Browsing by Author "Ahiavi, Kwame"
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- ItemImplementing a pipeline for analysing single-cell RNA sequencing data(Stellenbosch : Stellenbosch University, 2023-03) Ahiavi, Kwame; Tromp, Gerard; van der Spuy, Gian; Maasdorp, Elizna; Stellenbosch University. Faculty of Medicine and Health Sciences. Dept. of Biomedical Sciences. Molecular Biology and Human Genetics.ENGLISH ABSTRACT: Single-cell RNA sequencing (scRNA-seq) has permitted the dissection of gene expression at single-cell resolution and provides novel insights into the composition of apparently homogeneous cell types and transitions between cell states — thereby deepening our understanding of the cell as a functional unit. The data generated by scRNA-seq are characterised by sparsity, heterogeneity, and high-dimensionality as well as large scale. As a result of biological and technical limitations, scRNA-seq data are “noisier” and more complex than their bulk RNA-seq counterparts. Thus, analysing scRNA-seq data demands new statistical and computational methods. Analytical algorithms employed in scRNA-seq pipelines are prone to producing different results depending on the state at the start of the analysis and the number of iterations of computation, complicating reproducibility. I developed a highly robust, scalable, and reproducible analysis pipeline for scRNA-seq data, implemented in Nextflow — a workflow management system that complies with current best practices in bioinformatics. The pipeline implements pre-processing and comprehensive downstream analyses for scRNA-seq data. With the publicly available datasets used for testing, the pipeline identified cell types and differentially expressed genes that enabled the identification of cell subtypes. Trajectory inference also showed the differentiation trajectory of cells, identifying subclusters within cells. In addition, the pipeline documents all steps and transformations, records software packages and versions, and incorporates ontological metadata annotation. Containerisation of pipeline processes ensures that software dependencies are satisfied — contributing to consistent, robust, and reproducible science.