Augmented Doubly Robust Post-Imputation Inference for Proteomic Data
arxiv(2024)
摘要
Quantitative measurements produced by mass spectrometry proteomics
experiments offer a direct way to explore the role of proteins in molecular
mechanisms. However, analysis of such data is challenging due to the large
proportion of missing values. A common strategy to address this issue is to
utilize an imputed dataset, which often introduces systematic bias into
downstream analyses if the imputation errors are ignored. In this paper, we
propose a statistical framework inspired by doubly robust estimators that
offers valid and efficient inference for proteomic data. Our framework combines
powerful machine learning tools, such as variational autoencoders, to augment
the imputation quality with high-dimensional peptide data, and a parametric
model to estimate the propensity score for debiasing imputed outcomes. Our
estimator is compatible with the double machine learning framework and has
provable properties. In application to both single-cell and bulk-cell proteomic
data our method utilizes the imputed data to gain additional, meaningful
discoveries and yet maintains good control of false positives.
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