MetaboAnalystR

https://github.com/xia-lab/metaboanalystr
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MetaboAnalystR is an R package that provides a unified LC-MS workflow for global metabolomics data...
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### Install MetaboAnalystR from a local .tar.gz file

Source: https://github.com/xia-lab/metaboanalystr/blob/master/README.md

Install MetaboAnalystR by first downloading the .tar.gz file and then using the R CMD INSTALL command. This is useful for offline installations or specific versions.

```R
cd ~/Downloads
R CMD INSTALL MetaboAnalystR_3.0.3.tar.gz
```

--------------------------------

### Install MetaboAnalystR with devtools (with documentation)

Source: https://github.com/xia-lab/metaboanalystr/blob/master/README.md

Use this command to install MetaboAnalystR directly from GitHub, including all documentation and vignettes. Ensure the devtools package is installed first.

```R
# Step 1: Install devtools
install.packages("devtools")
library(devtools)

# Step 2: Install MetaboAnalystR with documentation
devtools::install_github("xia-lab/MetaboAnalystR", build = TRUE, build_vignettes = TRUE, build_manual =T)
```

--------------------------------

### Install dependencies using pacman

Source: https://github.com/xia-lab/metaboanalystr/blob/master/README.md

Uses the pacman package manager to install and load the required dependency list in one step.

```R
install.packages("pacman")

library(pacman)

pacman::p_load(c("impute", "pcaMethods", "globaltest", "GlobalAncova", "Rgraphviz", "preprocessCore", "genefilter", "sva", "limma", "KEGGgraph", "siggenes","BiocParallel", "MSnbase", "multtest","RBGL","edgeR","fgsea","httr","qs"))
```

--------------------------------

### Install MetaboAnalystR with devtools (without documentation)

Source: https://github.com/xia-lab/metaboanalystr/blob/master/README.md

This command installs MetaboAnalystR from GitHub without building or including documentation. This can be useful if you encounter issues with documentation builds.

```R
# Step 2: Install MetaboAnalystR without documentation
devtools::install_github("xia-lab/MetaboAnalystR", build = TRUE, build_vignettes = FALSE)
```

--------------------------------

### Install MetaboAnalystR from cloned GitHub repository

Source: https://github.com/xia-lab/metaboanalystr/blob/master/README.md

Clone the MetaboAnalystR repository from GitHub and then build and install it locally using R commands. This method provides the most up-to-date version.

```R
git clone https://github.com/xia-lab/MetaboAnalystR.git
R CMD build MetaboAnalystR
R CMD INSTALL MetaboAnalystR_4.0.0.tar.gz
```

--------------------------------

### Install MetaboAnalystR dependencies via custom function

Source: https://github.com/xia-lab/metaboanalystr/blob/master/README.md

Defines and executes a helper function to check for and install missing Bioconductor and CRAN dependencies.

```R
metanr_packages <- function(){

  metr_pkgs <- c("impute", "pcaMethods", "globaltest", "GlobalAncova", "Rgraphviz", "preprocessCore", "genefilter", "sva", "limma", "KEGGgraph", "siggenes","BiocParallel", "MSnbase", "multtest","RBGL","edgeR","fgsea","devtools","crmn","httr","qs")
  
  list_installed <- installed.packages()
  
  new_pkgs <- subset(metr_pkgs, !(metr_pkgs %in% list_installed[, "Package"]))
  
  if(length(new_pkgs)!=0){
    
    if (!requireNamespace("BiocManager", quietly = TRUE))
        install.packages("BiocManager")
    BiocManager::install(new_pkgs)
    print(c(new_pkgs, " packages added..."))
  }
  
  if((length(new_pkgs)<1)){
    print("No new packages added...")
  }
}
```

```R
metanr_packages()
```

--------------------------------

### Get MetaboAnalystR Citation Information

Source: https://github.com/xia-lab/metaboanalystr/blob/master/README.md

Use this R command to retrieve the citation details for the MetaboAnalystR package, which should be used when referencing the package in publications.

```R
citation("MetaboAnalystR")
```

--------------------------------

### Initialize and perform Quantitative Enrichment Analysis (QEA)

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Initializes data objects, reads text data, performs sanity checks, replaces minimum values, and normalizes data for QEA. Requires 'metabolomics_data.csv'.

```r
# Initialize for QEA
mSet <- InitDataObjects("conc", "msetqea", paired=FALSE)
mSet <- Read.TextData(mSet, "metabolomics_data.csv", format="rowu", lbl.type="disc")
mSet <- SanityCheckData(mSet)
mSet <- ReplaceMin(mSet)
mSet <- Normalization(mSet, "NULL", "LogNorm", "AutoNorm")
```

