Data for common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline

This upload contains the HZV029 Plasma and HZV029 Lipidomics datasets for reviewers of the "Data for common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline" submission. 

Both datasets will be uploaded to metabolomics workbench and the upload completed before final publication of the manuscript. For the he HZV029 Plasma datasets only the final run is included for any sample (i.e., failed injections or other samples with data quality issues that were reran during acquisition were omitted).

Also included in the upload is the source code for the MetDataModel and the pcpfm at the time of manuscript submission and the pcpfm itself. If you find this upload in the future, please check out the github repos for more updated versions:

https://github.com/shuzhao-li-lab/PythonCentricPipelineForMetabolomics

https://github.com/shuzhao-li-lab/metDataModel

The github repo does not store the input the data for space reasons, they only have the notebooks. However, the .zip here has both the notebooks by themselves in the notebook subdirectory and a separate directory with the notebooks and the data used to generate all the figures and results in the manuscript.

Some information that is needed to rerun this analysis:

Sequence files are critical to the functioning of the pipeline. The sequence files for all analyses are provided under ./sequence_files. These can be used to recapitulate the analysis by eitehr changing the filepath to each acquisition to where you put it on your sytem or by placing the sequence file in the same directory as the mzml or raw. In the latter case, the pipeline will search for filenames matching the sample names. The sequence files also store some sample metadata such as the type of sample a given acquisition is (unknown, pooled, qc, etc...)

.raw to .mzML conversion works well on MacOS but may not work well on other systems. You will need to use the ability to specify your own conversion command or convert files outside of the pipeline. 

To replicate the results, you do need to have the annotation sources downloaded which can be done using the pipeline.