Flow cytometry mainly used for detecting the characteristics of a number of biochemical substances based on the expression of specific markers in cells. It is particularly useful for detecting membrane surface receptors, antigens, ions, or during DNA/RNA expression. Not only can it be employed as a biomedical research tool for recognising distinctive types of cells in mixed populations, but it can also be used as a diagnostic tool for classifying abnormal cell populations connected with disease. Modern flow cytometers can rapidly analyse tens of thousands of cells at the same time while also measuring multiple parameters from a single cell. However, the rapid development of flow cytometers makes it challenging for conventional analysis methods to interpret flow cytometry data. Researchers need to be able to distinguish interesting-looking cell populations manually in multi-dimensional data collected from millions of cells. Thus, it is essential to find a robust approach for analysing flow cytometry data automatically, specifically in identifying cell populations automatically. This thesis mainly concerns discover the potential shortcoming of current automated-gating algorithms in both real datasets and synthetic datasets. Three representative automated clustering algorithms are selected to be applied, compared and evaluated by completely and partially automated gating. A subspace clustering ProClus also implemented in this thesis. The performance of ProClus in flow cytometry is not well, but it is still a useful algorithm to detect noise.
Clinical Validation of a Real-Time Machine Learning-based System for the Detection of Acute Myeloid Leukemia by Flow Cytometry
ARUP Laboratories（ARUP实验室）；Institute for Research and Innovation（研究与创新研究所）；Institute for Clinical and Experimental Pathology（临床与实验病理学研究所）；University of Utah（犹他大学）
Automated Identification of Cell Populations in Flow Cytometry Data with Transformers
TU Wien（维也纳工业大学）；St Anna Children’s Cancer Research Institute（圣安娜儿童癌症研究所）；Hospital de Pediatria “Dr. Juan P. Garrahan”（胡安·P·加拉汉博士儿科医院）；Charité Universitätsmedizin Berlin（柏林夏里特医学院）；HELIOS Klinikum Berlin-Buch（柏林布赫赫利俄斯医院）
Turtle shell clustering: A mixture approach to discriminative clustering with applications to flow cytometry and other data
Department of Mathematics & Statistics, McMaster University（数学与统计学系，麦斯特大学）；School of Computer Science & Statistics, Trinity College Dublin（计算机科学与统计学学院，三一学院都柏林）
Low dimensional representation of multi-patient flow cytometry datasets using optimal transport for minimal residual disease detection in leukemia
Institut de Mathématiques de Bordeaux（波尔多数学研究所）；Université de Bordeaux（波尔多大学）；CNRS（法国国家科学研究中心）；IRIT（图里尔信息与电信研究所）；Université de Toulouse（图卢兹大学）；CHU Bordeaux（波尔多大学医院中心）；Centre Hospitalier Universitaire de Bordeaux（波尔多大学医院中心）
Real-time deep learning phase imaging flow cytometer reveals blood cell aggregate biomarkers for haematology diagnostics
Technical University of Munich（慕尼黑工业大学）；TUMCREATE；Bioinformatics Institute, Agency for Science, Technology and Research（科技研究局生物信息研究所）；TUM University Hospital（TUM大学医院）；National University of Singapore（新加坡国立大学）；National University Hospital（新加坡国立大学医院）；German Center for Infection Research (DZIF)（德国感染研究中心）；Infectious Diseases Labs (AIDL), Agency for Science, Technology and Research（科技研究局传染病实验室）；Nanyang Technological University（南洋理工大学）；Centre for Frontier AI Research, Agency for Science, Technology and Research（科技研究局前沿人工智能研究中心）
When Conventional machine learning meets neuromorphic engineering: Deep Temporal Networks (DTNets) a machine learning frawmework allowing to operate on Events and Frames and implantable on Tensor Flow Like Hardware
Sorbonne Universitas（索邦大学）；University of Pittsburgh/Medical Center（匹兹堡大学医学中心）；Carnegie Mellon University（卡内基梅隆大学）
