| Autor: |
Omta, W., Heesbeen, R. van, Shen, I., Feelders, A., Brinkhuis, M., Egan, D., Spruit, M., Sub Software Production, Sub Algorithmic Data Analysis, Sub Natural Language Processing, Sub Softw.Techn. for Learning and Teach., Algorithmic Data Analysis, Natural Language Processing, Software Technology for Learning and Teaching |
| Přispěvatelé: |
Sub Software Production, Sub Algorithmic Data Analysis, Sub Natural Language Processing, Sub Softw.Techn. for Learning and Teach., Algorithmic Data Analysis, Natural Language Processing, Software Technology for Learning and Teaching |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Families, Systems and Health, 3(1), 1. Mary Ann Liebert inc |
| ISSN: |
1091-7527 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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