Biology subjects

Weinberger, E.

Publications and source records attributed to Weinberger, E..

7 recordsLinked to original sources

bioRxiv2025

An Explainable AI Framework for Identifying Universal Aging Signatures in Cell Embeddings

Aging is a complex biological process marked by progressive physiological decline and increased disease vulnerability. Single-cell RNA sequencing offers unprecedented resolution for studying aging, yet isolating aging-related signatures remains challenging because gene expression is primarily shaped by other factors such as cell type, tissue, and sex. We present ACE (Aging Cell Embeddings), an explainable deep generative framework that disentangles aging-related gene expression changes from background biological variation. ACE employs two latent representations: one capturing aging-related signatures and another representing non-aging-related variation in the data. Through explainable AI, ACE identifies key genes and pathways associated with aging amid dominant non-aging-related variations. Applied to large-scale mouse, fly, and human datasets, ACE uncovers aging signatures both within specific tissue-cell-type contexts and across all tissues and cell types, enabling accurate prediction of biological age. Moreover, ACE identifies aging genes conserved across species, highlighting its ability to reveal shared biological mechanisms of aging. Experimental RNAi knockdowns in C. elegans validate ACEs findings, confirming its ability to prioritize novel aging genes affecting lifespan. ACE reveals key pathways involved in proteostasis, immune regulation, and extracellular matrix remodeling, and identifies Uba52 through the cross-species model as an important aging gene, whose knockdown in C. elegans significantly shortens lifespan. By providing interpretable and generalizable aging embeddings, ACE establishes a foundation for cross-species single-cell aging studies and translational geroscience.

bioinformatics↗

bioRxiv2024

A deep generative model for capturing cell to phenotype relationships

Single-cell omics has proven to be a powerful instrument for exploring cellular diversity. With advances in sequencing protocols, single-cell studies are now routinely collected from large-scale donor cohorts consisting of samples from hundreds of donors with the goal of uncovering the molecular bases of higher-level donor phenotypes of interest. For example, to better understand the mechanisms behind Alzheimers disease, recent studies with up to hundreds of samples have investigated the relationships between single-cell omics measurements and donors neuropathological phenotypes (e.g. Braak staging) [4, 9, 3, 10]. In order to ensure the robustness of such findings, it may be desirable to aggregate data from multiple distinct donor cohorts. Unfortunately, doing so is not always straightforward, as different cohorts may be equipped with different sets of phenotype labels. Continuing the previous Alzheimers example, recent AD study cohorts have provided various subsets of neuropathological phenotypes, cognitive testing results, and APOE genotype. Thus, it is desirable to be able to infer any missing phenotype labels such that all available cell-level data in the study of a given phenotype of interest could be used. Moreover, beyond simply imputing missing phenotype information, it is often of interest to understand which groups of cells and/or molecular features may be most predictive of a given phenotype of interest. As such, there is a pressing need for computational methods that can connect cell-level measurements with donor-level labels. However, accomplishing this task is not straightforward. While a rich literature exists on learning meaningful low-dimensional representations of cells [7, 8, 1, 2] and for inferring corresponding cell-level labels (e.g. cell type) [11], the donor level prediction task introduces substantial additional complexity. For example, different numbers of cells may be recovered from each donor, and thus our prediction model must be able to handle arbitrary numbers of samples as input. Moreover, ideally our model would not a priori require any additional prior knowledge beyond our cell-level measurements, such as the importance of different cell types for a given prediction task. To resolve these issues, here we propose milVI (multiple instance learning variational inference), a deep generative modeling framework that explicitly accounts for donor-level phenotypes and enables inference of missing phenotype labels post-training. In order to handle varying numbers of cells per donor when inferring phenotype labels, milVI leverages recent advances in multiple instance learning. We validated milVI by applying to impute held-out Braak staging information from an Alzheimers disease study cohort from Mathys et al. [9], and we found that our method achieved lower error on this task compared to naive imputation methods.

bioinformatics↗

bioRxiv2023

Isolating structured salient variations in single-cell transcriptomic data with StrastiveVI

Single-cell RNA sequencing (scRNA-seq) has provided deeper insights into biological processes by highlighting differences at the cellular level. Within these single-cell omics measurements, researchers are often interested in identifying variations associated with a specific covariate. For instance, in aging research, it becomes vital to differentiate variations related to aging. To address this, we introduce StrastiveVI (Structured Contrastive Variational Inference; https://github.com/suinleelab/StrastiveVI), which effectively separates the variations of interest from other dominant biological signals in scRNA-seq datasets. When deployed on aging and Alzheimers disease (AD) datasets, StrastiveVI efficiently isolates aging and AD-associated patterns, distinguishing them from dominant variations linked to sex, tissue, and cell type that are unrelated to aging or AD. In doing so, it underscores both well-known genes and potential novel genes related to aging or AD.

