Article

The Overlooked Role of Specimen Preparation in Bolstering Deep Learning-Enhanced Spatial Transcriptomics Workflows

2023-10-09

Abstract excerpt

The application of deep learning methods to spatial transcriptomics has shown promise in unraveling the complex relationships between gene expression patterns and tissue architecture as they pertain to various pathological conditions. Deep learning methods that can infer gene expression patterns directly from tissue histomorphology can expand the capability to discern spatial molecular markers within tissue slides...

Topics

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

Identifiers and source

Literature Corpus work: 6c6bffed-02d1-5dbb-b717-84ebd835a74c
DOI: 10.1101/2023.10.09.23296700

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

The Overlooked Role of Specimen Preparation in Bolstering Deep Learning-Enhanced Spatial Transcriptomics WorkflowsDOI 10.1101/2023.10.09.23296700Read

Inferring spatial transcriptomics markers from whole slide images to characterize metastasis-related spatial heterogeneity of colorectal tumors: A pilot studySemantic proximity Read

Inferring Spatially Resolved Transcriptomics Data from Whole Slide Images for the Assessment of Colorectal Tumor Metastasis: A Feasibility StudySemantic proximity Read

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

A technical comparison of spatial transcriptomics platforms across six cancer typesSemantic proximity Read

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

Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentationSemantic proximity Read

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

DeepSpot: Leveraging Spatial Context for Enhanced Spatial Transcriptomics Prediction from H&E ImagesSemantic proximity Read

Morpho-FM: spatial molecular reconstruction from routine H&E histology using transcriptomic foundation-model priorsSemantic proximity Read

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

Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor HeterogeneitySemantic proximity Read

Integrating Histology with Spatial Molecular Programs Using a Multimodal Foundation ModelSemantic proximity Read