MetNormalizer

Installation

You can install MetNormalizer from Github.

# Install `MetNormalizer` from GitHub
if(!require(devtools)){
install.packages("devtools")
}
devtools::install_github("jaspershen/MetNormalizer")

We use the demo data in demoData package to show how to use MetNormalizer. Please install it first.

devtools::install_github("jaspershen/demoData")

Usage

Demo data

library(demoData)
library(MetNormalizer)
path <- system.file("MetNormalizer", package = "demoData")
file.copy(from = path, to = ".", overwrite = TRUE, recursive = TRUE)
new.path <- file.path("./MetNormalizer")

Run MetNormalizer 

metNor(
  ms1.data.name = "data.csv",
  sample.info.name = "sample.info.csv",
  minfrac.qc = 0,
  minfrac.sample = 0,
  optimization = TRUE,
  multiple = 5,
  threads = 4,
  path = new.path
)

All the results will be placed in the folder named as svr_normalization_result.

Need help?

If you have any questions about tidymass, please don’t hesitate to email me () or reach out me via the social medias below.

shenxt1990

Twitter

M339, Alway Buidling, Cooper Lane, Palo Alto, CA 94304

Citation

If you use MetNormalizer in you publication, please cite this publication:

Xiaotao Shen, Xiaoyun Gong, Yuping Cai, Yuan Guo, Jia Tu, Hao Li, Tao Zhang, Jialin Wang, Fuzhong Xue & Zheng-Jiang Zhu* (Corresponding Author), Normalization and integration of large-scale metabolomics data using support vector regression. Metabolomics volume 12, Article number: 89 (2016).

Thanks very much!