Check out the presentations of our latest and past innovations in two-dimensional chromatography data analysis, including new technologies and software solutions that support comprehensive analytics.
A key challenge for non-targeted, cross-sample analyses of gas chromatography/mass spectrometry (GC-MS or GCxGC-MS) data lies in finding the correspondence between compound features across samples. We extended our Investigator framework and demonstrate the effectiveness of our approach on two public GC-MS and GCxGC-MS datasets.
GCxGC-MS is increasingly used for cross-sample analyses. The center of analyses is to align peaks across all chromatograms and extract an aggregated peak table, which continues to be one of the most difficult problems. We developed a new workflow that extends the Investigator framework to use peak-region features along with ID and spectra matching to align peaks from the source chromatograms. The new approach ranks and tags all peaks with the peak-region features, which allows easier and quicker verification assisted by search tools.
GCxGC-MS produces highly complex data that require both interactive and automated comparative analysis methods. One common task is to compare two chromatograms to determine differences or changes due to chemical components or experimental conditions. A new comparative tool provides a detailed comparison of two chromatograms, utilizing advanced matching techniques and informative visualizations.
An important data analysis challenge for Comprehensive two-dimensional chromatography (such as GCxGC and LCxLC) is to select a few markers that can be used effectively for clustering and classifying multiple samples. A newly developed workflow and associated tools allow analysts to detect common and unique compounds across many samples with specialized detection and identification constraints that use chromatographic and mass spectral information to distinguish marker compounds.
Data produced by comprehensive two-dimensional chromatography is rich with information, but extracting and evaluating this information from multiple varying chromatograms can be a complicated challenge. Two new interactive tools provide rapid visual feedback that greatly accelerates the process of determining optimal settings for blob/peak detection and analyte pattern matching.
The information-rich data generated by Comprehensive two-dimensional liquid chromatography (LCxLC) is large and complex, so automated processing is essential. Therefore, LCxLC data processing operations must be flexible, but configuring automated processing and assuring the quality of results are challenging tasks. New methods and tools for optimizing automated processing and rapid quality assessment (QA) provide multiple visualizations and convenient graphical user interfaces (GUIs) to ensure more reliable data processing.
Quality assurance is especially important for complex and sophisticated analyses in challenging applications. A new informatics framework and associated tools are developed to support rapid and effective quality assurance with Comprehensive two-dimensional chromatographic data. The screening interface guides users through a sequence of tightly integrated visualizations that highlight pertinent aspects of the data analysis. During screening, the analyst can confirm acceptable results, make notes, reprocess data, reject unacceptable results, and generate reports.
The End-to-End Data Analysis Workflow (E2E) supports comprehensive comparative chemical analysis with comprehensive two-dimensional liquid chromatography (LCxLC). Comprehensive comparative analysis requires evaluation of every constituent in every sample and is the most general problem of analytical chemistry. E2E utilizes robust peak-region features and encompasses three principal steps: (1) Chromatogram Processing, (2) Feature Extraction, and (3) Comparative Analysis.
This research conducted experiments to compare performance of 2D peak detection algorithms. The evaluation results show that watershed algorithm outperforms two-step algorithm for 2D peak detection, in the sense that watershed algorithm is consistently more accurate for 2D peak detection with various levels of noise, peak widths, and retention-time shifts.
This poster describes initial results for group-type analysis by carbon number of diesel samples using GCxGC-MS and Smart Templates. Smart Templates describe the 2D retention-time pattern of expected peaks and utilize rules pertaining to the retention-times and mass spectra in order to match analyte peaks. Along with CLIC rules for group-type identification, they can distinguish group membership for peaks even where groups overlap in the retention-time plane.
Multi-type templates with peak sets, areas, and mesh objects are flexible and effective structures for GCxGC data analysis. Example analyses of petroleum samples demonstrate that the flexibility of multi-type templates is the basis for more effective and robust comprehensive analysis and automation of routine methods.
Advanced informatics are required to support interactive spatio-spectral analysis of three-dimensional chemical images generated by secondary ion mass spectrometry (SIMS). A new data visualization suite designed to provide an efficient, intuitive and powerful resource for the SIMS analyst working with three-dimensional (3D) data.
Template matching can be used for registering GCxGC data sets in order to identify chemicals in a sample or to identify differences between samples. Manually constructing peak templates can be tedious and time-consuming. This poster describes a method that automates construction of peak templates consisting of representative ‘marker’ peaks based on the inherent relationship between chemical structure and peak position on the retention time plane.
GCxGC provides increased separation capacity and multi-dimensional structure-retention relationships. This poster presents three software methods for extracting and visualizing chemical groups including clustering, mass-spectral colorization, and group identification with the Computer Language for Identifying Chemicals (CLIC).