[Barupal, D.K. and Fiehn, O., 2017. Chemical Similarity Enrichment Analysis (ChemRICH) as alternative to biochemical pathway mapping for metabolomic datasets Scientific Report 2017. (https://www.nature.com/articles/s41598-017-15231-w)
Note : If you want to compute the statistical results from raw data, use the next script (scroll down).
R version - R 3.5.1 or latest
Set up Java - follow these instructions
In RStudio, create a new project environment by clicking on File --> New Project
Use chemrich_input_stats.xlsx file as a template. Download it and replace it's content with your study's data. It has one sheet -
- input - details about compounds. First column of this sheet must be "CompoundID", which has to be a unique arbiterary compound identifier such as "CPD0001" The input file must contain KEGG IDs, SMILES codes, PubChem IDs, p-value and fold-changes. If you have received data from Metabolon, the file should already have these annotations.
Put "chemrich_input_stats.xlsx" file inside the R-studio project directory.
install.packages("https://github.com/barupal/chemrich/raw/master/ChemRICHWorkFlow_0.1.0.tar.gz", repos = NULL, type = "source")
library(ChemRICHWorkFlow)
project_name <- "chemrich_1" # Provide this analysis a name. This will be prefixed to all the exported files.
ChemRICHWorkFlow::load.ChemRICH.Packages()
ChemRICHWorkFlow::load.ChemRICH.databases()
Select below three lines and click on Run or press crtl+enter.
data_dict <- readxl::read_xlsx("chemrich_input_stats.xlsx", sheet="input") # Data Dictionary
Note : compounds having the SMILES codes will be used for the next steps.
chemrich.input.file <- ChemRICHWorkFlow::prepare.chemrich.input()
Note : If you already have chemical classes. Add a column "ChemicalClass" into the data_dict sheet in the chemrich_input.xlsx file.
chemrich.input.file <- ChemRICHWorkFlow::chemrich.getChemicalClass()
write.table(chemrich.input.file,paste0(project_name,"chemrich_input_file_with_clases.txt"), col.names = T, row.names = F, quote = F, sep="\t")
signif.chemrich.cluster <- ChemRICHWorkFlow::chemrich.GetSignificantClasses()
ChemRICHWorkFlow::export.chemrich.impactPlot(signif.chemrich.cluster)
ChemRICHWorkFlow::export.chemrich.interactivePlot(signif.chemrich.cluster)
ChemRICHWorkFlow::export.chemrich.similarityTree()
It should look like this -
ChemRICHWorkFlow::export.chemrich.tables(signif.chemrich.cluster)
See ChemRICH_results.xlsx file for expected results.
R version - R 3.5.1 or latest
Set up Java - follow these instructions
In RStudio, create a new project environment by clicking on File --> New Project
Use chemrich_input.xlsx file as a template. Download it and replace it's content with your study's data. It has three sheets -
-
data_dict - details about compounds. First column of this sheet must be "CompoundID" The data dictionary file must contain KEGG IDs, SMILES codes and chemical class or pathway annotation. If you have received data from Metabolon, the file should already have these annotations.
-
sample_metadata - details about samples. First column of this must be "Sample_ID"
-
data_matrix - metabolite numerical data. First column must be "CompoundID" and rest should be eash sample denoted by it's "Sample_ID"
Put "chemrich_input.xlsx" file inside the R-studio project directory.
install.packages("https://github.com/barupal/chemrich/raw/master/ChemRICHWorkFlow_0.1.0.tar.gz", repos = NULL, type = "source")
library(ChemRICHWorkFlow)
project_name <- "chemrich_1" # Provide this analysis a name. This will be prefixed to all the exported files.
ChemRICHWorkFlow::load.ChemRICH.Packages()
ChemRICHWorkFlow::load.ChemRICH.databases()
Select below three lines and click on Run or press crtl+enter.
data_dict <- readxl::read_xlsx("chemrich_input.xlsx", sheet="data_dict") # Data Dictionary
sample_metadata <- readxl::read_xlsx("chemrich_input.xlsx", sheet="sample_metadata") # Sample metadata
data_matrix <- readxl::read_xlsx("chemrich_input.xlsx", sheet="data_matrix") # Data matrix
colnames(sample_metadata)
grouping_variable <- "TISSUE TYPE"
table(sample_metadata[grouping_variable])
colnames(data_dict) # First check what variable names you have in the data dictionary
table(data_dict["SUPER PATHWAY"]) # metabolite count by super pathway
table(data_dict["SUB PATHWAY"]) # metabolite count by sub pathway
table(data_dict["PLATFORM"]) # metabolite count by analytical plateform
If you already have p-value and effect size (fold change or beta coefficient) for your analysis, skip this. Provide your results in the data_dict sheet in the chemrich_input.xlsx file. Add two columns - pvalue and fc. FC means fold-change. Negative beta values need to be converted to below 1 eg -2 will become 0.50.
