The traditional approaches to handling missing values in DIA proteomics are to either remove high-missingness proteins or impute them with statistical procedures. Both have their disadvantages—removal can limit statistical power, while imputation can introduce spurious correlations or dilute signal. We present an alternative approach based on imputing peptide retention times (RTs) rather than quantitations. For each missing value, we impute the RT boundaries, then obtain a quantitation by integrating the chromatographic signal within the imputed boundaries. Our method yields more accurate quantitations than existing proteomics imputation methods. RT boundary imputation also identifies differentially abundant peptides from key Alzheimer’s genes that were not identified with library search alone. RT boundary imputation improves the ability to estimate radiation exposure in biological tissues. RT boundary imputation significantly increases the number of peptides with quantitations, leading to increases in statistical power. Finally, RT boundary imputation better quantifies low abundance peptides than library search alone. Our RT boundary imputation method, called Nettle, is available as a standalone tool.

We obtained data from three sources (all available on Panorama). The first is a matrix-matched calibration curves experiment in which human peptides are spiked into a yeast background. The second is a study of cerebrospinal fluid derived from Alzheimer's disease patients. The third is a biodosimetry study of mice exposed to ionizing x-ray radiation. This folder contains Skyline documents used to apply our retention time boundary imputation method to each of these datasets.

Samples were acquired in DIA mode.