Dissertation/ Thesis
Effect of single-cell RNA sequencing parameters on cluster stability
| Τίτλος: | Effect of single-cell RNA sequencing parameters on cluster stability |
|---|---|
| Συγγραφείς: | Potgieter, Tiqvah Talya |
| Συνεισφορές: | Maasdorp, Elizna, Patterton, Hugh, Stellenbosch University. Faculty of Science. Center for Bioinformatics and Computational Biology. |
| Στοιχεία εκδότη: | Stellenbosch University |
| Έτος έκδοσης: | 2025 |
| Συλλογή: | Stellenbosch University: SUNScholar Research Repository |
| Θεματικοί όροι: | Gene expression -- Data processing, Cluster analysis -- Data processing, Stability, Nucleotide sequence -- Technique, Cells -- Analysis, Single-cell RNA sequencing, UCTD |
| Περιγραφή: | Thesis (MSc)--Stellenbosch University, 2025. ; Potgieter, T. T. 2025. Effect of Single-Cell RNA Sequencing Parameters on Cluster Stability. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/7220d82f-7182-4eb7-8dfd-90bd03c01c16 ; ENGLISH ABSTRACT: Single-cell RNA sequencing has allowed us to study gene expression at a single-cell level, enabling researchers to unravel the heterogeneity and complexity within individual cells. Clustering has made this possible as it identifies and groups different cells based on their expression profiles, clustering also plays a critical role in exploratory research and thus, understanding the quality of clustering and clustering outcomes is essential. However, cluster analysis remains one of the most challenging and fundamental tasks in a scRNA-seq analysis. This is mainly due to the fact that there is no ground truth or gold standard by which performance can be measured. Additionally, clustering itself is governed by a range of parameters and processing methods. Cluster stability assessments aim to address these challenges by defining how robust clustering results are under perturbations to the original dataset. The assumption is that stable clustering results represent the underlying structure of the data and thus should be consistent across an ensemble of perturbed datasets. We investigated the effects of various processing parameters on cluster stability in scRNA-seq data. For each parameter, cluster stability was quantified by generating a cluster ensemble with respect to different initializations and calculating the average scores using relevant stability metrics. Results presented here showed great variability, indicating that cluster stability is sensitive to the choices of parameter setting as well as the type of dataset. We emphasize that users should not rely on a once-off application of a clustering algorithm to their data but explore various parameter possibilities and assess the robustness of ... |
| Τύπος εγγράφου: | thesis |
| Περιγραφή αρχείου: | xi, 90 pages : illustrations; application/pdf |
| Γλώσσα: | English |
| Relation: | https://scholar.sun.ac.za/handle/10019.1/132494 |
| Διαθεσιμότητα: | https://scholar.sun.ac.za/handle/10019.1/132494 |
| Rights: | Stellenbosch University |
| Αριθμός Καταχώρησης: | edsbas.17575DAA |
| Βάση Δεδομένων: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://scholar.sun.ac.za/handle/10019.1/132494# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Items | – Name: Title Label: Title Group: Ti Data: Effect of single-cell RNA sequencing parameters on cluster stability – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Potgieter%2C+Tiqvah+Talya%22">Potgieter, Tiqvah Talya</searchLink> – Name: Author Label: Contributors Group: Au Data: Maasdorp, Elizna<br />Patterton, Hugh<br />Stellenbosch University. Faculty of Science. Center for Bioinformatics and Computational Biology. – Name: Publisher Label: Publisher Information Group: PubInfo Data: Stellenbosch University – Name: DatePubCY Label: Publication Year Group: Date Data: 2025 – Name: Subset Label: Collection Group: HoldingsInfo Data: Stellenbosch University: SUNScholar Research Repository – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Gene+expression+--+Data+processing%22">Gene expression -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+--+Data+processing%22">Cluster analysis -- Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Stability%22">Stability</searchLink><br /><searchLink fieldCode="DE" term="%22Nucleotide+sequence+--+Technique%22">Nucleotide sequence -- Technique</searchLink><br /><searchLink fieldCode="DE" term="%22Cells+--+Analysis%22">Cells -- Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Single-cell+RNA+sequencing%22">Single-cell RNA sequencing</searchLink><br /><searchLink fieldCode="DE" term="%22UCTD%22">UCTD</searchLink> – Name: Abstract Label: Description Group: Ab Data: Thesis (MSc)--Stellenbosch University, 2025. ; Potgieter, T. T. 2025. Effect of Single-Cell RNA Sequencing Parameters on Cluster Stability. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/7220d82f-7182-4eb7-8dfd-90bd03c01c16 ; ENGLISH ABSTRACT: Single-cell RNA sequencing has allowed us to study gene expression at a single-cell level, enabling researchers to unravel the heterogeneity and complexity within individual cells. Clustering has made this possible as it identifies and groups different cells based on their expression profiles, clustering also plays a critical role in exploratory research and thus, understanding the quality of clustering and clustering outcomes is essential. However, cluster analysis remains one of the most challenging and fundamental tasks in a scRNA-seq analysis. This is mainly due to the fact that there is no ground truth or gold standard by which performance can be measured. Additionally, clustering itself is governed by a range of parameters and processing methods. Cluster stability assessments aim to address these challenges by defining how robust clustering results are under perturbations to the original dataset. The assumption is that stable clustering results represent the underlying structure of the data and thus should be consistent across an ensemble of perturbed datasets. We investigated the effects of various processing parameters on cluster stability in scRNA-seq data. For each parameter, cluster stability was quantified by generating a cluster ensemble with respect to different initializations and calculating the average scores using relevant stability metrics. Results presented here showed great variability, indicating that cluster stability is sensitive to the choices of parameter setting as well as the type of dataset. We emphasize that users should not rely on a once-off application of a clustering algorithm to their data but explore various parameter possibilities and assess the robustness of ... – Name: TypeDocument Label: Document Type Group: TypDoc Data: thesis – Name: Format Label: File Description Group: SrcInfo Data: xi, 90 pages : illustrations; application/pdf – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: https://scholar.sun.ac.za/handle/10019.1/132494 – Name: URL Label: Availability Group: URL Data: https://scholar.sun.ac.za/handle/10019.1/132494 – Name: Copyright Label: Rights Group: Cpyrght Data: Stellenbosch University – Name: AN Label: Accession Number Group: ID Data: edsbas.17575DAA |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.17575DAA |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English Subjects: – SubjectFull: Gene expression -- Data processing Type: general – SubjectFull: Cluster analysis -- Data processing Type: general – SubjectFull: Stability Type: general – SubjectFull: Nucleotide sequence -- Technique Type: general – SubjectFull: Cells -- Analysis Type: general – SubjectFull: Single-cell RNA sequencing Type: general – SubjectFull: UCTD Type: general Titles: – TitleFull: Effect of single-cell RNA sequencing parameters on cluster stability Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Potgieter, Tiqvah Talya – PersonEntity: Name: NameFull: Maasdorp, Elizna – PersonEntity: Name: NameFull: Patterton, Hugh – PersonEntity: Name: NameFull: Stellenbosch University. Faculty of Science. Center for Bioinformatics and Computational Biology. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2025 Identifiers: – Type: issn-locals Value: edsbas |
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