In jaspershen/MetNormalizer: MetNormalizer

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE,
  fig.path = "man/figures/"
)

library("badger")

MetNormalizer

cat(
    badge_cran_release("MetNormalizer", "green"),
    badge_code_size(ref = "jaspershen/MetNormalizer"),
    badge_dependencies(),
    badge_lifecycle()
    # badge_cran_download("badger", "grand-total", "green"),
    # badge_cran_download("badger", "last-month", "green"),
    # badge_cran_download("badger", "last-week", "green")
)

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.

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