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Statistical methods for signal recovery and spatial reconstruction in single-cell and spatial omics Zhaojun Zhang
- Format:
- Book
- Thesis/Dissertation
- Author/Creator:
- Zhang, Zhaojun, author.
- Language:
- English
- Subjects (All):
- Statistics.
- Cellular biology.
- Biomedical engineering.
- Biostatistics.
- 0463.
- 0379.
- 0541.
- 0308.
- Local Subjects:
- Statistics.
- Cellular biology.
- Biomedical engineering.
- Biostatistics.
- 0463.
- 0379.
- 0541.
- 0308.
- Genre:
- Academic theses
- Physical Description:
- 1 online resource (174 pages)
- Contained In:
- Dissertations Abstracts International 87-12B
- Place of Publication:
- Ann Arbor : ProQuest Dissertations and Theses, 2026
- Language Note:
- English
- Summary:
- Advances in single-cell and spatial omics technologies have enabled characterization of cellular and tissue heterogeneity, however, they also introduce new statistical challenges. Single-cell technologies enable comparative analysis across individuals, conditions, and time points. To perform these comparisons, data from different samples and protocols need to be integrated. This task, known as batch correction, is a sensitive preprocessing step that, if performed too aggressively, can remove meaningful biological variations and also induce data distortions that can be harmful for downstream analyses. Spatial omics technologies, on the other hand, are developed to preserve features in the spatial context. However, the true biological structure is three dimensional, but the data generated from these technologies are often two-dimensional slices that are sparse and even incomplete. This dissertation develops statistical methods for recovering biological signal and spatial structure from technically imperfect omics measurements.The first part of the dissertation introduces CellANOVA, a post-integration statistical framework for recovering biological signals lost during single-cell batch integration. CellANOVA leverages experimental design through the construction of control pools to estimate a latent space of unwanted variation, and then restores between-sample variation that lies outside this batch space. Across case-control, longitudinal, and cross-protocol studies, CellANOVA reduces global and gene-level distortion while preserving effective batch correction. It returns batch-corrected expression matrices that remain suitable for downstream differential expression and pathway analysis, and the signals it recovers are supported by orthogonal flow cytometry measurements and by replication in independent datasets.The second part introduces 3D-Omics-Flow, a fault-tolerant generative framework for reconstructing continuous three-dimensional spatial-omics volumes from sparse two-dimensional sections. By combining prior-informed flow-based interpolation with mechanisms for feature restoration, tissue loss restoration, and calibration to auxiliary spatial information, 3D-Omics-Flow reconstructs 3D structure from incomplete 2D section stacks. The method enables virtual slicing, characterization of volumetric cellular neighborhoods, and analysis of three-dimensional signaling gradients that are inaccessible from isolated 2D sections.Together, these contributions show how principled statistical modeling can expand the scientific value of single-cell and spatial omics by recovering signals that would otherwise be lost to batch effects, sparse sampling, and experimental damage
- Notes:
- Source: Dissertations Abstracts International, Volume: 87-12, Section: B.
- Advisors: Zhang, Nancy R.; Ma, Zongming Committee members: Low, Mark G.; Wei, Yuting
- Ph.D. University of Pennsylvania 2026
- Vendor supplied data
- Local Notes:
- School code: 0175
- ISBN:
- 9798247982722
- Access Restriction:
- Restricted for use by site license
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