用图像直接预测基因表达图谱，突破传统点采样局限
From Spots to Pixels: Dense Spatial Gene Expression Prediction from Histology Images
- 从病理切片图像生成连续基因表达图谱，不依赖固定点采样
- 在四个数据集上优于现有方法，支持多尺度预测
- 适合生物医学图像分析与空间转录组研究者使用
空间转录组学（ST）在高分辨率下测量基因表达，揭示组织分子景观。以往方法通常从病理切片中裁剪感兴趣点，训练模型将每个点映射到对应的基因表达谱。但这些方法固有地损失了基因表达的空间分辨率：1）每个点常包含多个具有不同表达谱的细胞；2）点通常以固定空间分辨率定义，限制了在不同尺度上预测基因表达的能力。为此，本文提出PixNet，一种能够直接从病理切片图像中预测跨不同大小和尺度点的密集空间解析基因表达的网络。不同于以往将单个点映射到表达值的方法，我们从病理切片图像生成空间密集的连续基因表达图谱，并在感兴趣点内聚合值以预测基因表达。PixNet在四个常见ST数据集上，于多个空间尺度均优于现有最先进方法。源代码将公开。
原文摘要 · Abstract (English)
Spatial transcriptomics (ST) measures gene expression at fine-grained spatial resolution, offering insights into tissue molecular landscapes. Previous methods for spatial gene expression prediction typically crop spots of interest from histopathology slide images, and train models to map each spot to a corresponding gene expression profile. However, these methods inherently lose the spatial resolution in gene expression: 1) each spot often contains multiple cells with distinct gene expression profiles; 2) spots are typically defined at fixed spatial resolutions, limiting the ability to predict gene expression at varying scales. To address these limitations, this paper presents PixNet, a dense prediction network capable of predicting spatially resolved gene expression across spots of varying sizes and scales directly from histopathology slide images. Different from previous methods that map individual spots to gene expression values, we generate a spatially dense continuous gene expression map from the histopathology slide image, and aggregate values within spots of interest to predict the gene expression. Our PixNet outperforms state-of-the-art methods on four common ST datasets in multiple spatial scales. The source code will be publicly available.
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