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数据增强不变的流形学习AI 解读

Abstract Data augmentation is a widely used technique and an essential ingredient in the recent advance in self-supervised representation learning. By preserving the similarity between augmented data, the resulting data representation can improve various downstream analyses and achieve state-of-the-art performance in many applications. Despite the empirical effectiveness, most existing methods lack theoretical understanding under a general nonlinear setting. To fill this…

多视图数据中的自监督度量学习：下游任务视角AI 解读

Self-supervised metric learning has been a successful approach for learning a distance from an unlabeled dataset. The resulting distance is broadly useful for improving various distance-based downstream tasks, even when no information from downstream tasks is used in the metric learning stage. To gain insights into this approach, we develop a statistical framework to theoretically study how self-supervised metric learning can benefit downstream tasks in the context of…

组成数据的稳健差异丰度检验AI 解读

Summary Differential abundance tests for compositional data are essential and fundamental in various biomedical applications, such as single-cell, bulk RNA-seq and microbiome data analysis. However, because of the compositional constraint and the prevalence of zero counts in the data, differential abundance analysis on compositional data remains a complicated and unsolved statistical problem. This article proposes a new differential abundance test, the robust differential…

树上 Wasserstein 距离的最优估计及微生物组应用AI 解读

The weighted UniFrac distance, a plug-in estimator of the Wasserstein distance of read counts on a tree, has been widely used to measure the microbial community difference in microbiome studies. Our investigation however shows that such a plug-in estimator, although intuitive and commonly used in practice, suffers from potential bias. Motivated by this finding, we study the problem of optimal estimation of the Wasserstein distance between two distributions on a tree from the…

系统发育组成的假设检验：最小费用流视角AI 解读

Summary Quantitative comparison of microbial composition from different populations is a fundamental task in various microbiome studies. We consider two-sample testing for microbial compositional data by leveraging phylogenetic information. Motivated by existing phylogenetic distances, we take a minimum-cost flow perspective to study such testing problems. We first show that multivariate analysis of variance with permutation using phylogenetic distances, one of the most…

图上的组合假设检验及其在基因集富集分析中的应用AI 解读

Motivated by gene set enrichment analysis, we investigate the problem of combined hypothesis testing on a graph. A general framework is introduced to make effective use of the structural information of the underlying graph when testing multivariate means. A new testing procedure is proposed within this framework, and shown to be optimal in that it can consistently detect departures from the collective null at a rate that no other test could improve, for almost all graphs. We…