MetDNA2Vis

Knowledge-guided multi-layer network (KGMN) is a new approach leveraging knowledge-guided multi-layer networks to annotate known and unknown metabolites in untargeted metabolomics data. The goal of MetDNA2Vis package is to visualize, reproduce and investigate putatively annotated known and unknown metabolites from KGMN.

Detailed tutorial can be found here

Installation

You can install the development version of MetDNA2InSilicoTool like so:

if(!require(devtools)){
install.packages("devtools")
}

if(!require(BiocManager)){
install.packages("BiocManager")
}

# Install CRAN/Bioconductor packages
required_pkgs <- c("dplyr","tidyr","readr","CHNOSZ","igraph",
  "magrittr","ggplot2","ggraph","tidygraph")
list_installed <- installed.packages()

new_pkgs <- required_pkgs[!(required_pkgs %in% list_installed[,'Package'])]
if (length(new_pkgs) > 0) {
  BiocManager::install(new_pkgs)
} else {
  cat('Required CRAN/Bioconductor packages installed\n')
}


# Install ZhuLab packages
devtools::install_github("ZhuMetLab/SpectraTools")
devtools::install_github("ZhuMetLab/MetDNA2Vis")

Example

Here is a example which contains codes to help to reproduce above analysis quickly.

library(MetDNA2Vis)
library(SpectraTools)

# set working directory
setwd('D:/project/00_zhulab/01_metdna2/00_data/20220602_visualization_kgmn/Demo_MetDNA2_NIST_urine_pos/06_visualization/')

# Export global networks 
# construct network 1
reconstructNetwork1(is_unknown_annotation = TRUE)

# construct network 2
annotation_table <- reformatTable1()
reconstructNetwork2(annotation_table = annotation_table)

# construct network 3
reconstructNetwork3()

# Export subnetworks -----------------------------------------------------------
# network 1 of unknown peak subnetwork
# Note: the folder_output should keep same among different layer subnetworks
retrieveSubNetwork1(centric_met = c('C00082', 'KeggExd000923'), 
  is_unknown_annotation = TRUE, 
  folder_output = c('M182T541_M262T526'))


# network 2 of unknown peak subnetwork
retrieveSubNetwork2(from_peak = 'M182T541', 
  end_peak = 'M262T526', 
  folder_output = c('M182T541_M262T526'))

# network 3 of unknown peak subnetwork
retrieveSubNetwork3(base_peaks = c('M182T541', 'M262T526'),
  base_adducts = c('[M+H]+', '[M+H]+'),
  folder_output = c('M182T541_M262T526'))


# merge subnetwork
mergeSubnetwork(from_peak = 'M182T541', 
  end_peak = 'M262T526', 
  folder_output = 'M182T541_M262T526')

Citation

This free open-source software implements academic research by the authors and co-workers. If you use it, please support the project by citing the appropriate journal articles.

Zhiwei Zhou†, Mingdu Luo†, Haosong Zhang, Yandong Yin, Yuping Cai, and Zheng-Jiang Zhu*, Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking, Nature Communications, 2022, 13: 6656 Link

License

This work is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)