College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; BGI Research, Beijing 102601, China.
BGI Research, Beijing 102601, China; BGI Research, Shenzhen 518083, China.
Cell Syst. 2024 Oct 16;15(10):969-981.e6. doi: 10.1016/j.cels.2024.09.005. Epub 2024 Oct 7.
Spatially resolved transcriptomics (SRT) combines gene expression profiles with the physical locations of cells in their native states but suffers from unpredictable spatial noise due to cell damage during cryosectioning and exposure to reagents for staining and mRNA release. To address this noise, we developed SpotGF, an algorithm for denoising SRT data using optimal transport-based gene filtering. SpotGF quantifies diffusion patterns numerically, distinguishing widespread expression genes from aggregated expression genes and filtering out the former as noise. Unlike conventional denoising methods, SpotGF preserves raw sequencing data, thereby avoiding false positives that can arise from imputation. Additionally, SpotGF demonstrates superior performance in cell clustering, identifying potential marker genes, and annotating cell types. Overall, SpotGF has the potential to become a crucial preprocessing step in the downstream analysis of SRT data. The SpotGF software is freely available at GitHub. A record of this paper's transparent peer review process is included in the supplemental information.
空间分辨转录组学（SRT）将基因表达谱与细胞的原始位置的物理位置相结合，但由于在冷冻切片过程中细胞受损以及暴露于用于染色和 mRNA 释放的试剂，会产生不可预测的空间噪声。为了解决这个问题，我们开发了 SpotGF，这是一种使用基于最优传输的基因过滤对 SRT 数据进行去噪的算法。SpotGF 从数值上量化扩散模式，将广泛表达的基因与聚集表达的基因区分开来，并将前者过滤为噪声。与传统的去噪方法不同，SpotGF 保留了原始测序数据，从而避免了由于插补可能产生的假阳性。此外，SpotGF 在细胞聚类、识别潜在标记基因和注释细胞类型方面表现出优异的性能。总的来说，SpotGF 有可能成为 SRT 数据下游分析的一个关键预处理步骤。SpotGF 软件可在 GitHub 上免费获得。本论文的透明同行评审过程记录包含在补充信息中。
