Dataset Information
A data set of 255000 randomly selected and manually classified extracted ion chromatograms for evaluation of peak detection methods
Ontology highlight
ABSTRACT: Nontargeted mass spectrometry (MS) has become an important method over the last years in the fields of metabolomics and environmental research. While more and more algorithms and workflows become available to process a large number of data sets nontargeted, there still exist few manually evaluated universal test data sets for refining and evaluating these methods. The first step of nontargeted screening, peak detection (and refinement of it) is arguably the most important step for nontargeted screening. However, the absence of a model data set makes it harder for researchers to evaluate peak detection methods. In this Data Descriptor, we provide a manually checked data set consisting of 255000 EICs (5000 peaks randomly sampled from across 51 samples) for the evaluation on peak detection and gap filling algorithms. The data set was created from a previous real-world study, of which a subset was used to extract and manually classify ion chromatograms by three mass spectrometry experts. The data set consists of 51 converted mass spectral files in mzML format and an MZmine peaklist with annotations. Links:
INSTRUMENT(S): Liquid Chromatography MS - positive - reverse phase
SUBMITTER: Tobias Schulze
PROVIDER: MTBLS1455 | MetaboLights | 2020-05-26
REPOSITORIES: MetaboLights
Publications
A Data Set of 255,000 Randomly Selected and Manually Classified Extracted Ion Chromatograms for Evaluation of Peak Detection Methods.
Metabolites 20200422 4
Non-targeted mass spectrometry (MS) has become an important method over recent years in the fields of metabolomics and environmental research. While more and more algorithms and workflows become available to process a large number of non-targeted data sets, there still exist few manually evaluated universal test data sets for refining and evaluating these methods. The first step of non-targeted screening, peak detection and refinement of it is arguably the most important step for non-targeted sc ...[more]
