用第三人称点评一下：The rapid development of spatial transcriptomics allows the measurement of RNA abundance at a high spatial resolution making it possible to simultaneously profile gene expression spatial loc

The passage discusses the rapid development of spatial transcriptomics, a technique that allows for the measurement of RNA abundance with high spatial resolution. This advancement makes it possible to simultaneously profile gene expression, spatial locations of cells or spots, and corresponding histology images. The author mentions the potential to predict gene expression from histology images, which are relatively easy and cheap to obtain. However, existing methods have not fully captured the internal relations of the 2D vision features or spatial dependency between spots.

To address this, the author introduces Hist2ST, a deep learning-based model designed to predict RNA-seq expression from histology images. The model works by cropping the corresponding histology image around each sequenced spot and feeding it into a convolutional module to extract 2D vision features. Additionally, the model captures spatial relations using Transformer and graph neural network modules. These learned features are then used to predict gene expression by following the zero-inflated negative binomial distribution. To overcome the limitations of small spatial transcriptomics data, the model utilizes a self-distillation mechanism for efficient learning.

Through comprehensive tests on cancer and normal datasets, Hist2ST demonstrates superior performance compared to existing methods in terms of gene expression prediction and spatial region identification. Further pathway analyses indicate that the model retains biological information. Therefore, Hist2ST enables the generation of spatial transcriptomics data from histology images, facilitating the elucidation of molecular signatures in tissues.

Overall, the passage introduces the development of Hist2ST, a deep learning-based model that aims to predict RNA-seq expression from histology images. The author provides a clear explanation of the model's design and its potential applications in spatial transcriptomics research