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Algorithms for integration of single-cell transcriptomic data
 Ševčovičová, Zuzana ; Kolář, Michal (advisor) ; Abaffy, Pavel (referee)
Single-cell transcriptomics represents an innovative technique that allows for the examination of gene expression of individual cells in tissues or other complex biological samples. The data gen- erated through this approach offer crucial insights into various biological domains, such as em- bryology, oncology, and immunology. Understanding the functioning and mutual interactions of individual cells within tissues indeed requires knowledge of their individual expression profiles. Given the novelty of this method and technical challenges faced during data generation, com- parison of different samples may be difficult and affected by various batch effects. Effectively reconciling the information obtained regarding gene expression in distinct biological replicates or technical replicates generated by different methodologies may be onerous. The development of bioinformatics approaches to address this challenge is an ongoing process. The objective of this thesis is to: 1. survey the existing solutions for integration of single-cell transcriptomic data, 2. benchmark these solutions using publicly available datasets, specifically peripheral blood mononuclear cells, and 3. select the most suitable integration method to apply to a tumour microenvironment dataset generated by our laboratory. This bachelor...
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