Multi-Modal and Multi-Attribute Generation of Single Cells with CFGen (2407.11734v2)

Abstract: Generative modeling of single-cell RNA-seq data is crucial for tasks like trajectory inference, batch effect removal, and simulation of realistic cellular data. However, recent deep generative models simulating synthetic single cells from noise operate on pre-processed continuous gene expression approximations, overlooking the discrete nature of single-cell data, which limits their effectiveness and hinders the incorporation of robust noise models. Additionally, aspects like controllable multi-modal and multi-label generation of cellular data remain underexplored. This work introduces CellFlow for Generation (CFGen), a flow-based conditional generative model that preserves the inherent discreteness of single-cell data. CFGen generates whole-genome multi-modal single-cell data reliably, improving the recovery of crucial biological data characteristics while tackling relevant generative tasks such as rare cell type augmentation and batch correction. We also introduce a novel framework for compositional data generation using Flow Matching. By showcasing CFGen on a diverse set of biological datasets and settings, we provide evidence of its value to the fields of computational biology and deep generative models.

• The paper introduces CFGen, a conditional flow-based model that accurately generates multi-modal, multi-attribute single-cell counts.
• It leverages Flow Matching and discrete modeling techniques to outperform benchmarks like scVI, scDiffusion, and scGAN using metrics such as MMD and Wasserstein distances.
• CFGen enhances biological fidelity and supports applications like batch correction and rare cell type augmentation in single-cell RNA sequencing analysis.

An Expert Overview of CFGen: A Conditional Flow-Based Model for Single-Cell Data Generation

This academic paper introduces CFGen, a flow-based conditional generative model designed expressly for generating multi-modal and multi-attribute single-cell counts. The authors focus on addressing the deviation inherent in previous generative models that typically do not accommodate the discrete nature of single-cell RNA sequencing (scRNA-seq) data. The discrete characteristic of single-cell data is central to many downstream bioinformatics applications, including trajectory inference, batch effect removal, and robust noise model incorporation. In presenting CFGen, the authors propose a more statistically precise framework for synthetic single-cell data generation.

Methodological Innovation

CFGen builds upon Flow Matching techniques, a recent advancement in continuous normalizing flow models, which is well-suited to model statistical properties across multiple modalities in single-cell data. Specifically, this model handles both synthetic gene expression and DNA accessibility data by incorporating these aspects into compositional flow models, allowing conditional generation conditioned on multi-faceted biological and technical attributes. This feature notably extends the capabilities of existing flow-based systems by incorporating multi-attribute compositional guidance, which is new to literature on Flow Matching.

Quantitative and Qualitative Results

The numerical results reported in the paper indicate that CFGen achieves superior generative performance compared to established models such as scVI, scDiffusion, and scGAN across various benchmarks. Metrics such as the Mean Maximum Discrepancy (MMD) and the Wasserstein distances are utilized to measure the fidelity of generated data relative to real datasets. Highlights from the results showcase that CFGen often emerges as either the best or second-best performer in generating realistic cell-type distributions when compared to these benchmark models. Moreover, CFGen demonstrates capability in conserving critical biological data characteristics, such as the mean-variance relationship specific to genes, much more effectively than diffusion-based or GAN-based models.

Qualitative results further underscore CFGen's proficiency in generating data with realistic sparsity and over-dispersion properties, confirming its claims of exceeding previous methods which pre-process data into normalized forms.

Theoretical and Practical Implications

Theoretically, CFGen provides a better alignment with the intrinsic distribution properties of single-cell data through the use of negative binomial distributions and Bernoulli likelihoods. CFGen’s design endorses computational biology tasks that benefit from discrete data insights, such as batch effect correction and rare cell type augmentation. The flexibility to guide data generation conditionally with multiple attributes makes CFGen a versatile tool for single-cell data modeling.

Practically, CFGen exhibits potential as a means to enhance representation learning, as demonstrated in improvements of cell type classification in experimental frameworks. This capacity to augment training datasets effectively may improve the performance of large-scale models, including those employing LLMs for biological datasets.

Future Developments

Speculating on the future trajectory for CFGen, this model could presage enhancements in computational methodologies for single-cell analysis by leveraging its compositional flow architecture for even more specialized biological modeling tasks. Given CFGen's promising performance in the presented experiments, it is conceivable that such a framework could integrate additional biological modalities, refine technical noise modeling, and proliferate applications extending beyond single-cell technologies.

In conclusion, CFGen signifies a methodologically rigorous model that addresses previous limitations in discrete single-cell data generation. This presentation provides the academic groundwork for improved generative models in the scRNA-seq domain, with expansive potential applications in computational biology research and beyond.