Article

Towards Cross-Sample Alignment for Multi-Modal Representation Learning in Spatial Transcriptomics

2026-03-03

Abstract excerpt

<h4> A bstract </h4> The growing number of spatial transcriptomics (ST) datasets enables comprehensive multi-modal characterization of cell types across diverse biological and clinical contexts. However, integration across patient cohorts remains challenging, as local microenvironment, patient-specific variability, and technical batch effects can dominate signals. Here, we hypothesize that combining specialized...

Topics

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

Identifiers and source

Literature Corpus work: 173bfc88-4a35-52f0-9258-c94826be2b82
DOI: 10.64898/2026.03.02.709002

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Towards Cross-Sample Alignment for Multi-Modal Representation Learning in Spatial TranscriptomicsDOI 10.64898/2026.03.02.709002Read

scGPT-spatial: Continual Pretraining of Single-Cell Foundation Model for Spatial TranscriptomicsSemantic proximity Read

PathOmCLIP: Connecting tumor histology with spatial gene expression via locally enhanced contrastive learning of Pathology and Single-cell foundation modelSemantic proximity Read

Multi-Modal Disentanglement of Spatial Transcriptomics and Histopathology ImagingSemantic proximity Read

Robust and interpretable prediction of gene markers and cell types from spatial transcriptomics dataSemantic proximity Read

Inferring single-cell spatial gene expression with tissue morphology via explainable deep learningSemantic proximity Read

DeepSpot-M: a multimodal foundation model for transcriptome-wide virtual spatial transcriptomics from histologySemantic proximity Read

Comprehensive benchmarking of batch integration methods for spatial transcriptomics using a large-scale cancer atlasSemantic proximity Read

Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding spaceSemantic proximity Read

Riemannian Metric Learning for Alignment of Spatial MultiomicsSemantic proximity Read

Spatial Transcriptomics Expression Prediction from Histopathology Based on Cross-Modal Mask Reconstruction and Contrastive LearningSemantic proximity Read

A deep learning-based multiscale integration of spatial omics with tumor morphologySemantic proximity Read

H&Enium, Applying Foundation Models to Computational Pathology and Spatial Transcriptomics to Learn an Aligned Latent SpaceSemantic proximity Read