Estimating Relative Protein Expression Levels Sample Clauses

Estimating Relative Protein Expression Levels from In- complete Data Most statistical analyses of proteomics data do not address the issue of missing values beyond simple missing value imputation schemes, under the MCAR assumption. We believe that with proteomics data, MCAR is both too restrictive and unrealistic. We propose model based estimation strategies that are based on the less restrictive MAR assumption in two settings. First, we look at robustly estimating the true relative expression ratio of a protein based on incompletely observed peptide level data, using a multivariate t model. Secondly, we will look at a valid estimation strategy when only one peptide is available to uniquely identify a protein, using a bivariate normal model with a monotone missingness pattern. Since the sample sizes associated with single peptide proteins is relatively small, inferences based on the inverse of the observed information matrix are not ideal. Therefore, we also propose a Xxxxxxxx scheme for constructing confidence intervals for the estimated parameters under the bivariate normal model. We believe that our work is the first to investigate the use of these types of model based approaches for estimating the relative protein expression under MAR.
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