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MetProc  

Separate Metabolites into Likely Measurement Artifacts and True Metabolites
View on CRAN: Click here


Download and install MetProc package within the R console
Install from CRAN:
install.packages("MetProc")

Install from Github:
library("remotes")
install_github("cran/MetProc")

Install by package version:
library("remotes")
install_version("MetProc", "1.0.1")



Attach the package and use:
library("MetProc")
Maintained by
Mark Chaffin
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-05-19
Latest Update: 2016-05-19
Description:
Split an untargeted metabolomics data set into a set of likely true metabolites and a set of likely measurement artifacts. This process involves comparing missing rates of pooled plasma samples and biological samples. The functions assume a fixed injection order of samples where biological samples are randomized and processed between intermittent pooled plasma samples. By comparing patterns of missing data across injection order, metabolites that appear in blocks and are likely artifacts can be separated from metabolites that seem to have random dispersion of missing data. The two main metrics used are: 1. the number of consecutive blocks of samples with present data and 2. the correlation of missing rates between biological samples and flanking pooled plasma samples.
How to cite:
Mark Chaffin (2016). MetProc: Separate Metabolites into Likely Measurement Artifacts and True Metabolites. R package version 1.0.1, https://cran.r-project.org/web/packages/MetProc. Accessed 02 Nov. 2026.
Previous versions and publish date:
1.0.1 (2016-05-19 05:46), (2026-07-09 08:10)
Other packages that cited MetProc R package
View MetProc citation profile
Other R packages that MetProc depends, imports, suggests or enhances
Complete documentation for MetProc
Functions, R codes and Examples using the MetProc R package
Some associated functions: MetProc-package . corr_metric . get_group . get_missing . heatmap_res . met_proc . plot_metric . plot_pp_sample_missing . read.met . run_metric . sampledata . subset_met . write.met . 
Some associated R codes: functions_cleane.R .  Full MetProc package functions and examples
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