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Abstract Linking sequence-derived microbial taxa abundances to host (patho-)physiology or habitat characteristics in a reproducible and interpretable manner has remained a formidable challenge for the analysis of microbiome survey data. Here, we introduce a flexible probabilistic modeling framework, VI-MIDAS (variational inference for microbiome survey data analysis), that enables joint estimation of context-dependent drivers and broad patterns of associations of microbial taxon abundances from microbiome survey data. VI-MIDAS comprises mechanisms for direct coupling of taxon abundances with covariates and taxa-specific latent coupling, which can incorporate spatio-temporal information and taxon–taxon interactions. We leverage mean-field variational inference for posterior VI-MIDAS model parameter estimation and illustrate model building and analysis using Tara Ocean Expedition survey data. Using VI-MIDAS’ latent embedding model and tools from network analysis, we show that marine microbial communities can be broadly categorized into five modules, including SAR11-, nitrosopumilus-, and alteromondales-dominated communities, each associated with specific environmental and spatiotemporal signatures. VI-MIDAS also finds evidence for largely positive taxon–taxon associations in SAR11 or Rhodospirillales clades, and negative associations with Alteromonadales and Flavobacteriales classes. Our results indicate that VI-MIDAS provides a powerful integrative statistical analysis framework for discovering broad patterns of associations between microbial taxa and context-specific covariate data from microbiome survey data. 

Large language models (LLMs) demonstrate surprising capabilities, but we do not understand how they are implemented. One hypothesis suggests that these capabilities are primarily executed by small subnetworks within the LLM, known as circuits. Identifying these circuits is particularly useful in the context of building models that are robust to shortcut learning and distribution shifts. Identifying these shortcut encoding circuits allows us to "turn them off" by replacing their outputs with random values or zeros. Many papers have claimed to identify meaningful circuits in existing language models. In this paper, we focus on evaluating candidate circuits. Specifically, we formalize a set of criteria that a circuit is hypothesized to meet and develop a suite of hypothesis tests to evaluate how well circuits satisfy them. The criteria focus on the extent to which the LLM's behavior is preserved, the degree of localization of this behavior, and whether the circuit is minimal. We apply these tests to six circuits described in the research literature. We find that synthetic circuits -- circuits that are hard-coded in the model -- align with the idealized properties. Circuits discovered in Transformer models satisfy the criteria to varying degrees. To facilitate future empirical studies of circuits, we created the circuitry package, a wrapper around the TransformerLens library, which abstracts away lower-level manipulations of hooks and activations. The software is available at https://github.com/blei-lab/circuitry. 

Abstract Spatially resolved gene expression profiling provides insight into tissue organization and cell–cell crosstalk; however, sequencing-based spatial transcriptomics (ST) lacks single-cell resolution. Current ST analysis methods require single-cell RNA sequencing data as a reference for rigorous interpretation of cell states, mostly do not use associated histology images and are not capable of inferring shared neighborhoods across multiple tissues. Here we present Starfysh, a computational toolbox using a deep generative model that incorporates archetypal analysis and any known cell type markers to characterize known or new tissue-specific cell states without a single-cell reference. Starfysh improves the characterization of spatial dynamics in complex tissues using histology images and enables the comparison of niches as spatial hubs across tissues. Integrative analysis of primary estrogen receptor (ER)-positive breast cancer, triple-negative breast cancer (TNBC) and metaplastic breast cancer (MBC) tissues led to the identification of spatial hubs with patient- and disease-specific cell type compositions and revealed metabolic reprogramming shaping immunosuppressive hubs in aggressive MBC.