Dataset Information

Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control

ABSTRACT: Direct-infusion mass spectrometry (DIMS) metabolomics is an important approach for characterising molecular responses of organisms to disease, drugs and the environment. Increasingly large-scale metabolomics studies are being conducted, necessitating improvements in both bioanalytical and computational workflows to maintain data quality. This dataset represents a systematic evaluation of the reproducibility of a multi-batch DIMS metabolomics study of cardiac tissue extracts. It comprises of twenty biological samples (cow vs. sheep) that were analysed repeatedly, in 8 batches across 7 days, together with a concurrent set of quality control (QC) samples. Data are presented from each step of the workflow and are available in MetaboLights. The strength of the dataset is that intra- and inter-batch variation can be corrected using QC spectra and the quality of this correction assessed independently using the repeatedly-measured biological samples. Originally designed to test the efficacy of a batch-correction algorithm, it will enable others to evaluate novel data processing algorithms. Furthermore, this dataset serves as a benchmark for DIMS metabolomics, derived using best-practice workflows and rigorous quality assessment.

INSTRUMENT(S): LTQ-FT Ultra (Thermo Scientific)

PROVIDER: MTBLS79 | MetaboLights | 2022-05-16

REPOSITORIES: MetaboLights

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Dataset's files

Source:

			Action	DRS
	Dataset07__SFPM.xlsx	Xlsx
	Dataset08a__SFPM_PQN.xlsx	Xlsx
	Dataset08b__SFPM_PQN_BATCH.xlsx	Xlsx
	Dataset08c__SFPM_PQN_BATCH_CLEAN.xlsx	Xlsx
	Dataset10a__SFPM_PQN_KNN_GLOG.xlsx	Xlsx

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Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control.

Kirwan Jennifer A JA Weber Ralf J M RJ Broadhurst David I DI Viant Mark R MR

Scientific data 20140610

Direct-infusion mass spectrometry (DIMS) metabolomics is an important approach for characterising molecular responses of organisms to disease, drugs and the environment. Increasingly large-scale metabolomics studies are being conducted, necessitating improvements in both bioanalytical and computational workflows to maintain data quality. This dataset represents a systematic evaluation of the reproducibility of a multi-batch DIMS metabolomics study of cardiac tissue extracts. It comprises of twen ...[more]

PMID: 25977770

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