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Activity Number:
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535
- Contributed Poster Presentations: Section on Statistics in Genomics and Genetics
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #330204
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Title:
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Statistical Inference for Proteomics Data with Missing Peptide Concentrations
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Author(s):
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Hyeongseon Jeon* and Dan Nettleton
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Companies:
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and Iowa State University
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Keywords:
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Abstract:
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An experiment was conducted at Iowa State University to study the impact of selection for residual feed intake on the immune systems of pigs. The main research objective is to detect proteins whose mean abundance changes in response to a lipopolysaccharide injection intended to stimulate the immune systems of pigs selected for either high or low residual feed intake. Protein abundance is not directly observed in our label-free liquid chromatography-mass spectrometry data, so we propose a model based on the correspondence between observed peptides and latent proteins. The model includes peptide-specific yield rates and normalizing covariates. Imputation is used to manage peptide abundance values that are missing not at random. Simulation is used to compare the proposed approach to a mixed effect modeling approach, the TOP3 method, and the SCAMPI method.
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Authors who are presenting talks have a * after their name.