--------------------------------

### Initialize and set KEGG pathway library for analysis

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Initializes data objects, reads metabolomics data, performs sanity checks, and normalizes data for pathway analysis. It then sets the KEGG pathway library for a specified organism (e.g., human 'hsa').

```r
# Initialize for pathway analysis
mSet <- InitDataObjects("conc", "pathqea", paired=FALSE)
mSet <- Read.TextData(mSet, "metabolomics_data.csv", format="rowu", lbl.type="disc")
mSet <- SanityCheckData(mSet)
mSet <- Normalization(mSet, "NULL", "LogNorm", "AutoNorm")

# Compound mapping
mSet <- CrossReferencing(mSet, "name")
mSet <- CreateMappingResultTable(mSet)

# Set pathway library for human
mSet <- SetKEGG.PathLib(mSet, "hsa", "current")

# Other organism options: "mmu" (mouse), "rno" (rat), "ath" (arabidopsis), "eco" (e.coli), "sce" (yeast)
```

--------------------------------

### Initialize MetaboAnalyst Data Objects

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Creates the mSetObj container required for all subsequent analysis. Must be called first to define the data type and analysis module.

```r
library(MetaboAnalystR)

# Initialize for statistical analysis with concentration data
mSet <- InitDataObjects("conc", "stat", paired=FALSE)

# Initialize for pathway analysis
mSet <- InitDataObjects("conc", "pathqea", paired=FALSE)

# Initialize for metabolite set enrichment analysis (ORA)
mSet <- InitDataObjects("list", "msetora", paired=FALSE)

# Initialize for mummichog (MS peaks to pathways)
mSet <- InitDataObjects("mass_all", "mummichog", paired=FALSE)

# Data types: "list", "conc", "specbin", "pktable", "nmrpeak", "mspeak", "msspec"
# Analysis types: "stat", "pathora", "pathqea", "msetora", "msetqea", "mf", "roc"
```

--------------------------------

### Plot QEA overview

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Generates a PNG image summarizing the Quantitative Enrichment Analysis results. Requires mSet object with QEA results.

```r
# Plot QEA results
mSet <- PlotQEA.Overview(mSet, imgName="msea_qea", format="png", dpi=300, width=10)
```

--------------------------------

### Data Initialization

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Functions to initialize the MetaboAnalystR data object (mSetObj) and prepare it for different analysis types.

```APIDOC
## InitDataObjects - Initialize MetaboAnalyst Data Objects

### Description
Creates the main data container (mSetObj) required for all subsequent analysis. This function must be called first to specify the data type and analysis module.

### Method
Not applicable (R function)

### Endpoint
Not applicable (R function)

### Parameters
- **dataType** (string) - Required - Specifies the type of input data (e.g., "conc", "list", "mass_all").
- **analType** (string) - Required - Specifies the analysis module (e.g., "stat", "pathqea", "msetora", "mummichog").
- **paired** (boolean) - Optional - Indicates if the samples are paired (default is FALSE).

### Request Example
```r
library(MetaboAnalystR)

# Initialize for statistical analysis with concentration data
mSet <- InitDataObjects("conc", "stat", paired=FALSE)

# Initialize for pathway analysis
mSet <- InitDataObjects("conc", "pathqea", paired=FALSE)

# Initialize for metabolite set enrichment analysis (ORA)
mSet <- InitDataObjects("list", "msetora", paired=FALSE)

# Initialize for mummichog (MS peaks to pathways)
mSet <- InitDataObjects("mass_all", "mummichog", paired=FALSE)
```

### Response
- **mSet** (object) - The initialized MetaboAnalystR data object.

### Response Example
```r
# mSet object is returned and ready for further processing
```

## Read.TextData - Read CSV/TXT Data Files

### Description
Reads uploaded CSV or TXT files containing metabolomics data into the mSet object. Supports various formats for samples in rows or columns.

### Method
Not applicable (R function)

### Endpoint
Not applicable (R function)

### Parameters
- **mSet** (object) - Required - The initialized MetaboAnalystR data object.
- **filePath** (string) - Required - Path to the CSV or TXT data file.
- **format** (string) - Required - Specifies the data format (e.g., "rowu", "rowp", "colu", "colp").
- **lbl.type** (string) - Optional - Specifies the type of class labels (e.g., "disc" for discrete, "cont" for continuous).

### Request Example
```r
# Read data with samples in rows, unpaired, discrete class labels
mSet <- Read.TextData(mSet, "metabolomics_data.csv", format="rowu", lbl.type="disc")

# Read data with samples in columns, unpaired
mSet <- Read.TextData(mSet, "data.csv", format="colu", lbl.type="disc")

# Read data with continuous class labels (for regression)
mSet <- Read.TextData(mSet, "dose_response.csv", format="rowu", lbl.type="cont")
```

### Response
- **mSet** (object) - The MetaboAnalystR data object with data loaded.

### Response Example
```r
# mSet object is updated with the loaded data
```

## SanityCheckData - Data Integrity Check

### Description
Performs comprehensive data validation including checking for missing values, duplicates, and data format issues.

### Method
Not applicable (R function)

### Endpoint
Not applicable (R function)

### Parameters
- **mSet** (object) - Required - The MetaboAnalystR data object.

### Request Example
```r
# Perform sanity check on loaded data
mSet <- SanityCheckData(mSet)

