Accounting for undetected compounds in statistical analyses of mass spectrometry 'omic studies.

ABSTRACT: Mass spectrometry is an important high-throughput technique for profiling small molecular compounds in biological samples and is widely used to identify potential diagnostic and prognostic compounds associated with disease. Commonly, this data generated by mass spectrometry has many missing values resulting when a compound is absent from a sample or is present but at a concentration below the detection limit. Several strategies are available for statistically analyzing data with missing values. The accelerated failure time (AFT) model assumes all missing values result from censoring below a detection limit. Under a mixture model, missing values can result from a combination of censoring and the absence of a compound. We compare power and estimation of a mixture model to an AFT model. Based

SUBMITTER: Taylor SL 

PROVIDER: S-EPMC3905689 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature