代谢组数据有多种数据矫正方式：
（1）内标归一化 （2）基于样本总面积（3）QC归一化
这里采用MetNormalizer包进行数据前处理。具体R信息如下：
setwd('D:/R_code/MetNormalizer/normalization')
install.packages ("e1071") #安装相应软件包
install.packages("usethis")
install.packages("devtools")
install_github("jaspershen/MetNormalizer")
install_github("jaspershen/demoData")
devtools::install_github("jaspershen/demoData")
library ( e1071)
library(usethis)
library(devtools)
library(demoData)
library(MetNormalizer)
# 安装后，显示Error: package or namespace load failed for ‘MetNormalizer’:
# package ‘MetNormalizer’ was installed before R 4.0.0: please re-install it
# 看是否是安装了多个MetNormalizer包，导致存在冲突，因此查询安装路径
# 可能是路径的问题，查询路径
.libPaths()
#[1] "D:/Program Files/R/R-4.1.2/library"
.libPaths("D:/Program Files/R/R-4.1.2/library") #根据查询到的路径，设置library路径
# 未能解决问题，重新安装 MetNormalizer后，解决问题
# 示例 数据分析
path <- system.file("MetNormalizer", package = "demoData")
file.copy(from = path, to = ".", overwrite = TRUE, recursive = TRUE)
new.path <- file.path("./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
)
#######################################################################
# 自己的代谢组数据标准化code
setwd('D:/R_code/MetNormalizer/normalization')
new.path <-('D:/R_code/MetNormalizer/normalization')
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
)
# 503对代谢组数据标准化后数据保存在svr_normalization_result.
#######################################################################
# 备注：因为 devtools 缺少 rtools4 包， update rtools4
write('PATH="${RTOOLS40_HOME}\\usr\\bin;${PATH}"', file = "~/.Renviron", append = TRUE)
Sys.which("make")
## "C:\\rtools40\\usr\\bin\\make.exe"
install.packages("jsonlite", type = "source")
#update rtools4 完毕
参考内容：
guithub 的安装包和指导手册：https://github.com/jaspershen/MetNormalizer