论文标题
使用小波底座降低空间转录组学维度
Spatial Transcriptomics Dimensionality Reduction using Wavelet Bases
论文作者
论文摘要
空间分辨的转录组学（ST）测量基因表达以及测量的空间坐标。 ST数据的分析涉及显着的计算复杂性。在这项工作中，我们提出了保留空间结构的基因表达降低算法。我们将小波转化与基质分解结合在一起，以选择空间变化的基因。我们提取这些基因的低维表示。我们认为经验贝叶斯设置，通过因子基因的先前分布施加正则化。此外，我们还提供了捕获全局空间模式的提取表示基因的可视化。我们通过在模拟中的空间结构恢复和基因表达重建来说明方法的性能。在实际数据实验中，我们的方法确定了基因因素的空间结构，并且超过了有关重建误差的常规分解。我们发现基因模式的波动与小波技术之间的联系，提供了更平稳的可视化。我们开发包装并共享工作流程生成可再现的定量结果和基因可视化。该软件包可在https://github.com/oliverxuzy/wavest上找到。
Spatially resolved transcriptomics (ST) measures gene expression along with the spatial coordinates of the measurements. The analysis of ST data involves significant computation complexity. In this work, we propose gene expression dimensionality reduction algorithm that retains spatial structure. We combine the wavelet transformation with matrix factorization to select spatially-varying genes. We extract a low-dimensional representation of these genes. We consider Empirical Bayes setting, imposing regularization through the prior distribution of factor genes. Additionally, We provide visualization of extracted representation genes capturing the global spatial pattern. We illustrate the performance of our methods by spatial structure recovery and gene expression reconstruction in simulation. In real data experiments, our method identifies spatial structure of gene factors and outperforms regular decomposition regarding reconstruction error. We found the connection between the fluctuation of gene patterns and wavelet technique, providing smoother visualization. We develop the package and share the workflow generating reproducible quantitative results and gene visualization. The package is available at https://github.com/OliverXUZY/waveST.
