PurifyR: An R Package for Highly Automated, Reproducible Variable Extraction and Standardization

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.

Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening

“…Preprocessing data is a very tedious but important part of a data analysis process. 9,19,20 First, data are divided into meta features (information about the data) and analytical features (used for data analysis). Examples of analytical features are intensity, area, shape, and texture features on various channels (Thermo Scientific, 2010; retrieved from http://www.med.cam.ac.uk/wp-content/uploads/2016/02/ MorphologyExplorer_V4_LC06170800.pdf).…”