The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments.

ABSTRACT: An idealized version of a label-free discovery mass spectrometry proteomics experiment would provide absolute abundance measurements for a whole proteome, across varying conditions. Unfortunately, this ideal is not realized. Measurements are made on peptides requiring an inferential step to obtain protein level estimates. The inference is complicated by experimental factors that necessitate relative abundance estimation and result in widespread non-ignorable missing data. Relative abundance on the log scale takes the form of parameter contrasts. In a complete-case analysis, contrast estimates may be biased by missing data and a substantial amount of useful information will often go unused. To avoid problems with missing data, many analysts have turned to single imputation solutions. Unfort

SUBMITTER: O'Brien JJ 

PROVIDER: S-EPMC6249692 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature