A Comparison of Cell Type Identification for Single-Cell RNA Sequencing Data Analysis

dc.contributor.advisorHuang, Yufei
dc.contributor.authorLu, Weiming
dc.contributor.committeeMemberZhang, Michelle
dc.contributor.committeeMemberFlores, Mario
dc.date.accessioned2024-02-12T14:51:55Z
dc.date.available2024-02-12T14:51:55Z
dc.date.issued2021
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractSince single-cell RNA sequencing (scRNA-Seq) was introduced to the biology community, it has been a powerful tool for different applications. The developers in the community released several open-source software packages to analyze high-throughput biological data rapidly. The data analysis pipeline has become relatively straightforward to the community. However, the cell type identifying method remains the most challenging part throughout the scRNA-Seq analysis workflow and relies heavily on prior knowledge when defining cell type manually. Fortunately, more computational approaches on cell type annotation were releasing with recent advancements in technologies. However, the results appear to be varied when comparing one another approaches. In this dissertation, various computational techniques for cell-type identification are surveyed, and their performances are evaluated on benchmark scRNA-Seq datasets. This study facilitates the prospective users to select existing technologies for cell type identifications in single-cell scRNA-seq analysis.
dc.description.departmentElectrical and Computer Engineering
dc.format.extent49 pages
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/20.500.12588/4289
dc.languageen
dc.subjectCell type identification
dc.subjectSingle-cell RNA sequencing
dc.subjectData analysis
dc.subject.classificationComputer engineering
dc.subject.classificationBioinformatics
dc.titleA Comparison of Cell Type Identification for Single-Cell RNA Sequencing Data Analysis
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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