Linear Mixed Model Analysis of Proteomic Data with Many Missing Values

Project

Label-free proteomic data can be used to obtain abundance measurements for many proteins that can be compared across multiple biological samples.  Abundance measurements of peptides within a protein are combined together to provide a measurement of protein abundance.  Values for many peptides can be missing in one or more samples.  Missing values can be a sign of low abundance, but some missing values occur independently of protein abundance.  Thus, care must be used when interpreting missing values.  In a collaboration with Animal Science faculty (Dekkers, Lonergan, and Tuggle), Baker Center personnel (Jeon and Nettleton) are coupling imputation techniques with linear mixed model analyses to identify proteins whose abundance differs across pig genetic lines and/or across infection treatments.