hRUV is a package for normalisation of multiple batches of metabolomics data in a hierarchical strategy with use of samples replicates in large-scale studies. The tool utilises 2 types of replicates: intra-batch and inter-batch replicates to estimate the unwatned variation within and between batches with RUV-III. We have designed the replicate embedding arrangements within and between batches from http://shiny.maths.usyd.edu.au/hRUV. Our novel tool is a novel hierarchical approach to removing unwanted variation by harnessing information from sample replicates embedded in the seequence of experimental runs/batches and applying signal drift correction with robust linear or non-linear smoothers.
This package has been tested for macOS Big Sur (11.1) and Linux Debian 10 (buster) with R version 4.0.3.
Install the R package from GitHub using the
if (!("devtools" %in% rownames(installed.packages()))) install.packages("devtools") # Tested on devtools version 2.3.2. library(devtools) devtools::install_github("SydneyBioX/hRUV", build_vignettes=TRUE, dependencies = TRUE)
The vignette is available at https://sydneybiox.github.io/hRUV/. Alternatively, it can be accessed with
browseVignettes("hRUV") in your R console.
If you have any enquiries, especially about performing hRUV to integrate your metabolomics data, please contact email@example.com. We are also happy to receive any suggestions and comments.
Kim, T., Tang, O., Vernon, S. T., Kott, K. A., Koay, Y. C., Park, J., James, D. E., Grieve, S. M., Speed, T. P., Yang, P., Figtree, G. A., O’Sullivan, J. F., & Yang, J. Y. H. (2021). A hierarchical approach to removal of unwanted variation for large-scale metabolomics data. In Nature Communications (Vol. 12, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41467-021-25210-5