# Check messages for any issues
print(mSet$msgSet$check.msg)
```

### Response
- **mSet** (object) - The MetaboAnalystR data object with validation results.
- **mSet$msgSet$check.msg** (string) - Contains messages regarding data integrity issues.

### Response Example
```r
# "Data integrity check passed."
# or specific error/warning messages
```
```

--------------------------------

### Set library and perform GlobalTest Score calculation for QEA

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Sets the metabolic pathway library (e.g., smpdb_pathway) and then calculates the GlobalTest scores for quantitative enrichment analysis. Requires mSet object with mapped compounds.

```r
# Set library and perform QEA
mSet <- SetCurrentMsetLib(mSet, "smpdb_pathway", 2)
mSet <- CalculateGlobalTestScore(mSet)
```

--------------------------------

### Metabolite Set Library Selection

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Initializes data objects and selects the metabolite set library for enrichment analysis.

```r
# Initialize for MSEA
mSet <- InitDataObjects("list", "msetora", paired=FALSE)

# Set up compound list for analysis
mSet <- Setup.MapData(mSet, c("Glucose", "Pyruvate", "Lactate", "Citrate", "Succinate"))

# Perform compound name mapping to HMDB
mSet <- CrossReferencing(mSet, "name")
mSet <- CreateMappingResultTable(mSet)

# Set metabolite set library (pathway-based)
mSet <- SetCurrentMsetLib(mSet, "smpdb_pathway", 2)

# Other library options:
# "smpdb_pathway" - SMPDB pathways
# "kegg_pathway" - KEGG pathways
# "blood" - Blood metabolome
# "urine" - Urine metabolome
# "csf" - CSF metabolome
# "location" - Cellular location
# "disease" - Disease associations
```

--------------------------------

### Perform compound mapping and create mapping result table

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Maps compounds using their names and creates a table summarizing the mapping results. This is a prerequisite for QEA.

```r
# Perform compound mapping
mSet <- CrossReferencing(mSet, "name")
mSet <- CreateMappingResultTable(mSet)
```

--------------------------------

### Access and print QEA results

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Retrieves the quantitative enrichment analysis results matrix from the mSet object and prints it to the console. Columns include compound counts, statistics, and p-values.

```r
# Access results
qea_results <- mSet$analSet$qea.mat
print(qea_results)
# Columns: Total Cmpd, Hits, Statistic Q, Expected Q, Raw p, Holm p, FDR
```

--------------------------------

### Browse MetaboAnalystR Vignettes in a Web Browser

Source: https://github.com/xia-lab/metaboanalystr/blob/master/README.md

This command opens the MetaboAnalystR package vignettes in your default web browser, offering an alternative way to access the documentation.

```R
browseVignettes("MetaboAnalystR")
```

--------------------------------

### Plot MSEA overview and enrichment network

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Generates a PNG image for the MSEA ORA overview and another for the enrichment network. Requires mSet object with ORA results.

```r
mSet <- PlotORA(mSet, imgName="msea_ora", format="png", dpi=300, width=10)
```

```r
mSet <- PlotEnrichNet.Overview(mSet, imgName="msea_network", format="png", dpi=300, width=10)
```

--------------------------------

### Plot pathway analysis summary

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Generates a PNG image summarizing the pathway analysis results. Requires mSet object with pathway analysis results.

```r
# Plot pathway analysis results
mSet <- PlotPathSummary(mSet, imgName="pathway_summary", format="png", dpi=300, width=10)
```

--------------------------------

### Plot specific KEGG pathway with compound highlighting

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Generates a PNG image of a specific KEGG pathway map, highlighting matched compounds from the analysis. Requires mSet object with pathway analysis results.

```r
# Plot specific KEGG pathway
mSet <- PlotKEGGPath(mSet, pathName="Glycolysis / Gluconeogenesis", width=800, height=600,
                     format="png", dpi=300)

# The pathway image will highlight matched compounds in the analysis
```

--------------------------------

### Create Random Forest model for classification

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Builds a Random Forest classification model with specified parameters for trees and permutations, calculating variable importance using MDA. Requires mSet object with appropriate data.

```r
# Create random forest model
mSet <- CreateRandomForest(mSet, trees=500, permNum=100, importance="MDA")
```

--------------------------------

### Access and print pathway QEA results

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Retrieves the pathway quantitative enrichment analysis results matrix from the mSet object and prints it. Columns include compound counts, p-values, and impact scores.

```r
# Access results
pathway_qea <- mSet$analSet$qea.mat
print(pathway_qea)
# Columns: Total Cmpd, Hits, Raw p, -log10(p), Holm adjust, FDR, Impact
```

--------------------------------

### Perform pathway Quantitative Enrichment Analysis (QEA)

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Calculates pathway quantitative enrichment scores using either the GlobalTest ('gt') or GlobalAncova ('ga') method, with optional covariate adjustment. Requires mSet object with pathway library set.

```r
# Pathway QEA with GlobalTest
mSet <- CalculateQeaScore(mSet, nodeImp="rbc", method="gt")

# Alternative: GlobalAncova method
mSet <- CalculateQeaScore(mSet, nodeImp="rbc", method="ga")

# With covariate adjustment
mSet <- CalculateQeaScore(mSet, nodeImp="rbc", method="gt", covariates=c("Age", "Sex"))
```

