Dimensionality reduction by UMAP for visualizing and aiding in classification of imaging flow cytometry data

• DOI: 10.1016/j.isci.2022.105142
• Publication Date: 2022-09-15T16:53:09Z
• Authors: Ireneusz Stolarek; Anna Samelak-Czajka; Marek Figlerowicz; Paulina Jackowiak
• Keywords: Cell biology; 0303 health sciences; 03 medical and health sciences; Bioinformatics; Science; Q; Automation in bioinformatics; Article

Recent advances in imaging flow cytometry (IFC) have revolutionized high-throughput multiparameter analyses at single-cell resolution. Although enabling the discovery of population heterogeneities and the detection of rare events, IFC generates hyperdimensional datasets that demand innovative analytical approaches. Current methods work in a supervised manner, utilize only limited information content, or require large annotated reference datasets. Dimensionality reduction algorithms, including uniform manifold approximation and projection (UMAP), have been successfully applied to analyze the large number of parameters generated in various high-throughput techniques. Here, we apply a workflow incorporating UMAP to analyze different IFC datasets. We demonstrate that it out-competes other popular dimensionality reduction methods in speed and accuracy. Moreover, it enables fast visualization, clustering, and tagging of unannotated objects in large-scale experiments. We anticipate that our workflow will be a robust method to address complex IFC datasets, either alone or as an upstream addition to the deep learning approaches.