Article

DisConST: Deciphering Spatial Domains Using Distribution-aware Contrastive Learning for Spatial Transcriptomics

2025-03-15

Abstract excerpt

Spatial transcriptomics (ST) is a cutting-edge technology that provides comprehensive insights into gene expression patterns from a spatial perspective. A key research focus within this field is spatial domain identification, which is essential for exploring tissue organization, biological development, and disease mechanisms. Although methods have been developed, they still face challenges in modeling the gene exp...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: ab28c1f3-26cf-5c90-ac38-241e60edbb48
DOI: 10.1101/2025.03.13.642300

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

DisConST: Deciphering Spatial Domains Using Distribution-aware Contrastive Learning for Spatial TranscriptomicsDOI 10.1101/2025.03.13.642300Read

stGRL: spatial domain identification, denoising, and imputation algorithm for spatial transcriptome data based on multi-task graph contrastive representation learningSemantic proximity Read

Novae: a graph-based foundation model for spatial transcriptomics dataSemantic proximity Read

SORBET: Automated cell-neighborhood analysis of spatial transcriptomics or proteomics for interpretable sample classification via GNNSemantic proximity Read

HEARTSVG: a fast and accurate method for spatially variable gene identification in large-scale spatial transcriptomic dataSemantic proximity Read

CellContrast: Reconstructing Spatial Relationships in Single-Cell RNA Sequencing Data via Deep Contrastive LearningSemantic proximity Read

Identifying Differential Spatial Expression Patterns across Different Slices, Conditions and Developmental Stages with Interpretable Deep LearningSemantic proximity Read

HEARTSVG: a fast and accurate method for spatially variable gene identification in large-scale spatial transcriptomic dataSemantic proximity Read

EfNST: A composite scaling network of EfficientNet for improving spatial domain identification performanceSemantic proximity Read

SEDIST: Spatially Enhanced Domain Identification through Spatial TranscriptomicsSemantic proximity Read

An explanatory benchmark of spatial domain detection reveals key drivers of method performanceSemantic proximity Read

DenoiseST: A dual-channel unsupervised deep learning-based denoising method to identify spatial domains and functionally variable genes in spatial transcriptomicsSemantic proximity Read

SpatialMap: A Scalable Deep Learning Method for Cell Typing in Subcellular Spatial TranscriptomicsSemantic proximity Read