GraphsOutlier: Outlier analysis of PCA and PLS-DA

In AlzbetaG/Metabol: The statistical analysis of metabolomic data

Description

Outlier analysis of PCA and PLS-DA.

Usage

GraphsOutlier(data, name, groupnames, tsf = "clr", type = "pls",
  QCs = FALSE)

Arguments

data	Data table with variables (metabolites) in columns. Samples in rows are sorted according to specific groups.
name	A character string or expression indicating a name of data set. It occurs in names of every output.
groupnames	A character vector defining specific groups in data. Every string must be specific for each group and they must not overlap.
tsf	Data transformation must be defined by "clr" (default), "log", "log10", "PQN", "lnPQN", "pareto" or "none". See Details.
type	Definition which type of method do you want to use: "pls" (default) and "pca". If two groups are not nicely separated, the algorithm returns error. In this case set this parameter to "err".
QCs	logical. If FALSE (default) quality control samples (QCs) are automatically distinguished and skipped.

Details

Data transformation: with "clr" clr trasformation is used (see References), with "log" natural logarithm is used, with "log10" decadic logarithm is used, with "pareto" data are only scaled by Pareto scaling, with "PQN" probabilistic quotient normalization is done, with "lnPQN" natural logarithm of PQN transformed data is done, with "none" no tranformation is done.

Up to twenty different groups can be distinguished in data (including QCs).

Value

Plot of outliers.

References

Aitchison, J. (1986) The Statistical Analysis of Compositional Data Monographs on Statistics and Applied Probability. Chapman & Hall Ltd., London (UK). p. 416.

Thevenot E. A. et al., Analysis of the human adult urinary metabolome variations with age, body mass index and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses,Journal of Proteome Research 14, 3322-3335, 2015.

AlzbetaG/Metabol documentation built on May 31, 2019, 12:39 a.m.