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- All Subjects: Molecular genetics
- Creators: Guo, Jia
- Creators: Li, Jing
Description
Imaging genetics is an emerging and promising technique that investigates how genetic variations affect brain development, structure, and function. By exploiting disorder-related neuroimaging phenotypes, this class of studies provides a novel direction to reveal and understand the complex genetic mechanisms. Oftentimes, imaging genetics studies are challenging due to the relatively small number of subjects but extremely high-dimensionality of both imaging data and genomic data. In this dissertation, I carry on my research on imaging genetics with particular focuses on two tasks---building predictive models between neuroimaging data and genomic data, and identifying disorder-related genetic risk factors through image-based biomarkers. To this end, I consider a suite of structured sparse methods---that can produce interpretable models and are robust to overfitting---for imaging genetics. With carefully-designed sparse-inducing regularizers, different biological priors are incorporated into learning models. More specifically, in the Allen brain image--gene expression study, I adopt an advanced sparse coding approach for image feature extraction and employ a multi-task learning approach for multi-class annotation. Moreover, I propose a label structured-based two-stage learning framework, which utilizes the hierarchical structure among labels, for multi-label annotation. In the Alzheimer's disease neuroimaging initiative (ADNI) imaging genetics study, I employ Lasso together with EDPP (enhanced dual polytope projections) screening rules to fast identify Alzheimer's disease risk SNPs. I also adopt the tree-structured group Lasso with MLFre (multi-layer feature reduction) screening rules to incorporate linkage disequilibrium information into modeling. Moreover, I propose a novel absolute fused Lasso model for ADNI imaging genetics. This method utilizes SNP spatial structure and is robust to the choice of reference alleles of genotype coding. In addition, I propose a two-level structured sparse model that incorporates gene-level networks through a graph penalty into SNP-level model construction. Lastly, I explore a convolutional neural network approach for accurate predicting Alzheimer's disease related imaging phenotypes. Experimental results on real-world imaging genetics applications demonstrate the efficiency and effectiveness of the proposed structured sparse methods.
ContributorsYang, Tao (Author) / Ye, Jieping (Thesis advisor) / Xue, Guoliang (Thesis advisor) / He, Jingrui (Committee member) / Li, Baoxin (Committee member) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2017
Description
Spatial resolved detection and quantification of ribonucleic acid (RNA) molecules in single cell is crucial for the understanding of inherent biological issues, like mechanism of gene regulation or the development and maintenance of cell fate. Conventional methods for single cell RNA profiling, like single-cell RNA sequencing (scRNA-seq) or single-molecule fluorescent in situ hybridization (smFISH), suffer either from the loss of spatial information or the low detection throughput. In order to advance single-cell analysis, new approaches need to be developed with the ability to perform high-throughput detection while preserving spatial information of the subcellular location of target RNA molecules.
Novel approaches for highly multiplexed single cell in situ transcriptomic analysis were developed by our group to enable single-cell comprehensive RNA profiling in their native spatial contexts. Reiterative FISH was demonstrated to be able to detect >100 RNA species in single cell in situ, while more sophisticated approaches, consecutive FISH (C-FISH) and switchable fluorescent oligonucleotide based FISH (SFO-FISH), have the potential for whole transcriptome profiling at the single molecule sensitivity. The introduction of a cleavable fluorescent tyramide even enables sensitive RNA profiling in intact tissues with high throughput. These approaches will have wide applications in studies of systems biology, molecular diagnosis and targeted therapies.
Novel approaches for highly multiplexed single cell in situ transcriptomic analysis were developed by our group to enable single-cell comprehensive RNA profiling in their native spatial contexts. Reiterative FISH was demonstrated to be able to detect >100 RNA species in single cell in situ, while more sophisticated approaches, consecutive FISH (C-FISH) and switchable fluorescent oligonucleotide based FISH (SFO-FISH), have the potential for whole transcriptome profiling at the single molecule sensitivity. The introduction of a cleavable fluorescent tyramide even enables sensitive RNA profiling in intact tissues with high throughput. These approaches will have wide applications in studies of systems biology, molecular diagnosis and targeted therapies.
ContributorsXiao, Lu, Ph.D (Author) / Guo, Jia (Thesis advisor) / Wang, Xu (Committee member) / Borges, Chad (Committee member) / Arizona State University (Publisher)
Created2019