Generating Multi-Modal and Multi-Attribute Single-Cell Counts with CFGen
Keywords: scRNA-seq, Flow Matching, generative modeling, multiomics
TL;DR: We devise a new method to generate discrete multi-attribute and multi-modal single-cell data using Flow matching.
Abstract: Generative modeling of single-cell RNA-seq data has proven instrumental for tasks like trajectory inference, batch effect removal, and gene expression generation. However, the most 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 are underexplored. This work introduces Cell Flow for Generation (CFGen), a flow-based conditional generative model that accounts for the discrete nature of single-cell data. CFGen generates whole-genome multimodal single-cell counts 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.
Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.
Reciprocal Reviewing: I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Submission Number: 7686
Loading