SCALE is a conditional transport model that treats cells as unordered sets to predict treated cell populations without requiring cell-level matching. It uses a shared set-aware encoder and a conditional DiT backbone to learn latent transport, enabling endpoint supervision that is directly delta-aligned. Across diverse perturbation types—including genetic, chemical, developmental, and immune—SCALE accurately recovers gene‑expression changes, response directions, and population structure, outperforming competing methods on CRISPR data and successfully prioritizing cytokines that elicit distinct immune responses.
arXiv:2606. 12838v1 Announce Type: cross Abstract: Predicting single-cell transcriptional responses to genetic, chemical and cytokine perturbations is a fundamental challenge in computational biology and AI Virtual Cell (AIVC) modeling, with direct implications for drug discovery and the elucidation of gene regulatory networks.
arXiv:2608. 00152v1 Announce Type: new Abstract: Predicting the magnitude of a CRISPRi perturbation's transcriptomic effect on held-out target genes is an important open problem in single-cell biology.
arXiv:2608. 06824v1 Announce Type: cross Abstract: A central task in virtual cell modeling is predicting single-cell transcriptional responses to unseen genetic perturbations and drug combinations, and biological networks provide valuable priors on gene relationships.
arXiv:2606. 07760v1 Announce Type: new Abstract: Understanding cellular phenotypes and how they respond to perturbations is critical for disease biology and therapeutic design.
arXiv:2608. 06659v1 Announce Type: new Abstract: This paper shows that latent-space predictive pretraining can provide a scalable route to foundation models for spatial transcriptomics.
arXiv:2608. 14293v1 Announce Type: cross Abstract: High-content microscopy enables systematic profiling of cellular responses to chemical perturbations, but the scale of the chemical space makes exhaustive phenotypic characterization experimentally infeasible.
arXiv:2606. 27752v1 Announce Type: new Abstract: Single-cell perturbation models can reduce costly wet-lab screening by predicting how cells respond transcriptionally to interventions.
arXiv:2606. 30695v1 Announce Type: cross Abstract: Single-cell drug perturbation models should predict not only transcriptional response magnitude, but also whether a treatment alters the proliferative state of a cell.
The study evaluates a frozen Geneformer representation for predicting CRISPRi perturbation effects under a tightly controlled, pre‑registered protocol. While the representation shows significant predictive power within the Virtual Cell Challenge dataset, it fails to transfer to external screens, with negative zero‑shot Spearman correlations. The analysis also reveals that the VCC endpoint is heavily influenced by sampling depth, as cell count alone explains most of the variance, indicating a sampling‑depth entanglement that could mask transfer failures in less controlled settings.
CellRFT is a reinforcement fine‑tuning framework designed to improve single‑cell perturbation modeling by directly optimizing biological evaluation metrics. It employs policy‑gradient methods to learn from non‑differentiable biological rewards and aggregates multiple rewards hierarchically. Experiments show that CellRFT enhances perturbation prediction across various pretrained models and reveals interactions between different biological criteria, suggesting new ways to shape model behavior and evaluation design.
arXiv:2511. 02986v2 Announce Type: replace-cross Abstract: Computational modeling of single-cell gene expression is crucial for understanding cellular processes, but generating realistic expression profiles remains a major challenge.
arXiv:2608. 00985v1 Announce Type: new Abstract: The rapid growth of single-cell transcriptomic data has enabled the development of foundation models pretrained primarily by reconstructing masked expression values.