The Enhanced Prediction of Single-cell Metabolism through Consensus Methods
| dc.contributor.author | Amiss, John Michael | |
| dc.contributor.supervisor | Jabbari, Hosna | |
| dc.contributor.supervisor | Lum, Julian J. | |
| dc.date.accessioned | 2023-08-18T17:40:33Z | |
| dc.date.available | 2023-08-18T17:40:33Z | |
| dc.date.copyright | 2023 | en_US |
| dc.date.issued | 2023-08-18 | |
| dc.degree.department | Department of Computer Science | en_US |
| dc.degree.level | Master of Science M.Sc. | en_US |
| dc.description.abstract | Motivation: Altered metabolism is a key contributor to pathology in multiple dis ease states. In cancers, metabolic shifts are crucial to providing the tumor cells with the energy and substrates necessary for uncontrolled proliferation. Altered metabolism can exist within certain pathological cells, or within a population of cells. Unfortunately, direct measurements of single-cell metabolism are extremely limited. However, single-cell flux prediction software can be used to infer cellular metabolism from readily available transcriptome data. Two recent single-cell flux prediction tools, Single-cell Flux Estimation Analysis (“scFEA”) and Compass, have been used in re search and have been validated by direct metabolite measurements. The two systems make use of highly disparate methods. By adapting the outputs of these tools, I hypothesized that they can work in concert to provide a higher confidence flux pre diction at multiple levels of resolution within a metabolic pathway. Results: I developed a method to compare the outputs of Compass and scFEA and to determine the patterns of agreement between the systems. By creating and testing multiple methods of mapping metadata to reaction identifiers, and applying func tion composites to Compass outputs, I identified a method that showed the highest general similarities with the outputs of scFEA across reaction clusters. I demon strated the application of this method to the results from running the two systems on bulk and single-cell RNA-seq data. This analysis showed broad biological areas of agreement between the results, including processes related to cellular energy produc tion, and both nucleotide and amino acid metabolism. Bulk and imputed data was shown to sharply reduce the consensus between the systems. By creating a separate reaction-level method, I validated my consensus approach across individual reactions. This additionally revealed that the previous patterns of disagreement between the systems spanned entire groups of related reactions. I tested the consensus measures on two matched transcriptome and metabolomics datasets. The results suggest that consensus between the two systems may indicate that both are coherent with direct metabolic measurements. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/15271 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | Single-cell | en_US |
| dc.subject | Metabolism | en_US |
| dc.subject | Flux | en_US |
| dc.title | The Enhanced Prediction of Single-cell Metabolism through Consensus Methods | en_US |
| dc.type | Thesis | en_US |