single-cell

Jingtian Zhou builds single-cell and computational frameworks to probe the 3D genome, contributing to major publications in Science, Cell, and Cancer Cell that advance understanding of cell states and diseases.

This paper introduces bioMoR, a biology-guided Mixture-of-Recursions framework for genomic learning that integrates structured biological knowledge into recursive Transformer architectures, improving efficiency and accuracy across omics benchmarks.

BioM-JEPA introduces a joint-embedding predictive architecture that learns single-cell representations by predicting graph-connected gene blocks instead of individual genes, showing improved efficiency and downstream performance in perturbation-response tasks.

This arXiv paper introduces CohortHijack, a robustness audit that removes non-target cells from single-cell query cohorts to test how annotation tools can be manipulated without altering the target cell's expression profile. It shows that structured removal and search strategies can change refined labels in popular pipelines while preserving the target, identifying query cohort composition as a vulnerability surface.

This paper studies a self-supervised task for generating single-cell gene expression vectors using an autoregressive transformer with a quantized VAE tokenizer. It reports scaling laws and a compute-optimal frontier for single-cell foundation models, with potential fine-tuning for perturbation prediction.

This paper introduces CoCoS, a contrastive pretraining framework that learns whole-cell representations from complementary transcriptomic views, addressing limitations of masked gene reconstruction in single-cell foundation models. Experiments on cell-type annotation and gene regulatory network inference show competitive transfer performance.

This paper analyzes the Virtual Cell Challenge benchmark for held-out CRISPRi perturbation prediction, finding that simple magnitude-based scalar features outperform deep MLP encoders, and that magnitude-only predictors transfer better across cell types.

GSK expands its $110M AI drug discovery partnership with Relation Therapeutics, emphasizing that clean, disease-specific biological data from lab experiments is more valuable than larger models. The move highlights an industry-wide pivot toward proprietary data as a competitive moat in AI-driven pharma.

Arc Institute announces the first whole-body map of DNA methylation and 3D genome organization at single-cell resolution across 16 human tissues, published in Science.

This paper introduces CoDiffGRN, a co-evolutionary discrete diffusion framework for gene regulatory network inference, along with a new benchmark BEELINE-KGC for inductive evaluation. It achieves state-of-the-art performance in novel regulatory discovery.

SpaCellAgent is a self-evolving LLM-based multi-agent framework that automates end-to-end spatiotemporal trajectory analysis from natural language queries, achieving over 40% improvement in analytical efficiency while maintaining expert-level performance.

PerturbCellRL introduces a reinforcement learning framework that post-trains a pretrained single-cell transcriptomic generator using cell-level verifiers as rewards, improving biological consistency of perturbation predictions beyond distributional matching.

This paper introduces PolyILR, a canonical orthonormal decomposition of the Aitchison tangent space that aligns with any tree topology, providing stable and interpretable features for compositional data such as microbiome and single-cell profiles.

The article introduces FreeBridge, a variational Schrödinger Bridge framework for modeling cellular transitions from high-content imaging data, learning stochastic transport constrained within a fixed cellular manifold to achieve competitive endpoint fidelity and reduce intermediate support violations.

CellBRIDGE is a new method that enhances optimal transport for scRNA-seq trajectory inference by incorporating ligand-receptor interaction costs to model cell-cell communication, improving alignment and enabling interpretable in silico perturbations.

PerturbSpace is a spatial transcriptomics method that presses a tissue section onto a chip of barcoded microwells, using antibodies to tag cells with location codes before single-cell sequencing, achieving >90% confident spatial assignment.

A new preprint from the Arc Institute introduces PerturbSpace, a method for spatially resolved, multimodal whole-transcriptome CRISPR screens compatible with standard single-cell workflows.

Introduces scShapeBench, a benchmark dataset for shape detection in high-dimensional single-cell data, and scReebTower, a baseline method that uses diffusion geometry and Reeb graphs to classify data shapes into clusters, trajectories, multi-branches, and archetypes.

This paper introduces GATHER, a convergence-centric retrieval method for zero-shot cell-type annotation using knowledge graphs, which improves accuracy and reduces LLM costs compared to existing KG-RAG baselines.

This paper introduces Shesha, a geometric stability metric that quantifies directional coherence of single-cell CRISPR perturbation responses using mean cosine similarity, revealing regulatory architecture and predicting cellular stress across 2,200+ perturbations in five CRISPR datasets.