Multi-Modal and Multi-Attribute Generation of Single Cells with CFGen

Authors: Alessandro Palma, Till Richter, Hanyi Zhang, Manuel Lubetzki, Alexander Tong, Andrea Dittadi, Fabian J Theis
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: scRNA-seq, Flow Matching, Generative modeling, Multiomics

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 reliably generates whole-genome, multi-modal, single-cell data, 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.

Decision: accept (poster)
Reviewer scores: 8, 8, 5, 6

Reviews and discussion:
<thread>
creation date:
1730665891011

thread_type:
official_review

<thread_object>
creator: reviewer_fqvm

<content>
summary:
In summary, I initially rate this paper as  "5: marginally below the acceptance threshold". But I'm open to increase my score if authors properly answer my doubts in the rebuttal.

Summary of paper: The paper proposes a generative model for scRNA as well as accessibility modalities. The model can take in a combination of attributes, which suits the biological settings where for each cell only a subset of attributes are available. The method is evaluated in generation, handling label imbalance in cell type classification for rate cell types, and batch correction.

strengths:
- The model is tailored to real biological settings: it handles 2 modalities (scRNA and ATAC) and any number of attributes.
- The results properly support the good performance of the method. 
- Besides generation power, two very interesting applications are demonstrated: handing rare cell types in cell type classification and batch correction.

weaknesses:
- Handling discrete count data via negative binomial distribution is presented as a "contribution" of this paper. But there is a plethora of methods that make use of negative binomial (or alternatives like poisson distribution) to handle count data as well as over-dispersion. So why should it be listed as a contribution of this paper?
- According to the paper, "... the proposed factorisation is novel". In the factorisation of Eq. 5 what is the rational behind conditioning the latent factor z on library size?
- In proposition 1, the attributes $y_1$, $y_2$, ... are assumed to be conditionally independent given $z$, but with the factorisation of Eq. 5 the attributes are connected to $z$, hence $z$ forms a V-structure which according to d-separation causes the attributes to be dependant given $z$ ?
- Regarding the proposed guidance scheme, the only difference to the normal classifier-free guidance is that only some attributes (and one attribute during training) is fed to the decoder. Is this approach equivalent to the normal classifier-free guidance with all attributes plus some attributes being randomly dropped out? Even if so, it wouldn't decrease the value of the proposed method.
- In Table 1 scDiffusion is heavily outperformed by the proposed method, but one may say diffusion models may perform on par with flow matching (apart from training stability etc.). In the paper I'd recommend providing an explanation for the superior performance of the proposed method compared to scDiffusion.

questions:
Please see the "Weaknesses" part.
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</thread>
<thread>
creation date:
1730757338463

thread_type:
official_review

<thread_object>
creator: reviewer_eqfj

<content>
summary:
The authors of this paper present CFGen, a flow-based generative model designed for multi-modal single-cell data. CFGen addresses the challenges of generating discrete, multi-modal data while allowing conditional generation based on various biological attributes. The model extends the flow matching framework to handle compositional guidance across multiple attributes and provides promising results on tasks like data augmentation, rare cell type classification, and batch correction.

strengths:
- The authors nicely demonstrate practical applications of their method such as data augmentation in rare cell types, improving downstream classification, and performing batch correction. 

- The idea to extend flow matching for generation with multiple attributes is interesting and important for single-cell data.

- The paper is well-written, the related work is appropriately referenced, and the experimental setup is detailed.

weaknesses:
-  The authors do not discuss the computational complexity of the proposed method. A more detailed breakdown of computational requirements, including training and sampling times for the proposed method and the baselines, would improve the paper.

- One important task in single-cell data analysis is gene expression imputation, where missing or zero-inflated gene expression values are inferred to provide a more complete view of cellular states. It is unclear from the paper whether CFGen can effectively handle this task, given its focus on generating new cells rather than imputing missing data within existing cells. Could the authors clarify if CFGen’s architecture or the flow matching framework could be adapted for imputation?

questions:
- Can CFGen be applied to gene expression imputation tasks? If so, could the authors describe how the current framework could handle imputation, or if modifications would be needed?

- Could the authors provide details about the computational complexity of the model?
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</thread>
<thread>
creation date:
1730782442916

thread_type:
official_review

<thread_object>
creator: reviewer_du3l

<content>
summary:
The paper presents conditional flow-based generative models for single-cell RNA-seq and accessibility data. Single cell data is generally sparse, noisy, and has high feature variance. The authors suggest a flow matching based approach as a more expressive, and consistent generative model compared to VAEs, and GANs for generating synthetic cells. They also present a compositional variant of classifier-free guidance for flow-based models to allow conditioning on various attributes. Finally, they evaluate the model on two downstream tasks: (1) generating synthetic samples of rare cell-types and using them for data-augmentation,  (2) leveraging CFGen for batch correction.

strengths:
1. The paper addresses an important problem in single-cell data generation by generating raw count values, and further extending this to multimodal generation.
2. The paper is well-written, and the authors convey major limitations of their model clearly.
3. The results show that CFGen is able to capture characteristics of the training dataset and generate single cell data with similar statistical properties.
4. They also show the effectiveness of generating rare cell-types to improve classification performance for other models.

Post Rebuttal comments:

The authors have addressed my concerns regarding the presentation. They have also added the additional details I addressed in the weaknesses below. After going through their responses to other reviewers, I believe the paper will be a valuable addition to ICLR. I am raising my score to accept.

weaknesses:
1. Fig 3. is not really clear to me. Firstly, I suggest adding contrasting colors for points representing generated and real data. Secondly, what are the red points representing? I also suggest perhaps adding a quantitative metric (perhaps a oracle model that predicts the attributes) as well.
2. I also suggest removing the bars from Fig. 2b as they make it hard to observe the overlapping density curves which are easier to infer from.
3. For Sec 5.2, it might be worthwhile to also add a comparison with CFGen just trained on RNA-data in order to measure the effects of using multimodal data for training.
4. A comparison of inference times might also be useful in this case, especially to compare scDiffusion and CFGen, since both require multiple time steps. Adding approximate training times for each of the comparable models would also be valuable.
5. Fig.4 should also report the raw accuracy numbers for each of the cell-types to evaluate the effect of CFGen,

questions:
See weaknesses above.
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</thread>
<thread>
creation date:
1730961895693

thread_type:
official_review

<thread_object>
creator: reviewer_kehi

<content>
summary:
This paper proposes CFGen, which is a latent flow-matching generative model for single-cell data, where the latent space is first learned by an autoencoder. To capture statistical properties specific to single-cell data, the autoencoders learn to decode the parameters of a negative binomial distribution and Bernoulli distribution, for RNA-seq and ATAC-seq data, respectively. Conditional generation is achieved through classifier guidance. Empirical results demonstrate that CFGen outperform other single-cell generative models in terms of (1) data generation to approximate the real data distribution, (2) data generation for rare cell type classification, and (3) batch correction.

strengths:
- Adapting flow matching for single-cell data generation is a novel contribution.
- The proposed framework CFGen can be easily adapted for different uni- and multi-modal scenarios, as long as there are modality-specific autoencoders with a common latent space.

weaknesses:
- scVI should be included as a baseline in Figure 2 because scVI accounts for overdispersion and zero inflation, whereas the current baselines in Figure 2 (scDiffusion and scGAN) do not.
- For downstream applications that rely on conditional generation, it is unclear how the classifier guidance strength is determined.
- Quantitative results are lacking when evaluating the compositional classifier guidance in Section 5.3. The change in MMD and WD with respect to the target distribution when increasing guidance strength can suffice.

questions:
- For batch correction, is CFGen's performance (in terms of the Batch and Bio scores) sensitive to varying the guidance parameters? How does one tune the guidance parameters in practice?
- For cell type classification, simple models such as logistic regression (with or without regularization) are often used. Does data augmentation with CFGen improve performance for a logistic regression model?
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</thread>
<thread>
creation date:
1732154988766

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
General comment

comment:
We sincerely thank all the reviewers for their constructive feedback. As authors, we greatly appreciate the opportunity to enhance the scientific quality of our work by addressing reported inconsistencies, additional experiment requests, and general criticism. All experiments and specific suggestions were carefully addressed and will be discussed separately for each reviewer in the dedicated rebuttal sections.

Aside from providing key clarifications, our rebuttal output incorporates a considerable number of new experiments including:
* A detailed runtime analysis in comparison with baseline models (Appendix H.1).
* Quantitative evaluations of the multi-attribute setting (Appendix H.8).
* Cell type classification performance improvement using linear models (Appendix H.6).
* Comparison with CFGen trained on scRNA-seq only in the multi-modal setting (Tab. 2).
* The application of CFGen to the missing value imputation task (Appendix H.7).
* A deeper insight into the guidance strength selection for batch correction (Appendix H.9). 

We additionally supplied missing details and comparisons (e.g., the raw cell type classification accuracy before and after augmentation by CFGen) and improved the figures where suggested. 

All edits applied to the main manuscript are highlighted in blue. 

We thank again both the reviewers and the area chair for their valuable consideration and look forward to follow-up discussions.

Best regards, 

The Authors
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</thread>
<thread>
creation date:
1733226148370

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Summary of Discussion Outcomes and Manuscript Improvements

comment:
Dear AC and Reviewers,  

As the interaction period with reviewers nears its conclusion, we wish to provide a concise summary of the discussion outcomes and the improvements made to our manuscript in response to the insightful feedback received:  

- **Reviewer kEhi:**  
  We addressed additional concerns by extending model comparisons with scVI, elaborating on parameter selection for batch correction tasks, and demonstrating how multi-attribute guidance improves cell generation at the intersection of attributes. Evidence of CFGen's benefits for data augmentation in a linear classifier was also included. Following our rebuttal, Reviewer kEhi acknowledged the changes by increasing their score and suggesting further text additions, which we will incorporate.  

- **Reviewer DU3L:**  
  We clarified Figures 2b and 3, demonstrated that the multimodal component does not impact scRNA-seq generation quality (Tab. 2), and provided evidence of expected performance for multi-attribute guidance via classification of intermediate outputs at varying guidance strengths. Additional details on runtime and raw accuracy values for cell type classification pre- and post-augmentation were also provided. Reviewer DU3L acknowledged our responses and confirmed that their remaining concerns were addressed.  

- **Reviewer eqfJ:**  
  We included a detailed breakdown of model runtime, analyzing hyperparameter effects and benchmarking against baselines, which showed significant speedup over scDiffusion, our main ODE-based competitor. Additionally, we extended the scope to gene imputation, reporting promising results. While Reviewer eqfJ did not provide further responses, **all experimental concerns** raised during the review phase were thoroughly addressed.  

- **Reviewer FqvM:**  
  We elaborated on our contributions, including the choice of factorization, the conditional independence assumption, and additional details on the guidance approach. We also provided reasoning for scDiffusion's underperformance in our setting. Reviewer FqvM acknowledged our response and raised their score above the acceptance threshold.  

We are grateful to the AC for their continued support throughout the review and discussion process. We also extend our sincere thanks to the reviewers for their valuable time and thoughtful suggestions, which significantly improved our manuscript.  

Best regards,  

The Authors
</content>
</thread_object>

</thread>


Metareview:
metareview:
The paper presents a flow-based conditional generative model for single-cell RNA sequence data. The reviewers were generally positive about the strong: the adaptation of flow-matching to this setting is novel, the framework seems easily adaptable to many realistic downstream scenarios, and the empirical results are solid. While the paper's original presentation had a few weaknesses, the authors have adequately addressed these during the rebuttal period. Given this, I am enthusiastically recommending acceptance. Please incorporate the reviewers' feedback carefully in the final version.