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.

hRUV overview

Software requirements

This package has been tested for macOS Big Sur (11.1) and Linux Debian 10 (buster) with R version 4.0.3.

R package dependecies

CRAN

dplyr
tidyr
rlang
ggplot2
plotly
S4Vectors
tibble
DMwR2
MASS

Bioconductor

SummarizedExperiment
impute

Installation

Install the R package from GitHub using the devtools package:

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)

Vignette

The vignette is available at https://sydneybiox.github.io/hRUV/. Alternatively, it can be accessed with browseVignettes("hRUV") in your R console.

Contact us

If you have any enquiries, especially about performing hRUV to integrate your metabolomics data, please contact . We are also happy to receive any suggestions and comments.

Citation

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