MOTIVATION: Studies of microbial communities, represented by the relative abundances of taxa at various taxonomic levels, have underscored the significance of microbiota in numerous aspects of human health and disease. A pivotal challenge in microbiome research lies in pinpointing microbial taxa associated with disease outcomes, which could play crucial roles in prevention, detection, and treatment of various health conditions. Alongside these relative abundance data, taxonomic information sometimes offers a unique lens to explore the impact of shared evolutionary histories on patterns of microbial abundance. RESULTS: In pursuit of this goal, we utilize the tree structure to more flexibly identify taxa associated with disease outcomes. To enhance the accuracy of our selection process, we introduce auxiliary knockoff copies of microbiome features designated as noise. This approach allows for the assessment of false positives in the selection process and aids in refining it towards more precise outcomes. Extensive numerical simulations demonstrate that our methodology outperforms several existing methods in terms of selection accuracy. Furthermore, we demonstrate the practicality of our approach by applying it to a widely used gut microbiome dataset, identifying microbial taxa linked to body mass index. AVAILABILITY AND IMPLEMENTATION: TCVS R code is available at https://github.com/Yicong1225/TCVS.

Probabilistic modeling over the combinatorially large space of tree topologies remains a central challenge in phylogenetic inference. Previous approaches often necessitate pre-sampled tree topologies, limiting their modeling capability to a subset of the entire tree space. A recent advancement is ARTree, a deep autoregressive model that offers unrestricted distributions for tree topologies. However, its reliance on repetitive tree traversals and inefficient local message passing for computing topological node representations may hamper the scalability to large datasets. This paper proposes ARTreeFormer, a novel approach that harnesses fixed-point iteration and attention mechanisms to accelerate ARTree. By introducing a fixed-point iteration algorithm for computing the topological node embeddings, ARTreeFormer allows for fast vectorized computation, especially on CUDA devices. This, together with an attention-based global message passing scheme, significantly improves the computation speed of ARTree while maintaining great approximation performance. We demonstrate the effectiveness and efficiency of our method on a benchmark of challenging real data phylogenetic inference problems.

With the rapid proliferation of large datasets, efficient data compression has become more important than ever. Columnar compression techniques (e.g., dictionary encoding, run-length encoding, delta encoding) have proved highly effective for tabular data, but they typically compress individual columns without considering potential relationships among columns, such as functional dependencies and correlations. Semantic compression techniques, on the other hand, are designed to leverage such relationships to store only a subset of the columns necessary to infer the others, but existing approaches cannot effectively identify complex relationships across more than a few columns at a time. We propose DeepSqueeze, a novel semantic compression framework that can efficiently capture these complex relationships within tabular data by using autoencoders to map tuples to a lower-dimensional representation. DeepSqueeze also supports guaranteed error bounds for lossy compression of numerical data and works in conjunction with common columnar compression formats. Our experimental evaluation uses real-world datasets to demonstrate that DeepSqueeze can achieve over a 4x size reduction compared to state-of-the-art alternatives.