A User-friendly and Powerful R Analysis of Large-scale Datasets
| dc.contributor.author | Allison, Jessica | |
| dc.contributor.author | Zhang, Chong | |
| dc.contributor.author | Egoshi, Riki | |
| dc.contributor.author | Lu, Hua | |
| dc.date.accessioned | 2025-12-15T14:58:33Z | |
| dc.date.issued | 2025-11-04 | |
| dc.description.abstract | Large datasets are increasingly common in the scientific field. It is important to develop user-friendly tools to allow researchers to analyze these large datasets with ease. Here, we introduce a method involving an R script in the open-source software RStudio to analyze large-scale datasets obtained from time series experiments. This method requires minimal input from a user, allowing a beginner who does not have prior R knowledge or programming experience to use it. The detailed instructions described here and in the R script shall further guide users on how to use the method. The input data and the output results are stored in the same folder of a local computer, making it possible to do the analysis anywhere and anytime. The output results are organized into folders for easy interpretation, and they can be conveniently processed to generate figures for publications. This method has been successfully used to analyze circadian clock data and reactive oxygen species burst data, both containing large-scale datasets from time series experiments in a 96-well-plate format. We believe that this method provides a facile and powerful solution for researchers in analyzing similar large datasets obtained through time series experiments. | |
| dc.description.sponsorship | We thank the members of the Lu laboratory for their assistance in this work. We thank Min Gao and Matthew Fabian for the use of their unprocessed data and Benjamin Harris for assistance and/or guidance in making this R script. We thank John B. Hogenesch at Cincinnati Children's Hospital Medical Center for providing luminescence data from mammalian cells for Case study 2. We further thank John B. Hogenesch, Andrew Millar at The University of Edinburgh, and Mary Harrington at Smith College for helpful discussions during the development of this method. This work was partially supported by grants from the National Science Foundation, NSF 1456140 and NSF 2223886, to Hua Lu. | |
| dc.description.uri | https://app.jove.com/t/68868/a-user-friendly-and-powerful-r-analysis-of-large-scale-datasets | |
| dc.format.extent | 18 pages | |
| dc.genre | journal articles | |
| dc.identifier | doi:10.13016/m2vwrm-l0kw | |
| dc.identifier.citation | Allison, Jessica, Chong Zhang, Riki Egoshi, and Hua Lu. “A User-Friendly and Powerful R Analysis of Large-Scale Datasets.” Journal of Visualized Experiments (JoVE), no. 225 (November 2025): e68868. https://doi.org/10.3791/68868. | |
| dc.identifier.uri | https://dx.doi.org/10.3791/68868 | |
| dc.identifier.uri | http://hdl.handle.net/11603/41244 | |
| dc.language.iso | en | |
| dc.publisher | JoVE | |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Chemistry & Biochemistry Department | |
| dc.relation.ispartof | UMBC Biological Sciences Department | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | © 2025 JoVE Journal of Visualized Experiments | |
| dc.title | A User-friendly and Powerful R Analysis of Large-scale Datasets | |
| dc.type | Text | |
| dcterms.creator | https://orcid.org/0000-0002-7496-3200 | |
| dcterms.creator | https://orcid.org/0009-0004-3199-4762 |
