Spatial EcoTyper is a versatile framework for identifying spatially distinct multicellular communities, termed spatial ecotypes, from single-cell spatial transcriptomics data. In addition, it provides ...

This study addresses a critical challenge in spatial multi-omics: the effective integration of heterogeneous molecular modalities within complex tissue environments. By introducing SpaDDM, a ...

Graph Convolutional Networks (GCNs) are widely applied for spatial domain identification in spatial transcriptomics (ST), where node representations are learned by aggregating information from ...

Artificial intelligence (AI) has become a common tool for bioinformatics, with hundreds of methods published in recent years. Due to the training data demands of deep-learning algorithms, ...

Abstract: Spatial transcriptomics technology enables researchers to acquire both spatial location information and gene expression data within tissues, providing new perspectives for understanding ...

Layer 2/3 (L2/3) glutamatergic neurons are important sites of experience-dependent plasticity and learning in the mammalian cortex. Their properties vary continuously with cortical depth and depend ...

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.analchem.4c04462. Averaged mass spectrum from the rat brain tissue with ...

This study makes a valuable contribution to spatial transcriptomics by rigorously benchmarking cell-type deconvolution methods, assessing their performance across diverse datasets with a focus on ...

At AACR 2024, we explored the poster hall to pick out the posters that would interest the BioTechniques reader and those we found delivered the most interesting or surprising findings. Get our ...