| Autor: |
Danna V; Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States., Mitchell H; Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States., Anderson L; Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States., Godinez I; Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States., Gosline SJC; Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States., Teeguarden J; Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States., McDermott JE; Computational Biology Group, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.; Department of Molecular Microbiology and Immunology, Oregon Health & Sciences University, Portland, Oregon 97201, United States. |
| Abstrakt: |
A generalized goal of many high-throughput data studies is to identify functional mechanisms that underlie observed biological phenomena, whether they be disease outcomes or metabolic output. Increasingly, studies that rely on multiple sources of high-throughput data (genomic, transcriptomic, proteomic, metabolomic) are faced with a challenge of summarizing the data to generate testable hypotheses. However, this requires a time-consuming process to evaluate numerous statistical methods across numerous data sources. Here, we introduce the leapR package, a framework to rapidly assess biological pathway activity using diverse statistical tests and data sources, allowing facile integration of multisource data. The leapR package with a user manual and example workflow is available for download from GitHub (https://github.com/biodataganache/leapR). |
