Running Signal Correction (the shiftCor function) from the GUI

Meta File

Meta information includes the Sample name, class, batch and order. Do not change the name of each column

1. Class: The QC should be labeled as NA

2. Order : Injection sequence.

3. Batch: The analysis blocks or batches

4. Sample: Sample name should be consistent in Meta file and Profile file. *The QC sample name should be tagged with “QC”.

(See the example data)

Profile File

Expression data includes the sample name and expression data.(See the example data)

NA.Filter

Modified n precent rule function. A variable will be kept if it has a non-zero value for at least n precent of samples in any one group. (Default: 0.8)

MLmethod

The QC-based signal correction (i.e. QC-RFSC.) or QC-free methods (Combat)

Ntree

Number of trees to grow for QC-RFSC (Default: 500) .

Imputation

The parameter for imputation method.(i.e., nearest neighbor averaging, “KNN”; minimum values, “min”; Half of minimum values “minHalf” median values, “median”. (Default: KNN)

## Examples Code

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", imputeM = "KNN")
# Combat for QC-free datasets
samPeno2 <- paste(datpath,'MTBLS79_dQC_sampleList.csv', sep='/')
shiftCor_dQC(samPeno2,samFile, Frule = 0.8, MLmethod = "Combat")

See ?shiftCor for off-line help

Running Statistical Analysis (the statAnalysis function) from the GUI

Stat File

Expression data includes the sample name, group, and expression data with long format.

NA.Filter

Modified n precent rule function. A variable will be kept if it has a non-zero value for at least n precent of samples in any one group. (Default: 0.8)

Imputation

The parameter for imputation method.(i.e., nearest neighbor averaging, “KNN”; minimum values, “min”; Half of minimum values “minHalf”; median values, “median”. (Default: KNN)

Normalization

The parameter for normalization method (i.e probabilistic quotient normalization, “PQN”; integral normalization , “SUM”, and “none”).

Glog

Generalised logarithm (glog) transformation for Variance stabilization
(Default: TRUE)

Scaling Method

Scaling method before statistic analysis i.e. PCA or PLS(DA). Center can be used for specifying the Center scaling. Pareto can be used for specifying the Pareto scaling. Auto can be used for specifying the Auto scaling (or unit variance scaling). Vast can be used for specifying the vast scaling. Range can be used for specifying the Range scaling. (Default: Pareto)

Permutation times

The number of permutation times for cross-validation of PLS-DA model, and variable importance of randomforest model

PCs

PCs in the Xaxis or Yaxis: Principal components in PCA-PLS model for the x or y-axis (Default: 1 and 2)

nvarRF

The number of variables with top permutation importance in randomforest model. (Default: 20)

Labels

To show the name of sample or groups in the Score plot. (Default: TRUE)

Multiple testing

This multiple testing correction via false discovery rate (FDR) estimation with Benjamini-Hochberg method. The false discovery rate for conceptualizing the rate of type I errors in null hypothesis testing when conducting multiple comparisons. (Default: TRUE)

Volcano FC

The up or down -regulated metabolites using Fold Changes cut off values in the Volcano plot. (Default: > 2 or < 0.5)

Volcano Pvalue

The significance level for metabolites in the Volcano plot.(Default: 0.05)

## Examples Code

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")

Generation of input file (the transX function)

The transX function is to generate statTarget input file formats from Mass Spectrometry Data softwares, such as XCMS, MZmine2, SIEVE and SKYLINE. ‘?transX’ for off-line help.

## Examples Code

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

Random Forest classfication and variable importance measures

rForest provides the Breiman’s random forest algorithm for classification and permutation-based variable importance measures (PIMP-algorithm).

## Examples Code

library(statTarget)

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

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

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

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

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

graphics.off()

See ?rForest for off-line help

Once data files have been analysed it is time to investigate them. Please get this info. through the GitHub page. (URL: https://stattarget.github.io)

statTarget -- shiftCor 
-- After_shiftCor             # The fold for intergrated and corrected data 
   -- shift_all_cor.csv         # The corrected data of samples and QCs
   -- shift_QC_cor.csv          # The corrected data of QCs only
   -- shift_sample_cor.csv      # The corrected data of samples only
   -- loplot                    # The fold for quality control images
      -- *.pdf                  # The quality control images for each features

-- Before_shiftCor            # The fold for raw data
   -- shift_QC_raw.csv          # The raw data of QCs
   -- shift_sam_raw.csv         # The raw data of samples
   
-- RSDresult                  # The fold for variation analysis and quality assessment
   -- RSD_all.csv               # The RSD values of each feature
   -- RSDdist_QC_stat.csv       # The RSD distribution of QCs in each batch and all batches
   -- RSD distribution.pdf      # The RSD distribution plot in samples and QCs of all batches
   -- RSD variation.pdf         # The RSD variation plot for pre- and post- signal correction