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Multiplex immunofluorescence (mIF) imaging technology facilitates the studyof the tumour microenvironment in cancer patients. Due to the capabilities ofthis emerging bioimaging technique, it is possible to statistically analyse,for example, the co-varying location and functions of multiple different typesof immune cells. Complex spatial relationships between different immune cellshave been shown to correlate with patient outcomes and may reveal new pathwaysfor targeted immunotherapy treatments.  This tutorial reviews methods and procedures relating to spatial pointpatterns for complex data analysis. We consider tissue cells as a realisationof a spatial point process for each patient. We focus on proper functionaldescriptors for each observation and techniques that allow us to obtaininformation about inter-patient variation.  Ovarian cancer is the deadliest gynaecological malignancy and can resistchemotherapy treatment effective in cancers. We use a dataset of high-gradeserous ovarian cancer samples from 51 patients. We examine the immune cellcomposition (T cells, B cells, macrophages) within tumours and additionalinformation such as cell classification (tumour or stroma) and other patientclinical characteristics. Our analyses, supported by reproducible software,apply to other digital pathology datasets.

Analysing spatial point patterns in digital pathology: immune cells in high-grade serous ovarian carcinomas