集推断的结果更可能反映具有广泛适用性的生物学信号。我们探索了多种实现途径，并在模拟与真实宏基因组数据上证实，这些方法在信号保留与可解释性方面表现优异。