A Mixed-effects Model for Incomplete Data With Batch-Level   Abundance-Dependent Missing-Data Mechanism
  In mass spectrometry based quantitative proteomics research, the emerging iTRAQ technique has been widely adopted for high throughput protein profiling, as it enables one to measure multiple samples simultaneously in one multiplex experiment and thus greatly enhances the throughput of protein quantification. However, the technical variation across different iTRAQ multiplex experiments is often large due to the dynamic nature of MS instruments. This leads to strong batch effects in the iTRAQ data. Moreover, the iTRAQ data often contain substantial batch-level non-ignorable missingness. Specifically, the abundance measures of a given protein/peptide are often missing altogether in all the samples from the same batch, with the missing probability depending on the combined batch-level abundances. We term this unique missing-data mechanism as the Batch-level Abundance-Dependent Missing-data mechanism (BADMM). We introduce a new method, mixEMM, for analyzing iTRAQ data with batch effects and batch-level non-ignorable missingness. The mixEMM method employs a linear mixed-effects model and explicitly models the batch effects and the BADMM in the likelihood function. With simulation studies, we showed that compared with existing approaches that utilize relative abundances and ignore the missing batches under the missing completely at random assumption, the mixEMM method achieves more accurate parameter estimation and inference.We applied the method to an iTRAQ proteomics data from a breast cancer study and identified phosphopeptides differentially expressed between different breast cancer subtypes. The method can be applied to general clustered data with cluster level non ignorable missing-data mechanisms. 
Lin S. Chen, Jiebiao Wang, Xianlong Wang and Pei Wang
stat.AP