### Load and Prepare Data for statTarget

Source: https://stattarget.github.io/docs/my-new-doc

Loads example data from the statTarget package for subsequent analysis. Ensure the statTarget package is installed and loaded.

```r
library(statTarget)

datpath <- system.file('extdata',package = 'statTarget')
statFile <- paste(datpath,'data_example.csv', sep='/')
getFile <- read.csv(statFile,header=TRUE)
```

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

### Install statTarget from GitHub

Source: https://stattarget.github.io/docs/quick-start

Use this R code to install the latest version of statTarget from its GitHub repository. This requires the devtools package. This method is also suitable for Windows and Linux.

```r
if (!requireNamespace("devtools", quietly = TRUE))

 install.packages("devtools")

devtools::install_github("statTarget/statTarget2")

library(statTarget)

statTargetGUI()
```

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

### Install statTarget from Bioconductor

Source: https://stattarget.github.io/docs/quick-start

Use this R code to install the statTarget package from Bioconductor. Ensure you have BiocManager installed. This method is recommended for Windows and Linux.

```r
if (!requireNamespace("BiocManager", quietly = TRUE))

 install.packages("BiocManager")

BiocManager::install("statTarget", version = "3.8")

library(statTarget)  ## `Load statTarget`. There may be a prompt box for GTK2 Installation, click "OK"

statTargetGUI()  ## `Execute statTarget GUI` 
```

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

### Launch statTarget GUI

Source: https://stattarget.github.io/docs/my-new-doc

This function launches the graphical user interface for statTarget. For Mac users, XQuartz is required instead of X11. R version 3.3.0 and RGtk2 version 2.20.31 are recommended for RGtk2 installation.

```R
library(statTarget)

statTargetGUI()
```

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

### Input Files for Signal Correction and Statistic Analysis

Source: https://stattarget.github.io/docs/demo

Lists the required CSV files for different stages of analysis in statTarget. Ensure these files are correctly formatted before proceeding.

```text
# Input files for Signal Correction

PhenoFile.csv (or MetaFile.csv)

ProfileFile.csv

# Input files for Statistic Analysis

statFile_raw.csv
```

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

### Generate statTarget Input Files with transX

Source: https://stattarget.github.io/docs/my-new-doc

The transX function converts Mass Spectrometry data from various software (e.g., XCMS, Skyline) into statTarget input file formats. Use the second argument to specify the source software.

```R
library(statTarget)

datpath <- system.file('extdata',package = 'statTarget')
dataXcms <- paste(datpath,'xcmsOutput.tsv', sep='/')
dataSkyline <- paste(datpath,'skylineDemo.csv', sep='/')
transX(dataXcms,'xcms')
transX(dataSkyline,'skyline')

See ?transX for off-line help
```

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

### Run Signal Correction with shiftCor

Source: https://stattarget.github.io/docs/my-new-doc

Use the shiftCor function for QC-based signal correction. Ensure your meta and profile files are correctly formatted. Parameters like Frule, MLmethod, and QCspan control the correction process.

```R
library(statTarget)

datpath <- system.file('extdata',package = 'statTarget')
samPeno <- paste(datpath,'MTBLS79_sampleList.csv', sep='/')
samFile <- paste(datpath,'MTBLS79.csv', sep='/')
shiftCor(samPeno,samFile, Frule = 0.8, MLmethod = "QCRFSC", QCspan = 0,imputeM = "KNN")

See ?shiftCor for off-line help
```

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

### Generate HR Image with shiftCor in statTarget

Source: https://stattarget.github.io/faq

Use the shiftCor function with plot=TRUE to generate HR images when the statTargetGUI does not output them. Ensure correct file paths for sample and feature data.

```R
datpath <- system.file('extdata',package = 'statTarget')
samPeno <- paste(datpath,'MTBLS79_sampleList.csv', sep='/')
samFile <- paste(datpath,'MTBLS79.csv', sep='/')
shiftCor(samPeno,samFile, Frule = 0.8, MLmethod = 'QCRFSC', QCspan = 0,imputeM = ‘KNN’,plot=TRUE)
```

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

### Predict Test Data using Random Forest

Source: https://stattarget.github.io/docs/my-new-doc

Generates predictions on a subset of the data using the trained Random Forest model. Ensure the input data for prediction has the same feature structure as the training data.

```r
# Prediction of test data using random forest in statTarget.
predictOutput <- predict_RF(rFtest, getFile[1:19,3:8])
```

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

### Plot Variable Importance Measures

Source: https://stattarget.github.io/docs/my-new-doc

Generates plots for Gini importance and permutation-based variable Gini importance. These plots are crucial for understanding which features are most influential in the Random Forest model.

```r
# Create plots for Gini importance and permutation-based variable Gini importance measures.
pvimPlot(rFtest$randomForest,rFtest$pimpTest)
```

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

### Multi-dimensional Scaling Plot of Proximity Matrix

Source: https://stattarget.github.io/docs/my-new-doc

Creates an MDS plot from the proximity matrix generated by the Random Forest analysis. This helps visualize the relationships between samples based on the model's proximity calculations.

```r
# Multi-dimensional scaling plot of proximity matrix from randomForest.
mdsPlot(rFtest$randomForest,rFtest$pimpTest)
```

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

### Perform Statistical Analysis with statAnalysis

Source: https://stattarget.github.io/docs/my-new-doc

The statAnalysis function performs statistical analysis on expression data. It supports various imputation, normalization, and scaling methods. Glog transformation can be applied for variance stabilization.

```R
library(statTarget)

datpath <- system.file('extdata',package = 'statTarget')
file <- paste(datpath,'data_example.csv', sep='/')
statAnalysis(file,Frule = 0.8, normM = "NONE", imputeM = "KNN", glog = TRUE,scaling = "Pareto")
```

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

### Random Forest Classification with statTarget

Source: https://stattarget.github.io/docs/my-new-doc

Performs Random Forest classification on the loaded data. Adjust 'ntree' and 'times' parameters as needed for model training. This function is suitable for classification tasks.

```r
# Random Forest classfication 
rFtest <- rForest(getFile,ntree = 10,times = 5)
```

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

### Close Graphics Device

Source: https://stattarget.github.io/docs/my-new-doc

Closes the current graphics device to prevent further plots from being drawn to it. This is often used at the end of a plotting script.

```r
graphics.off()
```

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