High-dimensional single cell cytometry approach for immune system analysis

Abstract

Technological advancement allowed for the advent of single-cell technologies capable of measuring a large number of cellular features simultaneously. These technologies have been subsequently used to shed light on the heterogeneity of cellular systems previously considered homogeneous, identifying the exclusive features of individual cells within cellular niches. Today, single-cell technologies represent an essential tool for studying the underlying immunological mechanisms correlating with disease. In this context, cytometry is one of the diverse high-throughput methods capable of examining more than 50 features per cell. However, utilising cytometry at its full potential requires the development of optimized assays. Additionally, the resulting high-dimensional data represent a challenge for existing computational techniques. This thesis attempts to address these challenges. The first part of the thesis is focused on developing a non-linear embedding algorithm for rapid analysis of cytometry datasets called EmbedSOM. The comparison of EmbedSOM with other state-of-the-art algorithms suggested the superiority of EmbedSOM with faster runtime. This is critical for the analysis of large datasets with millions of cells. Furthermore, EmbedSOM has additional functionality such as landmark guided..