标题： 一种对空间蛋白质组学数据中免疫细胞聚类定量的稳健且可扩展的K-统计量 显示英文标题

标题： A robust, scalable K-statistic for quantifying immune cell clustering in spatial proteomics data

摘要： 基于点过程理论的空间汇总统计广泛用于量化单细胞空间蛋白质组学数据中细胞群体的空间组织。其中，Ripley 的 $K$ 是评估细胞是空间聚集还是随机分散的常用指标。然而，空间同质性的关键假设在空间蛋白质组学数据中经常被违反，导致细胞聚集和共定位的高估。为了解决这个问题，我们提出了一种新的基于 $K$ 的方法，称为 \textit{KAMP} （通过 \textbf{A}分析 \textbf{M}的 \textbf{P}排列分布的 \textbf{K}调整），用于量化空间蛋白质组学样本中细胞的空间组织。 \textit{KAMP}利用每个样本中的背景细胞以及 Ripley 的$K$的排列分布的一阶和二阶矩的新的闭式表示来估计经验 null 模型。 我们的方法对非均匀性具有鲁棒性，在大型数据集中计算效率高，并提供近似的$p$值以测试空间聚类和共定位。 该方法的发展源于一项针对 103 名卵巢癌女性患者的空间蛋白质组学研究，我们的\textit{KAMP}分析显示免疫细胞聚类与总体患者生存率之间存在正相关关系。 值得注意的是，我们还发现证据表明，如果不校正样本的非均匀性直接使用$K$，可能会在下游分析中使风险比估计产生偏差。 \textit{KAMP}仅需 5 分钟即可完成此分析，而唯一能够充分解决非均匀性的竞争方法需要 538 分钟。 显示英文摘要

摘要： Spatial summary statistics based on point process theory are widely used to quantify the spatial organization of cell populations in single-cell spatial proteomics data. Among these, Ripley's $K$ is a popular metric for assessing whether cells are spatially clustered or are randomly dispersed. However, the key assumption of spatial homogeneity is frequently violated in spatial proteomics data, leading to overestimates of cell clustering and colocalization. To address this, we propose a novel $K$-based method, termed \textit{KAMP} (\textbf{K} adjustment by \textbf{A}nalytical \textbf{M}oments of the \textbf{P}ermutation distribution), for quantifying the spatial organization of cells in spatial proteomics samples. \textit{KAMP} leverages background cells in each sample along with a new closed-form representation of the first and second moments of the permutation distribution of Ripley's $K$ to estimate an empirical null model. Our method is robust to inhomogeneity, computationally efficient even in large datasets, and provides approximate $p$-values for testing spatial clustering and colocalization. Methodological developments are motivated by a spatial proteomics study of 103 women with ovarian cancer, where our analysis using \textit{KAMP} shows a positive association between immune cell clustering and overall patient survival. Notably, we also find evidence that using $K$ without correcting for sample inhomogeneity may bias hazard ratio estimates in downstream analyses. \textit{KAMP} completes this analysis in just 5 minutes, compared to 538 minutes for the only competing method that adequately addresses inhomogeneity.