--------------------------------

### PlotPCALoading API

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Generates a PCA loading plot.

```APIDOC
## PlotPCALoading API

### Description
Generates a PCA loading plot.

### Method
Not applicable (R function call)

### Endpoint
Not applicable (R function call)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
```r
# Generate PCA loading plot
mSet <- PlotPCALoading(mSet, imgName="pca_loading", format="png", dpi=300, width=9, inx1=1, inx2=2)
```

### Response
#### Success Response (200)
Saves the PCA loading plot to a file.

#### Response Example
```r
# Plot saved to 'pca_loading.png'
```
```

--------------------------------

### Access MetaboAnalystR Vignettes within R

Source: https://github.com/xia-lab/metaboanalystr/blob/master/README.md

This command opens the package vignettes within your R environment, providing detailed tutorials and workflows for using MetaboAnalystR.

```R
vignette(package="MetaboAnalystR")
```

--------------------------------

### Generate PDF Analysis Report

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Creates a comprehensive PDF document containing all performed analyses, figures, and statistical summaries.

```r
# Generate comprehensive analysis report
mSet <- PreparePDFReport(mSet, usrName="User")

# Report includes all performed analyses with figures and statistical summaries
```

--------------------------------

### Read Metabolomics Data Files

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Imports CSV or TXT files into the mSet object, supporting various sample orientations and label types.

```r
# Read data with samples in rows, unpaired, discrete class labels
mSet <- Read.TextData(mSet, "metabolomics_data.csv", format="rowu", lbl.type="disc")

# Read data with samples in columns, unpaired
mSet <- Read.TextData(mSet, "data.csv", format="colu", lbl.type="disc")

# Read data with continuous class labels (for regression)
mSet <- Read.TextData(mSet, "dose_response.csv", format="rowu", lbl.type="cont")

# Format options:
# - "rowu" = samples in rows, unpaired
# - "rowp" = samples in rows, paired
# - "colu" = samples in columns, unpaired
# - "colp" = samples in columns, paired
```

--------------------------------

### Quantitative Enrichment Analysis (QEA)

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Functions for performing Quantitative Enrichment Analysis (QEA) using the GlobalTest method.

```APIDOC
## CalculateGlobalTestScore - Quantitative Enrichment Analysis

### Description
Performs quantitative enrichment analysis using the GlobalTest method.

### Method
POST (conceptual, as this is R code)

### Endpoint
N/A (R function)

### Parameters
None explicitly defined in the provided text for CalculateGlobalTestScore itself, but initialization and data reading steps are shown.

### Request Example
```R
# Initialize for QEA
mSet <- InitDataObjects("conc", "msetqea", paired=FALSE)
mSet <- Read.TextData(mSet, "metabolomics_data.csv", format="rowu", lbl.type="disc")
mSet <- SanityCheckData(mSet)
mSet <- ReplaceMin(mSet)
mSet <- Normalization(mSet, "NULL", "LogNorm", "AutoNorm")

# Perform compound mapping
mSet <- CrossReferencing(mSet, "name")
mSet <- CreateMappingResultTable(mSet)

# Set library and perform QEA
mSet <- SetCurrentMsetLib(mSet, "smpdb_pathway", 2)
mSet <- CalculateGlobalTestScore(mSet)
```

### Response
- **qea_results** (list) - Contains QEA results.
  - **Total Cmpd** (numeric) - Total number of compounds.
  - **Hits** (numeric) - Number of significant hits.
  - **Statistic Q** (numeric) - Q statistic.
  - **Expected Q** (numeric) - Expected Q value.
  - **Raw p** (numeric) - Raw p-value.
  - **Holm p** (numeric) - Holm-adjusted p-value.
  - **FDR** (numeric) - False Discovery Rate.

### Response Example
```R
print(qea_results)
```
```

```APIDOC
## PlotQEA.Overview

### Description
Plots the QEA results overview.

### Method
POST (conceptual, as this is R code)

### Endpoint
N/A (R function)

### Parameters
- **mSet** (object) - The MetaboAnalystR analysis object.
- **imgName** (string) - Name for the output image file.
- **format** (string) - Image format (e.g., "png").
- **dpi** (integer) - Dots per inch for the image.
- **width** (integer) - Width of the image.

### Request Example
```R
mSet <- PlotQEA.Overview(mSet, imgName="msea_qea", format="png", dpi=300, width=10)
```

### Response
Generates a plot image.
```

--------------------------------

### Perform SVM Analysis and Visualization

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Executes SVM classification with an RBF kernel and generates plots for classification results and important compounds.

```r
# Perform SVM analysis with RBF kernel
mSet <- RSVM.Anal(mSet, gamma=0.1, cost=1)

# Plot SVM classification results
mSet <- PlotRSVM.Classification(mSet, imgName="svm_classify", format="png", dpi=300, width=8)

# Plot important compounds
mSet <- PlotRSVM.Cmpd(mSet, imgName="svm_cmpd", format="png", dpi=300, width=8)
```

