variational autoencoder

RAPTORGraph: graph-based pathway modeling for causal discovery in single-cell perturbations

RAPTORGraph improves causal discovery in single-cell perturbations by modeling gene interactions and isolating perturbation effects, outperforming existing methods on downstream analyses.

How Much Can AI Virtual Cells Gain from Unified Perturbational Modeling?

This paper evaluates how unified perturbational modeling improves AI virtual cell predictors by integrating heterogeneous Perturb-seq datasets and achieving better performance across benchmarks.

SPATNIC enables H&E-grounded cancer-cell identification across spatial and single-cell transcriptomic modalities in colorectal cancer

SPATNIC is a variational autoencoder that identifies cancer cells from spatial and single-cell transcriptomic data in colorectal cancer, outperforming existing tools and identifying a rare cancer stem cell population.

LoCoFlow: Local-Context Flow Matching for Single-Cell Dynamics and Fate Inference

LoCoFlow improves trajectory inference in single-cell data by incorporating local geometry and fate uncertainty, achieving better performance than existing methods.

Quantum Hamiltonian-Based Generative Modeling of Single-Cell Transcriptomics for Gene Regulatory Network Inference

This paper introduces a quantum Hamiltonian-based model for gene regulatory network inference, showing improved performance on synthetic and real single-cell RNA-seq data.

Extracting interpretable single-cell metabolic states with graph-guided representation learning

The paper introduces MeRN, a graph-guided variational autoencoder for learning single-cell metabolic states from transcriptomes, and identifies metabolic drivers in various biological contexts.

Folate deficiency disrupts key metabolic transitions within the developing neural ectoderm

This study uses a deep-learning model to analyze scRNA-seq data and identifies how folate deficiency disrupts metabolic transitions in developing mouse neural ectoderm, leading to morphological defects.

Setting the SCENE for Interpretable Cell-Gene Embeddings in Single-Cell RNA-seq

This paper introduces SCENE, a probabilistic model for embedding cells and genes in scRNA-seq data, achieving state-of-the-art performance and interpretable visualization.

An Integrated Single-Nucleus Atlas Resolves Cell-Type-Specific Programs and Molecular Subtypes in Alzheimer's Disease

The paper constructs a single-nucleus transcriptomic atlas of Alzheimer's disease and identifies cell-type-specific molecular subtypes and therapeutic targets using a new framework.

scDRP: Disentangled representation learning for predicting single-cell responses to perturbations and estimating individual treatment effects

scDRP estimates individual treatment effects and predicts single-cell responses to perturbations using disentangled representation learning with a sparsity regularized β-VAE.

scDisent: regulatory-aware disentangled representation learning for multi-omic single-cell analysis

scDisent separates regulatory and expression variables in multi-omic single-cell data, improving integration and interpretability.

A Cytokine Receptor Signaling Atlas Reveals How STAT Mosaics Fine-Tune T Cell Function

A fully orthogonal IL-2 receptor platform isolates 36 cytokine receptors in T cells; receptor-specific STAT combinations specify distinct fates, and STAT3-dominant programs give curative antitumor responses.

Detection of Spatially Aberrant Cells in Spatial Transcriptomics Data by Conformal Prediction

The paper introduces SPADE, a framework that detects spatially aberrant cells in spatial transcriptomics data using variational autoencoders and conformal prediction.

Invasion status stratifies the composition of the human pancreatic cancer perineural niche

Xenium and single-nucleus profiling of 14 human pancreatic tumors shows invaded nerve neighborhoods enriched for classical cancer cells, myCAFs and lipid-handling macrophages but depleted of lymphocytes.

Deciphering Cell Cycle Dynamics and Cell States in Single-cell RNA-seq data with SPAE

This paper introduces SPAE, a model for analyzing cell cycle dynamics and states in scRNA-seq data, showing improved accuracy and robustness in cell cycle characterization and cancer cell cycle transition prediction.

CoLa-VAE: A Cell-Cell Communication-Aware Variational Autoencoder for Representation Learning and Expression Denoising

CoLa-VAE improves denoising and representation learning in single-cell data by integrating cell-cell communication information, enhancing downstream analyses like differential communication detection and spatial deconvolution.

ARCADIA Reveals Spatially Dependent Transcriptional Programs through Integration of scRNA-seq and Spatial Proteomics

ARCADIA integrates scRNA-seq and spatial proteomics to reveal spatially dependent transcriptional programs without cell barcode pairing or feature-level correspondence.

Elucidating the Design Space of Generative Models for Single-Cell Perturbation Prediction

This paper introduces eVAE, a discrete-latent model for single-cell perturbation prediction, achieving state-of-the-art results on multiple metrics and outperforming existing methods in perturbation ranking.

Temporally overlapping mechanisms diversify clonal B cell responses in vivo.

Single-cell RNA and BCR sequencing of infected mouse spleens shows class switching overlaps clonal expansion, GC-seeding clones also form plasmablasts, and SHM proceeds at constant rate.

GeroEngine: Generative single-cell aging trajectories reveal a bidirectionally traversable identity core and direction-specific inflammatory remodeling

GeroEngine reveals aging trajectories show bidirectional identity recovery and direction-specific inflammation, suggesting homeostatic restoration over inflammation suppression.

Cellpin enables reference-based imputation and denoising of spatial transcriptomes

Cellpin imputes and denoises spatial transcriptomes using a variational autoencoder trained on single-cell RNA-seq, outperforming existing methods without requiring cross-modality alignment.

Praxis-BGM: Clustering of Omics Data Using Semi-Supervised Transfer Learning for Gaussian Mixture Models via Natural-Gradient Variational Inference

Scalable multimodal mapping of macrophage regulatory architecture by integrating optical and transcriptomic pooled screens

The paper introduces PerturbPair, a platform integrating transcriptomic and optical screens to map macrophage regulatory networks, revealing cross-modal relationships and disease-relevant gene programs.

SRSA-VAE: Self-Attention-Based Feature Learning for Single-Cell Multimodal Clustering

SRSA-VAE improves single-cell clustering by using self-attention and residual connections, outperforming existing methods on scRNA-seq and CITE-seq data.

Geometry aware graph attention networks to explain single-cell chromatin state and gene expression

SEAGALL uses geometry-aware graph attention networks to extract biologically relevant features from single-cell data, revealing cell identity drivers beyond marker genes.

An integrated single cell and spatial omics atlas of human prenatal development

Human Developmental Cell Atlas integrates ~4.6 million cells/nuclei from 4-22 post-conception weeks into 454 refined cell types and maps 114 spatial niches across 6PCW whole-embryo sections.

Decoding Single-Cell Omics of Perturbation Responses Using DeSCOPE

DeSCOPE is a variational autoencoder framework for predicting genetic perturbation responses across multiple omics modalities, outperforming baselines in unseen gene and cell type scenarios.

Interpretable deep generative ensemble learning for single-cell omics with Hydra

Hydra is a deep generative framework for single-cell omics data that improves cell type annotation and feature selection, particularly for rare populations.

CoPhaser: generic modeling of biological cycles in scRNA-seq with context-dependent periodic manifolds

CoPhaser models biological cycles in scRNA-seq by decomposing data into periodic and non-periodic sources, revealing insights into cell cycle dynamics and other periodic processes across diverse contexts.

Simplex-Constrained Neural Topic VAEs with Flow Refinement for Interpretable Single-Cell Gene-Program Discovery

This paper introduces Topic-FM, a neural topic VAE for single-cell transcriptomics that improves interpretability and clustering performance across multiple datasets.