| Literature DB >> 33703901 |
Vincent Danna1, Hugh Mitchell1, Lindsey Anderson1, Iobani Godinez1, Sara J C Gosline1, Justin Teeguarden2, Jason E McDermott1,3.
Abstract
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).Entities:
Keywords: data integration; pathway analysis; phosphoproteomics; proteomics
Mesh:
Year: 2021 PMID: 33703901 PMCID: PMC9000964 DOI: 10.1021/acs.jproteome.0c00963
Source DB: PubMed Journal: J Proteome Res ISSN: 1535-3893 Impact factor: 4.466