--------------------------------

### Initialize and perform univariate ROC analysis

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Initializes data objects, reads biomarker data, performs sanity checks, and normalizes data for ROC analysis. It then performs univariate ROC analysis for a specified feature, generating plots and AUC values.

```r
# Initialize for ROC analysis
mSet <- InitDataObjects("conc", "roc", paired=FALSE)
mSet <- Read.TextData(mSet, "biomarker_data.csv", format="rowu", lbl.type="disc")
mSet <- SanityCheckData(mSet)
mSet <- Normalization(mSet, "NULL", "LogNorm", "AutoNorm")

# Perform univariate ROC for each feature
mSet <- Perform.UnivROC(mSet, feat.nm="Metabolite1", imgName="roc_univ",
                        format="png", dpi=300, isAUC=TRUE, isOpt=TRUE,
                        optMethod="closest.topleft", isPartial=FALSE)
```

--------------------------------

### Pathway Analysis

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Functions for pathway analysis, including setting pathway libraries and performing ORA and QEA on pathways.

```APIDOC
## SetKEGG.PathLib - Set Pathway Library

### Description
Sets up the KEGG pathway library for pathway analysis.

### Method
POST (conceptual, as this is R code)

### Endpoint
N/A (R function)

### Parameters
- **mSet** (object) - The MetaboAnalystR analysis object.
- **organism** (string) - Organism code (e.g., "hsa" for human, "mmu" for mouse).
- **lib.path** (string) - Library path, typically "current".

### Request Example
```R
# Initialize for pathway analysis
mSet <- InitDataObjects("conc", "pathqea", paired=FALSE)
mSet <- Read.TextData(mSet, "metabolomics_data.csv", format="rowu", lbl.type="disc")
mSet <- SanityCheckData(mSet)
mSet <- Normalization(mSet, "NULL", "LogNorm", "AutoNorm")

# Compound mapping
mSet <- CrossReferencing(mSet, "name")
mSet <- CreateMappingResultTable(mSet)

# Set pathway library for human
mSet <- SetKEGG.PathLib(mSet, "hsa", "current")
```

### Response
Updates the MetaboAnalystR object with the specified pathway library.
```

```APIDOC
## CalculateOraScore - Pathway ORA

### Description
Performs over-representation analysis on metabolic pathways.

### Method
POST (conceptual, as this is R code)

### Endpoint
N/A (R function)

### Parameters
- **mSet** (object) - The MetaboAnalystR analysis object.
- **nodeImp** (string) - Node importance measure (e.g., "rbc", "dgr").
- **method** (string) - Statistical test method (e.g., "fisher", "hyperg").

### Request Example
```R
# Perform pathway ORA with relative betweenness centrality and Fisher's test
mSet <- CalculateOraScore(mSet, nodeImp="rbc", method="fisher")

# Alternative: hypergeometric test with out-degree centrality
mSet <- CalculateOraScore(mSet, nodeImp="dgr", method="hyperg")
```

### Response
- **pathway_results** (list) - Contains pathway ORA results.
  - **Total** (numeric) - Total number of features.
  - **Expected** (numeric) - Expected number of hits.
  - **Hits** (numeric) - Number of significant hits.
  - **Raw p** (numeric) - Raw p-value.
  - **-log10(p)** (numeric) - -log10 transformed p-value.
  - **Holm adjust** (numeric) - Holm-adjusted p-value.
  - **FDR** (numeric) - False Discovery Rate.
  - **Impact** (numeric) - Pathway impact score.

### Response Example
```R
print(pathway_results)
```
```

```APIDOC
## CalculateQeaScore - Pathway QEA

### Description
Performs quantitative enrichment analysis on metabolic pathways.

### Method
POST (conceptual, as this is R code)

### Endpoint
N/A (R function)

### Parameters
- **mSet** (object) - The MetaboAnalystR analysis object.
- **nodeImp** (string) - Node importance measure (e.g., "rbc").
- **method** (string) - Quantitative enrichment method (e.g., "gt" for GlobalTest, "ga" for GlobalAncova).
- **covariates** (array of strings, optional) - Covariates for adjustment (e.g., c("Age", "Sex")).

### Request Example
```R
# Pathway QEA with GlobalTest
mSet <- CalculateQeaScore(mSet, nodeImp="rbc", method="gt")

