Yang, C.

Abstract Spatial transcriptomics analyses often require coordinating specialized Python and R methods. When analytical intent is translated through ad hoc scripts or unconstrained LLM generated code, workflows can be difficult to inspect, rerun, or compare. We present ChatSpatial, a schema enforced orchestration framework in which an LLM selects versioned tools and parameters rather than executable code. Built on the Model Context Protocol, ChatSpatial integrates 65 methods across 15 analytical categories into 20 typed tools with curated defaults. In two cancer case studies, ChatSpatial coordinated cross ecosystem workflows that recovered the analytical structure and central conclusions of published ovarian cancer and oral squamous cell carcinoma analyses. Across three LLMs, schema enforcement raised parameter validity from 20% to 98% and stabilized method selection. In end to end benchmarks against two code generation alternatives, it yielded higher execution success and output concordance.

Abstract Coarsening Visium HD resolution from 8 to 64 µ m can flip cell-type co-localization from negative to positive ( r = −0.12 → +0.80 ), yet many widely used compositional deconvolution workflows require coarsening or subsampling at million-bin scale. Here we introduce FlashDeconv, which combines leverage-score importance sampling with sparse spatial regularization to achieve competitive benchmark accuracy while processing 1.6 million bins in 153 seconds on commodity hardware. Systematic multi-resolution analysis of Visium HD mouse intestine reveals a tissue-specific resolution horizon (8–16 µ m)—the scale at which this sign inversion occurs—validated by Xenium ground truth. Below this horizon, FlashDeconv provides, to our knowledge, the first sequencing-based quantification of Tuft cell chemosensory niches (15.3-fold stem cell enrichment). In a 1.6-million-bin human colorectal cancer cohort, FlashDeconv uncovers neutrophil inflammatory microdomains co-localized with immunoregulatory dendritic cells (mRegDC) at the tumor–stroma interface—spatial niches largely missed by discrete-label summaries, with RCTD doublet mode labeling only 2.3% of hotspot bins as neutrophil singlets.