sacurine: Analysis of the human adult urinary metabolome variations...

In SamGG/ropls: PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data

Description

Urine samples from 183 human adults were analyzed by liquid chromatography coupled to high-resolution mass spectrometry (LTQ Orbitrap) in the negative ionization mode. A total of 109 metabolites were identified or annotated at the MSI level 1 or 2. After retention time alignment with XCMS, peaks were integrated with Quan Browser. After signal drift and batch effect correction of intensities, each urine profile was normalized to the osmolality of the sample. Finally, the data were log10 transformed.

Format

A list with the following elements:

• dataMatrix a 183 samples x 109 variables matrix of numeric type corresponding to the intensity profiles (values have been log10-transformed)

• sampleMetadata a 183 x 3 data frame, with the volunteers' age ('age', numeric), body mass index ('bmi', numeric), and gender ('gender', factor)

• variableMetadata a 109 x 3 data frame, with the metabolites' MSI identification level ('msiLevel': either 1 or 2), HMDB ID when available ('hmdb', character), chemical class according to the 'super class' taxonomy of HMDB ('chemicalClass', character)

Value

List containing the 'dataMatrix' matrix (numeric) of data (samples as rows, variables as columns), the 'sampleMetadata' data frame of sample metadata, and the variableMetadata data frame of variable metadata. Row names of 'dataMatrix' and 'sampleMetadata' are identical. Column names of 'dataMatrix' are identical to row names of 'variableMetadata'. For details see the 'Format' section above.

References

Thevenot E.A., Roux A., Xu Y., Ezan E. and Junot C. (2015). 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, DOI: 10.1021/acs.jproteome.5b00354

SamGG/ropls documentation built on May 29, 2019, 1:51 a.m.