Monday Webinar: A Data-Driven Framework for Metabolomics Quality Control скачать в хорошем качестве

Monday Webinar: A Data-Driven Framework for Metabolomics Quality Control

For any mass spectrometry based analytical assay it is considered best practice to include mechanisms for assessing the quality of acquired analyte concentrations. This is particularly important for untargeted metabolomics, where many hundreds of metabolites may be (relatively) quantified in parallel, with metabolite identification performed post hoc, making it is impossible to calibrate each metabolite to an internal standard gradient, and equally, making it impossible to ensure optimal peak-shape for all detected features. This leaves the acquired data open to unwanted within- and between-batch variation throughout a given experiment. For over a decade, the use of repeat-injection pooled quality control samples has proven to be a popular means of monitoring the precision of acquired data. In this webinar I will briefly outline current best practices and demonstrate a new software package (qcmxp.org) for assessing and potentially improving the precision of untargeted metabolomics data collect using these protocols. Biography: Dr. David Broadhurst is Professor of Metabolomic Epidemiology & Biosystems Data Science at Edith Cowan University, Perth, Western Australia. He has been an active member of the metabolomics community for over 25 years. In 2022 he was made a lifetime honorary fellow of the Metabolomics Society for his work promoting best practice in design of experiments, biostatistics and machine learning.