Cells are the basic units of life. Studying complex tissues and whole organs requires an understanding of cell heterogeneity and responses to stimuli at the single-cell level. Even the cells, which belong to the same cell type, behave differently at a specific moment and contain different amount of mRNA. Quantitative polymerase chain reaction (qPCR) is one the most sensitive methods for the detection of mRNA, however, gene expression profiling in single cells leads to a large amount of missing data due to the fact that the transcript is missing, or is below the level of detection. Therefore, it is necessary to establish a new statistical approach for analysis of single cells. In this thesis the potential of single-cell gene expression profiling using the high throughput instrument Biomark, focusing on data analysis and biological interpretation, is discussed. Data normalization and handling of missing data are two important steps in data analysis that are performed differently at the single-cell level. Single cells are not normalized by reference genes but the number of cells as a normalizer is applied. Missing data are replaced by value, which is equaled one quarter of transcript amount in the cell. Furthermore it is shown how single-cell gene expression data can be viewed and how subpopulations...
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:326143 |
| Date | January 2014 |
| Creators | Novosadová, Vendula |
| Contributors | Kubista, Mikael, Beneš, Vladimír, Vopálenský, Václav |
| Source Sets | Czech ETDs |
| Language | Czech |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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