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hrm

Easily install, load and update an R package ecosystem for performing analyses of high resolution metabolomics data.

hrm packages include:

  • metaboData - Example data sets for metabolomics analyses
  • grover - Web API Framework for Mass Spectrometry Data Transfer
  • binneR - Spectral Processing for High Resolution Flow Infusion Mass Spectrometry
  • metabolyseR - A tool kit for pre-treatment, modelling, feature selection and correlation analyses of metabolomics data
  • profilePro - Unified Input and Output for Processing of Metabolomic Profiling Experiments
  • mzAnnotation - Tools for putative annotation of accurate m/z from electrospray ionisation mass spectrometry data
  • assignments - Molecular formula assignment for high resolution ESI metabolomics
  • construction - Consensus structural classifications for putative molecular formula assignments
  • riches - Structural and functional enrichment for metabolomics data
  • metaboMisc - A collection of miscellaneous helper and linker functions for metabolomics analyses
  • metaboWorkflows - Workflow Project Templates for Metabolomics Analyses

Installation

Install the hrm package from GitHub using:

remotes::install_github('jasenfinch/hrm')

Usage

Loading the hrm packages will load the included R packages.

library(hrm)
#> ── Attaching packages ───────────────────────────────────────────── hrm 0.9.2 ──
#> ✔ chunky           0.1.1   ✔ projecttemplates 0.6.1 
#> ✔ metaboData       0.6.3   ✔ grover           1.1.3 
#> ✔ binneR           2.6.3   ✔ metabolyseR      0.15.0
#> ✔ profilePro       0.8.2   ✔ mzAnnotation     2.0.0 
#> ✔ assignments      1.0.0   ✔ construction     0.3.0 
#> ✔ riches           0.3.0   ✔ metaboMisc       0.6.1 
#> ✔ metaboWorkflows  0.10.0
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr     1.1.1     ✔ readr     2.1.4
#> ✔ forcats   1.0.0     ✔ stringr   1.5.0
#> ✔ ggplot2   3.4.1     ✔ tibble    3.2.1
#> ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
#> ✔ purrr     1.0.1     
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::collect()      masks xcms::collect()
#> ✖ dplyr::combine()      masks MSnbase::combine(), Biobase::combine(), BiocGenerics::combine()
#> ✖ tidyr::expand()       masks S4Vectors::expand()
#> ✖ dplyr::filter()       masks stats::filter()
#> ✖ dplyr::first()        masks S4Vectors::first()
#> ✖ dplyr::glimpse()      masks tibble::glimpse(), metaboWorkflows::glimpse()
#> ✖ dplyr::groups()       masks xcms::groups()
#> ✖ dplyr::lag()          masks stats::lag()
#> ✖ ggplot2::Position()   masks BiocGenerics::Position(), base::Position()
#> ✖ purrr::reduce()       masks metaboMisc::reduce(), MSnbase::reduce()
#> ✖ dplyr::rename()       masks S4Vectors::rename()
#> ✖ lubridate::second()   masks S4Vectors::second()
#> ✖ lubridate::second<-() masks S4Vectors::second<-()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Alteratively, these packages can be loaded using the following, without loading the hrm package directly.

hrm::hrmAttach()

A vector of the current hrm packages can be found by:

hrmPackages()
#>  [1] "chunky"           "projecttemplates" "metaboData"       "grover"          
#>  [5] "binneR"           "metabolyseR"      "profilePro"       "mzAnnotation"    
#>  [9] "assignments"      "construction"     "riches"           "metaboMisc"      
#> [13] "metaboWorkflows"

hrm associated packages can be updated using: