用隐式神经表示重建全脑基因表达图谱，实现高精度空间解析。
Brain-wide interpolation and conditioning of gene expression in the human brain using Implicit Neural Representations
- 基于隐式神经表征建模基因表达空间分布
- 在体素级分辨率下生成100个阿尔茨海默病相关基因的完整表达图
- 适用于脑科学中稀疏转录组数据的补全与跨区域分析
本文研究了近年来非局部、非线性图像插值与外推算法（特别是隐式神经表示，INR）在空间转录组数据分析中的有效性与实用性。利用健康人脑中稀疏采样的微阵列基因表达数据，目标是实现任意基因在整个大脑范围内的体素级分辨率空间表达图谱。我们首先选取了100个与阿尔茨海默病风险相关的基因，其基础空间转录轮廓来自艾伦人脑图谱（AHBA）。通过改进隐式神经表示模型，构建出可生成所有基因稳健体素级定量表达图谱的流程。实验以Abagen提供的插值结果作为基线参照，验证了方法的性能。
原文摘要 · Abstract (English)
In this paper, we study the efficacy and utility of recent advances in non-local, non-linear image interpolation and extrapolation algorithms, specifically, ideas based on Implicit Neural Representations (INR), as a tool for analysis of spatial transcriptomics data. We seek to utilize the microarray gene expression data sparsely sampled in the healthy human brain, and produce fully resolved spatial maps of any given gene across the whole brain at a voxel-level resolution. To do so, we first obtained the 100 top AD risk genes, whose baseline spatial transcriptional profiles were obtained from the Allen Human Brain Atlas (AHBA). We adapted Implicit Neural Representation models so that the pipeline can produce robust voxel-resolution quantitative maps of all genes. We present a variety of experiments using interpolations obtained from Abagen as a baseline/reference.
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