GraphsPCA3D: 3D principal component analysis (PCA)

In AlzbetaG/Metabol: The statistical analysis of metabolomic data

Description

Makes 3D principal component analysis (PCA).

Usage

GraphsPCA3D(data, name, groupnames, type = "points", tsf = "clr",
  QCs = TRUE)

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.
type	A type of plots must be defined by "points" (default) or "names".
tsf	Data transformation must be defined by "clr" (default), "log", "log10", "PQN", "lnPQN", "pareto" or "none". See Details.
QCs	logical. If TRUE (default) quality control samples (QCs) are are left in the graph. If FALSE 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).

If quality control samples (QCs) are present in data and QCs=TRUE, versions with QCs and without them are displayed. If QCs=TRUE and QCs are not present in data, this step is automatically skipped.

Value

3D score plot of PCA.

References

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

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