University of Regensburg

Comparative spatial transcriptomics usually partitions tissue into anatomical regions or clusters first and then tests differential expression inside those fixed units, separating localization from testing. SpaceDX instead learns gene-specific spatial attention weights while testing, aggregating spots into an attention-weighted pseudobulk that enters a hierarchical Bayesian logistic regression for subject-level association. In 19 stressed and 19 control mice it found 175 DE genes at 5% empirical FDR, 45.8% more than regional DESeq2's 120; in an Alzheimer's model the two found similar numbers alone, and combining them raised discovery 19.1% over DESeq2 alone.

Comparative spatial transcriptomics usually partitions tissue into anatomical regions or clusters first and then tests differential expression inside those fixed units, separating localization from testing. SpaceDX instead learns gene-specific spatial attention weights while testing, aggregating spots into an attention-weighted pseudobulk that enters a hierarchical Bayesian logistic regression for subject-level association. In 19 stressed and 19 control mice it found 175 DE genes at 5% empirical FDR, 45.8% more than regional DESeq2's 120; in an Alzheimer's model the two found similar numbers alone, and combining them raised discovery 19.1% over DESeq2 alone.

The work introduces an exemplar-free continual learning framework for whole-slide-image-to-report generation: it builds a compact domain footprint per domain in a frozen patch-embedding space (a k-means codebook, a slide-level code histogram bank, patch-count statistics, and a report-style prototype), uses it to synthesize pseudo-WSIs whose pseudo-reports come from an immediate teacher snapshot for generative replay, and conditions the language model through a style prefix; across multiple public continual learning benchmarks the approach outperforms exemplar-free and limited-buffer rehearsal baselines and supports domain-agnostic inference without explicit domain identifiers.

The work introduces an exemplar-free continual learning framework for whole-slide-image-to-report generation: it builds a compact domain footprint per domain in a frozen patch-embedding space (a k-means codebook, a slide-level code histogram bank, patch-count statistics, and a report-style prototype), uses it to synthesize pseudo-WSIs whose pseudo-reports come from an immediate teacher snapshot for generative replay, and conditions the language model through a style prefix; across multiple public continual learning benchmarks the approach outperforms exemplar-free and limited-buffer rehearsal baselines and supports domain-agnostic inference without explicit domain identifiers.