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Interactive clustering approaches in single-cell cytometry
Urban, Nicole Aemilia ; Šmelko, Adam (advisor) ; Stuchlý, Jan (referee)
Flow cytometry allows inexpensive monitoring of large and diverse cell populations using fluorescent markers, providing immense applications in studying biological properties of blood and tissues as well as diagnostics in the clinical setting. Recent methodological advances highlight automatic clustering as a tool of choice for data analysis, and many clustering algo- rithms were developed for various use cases. However, the applicability of such algorithms in biology and medicine remains challenging unless the tools expose user-friendly, interactive interfaces that are accessible to domain ex- perts. The goal of the thesis is to review the available methods that allow such interaction and supervision of the clustering process by the user, specif- ically focusing on interfaces desirable in clinical setting that does not require the user to interact with programming environments. As the main practi- cal result, the thesis should design a new tool that builds upon previously developed methodology (iDendro, gMHCA), allowing the application of the researched methodology on real datasets. By using proper data visualization techniques, the end user should be able to interact with the dataset in a way that is both intuitive and useful for producing biologically relevant results.
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Optimizations and applications of non-linear spectral unmixing in flow cytometry
Nemec, Matěj ; Musil, Jan (advisor) ; Stuchlý, Jan (referee)
Recent advances in flow cytometry techniques enable high-throughput single-cell experiments with extensive marker sets. In order to leverage this technology the measured signal must be unmixed to recover interpretable re- sults. Current approaches to unmixing typically leverage linear deconvolution algorithms such as fitting by ordinary least squares method, that tend have issues dealing with various noise sources inherit to the data collection pro- cess. This thesis evaluates the performance of a novel non-linear approach of unmixing called nougad. For the evaluation, we have generated realistic arti- ficial data with known ground truth for testing, implemented multi-threaded version of nougad and tuned its hyperparameters using Bayesian optimiza- tion, and collected several performance metrics of nougad and the other algorithms on the testing datasets. The results show that nougad is able to outperform the tested linear algorithms making this non-linear method more suitable for practical applications and a good candidate for further refinement and optimization efforts. 1
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Studying lymphocyte development using mass cytometry
Novák, David ; Stuchlý, Jan (advisor) ; Špidlen, Josef (referee)
Studying lymphocyte development using mass cytometry Abstract Development of mature lymphocytes, a white blood cell subtype, is crucial for the correct function of the human immune system. Currently, developmental pathways of lymphocytes can be studied using high-throughput single-cell measurements. In particular, mass cytometry enables the study of immunologically relevant pheno- typic and functional markers on a vast scale. In this work I present my individual contribution to tviblindi, a powerful software tool for analysis of cytometric data aimed at uncovering developmental trajectories. tviblindi is a package written in R, Python and C++. It provides a means to integrate prior knowledge with data analyses grounded in graph theory and algebraic topology. tviblindi is accessible to biological researchers without background in computer science or mathematics. It is an addition to the expanding field of trajectory inference in single-cell data. Furthermore, I review current knowledge of T-cell development and conduct a tviblindi analysis thereof using human thymus and peripheral blood datasets and evaluate the results. 1
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Studying lymphocyte development using mass cytometry
Novák, David ; Stuchlý, Jan (advisor) ; Špidlen, Josef (referee)
Studying lymphocyte development using mass cytometry Abstract Development of mature lymphocytes, a white blood cell subtype, is crucial for the correct function of the human immune system. Currently, developmental pathways of lymphocytes can be studied using high-throughput single-cell measurements. In particular, mass cytometry enables the study of immunologically relevant pheno- typic and functional markers on a vast scale. In this work I present my individual contribution to tviblindi, a powerful software tool for analysis of cytometric data aimed at uncovering developmental trajectories. tviblindi is a package written in R, Python and C++. It provides a means to integrate prior knowledge with data analyses grounded in graph theory and algebraic topology. tviblindi is accessible to biological researchers without background in computer science or mathematics. It is an addition to the expanding field of trajectory inference in single-cell data. Furthermore, I review current knowledge of T-cell development and conduct a tviblindi analysis thereof using human thymus and peripheral blood datasets and evaluate the results. 1
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Modern mathematical methods in the research of immune reconstitutions and immunodeficiencies
Stuchlý, Jan ; Kalina, Tomáš (advisor) ; Šinkora, Jiří (referee) ; Štěpánek, Ondřej (referee)
In this thesis we present new analytical and integrative approaches for broad spectrum of applications of flow cytometry. CVID Large (88 individuals) cohort of CVID patients was immunopheno- typed by flow cytometry. The "probability binning" algorithm was used to au- tomatically assess the immunophenotype which presented unprecedented stabi- lity. We were able to define sub-group of CVID patients with highly activated immunophenotype showing hallmarks of immunosenescence and common clinical characteristics (thrombocytopenia, lung fibrosis and bronchiectasis). The severity of clinical complications correlated quantitatively with the immunophenotype. Analysis of cellular proteome We have designed technics which allow for highly multiplexed (>1000 of antibodies) analysis of human proteome using af- finity proteomics. We analyzed the changes of proteome of human cell lines and characterized the proteome of acute leukemias. The yet undescribed stability of proteome with respect the sub-cellular localization is shown and theoretical and practical background for antibody standardization and validation for the use in a nity proteomics is presented. Topological analysis of cytometry data Multiparametric flow cytometry allows for description of complex topological relationships of the cells in di erent stages of...
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