Scalable Single-Cell Gene Expression Generation with Latent Diffusion Models

• URL: http://arxiv.org/abs/2511.02986v1
• Date: Tue, 04 Nov 2025 20:44:12 GMT
• Title: Scalable Single-Cell Gene Expression Generation with Latent Diffusion Models
• Authors: Giovanni Palla, Sudarshan Babu, Payam Dibaeinia, James D. Pearce, Donghui Li, Aly A. Khan, Theofanis Karaletsos, Jakub M. Tomczak,
• Abstract summary: We introduce a scalable latent diffusion model for single-cell gene expression data, which we refer to as scLDM.<n>We show its superior performance in a variety of experiments for both observational and perturbational single-cell data, as well as downstream tasks like cell-level classification.
• Score: 11.343106383645441
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computational modeling of single-cell gene expression is crucial for understanding cellular processes, but generating realistic expression profiles remains a major challenge. This difficulty arises from the count nature of gene expression data and complex latent dependencies among genes. Existing generative models often impose artificial gene orderings or rely on shallow neural network architectures. We introduce a scalable latent diffusion model for single-cell gene expression data, which we refer to as scLDM, that respects the fundamental exchangeability property of the data. Our VAE uses fixed-size latent variables leveraging a unified Multi-head Cross-Attention Block (MCAB) architecture, which serves dual roles: permutation-invariant pooling in the encoder and permutation-equivariant unpooling in the decoder. We enhance this framework by replacing the Gaussian prior with a latent diffusion model using Diffusion Transformers and linear interpolants, enabling high-quality generation with multi-conditional classifier-free guidance. We show its superior performance in a variety of experiments for both observational and perturbational single-cell data, as well as downstream tasks like cell-level classification.

Related papers

• Unlasting: Unpaired Single-Cell Multi-Perturbation Estimation by Dual Conditional Diffusion Implicit Bridges [68.98973318553983]
  We propose a framework based on Dual Diffusion Implicit Bridges (DDIB) to learn the mapping between different data distributions.<n>We integrate gene regulatory network (GRN) information to propagate perturbation signals in a biologically meaningful way.<n>We also incorporate a masking mechanism to predict silent genes, improving the quality of generated profiles.
  arXiv  Detail & Related papers  (2025-06-26T09:05:38Z)
• Bidirectional Mamba for Single-Cell Data: Efficient Context Learning with Biological Fidelity [0.39945675027960637]
  We introduce GeneMamba, a scalable and efficient foundation model for single-cell transcriptomics built on state space modeling.<n>GeneMamba captures bidirectional gene context with linear-time complexity, offering substantial computational gains over transformer baselines.<n>We evaluate GeneMamba across diverse tasks, including multi-batch integration, cell type annotation, and gene-gene correlation, demonstrating strong performance, interpretability, and robustness.
  arXiv  Detail & Related papers  (2025-04-22T20:34:47Z)
• scReader: Prompting Large Language Models to Interpret scRNA-seq Data [12.767105992391555]
  We propose an innovative hybrid approach that integrates the general knowledge capabilities of large language models with domain-specific representation models for single-cell omics data interpretation.<n>By inputting single-cell gene-level expression data with prompts, we effectively model cellular representations based on the differential expression levels of genes across various species and cell types.
  arXiv  Detail & Related papers  (2024-12-24T04:28:42Z)
• A Non-negative VAE:the Generalized Gamma Belief Network [49.970917207211556]
  The gamma belief network (GBN) has demonstrated its potential for uncovering multi-layer interpretable latent representations in text data. We introduce the generalized gamma belief network (Generalized GBN) in this paper, which extends the original linear generative model to a more expressive non-linear generative model. We also propose an upward-downward Weibull inference network to approximate the posterior distribution of the latent variables.
  arXiv  Detail & Related papers  (2024-08-06T18:18:37Z)
• Multi-Modal and Multi-Attribute Generation of Single Cells with CFGen [76.02070962797794]
  This work introduces CellFlow for Generation (CFGen), a flow-based conditional generative model that preserves the inherent discreteness of single-cell data.<n>CFGen generates whole-genome multi-modal single-cell data reliably, improving the recovery of crucial biological data characteristics.
  arXiv  Detail & Related papers  (2024-07-16T14:05:03Z)
• Semantically Rich Local Dataset Generation for Explainable AI in Genomics [0.716879432974126]
  Black box deep learning models trained on genomic sequences excel at predicting the outcomes of different gene regulatory mechanisms. We propose using Genetic Programming to generate datasets by evolving perturbations in sequences that contribute to their semantic diversity.
  arXiv  Detail & Related papers  (2024-07-03T10:31:30Z)
• Mixed Models with Multiple Instance Learning [51.440557223100164]
  We introduce MixMIL, a framework integrating Generalized Linear Mixed Models (GLMM) and Multiple Instance Learning (MIL) Our empirical results reveal that MixMIL outperforms existing MIL models in single-cell datasets.
  arXiv  Detail & Related papers  (2023-11-04T16:42:42Z)
• Improving Out-of-Distribution Robustness of Classifiers via Generative Interpolation [56.620403243640396]
  Deep neural networks achieve superior performance for learning from independent and identically distributed (i.i.d.) data. However, their performance deteriorates significantly when handling out-of-distribution (OoD) data. We develop a simple yet effective method called Generative Interpolation to fuse generative models trained from multiple domains for synthesizing diverse OoD samples.
  arXiv  Detail & Related papers  (2023-07-23T03:53:53Z)
• DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion [66.21290235237808]
  We introduce an energy constrained diffusion model which encodes a batch of instances from a dataset into evolutionary states. We provide rigorous theory that implies closed-form optimal estimates for the pairwise diffusion strength among arbitrary instance pairs. Experiments highlight the wide applicability of our model as a general-purpose encoder backbone with superior performance in various tasks.
  arXiv  Detail & Related papers  (2023-01-23T15:18:54Z)
• Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling [3.2435888122704037]
  We propose a deep generative model of single-cell gene expression data for which each perturbation is treated as an intervention targeting an unknown, but sparse, subset of latent variables. We benchmark these methods on simulated single-cell data to evaluate their performance at latent units recovery, causal target identification and out-of-domain generalization.
  arXiv  Detail & Related papers  (2022-11-07T15:47:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.