README.md

In jaspershen/MetNormalizer: MetNormalizer

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 ([e-mail]) or reach out me via the social medias below.

shenxt1990

[e-mail]

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!

jaspershen/MetNormalizer documentation built on March 7, 2021, 6:53 p.m.