Description Usage Arguments Details Value References Examples
Evaluate correlations between all pairs of variables and degree of class separation.
1 | GraphsCorrDSC(data, name, groupnames, tsf = "clr", QCs = FALSE)
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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. |
QCs |
logical. If FALSE (default) quality control samples (QCs) are automatically distinguished and skipped. |
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).
Correlation graph with evaluated clusters.
Excel file with degree of class separation (DCS) of all possible pairs of groups in data for all pairs of comparisons of two variables.
Aitchison, J. (1986) The Statistical Analysis of Compositional Data Monographs on Statistics and Applied Probability. Chapman & Hall Ltd., London (UK). p. 416.
Pierce, K.M. et al. (2005) Classification of gasoline data obtained by gas chromatography using a piecewise alignment algorithm combined with feature selection and principal component analysis, J CHROMATOGR A 1096, p. 101-110.
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