Systematic Design, Generation, and Application of Synthetic Datasets for Flow Cytometry

Application of synthetic datasets in training and validation of analysis tools has led to improvements in many decision-making tasks in a range of domains from computer vision to digital pathology. Synthetic datasets overcome the constraints of real-world datasets, namely difficulties in collection and labeling, expense, time, and privacy concerns. In flow cytometry, real cell-based datasets are limited by properties such as size, number of parameters, distance between cell populations, and distributions and are often focused on a narrow range of disease or cell types. Researchers in some cases have designed these desired properties into synthetic datasets; however, operators have implemented them in inconsistent approaches, and there is a scarcity of publicly available, high-quality synthetic datasets. In this research, we propose a method to systematically design and generate flow cytometry synthetic datasets with highly controlled characteristics. We demonstrate the generation of two-cluster synthetic datasets with specific degrees of separation between cell populations, and of non-normal distributions with increasing levels of skewness and orientations of skew pairs. We apply our synthetic datasets to test the performance of a popular automated cell populations identification software, SPADE3, and define the region where the software performance decreases as the clusters get closer together. Application of the synthetic skewed dataset suggests the software is capable of processing non-normal data. We calculate the classification accuracy of SPADE3 with robustness not achievable with real-world datasets. Our approach aims to advance research toward generation of high-quality synthetic flow cytometry datasets and to increase their awareness among the community. The synthetic datasets can be used in benchmarking studies that critically evaluate cell population identification tools and help illustrate potential digital platform inconsistencies. These datasets have the potential to improve cell characterization workflows that integrate automated analysis in clinical diagnostics and cell therapy manufacturing.