Exemplar Guided Deep Neural Network for Spatial Transcriptomics Analysis of Gene Expression Prediction

• URL: http://arxiv.org/abs/2210.16721v1
• Date: Sun, 30 Oct 2022 02:22:20 GMT
• Title: Exemplar Guided Deep Neural Network for Spatial Transcriptomics Analysis of Gene Expression Prediction
• Authors: Yan Yang and Md Zakir Hossain and Eric A Stone and Shafin Rahman
• Abstract summary: This paper proposes an Exemplar Guided Network (EGN) to accurately and efficiently predict gene expression directly from each window of a tissue slide image. Our EGN framework composes of three main components: 1) an extractor to structure a representation space for unsupervised retrievals; 2) a vision transformer (ViT) backbone to progressively extract representations of the input window; and 3) an Exemplar Bridging (EB) block to adaptively revise the intermediate ViT representations by using the nearest exemplars.
• Score: 9.192169460752805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatial transcriptomics (ST) is essential for understanding diseases and developing novel treatments. It measures gene expression of each fine-grained area (i.e., different windows) in the tissue slide with low throughput. This paper proposes an Exemplar Guided Network (EGN) to accurately and efficiently predict gene expression directly from each window of a tissue slide image. We apply exemplar learning to dynamically boost gene expression prediction from nearest/similar exemplars of a given tissue slide image window. Our EGN framework composes of three main components: 1) an extractor to structure a representation space for unsupervised exemplar retrievals; 2) a vision transformer (ViT) backbone to progressively extract representations of the input window; and 3) an Exemplar Bridging (EB) block to adaptively revise the intermediate ViT representations by using the nearest exemplars. Finally, we complete the gene expression prediction task with a simple attention-based prediction block. Experiments on standard benchmark datasets indicate the superiority of our approach when comparing with the past state-of-the-art (SOTA) methods.

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