具有批次丰度依赖缺失数据机制的不完全数据混合效应模型
A Mixed-effects Model for Incomplete Data With Batch-Level Abundance-Dependent Missing-Data Mechanism
摘要：
在基于质谱的定量蛋白质组学研究中，新兴的iTRAQ技术已被广泛用于高通量蛋白质分析，因为它使人们能够在一个多重实验中同时测量多个样品，从而大大提高了蛋白质定量的通量。然而，由于MS仪器的动态性质，不同iTRAQ多重实验之间的技术差异通常很大。这导致iTRAQ数据中存在强烈的批次效应。此外，iTRAQ数据通常包含大量批次级不可重复缺失。具体而言，给定蛋白质/肽的丰度测量通常在来自同一批次的所有样品中完全缺失，缺失概率取决于组合的批次水平丰度。我们将这种独特的缺失数据机制称为批次水平丰度依赖的缺失数据机制（BADMM）。我们介绍了一种新的方法，mixEMM，用于分析iTRAQ数据的批次效应和批次级的不可重复的缺失。mixEMM方法采用线性混合效应模型，并在似然函数中明确建模批次效应和BADMM。通过模拟研究，我们发现与现有的利用相对丰度并在完全随机缺失假设下忽略缺失批次的方法相比，mixEMM方法实现了更准确的参数估计和推断。我们将该方法应用于来自乳腺癌研究的iTRAQ蛋白质组学数据，并确定了不同乳腺癌亚型之间差异表达的磷酸肽。该方法适用于具有簇级不可恢复缺失数据机制的一般聚类数据。
英文摘要：
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.
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