Generalized cell phenotyping for spatial proteomics with language-informed vision models

Abstract excerpt

We present DeepCell Types, a novel approach to cell phenotyping for spatial proteomics that addresses the challenge of generalization across diverse datasets with varying marker panels collected across different platforms. Our approach utilizes a transformer with channel-wise attention to create a language-informed vision model; this model’s semantic understanding of the underlying marker panel enables it to learn...

Topics

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• Aging
• COVID-19
• Deep Learning
• Leukocytes, Mononuclear
• Phenotype
• Single-Cell Analysis
• Single-Cell Gene Expression Analysis

Identifiers and source

Literature Corpus work

c4b24398-82cf-5f87-ba1a-6dcf623c5194

DOI

10.1101/2024.11.02.621624