bioinformatics↗

bioRxiv2022

Disentangling shared and group-specific variations in single-cell transcriptomics data with multiGroupVI

Single-cell RNA sequencing (scRNA-seq) technologies have enabled a greater understanding of previously unexplored biological diversity. Based on the design of such experiments, individual cells from scRNA-seq datasets can often be attributed to non-overlapping "groups". For example, these group labels may denote the cells tissue or cell line of origin. In this setting, one important problem consists in discerning patterns in the data that are shared across groups versus those that are group-specific. However, existing methods for this type of analysis are mainly limited to (generalized) linear latent variable models. Here we introduce multiGroupVI, a deep generative model for analyzing grouped scRNA-seq datasets that decomposes the data into shared and group-specific factors of variation. We first validate our approach on a simulated dataset, on which we significantly outperform state-of-the-art methods. We then apply it to explore regional differences in an scRNA-seq dataset sampled from multiple regions of the mouse small intestine. We implemented multiGroupVI using the scvi-tools library [1], and released it as open-source software at https://github.com/Genentech/multiGroupVI.

bioinformatics↗

bioRxiv2021

Isolating salient variations of interest in single-cell transcriptomic data with contrastiveVI

Single-cell datasets are routinely collected to investigate changes in cellular state between control cells and corresponding cells in a treatment condition, such as exposure to a drug or infection by a pathogen. To better understand heterogeneity in treatment response, it is desirable to disentangle latent structures and variations uniquely enriched in treated cells from those shared with controls. However, standard computational models of single-cell data are not designed to explicitly separate these variations. Here, we introduce Contrastive Variational Inference (contrastiveVI; https://github.com/suinleelab/contrastiveVI), a framework for analyzing treatment-control scRNA-seq datasets that explicitly disentangles the data into shared and treatment-specific latent variables. Using four treatment-control scRNA-seq dataset pairs, we apply contrastiveVI to perform a broad set of standard analysis tasks, including visualization, clustering, and differential expression testing. In each case, we find that our method consistently achieves results that agree with known biological ground truths, while previously proposed methods often fail to do so. We conclude by generalizing our framework to multimodal measurements and applying it to analyze a single-cell dataset with joint transcriptome and surface protein measurements.

bioinformatics↗

bioRxiv2021

Transferable representations of single-cell transcriptomic data

Advances in single-cell RNA-seq (scRNA-seq) technologies are enabling the construction of large-scale, human-annotated reference cell atlases, creating unprecedented opportunities to accelerate future research. However, effectively leveraging information from these atlases, such as clustering labels or cell type annotations, remains challenging due to substantial technical noise and sparsity in scRNA-seq measurements. To address this problem, we present HD-AE, a deep autoencoder designed to extract integrated low-dimensional representations of scRNA-seq measurements across datasets from different labs and experimental conditions (https://github.com/suinleelab/HD-AE). Unlike previous approaches, HD-AEs representations successfully transfer to new query datasets without needing to retrain the model. Researchers without substantial computational resources or machine learning expertise can thus leverage the robust representations learned by pretrained HD-AE models to compare embeddings of their own data with previously generated sets of reference embeddings.

bioinformatics↗

bioRxiv2020

Unified AI framework to uncover deep interrelationships between gene expression and Alzheimer's disease neuropathologies

Deep neural networks offer a promising approach for capturing complex, non-linear relationships among variables. Because they require immense sample sizes, their potential has yet to be fully tapped for understanding complex relationships between gene expression and human phenotypes. Encouragingly, a growing number of diseases are being studied through consortium efforts. Here we introduce a new analysis framework, namely MD-AD (Multi-task Deep learning for Alzheimers Disease neuropathology), which leverages an unexpected synergy between deep neural networks and multi-cohort settings. In these settings, true joint analysis can be stymied using conventional statistical methods, which (1) require "harmonized" phenotypes (i.e., measured in a highly consistent manner) and (2) tend to capture cohort-level variations, obscuring the subtler true disease signals. Instead, MD-AD incorporates multiple related phenotypes sparsely measured across cohorts, and learns complex, non-linear interactions between genes and phenotypes not discovered using conventional expression data analysis methods (e.g., component analysis and module detection), enabling the model to capture subtler signals than cohort-level variations. Applied to the largest available collection of brain samples (N=1,758), we demonstrate that MD-AD learns a truly generalizable relationship between gene expression program and AD-related neuropathology. The learned program generalizes in several important ways, including recapitulation of the disease progress in animal models and across tissue types, and we show that such generalizability is not achieved by previous statistical paradigms. Its ability to identify genes with high non-linear relevance to neuropathology enabled us to identify a sex-specific relationship between neuropathology and immune response across microglia, providing a nuanced context for association between inflammatory genes and AD.

systems biology↗