https://github.com/yufree/envigcms
GC/LC-MS data analysis for environmental science
https://github.com/yufree/envigcms
cran environment mass-spectrometry metabolomics r
Last synced: 4 months ago
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GC/LC-MS data analysis for environmental science
- Host: GitHub
- URL: https://github.com/yufree/envigcms
- Owner: yufree
- Created: 2015-12-17T07:16:46.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2025-01-14T20:31:06.000Z (6 months ago)
- Last Synced: 2025-02-28T12:24:42.753Z (5 months ago)
- Topics: cran, environment, mass-spectrometry, metabolomics, r
- Language: R
- Homepage: http://yufree.github.io/enviGCMS/
- Size: 182 MB
- Stars: 17
- Watchers: 3
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
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README
enviGCMS: GC-MS Data Analysis for Environmental Science
================[](https://cran.r-project.org/package=enviGCMS) [](https://cran.r-project.org/package=enviGCMS) [](https://cran.r-project.org/package=enviGCMS) [](https://www.repostatus.org/#active)
`enviGCMS` provides functions for GC/LC-MS data analysis for environmental sciences.
Installation
------------You can either use the stable version of `enviGCMS` from CRAN,
``` {r}
install.packages("enviGCMS")
```or the current development snapshot from this GitHub repository:
``` {r}
remotes::install_github("yufree/enviGCMS")
```Usage
-----Check this [vignette](http://yufree.github.io/enviGCMS/articles/GCMSDA.html) for Data analysis of GC-MS and LC-MS in Environmental Science.
Check this [vignette](http://yufree.github.io/enviGCMS/articles/PooledQC.html) for Pooled QC analysis in Environmental Science.
- get the mean and RSD of one sample for 5 technique replicate
~~~
# enviGCMS use functions in xcms to import the data, just type the path to your single sample
data1 <- enviGCMS:::getmd(‘sample1-1’)
data2 <- enviGCMS:::getmd(‘sample1-2’)
data3 <- enviGCMS:::getmd(‘sample1-3’)
data4 <- enviGCMS:::getmd(‘sample1-4’)
data5 <- enviGCMS:::getmd(‘sample1-5’)
~~~- get the mean
~~~
data <- (data1+data2+data3+data4+data5)/5
~~~- get the standard deviation
~~~
datasd <- sqrt(((data1-data)^2+(data2-data)^2+(data3-data)^2+(data4-data)^2+(data5-data)^2)/4)
~~~- get the RSD
~~~
databrsd <- datasd/data
~~~- plot the smooth scatter
~~~
plotsms(datarsd)
~~~- plot the heatmap
~~~
plotms(data)
~~~- plot the mz-rt scatter plot
~~~
plotmz(data)
~~~- plot the boundary model
~~~
findline(data)
~~~Detailed usage of those functions in Environmental analysis could be found in this [paper](https://www.sciencedirect.com/science/article/pii/S0039914016309298) and the vignettes in this package.