/tidymass

tidymass

Primary LanguageRMIT LicenseMIT

tidymass: R packages for MS data processing and analysis

Dependencies


About

The tidymass is a collection of R packages designed for MS-based untargeted metabolomics data processing. All packages share an underlying design philosophy, grammar, and data structures.

Tidymass is a comprehensive computational framework for MS-based untargeted metabolomics data processing and analysis, including raw data processing (peak detecting), data cleaning (missing value processing, data normalization, and integration), statistical analysis, metabolite annotation, and biological function mining (pathway enrichment, feature-based metabolic module analysis).


Installation

You can install tidymass from GitHub.

if(!require(devtools)){
install.packages("devtools")
}
devtools::install_github("tidymass/tidymass")

Then you can use tidymass_install() to install all the packages in tidymass.

library(tidymass)
tidymass::tidymass_install(from = "github", force = FALSE)

Packages

Now, tidymass contains 9 packages, which are listed below:

massConverter


massconverter is used to convert mass spectrometry raw data to other format data (mzXML, mxML, etc.).


massDataset


massdataset is used organize metabolomics experiment data into a mass_dataset class object, that can be processed by all the tidymass packages.


massProcesser


massprocesser is used for LC-MS based untargeted metabolomics raw data processing.


massCleaner


masscleaner is used for data cleaning of metabolomics.



massQC


massqc is used for data quality assessment and control.



metID


metid is used for metabolite database construction and metabolite annotation.



massStat


massstat is used for statistical analysis of untargeted metabolomics.



metPath


metpath is used for pathway enrichment analysis of metabolomics data.



tinTtools


tinytools is a collection of useful tiny tools for mass spectrometry data processing and analysis.


Need help?

If you have any questions about tidymass, please don’t hesitate to email me (shenxt@stanford.edu) or reach out me via the social medias below.

shenxt1990

shenxt@stanford.edu

Twitter

M339, Alway Buidling, Cooper Lane, Palo Alto, CA 94304

Citation

If you use tidymass in you publications, please cite this publication:

X. Shen, R. Wang, X. Xiong, Y. Yin, Y. Cai, Z. Ma, N. Liu, and Z.-J. Zhu* (Corresponding Author), Metabolic Reaction Network-based Recursive Metabolite Annotation for Untargeted Metabolomics, Nature Communications, 2019, 10: 1516.
Web Link.

Thank you very much!