Abstract
MOTIVATION: methyLImp, a method we recently introduced for the missing value estimation of DNA methylation data, has demonstrated competitive performance in data imputation compared to the existing, general-purpose, approaches. However, imputation running time was considerably long and unfeasible in case of large datasets with numerous missing values. RESULTS: methyLImp2 made possible computations that were previously unfeasible. We achieved this by introducing two important modifications that have significantly reduced the original running time without sacrificing prediction performance. First, we implemented a chromosome-wise parallel version of methyLImp. This parallelization reduced the runtime by several 10-fold in our experiments. Then, to handle large datasets, we also introduced a mini-batch approach that uses only a subset of the samples for the imputation. Thus, it further reduces the running time from days to hours or even minutes in large datasets. AVAILABILITY AND IMPLEMENTATION: The R package methyLImp2 is under review for Bioconductor. It is currently freely available on Github https://github.com/annaplaksienko/methyLImp2.