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Computational Methods for Analyzing High-Dimensional Datasets Derived from Molecular Profiling of Biological Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computational Methods for Analyzing High-Dimensional Datasets Derived from Molecular Profiling of Biological Systems./
作者:	Huang, Xingfan.
面頁冊數:	1 online resource (279 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-10, Section: B.
Contained By:	Dissertations Abstracts International84-10B.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30313720click for full text (PQDT)
ISBN:	9798379411053

Computational Methods for Analyzing High-Dimensional Datasets Derived from Molecular Profiling of Biological Systems.
Huang, Xingfan.

Computational Methods for Analyzing High-Dimensional Datasets Derived from Molecular Profiling of Biological Systems. - 1 online resource (279 pages)

Source: Dissertations Abstracts International, Volume: 84-10, Section: B.

Thesis (Ph.D.)--University of Washington, 2023.

Includes bibliographical references

Embryonic development is the process by which a single fertilized egg gives rise to all cells in a complex multicellular organism. Recent advances in single-cell sequencing techniques allow researchers to generate genomic profiles of large numbers of single cells and study metazoan development and complex tissues at unprecedented resolution. The objectives of my research projects are to develop computational methods to analyze high-dimensional genomics datasets, and direct those methods towards understanding metazoan embryonic development and complex tissues, in both normal and disease related genetic backgrounds. In this thesis, we introduce three projects: 1) Drosophila Embryonic Atlas Project (DEAP), a continuous, single-cell atlas of chromatin accessibility and gene expression that spans Drosophila embryogenesis; 2) Mouse Mutant Cell Atlas (MMCA), a single-cell atlas of gene expression of mouse embryos in both normal and developmental disease related genetic backgrounds at the end of organogenesis; and 3) Macaque Brain Atlas, a multi-omic single-cell atlas of adult macaque brain samples to study regional heterogeneity and regulatory grammar of diverse brain cell types. We introduce several novel computational methods: 1) A neural network to infer the precise time in development of individual nuclei and understand continuous gene expression and regulatory dynamics in Drosophila embryogenesis; 2) a novel statistic lochNESS to quantify sample specific effects in single-cell data based on k-NN graphs, allowing systematic comparison of molecular profiles from normal and disease datasets to investigate effects of genetic perturbations; and 3) an extension of lochNESS for multiple comparisons to identify brain region specific transcriptional programs in the Macaque Brain Atlas. With these novel methods, combined with existing computational tools and packages, we derive biological insights from the various single-cell atlases and add to our understanding of the various biological systems.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379411053Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Embryonic developmentIndex Terms--Genre/Form:

542853
Electronic books.

Computational Methods for Analyzing High-Dimensional Datasets Derived from Molecular Profiling of Biological Systems.
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