Abstract
IntroductionData processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.ObjectivesMerge in the same platform the steps required for metabolomics data processing.MethodsKniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.ResultsThe approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.ConclusionKniMet provides the user with a local, modular and customizable workflow for the processing of both GC–MS and LC–MS open profiling data.
Highlights
Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow
Among the several analytical techniques employed within metabolomics, gas and liquid chromatography coupled with mass spectrometry (GC– and LC–MS) are the most commonly used in metabolomics studies as they allow the identification of a large number of diverse molecular species
An appropriate evaluation of the reasons behind the presence of missing values in the data matrix, and their consecutive imputation, is fundamental to avoid biased statistical results (Di Guida et al 2016; Gromski et al 2014). In this application, missing values imputation can be performed with either Random Forest (RF) or K-Nearest Neighbour (KNN) algorithms, implemented as R scripts using the libraries missForest (Stekhoven and Buhlmann 2012) and impute (Hastie et al 2016) respectively, or Small Value replacement (SV), i.e. half of the minimum value found for a given feature in given sample
Summary
Among the several analytical techniques employed within metabolomics, gas and liquid chromatography coupled with mass spectrometry (GC– and LC–MS) are the most commonly used in metabolomics studies as they allow the identification of a large number of diverse molecular species. The plethora of samples analyzed during high-throughput screenings, the number of processing steps, and the required computational competences and resources often represent a bottleneck that renders these analyses slow. For these reasons, the KNIME Analytics Platform (Berthold et al 2007) was used to build a vendor-independent processing workflow. KniMet (Liggi 2017) joins several steps required to process GC– and LC–MS metabolomics data, outputting a data matrix normalized, annotated and filtered from inconsistently detected features in a semi-automated, documented and reproducible analysis
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