statAnalysis for GUI

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Description

statAnalysis provide the statistical analysis for metabolomics data or others.

Usage

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statAnalysis(file, Frule = 0.8, imputeM = "KNN", glog = TRUE,
  test.multi = TRUE, FDR = TRUE, nvarRF = 10, scaling = "Pareto",
  silt = 500, pcax = 1, pcay = 2, Labels = TRUE, upper.lim = 1.5,
  lower.lim = 0.5, sig.lim = 0.05)

Arguments

file

The file with the expression information.

Frule

The cut-off value for missing value filter function.

imputeM

The parameter for imputation method.(i.e., nearest neighbor averaging, "KNN"; minimum values for imputed variables, "min", median values for imputed variables (Group dependent) "median").

glog

Generalised logarithm (glog) transformation, with the default value TRUE.

test.multi

Multiple statistical analysis, with the default value TRUE.

FDR

The false discovery rate for conceptualizing the rate of type I errors in null hypothesis testing when conducting multiple comparisons.

nvarRF

The number of variables in Gini plot of Randomforest model (=< 100).

scaling

Scaling method before statistic analysis (PCA or PLS-DA). 'pareto', 'Pareto', 'p' or 'P' can be used for specifying the Pareto scaling. 'auto', 'Auto', 'auto', 'a' or 'A' can be used for specifying the Auto scaling (or unit variance scaling). 'vast', 'Vast', 'v' or 'V' can be used for specifying the vast scaling. 'range', 'Range', 'r' or 'R' can be used for specifying the Range scaling.

silt

The number of permutation times for PLS-DA model

pcax

Principal components in PCA model for the x-axis.

pcay

Principal components in PCA model for the y-axis.

Labels

Name labels for score plot of multiple statistical analysis

upper.lim

The up-regulated metabolites using Fold Changes cut off values in the Volcano plot.

lower.lim

The down-regulated metabolites using Fold Changes cut off values in the Volcano plot.

sig.lim

The significance level for metabolites in the Volcano plot.

Value

A object of statAnalysis

Examples

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datpath <- system.file("extdata",package = "statTarget")
file <- paste(datpath,"data_example.csv", sep="/")
statAnalysis(file,nvarRF =5)

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