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

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 computatio...

Full description

Saved in:
Bibliographic Details
Published in:Scientific data 2014-06, Vol.1 (1), p.140012-140012, Article 140012
Main Authors: Kirwan, Jennifer A, Weber, Ralf J M, Broadhurst, David I, Viant, Mark R
Format: Article
Language:English
Subjects:
631/114/2402
631/45/320
Animals
Benchmarking
Cattle
Data Descriptor
Humanities and Social Sciences
Mass Spectrometry
Metabolomics
multidisciplinary
Myocardium - chemistry
Reproducibility of Results
Science
Sheep
Tissue Extracts
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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. Design Type(s) replicate design • reference design • parallel group design Measurement Type(s) metabolite profiling Technology Type(s) mass spectrometry assay Factor Type(s) Batch • material entity • Day of assay Sample Characteristic(s) Bos taurus • Ovis aries • Right ventricle of heart Machine-accessible metadata file describing the reported data (ISA-Tab format)
ISSN:2052-4463
2052-4463
DOI:10.1038/sdata.2014.12