4 articles tagged with #spatial-transcriptomics. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers propose a foundation model approach using adversarial fine-tuning to translate between unpaired spatial transcriptomics and single-cell RNA sequencing data. The method addresses the scarcity of paired datasets by leveraging the abundance of individual modalities, outperforming existing multi-omics translation approaches.
Researchers propose CAMMST, a Masked Autoencoder framework that predicts gene expression from histology images by leveraging small amounts of spatial transcriptomics data as genetic anchors. The method combines visual and genetic modalities through contrastive learning, achieving superior performance with minimal transcriptomic coverage and addressing the cost limitations of spatial transcriptomics profiling.
Researchers introduce SNR-ST-Mix, a data augmentation framework designed specifically for spatial transcriptomics that uses geometry-aware and expression-aware mixing to improve deep neural network performance. The method constrains data interpolation to k-nearest spatial neighbors and weights coefficients by expression similarity, enabling more biologically plausible synthetic training samples that enhance prediction accuracy without architectural changes.
Researchers introduce GC-MoE, a machine learning framework that predicts individual cell gene expression from histopathology images and spatial data, addressing limitations of existing methods that only work at the spot level. The approach combines cell-type-specific expert models with genomic guidance to capture cellular expression variability more accurately than current baselines.