metsig.df <- ChemRICHWorkFlow::getSignifMetabolites(grouping_variable)
write.table(metsig.df,paste0(project_name,"_significant_metabolites.txt"), col.names = T, row.names = F, quote = F, sep="\t")
Note : compounds having the SMILES codes will be used for the next steps.
chemrich.input.file <- ChemRICHWorkFlow::prepare.chemrich.input()
Note : If you already have chemical classes. Add a column "ChemicalClass" into the data_dict sheet in the chemrich_input.xlsx file.
chemrich.input.file <- ChemRICHWorkFlow::chemrich.getChemicalClass()
write.table(chemrich.input.file,paste0(project_name,"chemrich_input_file_with_clases.txt"), col.names = T, row.names = F, quote = F, sep="\t")
signif.chemrich.cluster <- ChemRICHWorkFlow::chemrich.GetSignificantClasses()
ChemRICHWorkFlow::export.chemrich.impactPlot(signif.chemrich.cluster)
It should look like this -
ChemRICHWorkFlow::export.chemrich.interactivePlot(signif.chemrich.cluster)
ChemRICHWorkFlow::export.chemrich.similarityTree()
It should look like this -
ChemRICHWorkFlow::export.chemrich.tables(signif.chemrich.cluster)
See ChemRICH_results.xlsx file for expected results.
ChemRICHWorkFlow::chemrich.getIntegratedNetwork(moduleName,compoundLabel)
for example -
ChemRICHWorkFlow::chemrich.getIntegratedNetwork("Adenine Nucleotides", "BIOCHEMICAL NAME")
ChemRICHWorkFlow::chemrich.generateBWplots(moduleName,compoundLabel)
for example
ChemRICHWorkFlow::chemrich.generateBWplots("Adenine Nucleotides", "BIOCHEMICAL NAME")
If you do not have SMILES code or InChiKeys for some compounds in your dataset but you do have the class information, you can use this small workflow to perform the ChemRich analysis for your study.
R version - R 3.5.1 or latest
Set up Java - follow these instructions
In RStudio, create a new project environment by clicking on File --> New Project
Use chemrich_class_template.xlsx file as a template. Download it and replace it's content with your study's data.
Put "chemrich_class_template.xlsx" file inside the R-studio project directory.
install.packages("https://github.com/barupal/chemrich/raw/master/ChemRICHWorkFlow_0.1.0.tar.gz", repos = NULL, type = "source")
library(ChemRICHWorkFlow)
project_name <- "demo_" # Provide this analysis a name. This will be prefixed to all the exported files.
ChemRICHWorkFlow::load.ChemRICH.Packages()
ChemRICHWorkFlow::runChemRICHClass("chemrich_class_template.xlsx",project_name)
Results should have been exported.
R version - R 3.5.1 or latest
Set up Java - follow these instructions
In RStudio, create a new project environment by clicking on File --> New Project
Use chemrich_multi_input.xlsx file as a template. Download it and replace it's content with your study's data. It has one sheet-
- data_dict - details about compounds. Provide pvalue and foldchange columns for each statistical comparison.
Put "chemrich_multi_input.xlsx" file inside the R-studio project directory.
install.packages("https://github.com/barupal/chemrich/raw/master/ChemRICHWorkFlow_0.1.0.tar.gz", repos = NULL, type = "source")
library(ChemRICHWorkFlow)
ChemRICHWorkFlow::load.ChemRICH.Packages()
ChemRICHWorkFlow::load.ChemRICH.databases()
project_name <- "chemrich_multi_1" # Provide this analysis a name. This will be prefixed to all the exported files.
classVariable <- "Class"
inputfile <- "chemrich_multi_input.xlsx"
ChemRICHWorkFlow::chemrih_multi_group(inputfile)
Make sure you have latest JAVA (JDK and JRE both) installed on your computer. Latest Java
In R, run the following code.
if (!require("devtools"))
install.packages('devtools', repos="http://cran.rstudio.com/")
if (!require("opencpu"))
install.packages('opencpu', repos="http://cran.rstudio.com/")
if (!require("RCurl"))
install.packages('RCurl', repos="http://cran.rstudio.com/")
if (!require("pacman"))
install.packages('pacman', repos="http://cran.rstudio.com/")
library(devtools)
library(RCurl)
library(pacman)
source('https://bioconductor.org/biocLite.R')
pacman::p_load(grid,rcdk, RJSONIO,openxlsx, RCurl, rvg, magrittr, dynamicTreeCut,ape,ggplot2, ggrepel,ReporteRs, officer,phytools, plotrix, plotly, htmlwidgets,DT,extrafont,XLConnect)
install_github('barupal/chemrich')
library(ChemRICH)
library(opencpu)
opencpu::ocpu_start_server()
Then go to : ChemRICH Local Version