Dataset Information

A benchmark spike-in data set for biomarker identification in metabolomics

ABSTRACT: The development and the validation of innovative approaches for biomarker selection are of paramount importance in many -omics technologies. Unfortunately, the actual testing of new methods on real data is difficult, because in real data sets, one can never be sure about the “true” biomarkers. In this paper, we present a publicly available metabolomic ultra performance liquid chromatography–mass spectrometry spike-in data set for apples. The data set consists of 10 control samples and three spiked sets of the same size (G1,G2,G3), where naturally occurring compounds are added in different concentrations. In this sense, the data set can serve as a test bed to assess the performance of new algorithms (eg. biomarker selection, peak picking, etc.) and compare them with previously published results. The dataset includes the raw CDF files for all the injections. The raw data are organized in four assays: positive ion mode, negative ion mode, injections of the standard mixes (in both positive and negative ion modes). The spiked compounds are: epicatechin (CHEBI 90), catechin (CHEBI 15600), cyanidin-3-galactoside (CHEBI 27475), quercetin-3-galactoside (CHEBI 67486), quercetin-3-glucoside (CHEBI 28299), quercetin-3-rhamnoside (CHEBI 17558), resveratrol (CHEBI 27881), phloridzin (CHEBI 8113), quercetin (CHEBI 16243). All the compounds except resveratrol and cyanidin-3-galactoside are known to be present in the apple extracts. Spiking concentrations have been defined taking as a reference the ones measured in a pooled apple extract. A detailed description of the spiking concentration in the three group is included in the spiking.pdf file.

INSTRUMENT(S): SYNAPT HDMS (Waters)

SUBMITTER: Pietro Franceschi

PROVIDER: MTBLS59 | MetaboLights | 2013-10-24

REPOSITORIES: MetaboLights

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Dataset's files

Source:

			Action	DRS
	MTBLS59	Other
	FILES	Other
	a_MTBLS59_neg_mass_spectrometry.txt	Txt
	a_MTBLS59_neg_std_mix_mass_spectrometry.txt	Txt
	a_MTBLS59_pos_mass_spectrometry.txt	Txt

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Publications

Stability-based biomarker selection.

Wehrens Ron R Franceschi Pietro P Vrhovsek Urska U Mattivi Fulvio F

Analytica chimica acta 20110201 1-2

Biomarker identification, i.e., finding those variables that indicate true differences between two or more populations, is an ever more important topic in the omics sciences. In most cases, the number of variables far exceeds the number of samples, making biomarker identification extremely difficult. We present a strategy based on the stability of putative biomarkers under perturbation of the data, and show that in several cases important gains can be achieved. The strategy is very general and c ...[more]

PMID: 21962342

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