Position-informed contrastive learning for spatially resolved omics deciphers hierarchical tissue structure at both cellular and niche levels

Abstract excerpt

Spatial omics and multiplexed imaging technologies provide unprecedented spatial context to characterize molecular variation in complex tissues. Developing unsupervised computational pipeline in a discovery mode is of vital importance to interpret spatially resolved data and derive novel biological insights. We develop Stereo, a unified framework leveraging contrastive learning to jointly model molecular and spati...

Topics

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• Algorithms
• Carcinoma, Non-Small-Cell Lung
• Cluster Analysis
• Lung Neoplasms
• Neural Networks, Computer
• Phenotype
• Single-Cell Analysis
• Single-cell and spatial transcriptomics

Identifiers and source

Literature Corpus work

02e3b3aa-d4a6-5d30-b5f8-c391e15bcbaa

DOI

10.21203/rs.3.rs-1067780/v1