Characterization of Stability of Non-Negative Matrix Factorization Models: An Application to Single-Cell Data

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dc.contributor.advisorZhang, Qingrun
dc.contributor.authorLiu, Alexander EJ
dc.contributor.committeememberWu, Jingjing
dc.contributor.committeememberXu, Yuan
dc.contributor.committeememberZhang, Qingrun
dc.date2023-11
dc.date.accessioned2023-09-08T15:46:39Z
dc.date.available2023-09-08T15:46:39Z
dc.date.issued2023-08-21
dc.description.abstractThe non-negative matrix factorization (NMF) is a powerful machine learning technique used in mathematics, computer science, and data science. This technique has applications in a wide range of fields including recommender systems, image processing, signal processing, machine learning and genetics. Recently, NMF has gained popularity in the analysis of single-cell gene expression data to identify cell types and gene expression patterns. In this thesis, we have studied the NMF, its rank estimation, classification, and stability using both simulated data and real single-cell gene expression data. We have designed two simulated data sets with desired features and tested two seeding methods, eight NMF algorithms and five rank estimation criteria. Additionally, a real single-cell gene expression data has been used to further characterize the NMF algorithms. We have also investigated the stability of NMF, first over the sample size consideration and then on initialization. The detailed conditions that have been revealed by this thesis may generate practical impact in directing the appropriate use of NMF in analyzing single-cell gene expression data.
dc.identifier.citationLiu, A. E. J. (2023). Characterization of stability of non-negative matrix factorization models: an application to single-cell data (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.urihttps://hdl.handle.net/1880/116998
dc.identifier.urihttps://dx.doi.org/10.11575/PRISM/41842
dc.language.isoen
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgary
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject.classificationStatistics
dc.titleCharacterization of Stability of Non-Negative Matrix Factorization Models: An Application to Single-Cell Data
dc.typemaster thesis
thesis.degree.disciplineMathematics & Statistics
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.thesis.accesssetbystudentI do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.
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