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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.