# Alternative: GlobalAncova method
mSet <- CalculateQeaScore(mSet, nodeImp="rbc", method="ga")

# With covariate adjustment
mSet <- CalculateQeaScore(mSet, nodeImp="rbc", method="gt", covariates=c("Age", "Sex"))
```

### Response
- **pathway_qea** (list) - Contains pathway QEA results.
  - **Total Cmpd** (numeric) - Total number of compounds.
  - **Hits** (numeric) - Number of significant hits.
  - **Raw p** (numeric) - Raw p-value.
  - **-log10(p)** (numeric) - -log10 transformed p-value.
  - **Holm adjust** (numeric) - Holm-adjusted p-value.
  - **FDR** (numeric) - False Discovery Rate.
  - **Impact** (numeric) - Pathway impact score.

### Response Example
```R
print(pathway_qea)
```
```

```APIDOC
## PlotPathSummary

### Description
Plots a summary of pathway analysis results.

### Method
POST (conceptual, as this is R code)

### Endpoint
N/A (R function)

### Parameters
- **mSet** (object) - The MetaboAnalystR analysis object.
- **imgName** (string) - Name for the output image file.
- **format** (string) - Image format (e.g., "png").
- **dpi** (integer) - Dots per inch for the image.
- **width** (integer) - Width of the image.

### Request Example
```R
mSet <- PlotPathSummary(mSet, imgName="pathway_summary", format="png", dpi=300, width=10)
```

### Response
Generates a plot image.
```

```APIDOC
## PlotKEGGPath - KEGG Pathway Visualization

### Description
Generates KEGG pathway maps with compound highlighting.

### Method
POST (conceptual, as this is R code)

### Endpoint
N/A (R function)

### Parameters
- **mSet** (object) - The MetaboAnalystR analysis object.
- **pathName** (string) - The name of the KEGG pathway to plot (e.g., "Glycolysis / Gluconeogenesis").
- **width** (integer) - Width of the image.
- **height** (integer) - Height of the image.
- **format** (string) - Image format (e.g., "png").
- **dpi** (integer) - Dots per inch for the image.

### Request Example
```R
# Plot specific KEGG pathway
mSet <- PlotKEGGPath(mSet, pathName="Glycolysis / Gluconeogenesis", width=800, height=600,
                     format="png", dpi=300)
```

### Response
Generates a KEGG pathway image highlighting matched compounds.
```

--------------------------------

### Plot Random Forest classification results

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Generates a PNG image visualizing the results of the Random Forest classification model. Requires mSet object with a trained RF model.

```r
# Plot RF classification results
mSet <- PlotRF.Classify(mSet, imgName="rf_classify", format="png", dpi=300, width=8)
```

--------------------------------

### Visualize Normalization Effects

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Generates plots to compare feature distributions or sample views before and after normalization.

```r
# Generate normalization summary plot (feature view)
mSet <- PlotNormSummary(mSet, imgName="norm_summary", format="png", dpi=300, width=10)

# Generate sample view normalization summary
mSet <- PlotSampleNormSummary(mSet, imgName="sample_norm", format="png", dpi=300, width=10)
```

--------------------------------

### K-means Clustering

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Performs k-means clustering to identify metabolite patterns.

```r
# K-means clustering with 5 clusters
mSet <- Kmeans.Anal(mSet, clust.num=5)

# Plot K-means results
mSet <- PlotKmeans(mSet, imgName="kmeans", format="png", dpi=300, width=10)

# Access cluster assignments
cluster_info <- mSet$analSet$kmeans$clusters
```

--------------------------------

### Access and print pathway ORA results

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Retrieves the pathway over-representation analysis results matrix from the mSet object and prints it. Columns include counts, p-values, and adjusted p-values.

```r
# Access pathway results
pathway_results <- mSet$analSet$ora.mat
print(pathway_results)
# Columns: Total, Expected, Hits, Raw p, -log10(p), Holm adjust, FDR, Impact
```

--------------------------------

### Perform T-test Analysis

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Executes various t-test configurations and generates visualization plots.

```r
# Perform Welch's t-test with FDR correction (p < 0.05)
mSet <- Ttests.Anal(mSet, nonpar=FALSE, threshp=0.05, paired=FALSE,
                    equal.var=FALSE, pvalType="fdr", all_results=TRUE)

# Perform Student's t-test (equal variance assumed)
mSet <- Ttests.Anal(mSet, nonpar=FALSE, threshp=0.05, paired=FALSE,
                    equal.var=TRUE, pvalType="raw", all_results=FALSE)

# Non-parametric Wilcoxon rank-sum test
mSet <- Ttests.Anal(mSet, nonpar=TRUE, threshp=0.05, paired=FALSE,
                    equal.var=TRUE, pvalType="fdr", all_results=TRUE)

# Paired t-test
mSet <- Ttests.Anal(mSet, nonpar=FALSE, threshp=0.05, paired=TRUE,
                    equal.var=TRUE, pvalType="fdr", all_results=FALSE)

# Generate t-test plot
mSet <- PlotTT(mSet, imgName="ttest_plot", format="png", dpi=300, width=8)
```

--------------------------------

### Hierarchical Clustering Heatmap

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Generates heatmaps with hierarchical clustering for pattern visualization.

```r
# Generate heatmap with default settings
mSet <- PlotHeatMap(mSet, imgName="heatmap", format="png", dpi=300, width=10,
                    dataOpt="norm", scaleOpt="row", smplDist="euclidean",
                    clstDist="ward.D", palette="bwm", viewOpt="overview",
                    rowV=TRUE, colV=TRUE, drawBorder=TRUE)

# Heatmap with correlation distance and complete linkage
mSet <- PlotHeatMap(mSet, imgName="heatmap2", format="png", dpi=300, width=10,
                    dataOpt="norm", scaleOpt="row", smplDist="correlation",
                    clstDist="complete", palette="gbr", viewOpt="detail",
                    rowV=TRUE, colV=TRUE, drawBorder=FALSE)
```

