Dissertation/ Thesis
Large scale single cell methods and pan-cancer analysis
| Title: | Large scale single cell methods and pan-cancer analysis |
|---|---|
| Authors: | Fang, Xiunan, 方秀南 |
| Contributors: | Ho, JWK |
| Publisher Information: | The University of Hong Kong (Pokfulam, Hong Kong) |
| Publication Year: | 2024 |
| Collection: | University of Hong Kong: HKU Scholars Hub |
| Subject Terms: | Nucleotide sequence - Data processing, Cancer - Data processing |
| Description: | The widespread adoption of single cell omic technologies has enabled generation of large cell atlases that often consists of more than hundreds of thousands of cells. The large data size and heterogeneity pose significant computational and scientific challenges. With limited computational resources, new scalable algorithms and software are required to properly analyze these atlas-scale single cell data sets. In this thesis, we present a collection of new algorithms, bioinformatics tools, and applications related to large-scale analysis of single cell omic data. • A scalable clustering method called FlowGrid that can significantly reduce run time when analyzing very large single cell omic data. • A web-based immersive visualization tool called StarmapVis that enables effective exploration of single cell and spatial omic data. • A high-throughput and sensitive single-nucleus total RNA sequencing technique called HHseq is developed for the detection of total RNA from frozen clinical samples such as tumors. Using HHseq, we collected more than 730,000 single nuclei from 32 patients with various tumor types. An innovative analysis platform has been established to specifically encompass the entire process from upstream to downstream. • The pan-cancer analysis focuses on dissecting the transcriptional changes happened in malignant cells. The functions of key cell types and regulatory genes are analyzed. Specifically, pan-cancer malignant cell sub-cluster are detected, ciliated-like malignant groups show important functions in cancer proliferating and signaling. Furthermore, the investigation reveals mutual mutations in small nuclear RNAs (RNU4-1, RNU6-31P) among malignant cells, highlighting their potential splicing effects on cancer markers implicated in tumorigenesis mechanisms. Overall, the thesis highlights the importance of large-scale single-cell analysis in understanding complex biological systems such as cancer. The insights gained from this research can have important implications for developing suitable ... |
| Document Type: | doctoral or postdoctoral thesis |
| Language: | English |
| Relation: | HKU Theses Online (HKUTO); 991044829506303414; https://hub.hku.hk/handle/10722/344190 |
| Availability: | https://hub.hku.hk/handle/10722/344190 |
| Rights: | The author retains all proprietary rights, (such as patent rights) and the right to use in future works. ; This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. |
| Accession Number: | edsbas.A2945F2C |
| Database: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://hub.hku.hk/handle/10722/344190# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Header | DbId: edsbas DbLabel: BASE An: edsbas.A2945F2C RelevancyScore: 872 AccessLevel: 3 PubType: Dissertation/ Thesis PubTypeId: dissertation PreciseRelevancyScore: 871.7080078125 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Large scale single cell methods and pan-cancer analysis – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Fang%2C+Xiunan%22">Fang, Xiunan</searchLink><br /><searchLink fieldCode="AR" term="%22方秀南%22">方秀南</searchLink> – Name: Author Label: Contributors Group: Au Data: Ho, JWK – Name: Publisher Label: Publisher Information Group: PubInfo Data: The University of Hong Kong (Pokfulam, Hong Kong) – Name: DatePubCY Label: Publication Year Group: Date Data: 2024 – Name: Subset Label: Collection Group: HoldingsInfo Data: University of Hong Kong: HKU Scholars Hub – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Nucleotide+sequence+-+Data+processing%22">Nucleotide sequence - Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Cancer+-+Data+processing%22">Cancer - Data processing</searchLink> – Name: Abstract Label: Description Group: Ab Data: The widespread adoption of single cell omic technologies has enabled generation of large cell atlases that often consists of more than hundreds of thousands of cells. The large data size and heterogeneity pose significant computational and scientific challenges. With limited computational resources, new scalable algorithms and software are required to properly analyze these atlas-scale single cell data sets. In this thesis, we present a collection of new algorithms, bioinformatics tools, and applications related to large-scale analysis of single cell omic data. • A scalable clustering method called FlowGrid that can significantly reduce run time when analyzing very large single cell omic data. • A web-based immersive visualization tool called StarmapVis that enables effective exploration of single cell and spatial omic data. • A high-throughput and sensitive single-nucleus total RNA sequencing technique called HHseq is developed for the detection of total RNA from frozen clinical samples such as tumors. Using HHseq, we collected more than 730,000 single nuclei from 32 patients with various tumor types. An innovative analysis platform has been established to specifically encompass the entire process from upstream to downstream. • The pan-cancer analysis focuses on dissecting the transcriptional changes happened in malignant cells. The functions of key cell types and regulatory genes are analyzed. Specifically, pan-cancer malignant cell sub-cluster are detected, ciliated-like malignant groups show important functions in cancer proliferating and signaling. Furthermore, the investigation reveals mutual mutations in small nuclear RNAs (RNU4-1, RNU6-31P) among malignant cells, highlighting their potential splicing effects on cancer markers implicated in tumorigenesis mechanisms. Overall, the thesis highlights the importance of large-scale single-cell analysis in understanding complex biological systems such as cancer. The insights gained from this research can have important implications for developing suitable ... – Name: TypeDocument Label: Document Type Group: TypDoc Data: doctoral or postdoctoral thesis – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: HKU Theses Online (HKUTO); 991044829506303414; https://hub.hku.hk/handle/10722/344190 – Name: URL Label: Availability Group: URL Data: https://hub.hku.hk/handle/10722/344190 – Name: Copyright Label: Rights Group: Cpyrght Data: The author retains all proprietary rights, (such as patent rights) and the right to use in future works. ; This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. – Name: AN Label: Accession Number Group: ID Data: edsbas.A2945F2C |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.A2945F2C |
| RecordInfo | BibRecord: BibEntity: Languages: – Text: English Subjects: – SubjectFull: Nucleotide sequence - Data processing Type: general – SubjectFull: Cancer - Data processing Type: general Titles: – TitleFull: Large scale single cell methods and pan-cancer analysis Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Fang, Xiunan – PersonEntity: Name: NameFull: 方秀南 – PersonEntity: Name: NameFull: Ho, JWK IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2024 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
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