quantro Additional Materials and Scripts
The additional materials (R code and scripts) for the paper When to use Quantile Normalization? authored by Stephanie C. Hicks and Rafael A. Irizarry are provided here.
R-packages
quantro
The quantro R/Bioconductor package is discussed in detail in the Supplementary Section 1 of the paper. This package contains a data-driven test for global differences between groups of distributions which asses whether global normalization methods such as quantile normalization should be applied.
- quantro_0.99.1.tar.gz (archived)
- quantro vignette
- Most recent version available on Bioconductor
quantroSim
The quantroSim R package is discussed in detail in the Supplementary Section 3 of the paper. This is the supporting data simulation package for the quantro R/Bioconductor package which can be used to simulate gene expression and DNA methylation data.
- quantroSim_0.0.1.tar.gz (archived)
- quantroSim vignette
- Most recent version available on Github
Scripts
Applications to gene expression and DNA methylation
In this section, we apply quantro to several gene expression and DNA methylation datasets. For a description of all the data used, see Table 1 in the Supplementary Material. Below are Rmarkdown files containing the analysis using quantro.
Gene expression (RNA-Seq)
Gene expression (Microarrays)
- alveolarSmokingAffyData
- lungCOPDAffyData
- brainParkinsonsAffyData
- liverNAFLDAffyData
- mycAffyData
- Tumor/Normal examples
DNA methylation (Microarrays)
Simulation studies
In this section, we perform several Monte Carlo simulation studies to assess the performance of our method in the quantro R/Bioconductor package using the quantroSim R-package (discussed in further detail in Supplementary Section 4).
- Script for Supplementary Figures 21-22: Bias and MSE
- Script for Supplementary Figure 23: False discovery plots
- Script for Supplementary Figure 24: ROC curves
- Helper functions needed for simulation studies