In this review, we apply multiple imputation strategies to various label-free LC-MS experimental datasets to evaluate the accuracy in respect to mean square error (MSE) and classification of experimental groups, as well as the robustness and run times of the various imputation approaches. We examine these commonly used imputation approaches for their individual merits and discuss the caveats and merits of each approach in respect to LC-MS proteomics data.
Revised: January 7, 2016 |
Published: May 1, 2015
Citation
Webb-Robertson B.M., H.K. Wiberg, M.M. Matzke, J.N. Brown, J. Wang, J.E. McDermott, and R.D. Smith, et al. 2015.Review, Evaluation, and Discussion of the Challenges of Missing Value Imputation for Mass Spectrometry-Based Label-Free Global Proteomics.Journal of Proteome Research 14, no. 5:1993-2001. PNWD-SA-10158. doi:10.1021/pr501138h