Indiana University-Purdue University Indianapolis (IUPUI) / The successful treatment and potential eradication of many complex diseases,
such as cancer, begins with elucidating the convoluted mapping of molecular profiles
to phenotypical manifestation. Our observed molecular profiles (e.g., genomics,
transcriptomics, epigenomics) are often high-dimensional and are collected from patient
samples falling into heterogeneous disease subtypes. Interpretable learning from
such data calls for sparsity-driven models. This dissertation addresses the high dimensionality,
sparsity, and heterogeneity issues when analyzing multiple-omics data,
where each method is implemented with a concomitant R package. First, we examine
challenges in submatrix identification, which aims to find subgroups of samples
that behave similarly across a subset of features. We resolve issues such as two-way
sparsity, non-orthogonality, and parameter tuning with an adaptive thresholding procedure
on the singular vectors computed via orthogonal iteration. We validate the
method with simulation analysis and apply it to an Alzheimer’s disease dataset.
The second project focuses on modeling relationships between large, matched
datasets. Exploring regressional structures between large data sets can provide insights
such as the effect of long-range epigenetic influences on gene expression. We
present a high-dimensional version of mixture multivariate regression to detect patient
clusters, each with different correlation structures of matched-omics datasets.
Results are validated via simulation and applied to matched-omics data sets. In the third project, we introduce a novel approach to modeling spatial transcriptomics
(ST) data with a spatially penalized multinomial model of the expression
counts. This method solves the low-rank structures of zero-inflated ST data with
spatial smoothness constraints. We validate the model using manual cell structure
annotations of human brain samples. We then applied this technique to additional
ST datasets. / 2025-05-22
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/33295 |
| Date | 05 1900 |
| Creators | White, Alexander James |
| Contributors | Cao, Sha, Tu, Wanzhu, Zhang, Chi, Zhao, Yi |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
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