Expression Quantitative Trait Loci Analysis in Single-Cell RNA- Sequencing Data
Name
Peep Kolberg
Abstract
Inter-individual genotypic variation is great and the effects of DNA on the phenotype complex. Studies measure individuals’ traits, map the genome and find associations between the two in order to gain insights. One quantifiable trait is RNA expression.
Presently, RNA expression can be measured at the single cell level which provides a more detailed view of the heterogeneity of cells and allows to find stronger associations with the genome. Thus, eQTL-analysis on single-cell RNA sequencing data is a powerful method for explaining the effects of DNA. However, when independent laboratories use different analysis pipelines, the published results are neither comparable nor combinable. To use samples to their fullest, data should be analyzed uniformly or through metaanalysis. The thesis used data from three single-cell RNA sequencing studies and showed that eQTL signals can be found by using the same pipeline on all datasets. The results confirm that the datasets are suitable for metaanalysis. By combining data from different sources and analyzing it together, there is greater statistical power to find significant associations.
Presently, RNA expression can be measured at the single cell level which provides a more detailed view of the heterogeneity of cells and allows to find stronger associations with the genome. Thus, eQTL-analysis on single-cell RNA sequencing data is a powerful method for explaining the effects of DNA. However, when independent laboratories use different analysis pipelines, the published results are neither comparable nor combinable. To use samples to their fullest, data should be analyzed uniformly or through metaanalysis. The thesis used data from three single-cell RNA sequencing studies and showed that eQTL signals can be found by using the same pipeline on all datasets. The results confirm that the datasets are suitable for metaanalysis. By combining data from different sources and analyzing it together, there is greater statistical power to find significant associations.
Graduation Thesis language
Estonian
Graduation Thesis type
Master - Computer Science
Supervisor(s)
Kaur Alasoo
Defence year
2023