--------------------------------

### PlotPCAScree API

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Generates a scree plot for PCA.

```APIDOC
## PlotPCAScree API

### Description
Generates a scree plot for PCA.

### Method
Not applicable (R function call)

### Endpoint
Not applicable (R function call)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
```r
# Generate scree plot
mSet <- PlotPCAScree(mSet, imgName="pca_scree", format="png", dpi=300, width=10, scree.num=10)
```

### Response
#### Success Response (200)
Saves the scree plot to a file.

#### Response Example
```r
# Plot saved to 'pca_scree.png'
```
```

--------------------------------

### PlotVolcano API

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Generates a volcano plot for differential analysis.

```APIDOC
## PlotVolcano API

### Description
Generates a volcano plot for differential analysis.

### Method
Not applicable (R function call)

### Endpoint
Not applicable (R function call)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
```r
# Generate volcano plot with labels
mSet <- PlotVolcano(mSet, imgName="volcano", plotLbl=1, plotTheme=0, format="png", dpi=300, width=10, labelNum=10)
```

### Response
#### Success Response (200)
Saves the volcano plot to a file.

#### Response Example
```r
# Plot saved to 'volcano.png'
```
```

--------------------------------

### Self-Organizing Maps (SOM)

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Performs SOM analysis for clustering and visualization.

```r
# SOM analysis with 3x3 grid
mSet <- SOM.Anal(mSet, x.dim=3, y.dim=3, initMethod="pca", neighType="gaussian")

# Plot SOM results
mSet <- PlotSOM(mSet, imgName="som", format="png", dpi=300, width=10)
```

--------------------------------

### Retrieve Analysis Command History

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Extracts the history of executed R commands from the mSet object for reproducibility.

```r
# Get command history
cmd_history <- GetRCommandHistory(mSet)
print(cmd_history)

# Commands are also automatically saved to "Rhistory.R"
```

--------------------------------

### Perform PCA Analysis

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Executes Principal Component Analysis and generates various diagnostic and score plots.

```r
# Perform PCA analysis
mSet <- PCA.Anal(mSet)

# Generate 2D PCA score plot (PC1 vs PC2)
mSet <- PlotPCA2DScore(mSet, imgName="pca_score2d", format="png", dpi=300,
                       width=9, pcx=1, pcy=2, reg=0.95, show=0, grey.scale=0)

# Generate PCA loading plot
mSet <- PlotPCALoading(mSet, imgName="pca_loading", format="png", dpi=300,
                       width=9, inx1=1, inx2=2)

# Generate scree plot
mSet <- PlotPCAScree(mSet, imgName="pca_scree", format="png", dpi=300,
                     width=10, scree.num=10)

# Generate PCA biplot with top 10 contributing features
mSet <- PlotPCABiplot(mSet, imgName="pca_biplot", format="png", dpi=300,
                      width=9, inx1=1, inx2=2, topnum=10)

# Generate 3D PCA score plot (JSON for interactive visualization)
mSet <- PlotPCA3DScore(mSet, imgName="pca_score3d", format="json", inx1=1, inx2=2, inx3=3)

# Access variance explained
print(mSet$analSet$pca$variance)
# Access PERMANOVA results
print(mSet$analSet$pca$permanova.res$stat.info)
```

--------------------------------

### PlotANOVA API

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Generates a plot for ANOVA results.

```APIDOC
## PlotANOVA API

### Description
Generates a plot for ANOVA results.

### Method
Not applicable (R function call)

### Endpoint
Not applicable (R function call)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
```r
# Generate ANOVA plot
mSet <- PlotANOVA(mSet, imgName="anova_plot", format="png", dpi=300, width=9)
```

### Response
#### Success Response (200)
Saves the ANOVA plot to a file.

#### Response Example
```r
# Plot saved to 'anova_plot.png'
```
```

--------------------------------

### Perform ORA enrichment and access results

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Calculates hypergeometric scores for ORA and accesses the resulting matrix. Assumes mSet object is already initialized and processed.

```r
mSet <- CalculateHyperScore(mSet)

# Access enrichment results
ora_results <- mSet$analSet$ora.mat
print(ora_results)
# Columns: total, expected, hits, Raw p, Holm p, FDR
```

--------------------------------

### Calculate.ANOVA.posthoc API

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Performs post-hoc analysis for ANOVA results using Fisher's LSD or Tukey's HSD.

```APIDOC
## Calculate.ANOVA.posthoc API

### Description
Performs post-hoc analysis for ANOVA results.

### Method
Not applicable (R function call)

### Endpoint
Not applicable (R function call)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
```r
# Post-hoc analysis with Fisher's LSD
mSet <- Calculate.ANOVA.posthoc(mSet, post.hoc="fisher", thresh=0.05)

