hSNMF：用于图像衍生空间转录组的混合空间正则化NMF
hSNMF: Hybrid Spatially Regularized NMF for Image-Derived Spatial Transcriptomics
AI总结：
hSNMF通过混合空间正则化NMF提升空间转录组分析的精度与生物相干性
摘要：
高分辨率空间转录组平台，如Xenium，生成单细胞图像，能够捕捉分子和空间背景信息，但其极高的维度性对表示学习和聚类提出了重大挑战。在本研究中，我们分析了Xenium平台的数据，该平台捕捉到高分辨率的肿瘤微阵列（TMA）组织图像，并将其转换为适合计算分析的细胞-基因矩阵。我们通过引入两种空间正则化的变体来评估和扩展非负矩阵分解（NMF）用于空间转录组学。首先，我们提出了空间NMF（SNMF），一种轻量级的基线方法，通过将每个细胞的NMF因子向量扩散到其空间邻域来强制局部空间平滑性。其次，我们引入了混合空间NMF（hSNMF），该方法在混合邻接矩阵上执行空间正则化的NMF，该邻接矩阵通过可调混合参数alpha整合空间接近性（通过接触半径图）和转录组相似性。在胆管癌数据集上评估，SNMF和hSNMF在空间紧凑性（CHAOS < 0.004，Moran's I > 0.96）、聚类分离度（Silhouette > 0.12，DBI < 1.8）和生物相干性（CMC和富集）方面显著优于其他空间基线方法。可用性与实现：https://github.com/ishtyaqmahmud/hSNMF
英文摘要：
High-resolution spatial transcriptomics platforms, such as Xenium, generate single-cell images that capture both molecular and spatial context, but their extremely high dimensionality poses major challenges for representation learning and clustering. In this study, we analyze data from the Xenium platform, which captures high-resolution images of tumor microarray (TMA) tissues and converts them into cell-by-gene matrices suitable for computational analysis. We benchmark and extend nonnegative matrix factorization (NMF) for spatial transcriptomics by introducing two spatially regularized variants. First, we propose Spatial NMF (SNMF), a lightweight baseline that enforces local spatial smoothness by diffusing each cell's NMF factor vector over its spatial neighborhood. Second, we introduce Hybrid Spatial NMF (hSNMF), which performs spatially regularized NMF followed by Leiden clustering on a hybrid adjacency that integrates spatial proximity (via a contact-radius graph) and transcriptomic similarity through a tunable mixing parameter alpha. Evaluated on a cholangiocarcinoma dataset, SNMF and hSNMF achieve markedly improved spatial compactness (CHAOS < 0.004, Moran's I > 0.96), greater cluster separability (Silhouette > 0.12, DBI < 1.8), and higher biological coherence (CMC and enrichment) compared to other spatial baselines. Availability and implementation: https://github.com/ishtyaqmahmud/hSNMF
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
