Statistical Inference and Community Detection in Proximity and Spatial Proteomics: Resolving the Organization of the Neuronal Proteome

Soderling, Scott H

Bradshaw, Tyler Wesley

2021-05-19T18:08:19Z

2021-11-17T09:17:08Z

2021

Neurobiology

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.

https://hdl.handle.net/10161/23039

Neurosciences

Molecular biology

Animal diseases

Community detection

Hypothesis testing

Neurobiology of disease

Proximity Proteomics

Spatial Proteomics

Statistical Inference and Community Detection in Proximity and Spatial Proteomics: Resolving the Organization of the Neuronal Proteome

Dissertation

5.950684931506849