Abstract
Life science experiments that employ automated technologies, such as high-content screens, frequently produce large datasets that require substantial amounts of preprocessing before analysis can be carried out. Standardization of this preprocessing becomes impossible as the dataset size increases if there are manual steps involved. Virtually no standards for preprocessing currently exist and few user-friendly tools are available that allow the cleaning of data files in a simple and transparent manner while also allowing for reproducibility. We demonstrate in a publicly available R package, PurifyR, how preprocessing steps can be streamlined and automated. PurifyR supports multithreading and the standardization of large-matrix preprocessing. These steps provide transparent and reproducible preprocessing for matrix-oriented datasets. The PurifyR package is open source and can be downloaded from github.
Highlights
Machine-generated datasets, such as those from highcontent screens (HCSs), are increasingly unavoidable in life science and bioinformatic experiments[1,2] and, given their size and complexity, are challenging to both maintain and process without automated preprocessing tool kits.[3]
PurifyR Package Components In this study, we present PurifyR, an R package for big data preprocessing, for preparing highdimensional datasets in a dynamic, repeatable, and autonomous manner to avoid reinventing the wheel
The PurifyR package can be installed from GitHub, see Supplementary Data S6, or a live Shiny implementation can be seen at https://purifyr.stratominer.com/ Shiny Users can call three specific functions to automate preprocessing steps
Summary
Machine-generated datasets, such as those from highcontent screens (HCSs), are increasingly unavoidable in life science and bioinformatic experiments[1,2] and, given their size and complexity, are challenging to both maintain and process without automated preprocessing tool kits.[3]. Datasets must be clean for analysis and prepared before the use of any later machine learning methods.[18] Time-consuming cleansing processes require focus, which can become a distraction from the original purpose of the experiment.
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