Multiplex immunofluorescence (mIF) imaging technology facilitates the study of the tumour microenvironment in cancer patients. Due to the capabilities of this emerging bioimaging technique, it is possible to statistically analyse, for example, the co-varying location and functions of multiple different types of immune cells. Complex spatial relationships between different immune cells have been shown to correlate with patient outcomes and may reveal new pathways for targeted immunotherapy treatments. This tutorial reviews methods and procedures relating to spatial point patterns for complex data analysis. We consider tissue cells as a realisation of a spatial point process for each patient. We focus on proper functional descriptors for each observation and techniques that allow us to obtain information about inter-patient variation. Ovarian cancer is the deadliest gynaecological malignancy and can resist chemotherapy treatment effective in cancers. We use a dataset of high-grade serous ovarian cancer samples from 51 patients. We examine the immune cell composition (T cells, B cells, macrophages) within tumours and additional information such as cell classification (tumour or stroma) and other patient clinical characteristics. Our analyses, supported by reproducible software, apply to other digital pathology datasets.

翻译：多重免疫荧光（mIF）成像技术有助于研究癌症患者的肿瘤微环境。凭借这种新兴生物成像技术的能力，我们可以对多种不同类型免疫细胞的共变位置和功能等进行统计分析。不同免疫细胞之间的复杂空间关系已被证明与患者预后相关，并可能揭示靶向免疫治疗的新途径。本教程回顾了与复杂数据分析中空间点模式相关的方法和步骤。我们将组织细胞视为每位患者空间点过程的一种实现，重点关注每个观测的适当功能描述符以及能够获取患者间变异信息的技术。卵巢癌是最致命的妇科恶性肿瘤，且可能抵抗对某些癌症有效的化疗。我们使用来自51名患者的高级别浆液性卵巢癌样本数据集，检查肿瘤内免疫细胞组成（T细胞、B细胞、巨噬细胞）以及额外的信息，如细胞分类（肿瘤或间质）和其他患者临床特征。我们的分析由可重复软件支持，并可应用于其他数字病理学数据集。