How does data analysis with Progenesis CoMet work?
Importing your data
Progenesis CoMet supports many of the common data formats produced by LC-MS machines, such as Waters, Thermo, Agilent and Bruker. It also supports cross-vendor file formats including mzXML, mzML and NetCDF. Data can be in profile or centroid form, be high resolution or low resolution and have positive, negative or mixed ionization polarity.
We’re always looking to increase the number of file formats supported so if your instrument or file format isn't supported yet, contact us and we'll see what we can do to help.
Select your possible adducts
Prior to data import you are prompted to select from a list of possible adducts present in your samples. If a specific adduct is not listed it can easily be added to the list.
Ion intensity maps
After being imported, each run in your experiment is shown as an ion intensity map which is representative of the sample's MS signal by m/z and retention time. This gives an immediate visual quality check, highlighting any problems experienced in sample running.
The ability to combine data from multiple mass spec runs is required for comparative abundance profiling studies. This enables the comparison of different experimental conditions using a high number of replicates. To combine and compare results from different runs, Progenesis CoMet aligns them to compensate for between-run variation in the chromatography.
To ensure consistent peak picking and matching across all data files, an aggregate data set is created from the aligned runs. This contains all peak information from all sample files, allowing the detection of a single map of compound ions. This map is then applied to each sample, giving 100% matching of peaks with no missing values, so you can generate reliable results using valid multivariate statistical analysis.
This is where the results of quantification and identification, including any structures from SDF searches (see below), are automatically brought together. All the compound ions are automatically deconvoluted to provide accurate quantitation of each compound.
After detection, the ion abundance measurements are normalised so we can make comparisons between the runs and find compounds of biological interest. You can choose the compound ions you want to identify based on the significance measures e.g. Anova p-value, fold change, power.
Ion intensity maps, 3D views, mass spectra and chromatograms are displayed for each compound ion on all runs to provide quality assurance of peak picking and alignment. You can edit peak picking on any run and make the same change to the same feature across all runs.
Compounds which have more than one possible identification can be reviewed so you can narrow down the results and select the correct identification. This review step also displays the measured isotope distribution compared to the theoretical. This isotope distribution match contributes to the overall compound identification score.
Once you have a list of detected compound ions that you want to identify, MetaScope is fully integrated for searching your own compound data and directly returning results back into the workflow. The software runs a search of neutral-mass or m/z, and RT, with tolerances selected, against a flat file structure database. The results, with a compound score applied, are automatically reimported into Progenesis CoMet and linked to your quantified compound ions.
Metascope can also search SDF databases to return compound structures alongside identification and quantification data. There is the added flexibility of exporting data to search for compound identifications using the online METLIN search.
Here you can review all the compound ions, used to quantify and identify a compound, as a montage of detected features. Mass spectra and extracted ion chromatograms are also displayed for each compound ion, showing how they similar they are. This is useful in visually checking the quality of data underlying each quantified compound.
If any compounds have an ion whose profile appears as an outlier in terms of its m/z and RT characteristics, within expected limits, it can be removed. Likewise, you have the opportunity to look if a compound ion appears at an expected position on the ion intensity map and, if it is present, add it to the compound quantification calculation.
Multivariate statistical analysis and integrate results with other bioinformatics
Finally, you can explore the list of quantified and identified compounds using an in-built set of easy-to-use multivariate statistical tools to generate results including: Principal Components Analysis, Correlation Analysis, False Discovery Rate q-values and a view of how adduct abundance varies between runs.
Progenesis CoMet also helps you to put your results into a wider biological context. Quantification data, including raw abundance of every isotope peak for every adduct, and identification results can be easily exported and linked with other ‘omics data or used as the basis for further bioinformatics. Results from any downstream bioinformatics can be imported back into your analysed experiment to combine all your compound data into one table to review or share.