# Post-hoc analysis with Tukey's HSD
mSet <- Calculate.ANOVA.posthoc(mSet, post.hoc="tukey", thresh=0.05)
```

### Response
#### Success Response (200)
Modified mSet object with post-hoc analysis results.

#### Response Example
```r
# mSet object after post-hoc analysis
```
```

--------------------------------

### Volcano.Anal API

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Combines fold change and t-test results for comprehensive differential analysis visualization.

```APIDOC
## Volcano.Anal API

### Description
Combines fold change and t-test results for comprehensive differential analysis visualization.

### Method
Not applicable (R function call)

### Endpoint
Not applicable (R function call)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
```r
# Volcano analysis: FC threshold=2, p-value threshold=0.05
mSet <- Volcano.Anal(mSet, paired=FALSE, fcthresh=2, cmpType=0, nonpar=FALSE, threshp=0.05, equal.var=TRUE, pval.type="fdr")
```

### Response
#### Success Response (200)
Modified mSet object with volcano plot analysis results.

#### Response Example
```r
# mSet object after volcano analysis
```
```

--------------------------------

### PCA.Anal API

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Performs Principal Component Analysis (PCA) for unsupervised exploration of metabolomics data patterns.

```APIDOC
## PCA.Anal API

### Description
Performs Principal Component Analysis (PCA) for unsupervised exploration of metabolomics data patterns.

### Method
Not applicable (R function call)

### Endpoint
Not applicable (R function call)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
```r
# Perform PCA analysis
mSet <- PCA.Anal(mSet)
```

### Response
#### Success Response (200)
Modified mSet object with PCA analysis results.

#### Response Example
```r
# mSet object after PCA analysis
```
```

--------------------------------

### Generate univariate ROC boxplot

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Creates a PNG image of a boxplot summarizing univariate ROC analysis results. Requires mSet object with ROC analysis results.

```r
# Generate univariate ROC boxplot
mSet <- PlotRocUnivBoxPlot(mSet, imgName="roc_boxplot", format="png", dpi=300, width=8)
```

--------------------------------

### Export Processed Data

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Saves the current state of the mSet object data to CSV files representing different stages of the pipeline.

```r
# Save all transformed data to CSV files
mSet <- SaveTransformedData(mSet)

# Files generated:
# - data_original.csv (original data)
# - data_processed.csv (after processing)
# - data_normalized.csv (after normalization)
```

--------------------------------

### Plot Random Forest variable importance

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Generates a PNG image displaying the variable importance scores from the Random Forest model. Requires mSet object with a trained RF model.

```r
# Plot variable importance
mSet <- PlotRF.VIP(mSet, imgName="rf_vip", format="png", dpi=300, width=8)
```

--------------------------------

### ORA Enrichment Analysis

Source: https://context7.com/xia-lab/metaboanalystr/llms.txt

Functions for performing Over-Representation Analysis (ORA) on metabolic pathways.

```APIDOC
## CalculateHyperScore / ORA Enrichment Analysis

### Description
Performs ORA enrichment analysis.

### Method
POST (conceptual, as this is R code)

### Endpoint
N/A (R function)

### Parameters
None explicitly defined in the provided text for CalculateHyperScore itself.

### Request Example
```R
mSet <- CalculateHyperScore(mSet)
```

### Response
- **ora_results** (list) - Contains ORA enrichment results.
  - **total** (numeric) - Total number of features.
  - **expected** (numeric) - Expected number of hits.
  - **hits** (numeric) - Number of significant hits.
  - **Raw p** (numeric) - Raw p-value.
  - **Holm p** (numeric) - Holm-adjusted p-value.
  - **FDR** (numeric) - False Discovery Rate.

### Response Example
```R
print(ora_results)
```
```

```APIDOC
## PlotORA

### Description
Plots the ORA enrichment overview.

### Method
POST (conceptual, as this is R code)

### Endpoint
N/A (R function)

### Parameters
- **mSet** (object) - The MetaboAnalystR analysis object.
- **imgName** (string) - Name for the output image file.
- **format** (string) - Image format (e.g., "png").
- **dpi** (integer) - Dots per inch for the image.
- **width** (integer) - Width of the image.

### Request Example
```R
mSet <- PlotORA(mSet, imgName="msea_ora", format="png", dpi=300, width=10)
```

### Response
Generates a plot image.
```

```APIDOC
## PlotEnrichNet.Overview

### Description
Plots the enrichment network overview.

### Method
POST (conceptual, as this is R code)

### Endpoint
N/A (R function)

### Parameters
- **mSet** (object) - The MetaboAnalystR analysis object.
- **imgName** (string) - Name for the output image file.
- **format** (string) - Image format (e.g., "png").
- **dpi** (integer) - Dots per inch for the image.
- **width** (integer) - Width of the image.

### Request Example
```R
mSet <- PlotEnrichNet.Overview(mSet, imgName="msea_network", format="png", dpi=300, width=10)
```

### Response
Generates a plot image.
```
Tokens:
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