Automating and Extending Comprehensive Two-Dimensional Gas Chromatography Data Processing by Interfacing Open-Source and Commercial Software.
Michael J WildeBo ZhaoRebecca L CordellWadah IbrahimAmisha SingapuriNeil J GreeningChris E BrightlingSalman SiddiquiPaul S MonksRobert C FreePublished in: Analytical chemistry (2020)
Comprehensive two-dimensional gas chromatography (GC×GC) is a powerful analytical tool for both nontargeted and targeted analyses. However, there is a need for more integrated workflows for processing and managing the resultant high-complexity datasets. End-to-end workflows for processing GC×GC data are challenging and often require multiple tools or software to process a single dataset. We describe a new approach, which uses an existing underutilized interface within commercial software to integrate free and open-source/external scripts and tools, tailoring the workflow to the needs of the individual researcher within a single software environment. To demonstrate the concept, the interface was successfully used to complete a first-pass alignment on a large-scale GC×GC metabolomics dataset. The analysis was performed by interfacing bespoke and published external algorithms within a commercial software environment to automatically correct the variation in retention times captured by a routine reference standard. Variation in 1tR and 2tR was reduced on average from 8 and 16% CV prealignment to less than 1 and 2% post alignment, respectively. The interface enables automation and creation of new functions and increases the interconnectivity between chemometric tools, providing a window for integrating data-processing software with larger informatics-based data management platforms.
Keyphrases
- gas chromatography
- mass spectrometry
- tandem mass spectrometry
- data analysis
- high resolution mass spectrometry
- electronic health record
- liquid chromatography
- big data
- gas chromatography mass spectrometry
- solid phase extraction
- machine learning
- high resolution
- simultaneous determination
- cancer therapy
- randomized controlled trial
- deep learning
- drug delivery
- single cell