TrendBio

#1 · Transcriptomics

Separating spatial context from cell identity in spatial transcriptomic variation

9 preprints · 3 published · ↑ 2.7× vs 4-wk avg
Items per day, last 8 weekstoday: 12

The dominant thread is attributing transcriptomic variation to spatial context as distinct from cell identity or composition. ARCADE separates cell-type abundance from continuous intratype transcriptional state. SpatioCAD uses cell-density-aware graph diffusion to reduce false-positive spatially variable gene calls in heterogeneous tissues. SpaceDX couples subject-level differential expression testing with gene-specific attention that localizes where effects occur, and reports better performance than cluster-based approaches. Domain identification is converging on niche-aware representations. Kontext jointly embeds cells and local environments through ligand-receptor interactions. TRACER uses twin-contrastive graph autoencoding to recover rare and fine-grained domains. smoothclust adds a boundary-density summary of label transitions. FICTURE2 pushes attribution to individual RNA molecules, inferring each molecule's program from gene identity and spatial context. CRISP characterizes cross-signal interactions in single-cell and spatial data. An LCM-Spatial-ATAC-seq study of zonated hepatocytes separates an invariant cell-type sub-network from state-level regulation. HiCONA, C-Fetch and MAGE address 3D genome and regulatory-network questions outside this thread.

Published papers

Preprints

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