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MGMS2
View on CRAN: Click
here
Download and install MGMS2 package within the R console
Install from CRAN:
install.packages("MGMS2")
Install from Github:
library("remotes")
install_github("cran/MGMS2")
Install by package version:
library("remotes")
install_version("MGMS2", "1.0.2")
Attach the package and use:
library("MGMS2")
Maintained by
George Wendt
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-12-16
Latest Update: 2020-04-24
Description:
A glycolipid mass spectrometry technology has the potential to accurately identify individual bacterial species from polymicrobial samples. To develop bacterial identification algorithms (e.g. machine learning) using this glycolipid technology, it is necessary to generate a large number of various in-silico polymicrobial mass spectra that are similar to real mass spectra. 'MGMS2' (Membrane Glycolipid Mass Spectrum Simulator) generates such in-silico mass spectra, considering errors in m/z (mass-to-charge ratio) and variances of intensity values, occasions of missing signature ions, and noise peaks. It estimates summary statistics of monomicrobial mass spectra for each strain or species and simulates polymicrobial glycolipid mass spectra using the summary statistics of monomicrobial mass spectra. References: Ryu, S.Y., Wendt, G.A., Chandler, C.E., Ernst, R.K. and Goodlett, D.R. (2019) "Model-based Spectral Library Approach for Bacterial Identification via Membrane Glycolipids." Gibb, S. and Strimmer, K. (2012) "MALDIquant: a versatile R package for the analysis of mass spectrometry data."
How to cite:
George Wendt (2019). MGMS2: 'MGMS2' for Polymicrobial Samples. R package version 1.0.2, https://cran.r-project.org/web/packages/MGMS2. Accessed 21 Nov. 2024.
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imports, suggests or enhances
Complete documentation for MGMS2
Functions, R codes and Examples using
the MGMS2 R package
Some associated functions: characterize_peak . create_insilico_mixture_template . filtermass . gather_summary . gather_summary_file . preprocessMS . process_monospectra . simulate_ind_spec_single . simulate_many_poly_spectra . simulate_poly_spectra . summarize_monospectra . summary_mono .
Some associated R codes: SpecFunctions.R . Full MGMS2 package functions and examples
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