diaplasma: Analysis of plasma from diabetic patients by LC-HRMS

Description Format Value Source References


Plasma samples from 69 diabetic patients were analyzed by reversed-phase liquid chromatography coupled to high-resolution mass spectrometry (Orbitrap Exactive) in the negative ionization mode. The raw data were pre-processed with XCMS and CAMERA (5,501 features), corrected for signal drift, log10 transformed, and annotated with an in-house spectral database. The patient's age, body mass index, and diabetic type are recorded. These three clinical covariates are strongly associated, most of the type II patients being older and with a higher bmi than the type I individuals.


A list with the following elements:

  • dataMatrix: a 69 samples x 5,501 features matrix of numeric type corresponding to the intensity profiles (values have been log10-transformed),

  • sampleMetadata: a 69 x 3 data frame, with the patients' diabetic type ('type', factor), age ('age', numeric), and body mass index ('bmi', numeric),

  • variableMetadata: a 5,501 x 8 data frame, with the median m/z ('mzmed', numeric) and the median retention time in seconds ('rtmed', numeric) from XCMS, the 'isotopes' (character), 'adduct' (character) and 'pcgroups' (numeric) annotations from CAMERA, and the names of the m/z and RT matching compounds from an in-house database of pure spectra from commercial metabolites ('spiDb', character).


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.


'diaplasma' dataset.


Rinaudo P., Boudah S., Junot C. and Thevenot E.A. (2016). biosigner: a new method for the discovery of significant molecular signatures from omics data. Frontiers in Molecular Biosciences 3. doi:10.3389/fmolb.2016.00026

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