Technological advances in protein mass spectrometry (MS), aka proteomics, haveenabled high-throughput quantification of spatially-resolved, subcellular-specific proteomes. Biological insight in these experiments depends upon sound statistical analysis. Despite the myriad of existing proprietary and open-source software solutions for statistical analysis of proteomics data, these tools suffer a drawback inherent in any general solution: a loss of specificity. These tools often fail to be easily adapted to analyze experiment-specific designs. I present a flexible, linear mixed-effects model framework for assessing differential abundance in protein mass spectrometry experiments. Combined with methods to identify communities of proteins in biological networks, I extend this framework to perform inference at the level of protein groups or modules. Using these software tools, I demonstrate how module-level insight in proximity and spatial proteomics generates hypotheses that identify foci of biological function and dysfunction which may underlie the neuropathology of disease.
","abstract_html":"<p>Technological advances in protein mass spectrometry (MS), aka proteomics, haveenabled high-throughput quantification of spatially-resolved, subcellular-specific proteomes. Biological insight in these experiments depends upon sound statistical analysis. Despite the myriad of existing proprietary and open-source software solutions for statistical analysis of proteomics data, these tools suffer a drawback inherent in any general solution: a loss of specificity. These tools often fail to be easily adapted to analyze experiment-specific designs. I present a flexible, linear mixed-effects model framework for assessing differential abundance in protein mass spectrometry experiments. Combined with methods to identify communities of proteins in biological networks, I extend this framework to perform inference at the level of protein groups or modules. Using these software tools, I demonstrate how module-level insight in proximity and spatial proteomics generates hypotheses that identify foci of biological function and dysfunction which may underlie the neuropathology of disease.</p>","abstract_has_math":false,"creators":["Bradshaw, Tyler Wesley"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Soderling, Scott H"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021","date_published":"2021","updated_at":"2026-07-24T02:07:07Z","subjects":["Neurosciences","Molecular biology","Animal diseases","Community Detection","Hypothesis Testing","Neurobiology of disease","Proximity Proteomics","Spatial Proteomics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10161/23039","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Soderling, Scott H"]},{"key":"dc:creator","label":"Author","values":["Bradshaw, Tyler Wesley"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-05-19T18:08:19Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-11-17T09:17:08Z"]},{"key":"dc:date.issued","label":"Date","values":["2021"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Neurosciences","Molecular biology","Animal diseases","Community Detection","Hypothesis Testing","Neurobiology of disease","Proximity Proteomics","Spatial Proteomics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10161/23039"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Technological advances in protein mass spectrometry (MS), aka proteomics, haveenabled high-throughput quantification of spatially-resolved, subcellular-specific proteomes. Biological insight in these experiments depends upon sound statistical analysis. Despite the myriad of existing proprietary and open-source software solutions for statistical analysis of proteomics data, these tools suffer a drawback inherent in any general solution: a loss of specificity. These tools often fail to be easily adapted to analyze experiment-specific designs. I present a flexible, linear mixed-effects model framework for assessing differential abundance in protein mass spectrometry experiments. Combined with methods to identify communities of proteins in biological networks, I extend this framework to perform inference at the level of protein groups or modules. Using these software tools, I demonstrate how module-level insight in proximity and spatial proteomics generates hypotheses that identify foci of biological function and dysfunction which may underlie the neuropathology of disease.
"]},{"key":"dc:title","label":"Title","values":["Statistical Inference and Community Detection in Proximity and Spatial Proteomics: Resolving the Organization of the Neuronal Proteome"]}]}],"canonical_facts":{"dc:contributor.advisor":["Soderling, Scott H"],"dc:creator":["Bradshaw, Tyler Wesley"],"dc:date.accessioned":["2021-05-19T18:08:19Z"],"dc:date.available":["2021-11-17T09:17:08Z"],"dc:date.issued":["2021"],"dc:description.abstract":["Technological advances in protein mass spectrometry (MS), aka proteomics, haveenabled high-throughput quantification of spatially-resolved, subcellular-specific proteomes. Biological insight in these experiments depends upon sound statistical analysis. Despite the myriad of existing proprietary and open-source software solutions for statistical analysis of proteomics data, these tools suffer a drawback inherent in any general solution: a loss of specificity. These tools often fail to be easily adapted to analyze experiment-specific designs. I present a flexible, linear mixed-effects model framework for assessing differential abundance in protein mass spectrometry experiments. Combined with methods to identify communities of proteins in biological networks, I extend this framework to perform inference at the level of protein groups or modules. Using these software tools, I demonstrate how module-level insight in proximity and spatial proteomics generates hypotheses that identify foci of biological function and dysfunction which may underlie the neuropathology of disease.
"],"dc:identifier.uri":["https://hdl.handle.net/10161/23039"],"dc:subject":["Neurosciences","Molecular biology","Animal diseases","Community Detection","Hypothesis Testing","Neurobiology of disease","Proximity Proteomics","Spatial Proteomics"],"dc:title":["Statistical Inference and Community Detection in Proximity and Spatial Proteomics: Resolving the Organization of the Neuronal Proteome"],"dc:type":["Dissertation"]},"updated_at":"2026-07-24T02:07:07Z"}