Interactive environment for flow-cytometry data analysis

• Thesis title in Czech: Interaktivní prostředí pro analýzu dat z průtokové cytometrie
• Thesis title in English: Interactive environment for flow-cytometry data analysis
• Key words: bioinformatika, shlukování, vizualizace, průtoková cytometrie
• English key words: bioinformatics, clustering, visualisations, flow cytometry
• Academic year of topic announcement: 2019/2020
• Thesis type: Bachelor's thesis
• Thesis language: angličtina
• Department: Department of Software Engineering (32-KSI)
• Supervisor: RNDr. Miroslav Kratochvíl, Ph.D.
• Author: hidden - assigned and confirmed by the Study Dept.
• Date of registration: 15.11.2019
• Date of assignment: 18.11.2019
• Confirmed by Study dept. on: 16.03.2020
• Date and time of defence: 07.07.2020 09:00
• Date of electronic submission: 04.06.2020
• Date of submission of printed version: 04.06.2020
• Date of proceeded defence: 07.07.2020
• Opponents: RNDr. Jan Pacovský

Guidelines

Recent technological advances in flow cytometry have allowed experiments that precisely measure tens of protein surface markers present in various combinations on millions of individual cells, which can be used to study various biologically relevant properties of the cells of living organisms. Current computational approaches for evaluating the resulting data sets aim to analyze the multidimensional space of the surface marker expression using various unsupervised clustering and dimensionality-reduction methods. Although these often provide better results than manual analysis by simple "gating", their use is complicated both by computational complexity of the algorithms, and by limited capabilities of the presently available user interfaces. The main aim of the thesis is to implement an efficient and fast user-friendly environment for viewing, dissecting and analyzing the data. Performance of dataset browsing and viewing should be sufficient for interactive work with several millions of individual cells at once, which will be demonstrated on a suitable dataset. The software will also integrate several existing high-performance cell analysis algorithms.

References

Van Gassen, S., Callebaut, B., Van Helden, M. J., Lambrecht, B. N., Demeester, P., Dhaene, T., & Saeys, Y. (2015). FlowSOM: Using self‐organizing maps for visualization and interpretation of cytometry data. Cytometry Part A, 87(7), 636-645.

Weber, L. M., & Robinson, M. D. (2016). Comparison of clustering methods for high‐dimensional single‐cell flow and mass cytometry data. Cytometry Part A, 89(12), 1084-1096.

Bashashati, A., & Brinkman, R. R. (2009). A survey of flow cytometry data analysis methods. Advances in bioinformatics, 2009.

Sanftmann, H., & Weiskopf, D. (2009, June). Illuminated 3D scatterplots. In Computer Graphics Forum (Vol. 28, No. 3, pp. 751-758). Oxford, UK: Blackwell Publishing Ltd.

The OpenGL Shading Language. https://www.khronos.org/registry/OpenGL/specs/gl/GLSLangSpec.4.40.pdf

Vulkan 1.1.127 - A Specification. https://www.khronos.org/registry/vulkan/specs/1.1/pdf/vkspec.pdf