title: Single and multi-subject clustering of flow cytometry data for cell-type identification and anomaly detection.
abstract: Measurement of various markers of single cells using flow cytometry has several biological applications. These applications include improving our understanding of behavior of cellular systems, identifying rare cell populations and personalized medication. A common critical issue in the existing methods is identification of the number of cellular populations which heavily affects the accuracy of results. Furthermore, anomaly detection is crucial in flow cytometry experiments. In this work, we propose a two-stage clustering technique for cell type identification in single subject flow cytometry data and extend it for anomaly detection among multiple subjects.
  
  
  
  Our experimentation on 42 flow cytometry datasets indicates high performance and accurate clustering (F-measure > 91 %) in identifying main cellular populations. Furthermore, our anomaly detection technique evaluated on Acute Myeloid Leukemia dataset results in only <2 % false positives.
authors: Quality of Life Technology Laboratory, The University of Texas at Dallas, Richardson, Texas, USA.
authors: Maziyar Baran
authors: MB
authors: Pouyan
authors: Quality of Life Technology Laboratory, The University of Texas at Dallas, Richardson, Texas, USA.|Department of Computer Science, The University of Texas at Dallas, RichardsonTexas, USA.
authors: Vasu
authors: V
authors: Jindal
authors: Quality of Life Technology Laboratory, The University of Texas at Dallas, Richardson, Texas, USA.
authors: Javad
authors: J
authors: Birjandtalab
authors: Quality of Life Technology Laboratory, The University of Texas at Dallas, Richardson, Texas, USA. nourani@utdallas.edu.
authors: Mehrdad
authors: M
authors: Nourani
mesh_terms: D015415:Biomarkers / Q000032:analysis; D002477:Cells / Q000145:classification* / Q000166:cytology; D016000:Cluster Analysis; D005434:Flow Cytometry / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000473:pathology; D008390:Markov Chains
chemical_list: D015415:Biomarkers
keywords: Anomaly detection; Biaxial gating; Cell-type population; Flow cytometry; Single-cell technology; Two-stage clustering