# scDiffusion: conditional generation of high-quality single-cell data using diffusion model arXiv: https://arxiv.org/abs/2401.03968 ยท HTML: https://mlsanity.com/paper/2401.03968/ ## Key facts - Cited by 24 papers in the map (data through 14 Oct 2026). - Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. - Builds on 14 of its 71 references that are in the map. - First posted on arXiv 8 Jan 2024. ## Placed in - Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/) ## Builds on (most cited first, 14 of 14) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020) - [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021) - [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021) - [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018) - [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018) - [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022) - [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022) - [Text-to-image Diffusion Models in Generative AI: A Survey](https://mlsanity.com/paper/2303.07909/) (2023) - [Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models](https://mlsanity.com/paper/2103.04922/) (2021) - [A Survey on Generative Diffusion Model](https://mlsanity.com/paper/2209.02646/) (2022) - [Geometry-Based Data Generation](https://mlsanity.com/paper/1802.04927/) (2018) ## Built on by (most cited first, 24 of 24) - [Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images](https://mlsanity.com/paper/2501.15598/) (2025) - [Diffuse Everything: Multimodal Diffusion Models on Arbitrary State Spaces](https://mlsanity.com/paper/2506.07903/) (2025) - [ViPer: Visual Personalization of Generative Models via Individual Preference Learning](https://mlsanity.com/paper/2407.17365/) (2024) - [Multi-Modal and Multi-Attribute Generation of Single Cells with CFGen](https://mlsanity.com/paper/2407.11734/) (2024) - [Language-Enhanced Representation Learning for Single-Cell Transcriptomics](https://mlsanity.com/paper/2503.09427/) (2025) - [From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research](https://mlsanity.com/paper/2507.09028/) (2025) - [scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction](https://mlsanity.com/paper/2510.11726/) (2025) - [Lingshu-Cell: A generative cellular world model for transcriptome modeling toward virtual cells](https://mlsanity.com/paper/2603.25240/) (2026) - [Generative AI for Biosciences: Emerging Threats and Roadmap to Biosecurity](https://mlsanity.com/paper/2510.15975/) (2025) - [STRAND: Sequence-Conditioned Transport for Single-Cell Perturbations](https://mlsanity.com/paper/2602.10156/) (2026) - [Flow Matching for Count Data](https://mlsanity.com/paper/2605.07746/) (2026) - [Scalable Single-Cell Gene Expression Generation with Latent Diffusion Models](https://mlsanity.com/paper/2511.02986/) (2025) - [CountsDiff: A Diffusion Model on the Natural Numbers for Generation and Imputation of Count-Based Data](https://mlsanity.com/paper/2604.03779/) (2026) - [Sparse Data Diffusion for Scientific Simulations in Biology and Physics](https://mlsanity.com/paper/2502.02448/) (2025) - [LSDM: LLM-Enhanced Spatio-temporal Diffusion Model for Service-Level Mobile Traffic Prediction](https://mlsanity.com/paper/2507.17795/) (2025) - [Latent Gene Diffusion for Spatial Transcriptomics Completion](https://mlsanity.com/paper/2509.01864/) (2025) - [Remapping and navigation of an embedding space via error minimization: a fundamental organizational principle of cognition in natural and artificial systems](https://mlsanity.com/paper/2601.14096/) (2026) - [ScDiVa: Masked Discrete Diffusion for Joint Modeling of Single-Cell Identity and Expression](https://mlsanity.com/paper/2602.03477/) (2026) - [Clustering by Denoising: Latent plug-and-play diffusion for single-cell data](https://mlsanity.com/paper/2510.22835/) (2025) - [SAVE: A Generalizable Framework for Multi-Condition Single-Cell Generation with Gene Block Attention](https://mlsanity.com/paper/2604.16776/) (2026) - [AblateCell: A Reproduce-then-Ablate Agent for Virtual Cell Repositories](https://mlsanity.com/paper/2604.19606/) (2026) - [RNA-FM: Flow-Matching Generative Model for Genome-wide RNA-Seq Prediction](https://mlsanity.com/paper/2605.11622/) (2026) - [scCBGM: Interpretable Single-Cell Counterfactual Editing](https://mlsanity.com/paper/2606.07760/) (2026) - [Improving scDiffusion with Sparsity-Biased Classifier-Free Guidance](https://mlsanity.com/paper/2607.29043/) (2026) ## Abstract (arXiv) Single-cell RNA sequencing (scRNA-seq) data are important for studying the laws of life at single-cell level. However, it is still challenging to obtain enough high-quality scRNA-seq data. To mitigate the limited availability of data, generative models have been proposed to computationally generate synthetic scRNA-seq data. Nevertheless, the data generated with current models are not very realistic yet, especially when we need to generate data with controlled conditions. In the meantime, the Diffusion models have shown their power in generating data at high fidelity, providing a new opportunity for scRNA-seq generation. In this study, we developed scDiffusion, a generative model combining diffusion model and foundation model to generate high-quality scRNA-seq data with controlled conditions. We designed multiple classifiers to guide the diffusion process simultaneously, enabling scDiffusion to generate data under multiple condition combinations. We also proposed a new control strategy called Gradient Interpolation. This strategy allows the model to generate continuous trajectories of cell development from a given cell state. Experiments showed that scDiffusion can generate single-cell gene expression data closely resembling real scRNA-seq data. Also, scDiffusion can conditionally produce data on specific cell types including rare cell types. Furthermore, we could use the multiple-condition generation of scDiffusion to generate cell type that was out of the training data. Leveraging the Gradient Interpolation strategy, we generated a continuous developmental trajectory of mouse embryonic cells. These experiments demonstrate that scDiffusion is a powerful tool for augmenting the real scRNA-seq data and can provide insights into cell fate research.