Abstract Motivation There is a growing need for efficient imputation methods for high-dimensional DNA methylation (DNAm) datasets. Existing microarray imputation approaches, such as k-nearest neighbors (K-NN) or principal component analysis (PCA)-based methods, provide high accuracy but can be computationally intensive, while methods for whole-genome data are not designed for large cohorts. We developed slideimp, an R package that implements sliding window, groupable, parallelized K-NN and optimized PCA imputation to address these limitations. Results Benchmarks on microarray DNAm datasets demonstrate that slideimp achieves up to 150x faster runtime and 10x-100x lower memory usage while maintaining comparable or superior accuracy over existing methods and implementations. For K-NN and PCA imputation, slideimp supports grouped imputation which enhances imputation efficiency and accuracy. In a whole-genome dataset, sliding window K-NN imputation substantially increased the correlation of the Horvath 2013 clock with chronological age from 0.131 to 0.477. Additional features include targeted imputation of CpG subsets for K-NN and estimation of imputation accuracy via repeated cross-validation. The efficient and flexible DNAm imputation methods implemented by slideimp can easily be applied to other high-dimensional data types. Availability and Implementation The code to fully reproduce all analyses presented in this paper is available on GitHub at https://github.com/hhp94/slideimp_paper . The R package slideimp is also available on GitHub at https://github.com/hhp94/slideimp . Supplemental figures are available online.

The analysis of proteome profiles offers a new approach to understanding how cellular machinery functions and responds under certain conditions. By combining two-dimensional electrophoresis with mass spectrometry (MS), a snapshot of the cell's protein expression status and quantitative proteome profiling can be provided. As the cell's proteome becomes defined in normal and altered states, possible utilization of MS proteome profiling as a diagnostic tool becomes a reality. The ability of Matrix Assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS) to generate a spectrum with thousands of data points, necessitate the development of sophisticated analytical algorithms. In this paper, we describe how MALDI-MS can be used in monitoring proteomic profile in patients before and after treatment using a non- invasive sampling method. Because data analysis in this process possesses a challenge, we present a novel mathematical approach for analyzing data produced by MALDI MS, and discuss current applications of mass spectrometry in clinical medicine as well as challenges faced during procedures and experimentation. As a case study, we analyze protein expression patterns in premenopausal versus postmenopausal women. We also provide a proteomic profiling of premenopausal women versus postmenopausal women treated with estrogen as a hormone replacement therapy. * Corresponding authors: