In this thesis, I present a comprehensive framework for the analysis of single-cell (SC)morphological data, specifically focusing on the Cell Painting assay. SC technologies haverevolutionized biological research by enabling high-throughput and high-content screeningat the cellular level. Here, the computational challenges and opportunities associated withSC morphological profiling are explored, leveraging both traditional tools like CellProfilerand advanced deep learning methods such as DeepProfiler. This study investigates the potential of SC morphological data to uncover cellular hetero-geneity and identify distinct sub populations within complex datasets. To attain this goal, various feature extraction, normalization, and filtering techniques are employed, followedby unsupervised and supervised learning methods to analyze the extracted features. The results demonstrate the effectiveness of the deep-learning model DeepProfiler in cap-turing intricate cellular features, outperforming the traditional method CellProfiler in most tasks including mechanism of action predictions by as much as 30% macro F1. Thiswork also highlights the importance of efficient computational resources and robust dataprocessing pipelines to handle the large-scale datasets typically generated in SC research.Additionally, I propose a combination of metrics, namely e-distance and SC grit score,for evaluating perturbation strength and filtering morphological data. These metrics, inconjunction with advanced analysis tools such as UMAP and the introduced CellViewer,enhance the interpretability of results, offering a deeper insight into the morphologicalchanges induced by various treatments and subsequent biological implications.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-534126 |
| Date | January 2024 |
| Creators | Frey, Benjamin |
| Publisher | Uppsala universitet, Institutionen för biologisk grundutbildning |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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