Article

conST: an interpretable multi-modal contrastive learning framework for spatial transcriptomics

2022-01-17

Abstract excerpt

<h4>Motivation</h4> Spatially resolved transcriptomics (SRT) shows its impressive power in yielding biological insights into neuroscience, disease study, and even plant biology. However, current methods do not sufficiently explore the expressiveness of the multi-modal SRT data, leaving a large room for improvement of performance. Moreover, the current deep learning based methods lack interpretability due to the “...

Topics

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

Identifiers and source

Literature Corpus work: 824ebc59-c2a5-55b2-b67e-b253e1260591
DOI: 10.1101/2022.01.14.476408

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

conST: an interpretable multi-modal contrastive learning framework for spatial transcriptomicsDOI 10.1101/2022.01.14.476408Read

Unsupervised Spatially Embedded Deep Representation of Spatial TranscriptomicsSemantic proximity Read

Learning context-aware, distributed gene representations in spatial transcriptomics with SpaCEXSemantic proximity Read

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

Unsupervised Spatially Embedded Deep Representation of Spatial TranscriptomicsSemantic proximity Read

DisConST: Deciphering Spatial Domains Using Distribution-aware Contrastive Learning for Spatial TranscriptomicsSemantic proximity Read

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

PathCLAST: Pathway-Augmented Contrastive Learning with Attention for Spatial TranscriptomicsSemantic proximity Read

BertST: BERT-based Spatial Domain Identification in Patient DataSemantic proximity Read

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

Towards Cross-Sample Alignment for Multi-Modal Representation Learning in Spatial TranscriptomicsSemantic proximity Read

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

SEDIST: Spatially Enhanced Domain Identification through Spatial TranscriptomicsSemantic proximity Read