ANALYTICAL APPROACHES FOR COMPLEX MULTI-BATCH -OMICS DATASETS AND THEIR APPLICATION TO NEURONAL DEVELOPMENT

dc.contributor.advisorSpeer, Colenso Men_US
dc.contributor.advisorEl-Sayed, Najib Men_US
dc.contributor.authorAlexander, Theresa Annen_US
dc.contributor.departmentBiologyen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2023-06-26T05:30:46Z
dc.date.available2023-06-26T05:30:46Z
dc.date.issued2023en_US
dc.description.abstractHigh-throughput sequencing methods are extremely powerful tools to quantify gene expression in bulk tissue and individual cells. Experimental designs often aim to quantify expression shifts to characterize developmental trajectories, disease states, or cellular drug responses. Experimental and genetic methods are also rapidly evolving to capture specific aspects of gene expression such as in targeting individual cell types, regulatory stages, and spatially resolved cell subcompartments. These studies frequently involve a variety of experimental conditions that require many samples to guarantee sufficient statistical power for subsequent analyses. These studies are frequently processed in multiple batches due to limitations on the number of samples that can be collected, processed, and sequenced at once. To eliminate erroneous results in subsequent analyses, it is necessary to deconvolve non-biological variation (batch effect) from biological signal. Here, we explored variational contributions in multi-batch high throughput sequencing experiments by developing new methods, evaluating heterogeneity-contributors in an axon-TRAP-RiboTag protocol case-study, and highlighting biological results from this protocol. First, we describe iDA, a novel dimensionality reduction method for high-throughput sequencing data. High-dimensional data in complex, multi-batch experiments often result in discrete clustering of samples or cells. Existing unsupervised linear dimensionality reduction methods like PCA often do not resolve discreteness simply with projections of maximum variance. We show that iDA can produce better projections for separating discrete clustering that correlates with known experimental biological and batch factors. Second, we provide a case study of special considerations for a complex, multi-batch high throughput experiment. We investigated the multi-faceted heterogenic contributions of a study using the axon-TRAP-RiboTag translatomic isolation protocol in a neuronal cell type. We show that popular batch-correction methods may reduce signal due to true biological heterogeneity in addition to technical noise. We offer metrics to help identify biological signal-driven batch-differences. Lastly, we employ our understanding of variational contributions in the intrinsically photosensitive retinal ganglion cell (ipRGC) -omics case study to explore the biological transcriptomic and translatomic coordination. Our analysis revealed ipRGCs participate in subcompartment-specific local protein translation. Genetic perturbations of photopigment-driven neuronal activity led to global tissue transcriptomic shifts in both the retina and brain targets, but the ipRGC axonal-specific translatome was unaltered.en_US
dc.identifierhttps://doi.org/10.13016/dspace/nrui-9ysh
dc.identifier.urihttp://hdl.handle.net/1903/30168
dc.language.isoenen_US
dc.subject.pqcontrolledBioinformaticsen_US
dc.subject.pqcontrolledBiologyen_US
dc.subject.pquncontrolledbioinformaticsen_US
dc.subject.pquncontrolledcomputational biologyen_US
dc.titleANALYTICAL APPROACHES FOR COMPLEX MULTI-BATCH -OMICS DATASETS AND THEIR APPLICATION TO NEURONAL DEVELOPMENTen_US
dc.typeDissertationen_US

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