Processed metabolomic data from the EXPOsOMICS Personal Exposure Monitoring study

Metabolomic data from the 'Variability of the Human Serum Metabolome over 3 Months in the EXPOsOMICS Personal Exposure Monitoring Study' paper DOI: 10.1021/acs.est.3c03233 . 

The data was originally collected and generated by the multicenter EXPOsOMICS Personal Exposure Monitoring study. Details on data collection and processing are described in the aforementioned paper. The statistical analysis from that paper is available at https://github.com/moosterwegel/variability-metabolites-paper and may contain useful information/code to work with this data.

`processed_covariate_data.csv`:
```
Rows: 298
Columns: 7
$ subjectid: hashed identifier subject
$ sample_code: indicates if it's the first (A) or second (B) blood sample
$ centre: indicates in which centre the data was collected
$ age_cat: indicates age category at the time of a PEM session
$ sq_sex:  indicates the sex of the participant (male, female) as filled in during the screening questionaire
$ traf: indicates the exposure to traffic (PM2.5 and UFP) as measured during the PEM sessions. 
$ bmi_cat: indicates BMI category at the time of a PEM session
```

`processed_lcms_data data.csv` contains the processed LCMS data:
```
Rows: 298
Columns: 4297
$ subjectid: hashed identifier subject
$ sample_code: indicates if it's the first (A) or second (B) blood sample
$ centre: indicates in which centre the data was collected
$ compounds: measured features (compounds) are prefixed by the letter X. The name contains information on the measured monoisotopicmass_retentiontime.
Non-detects (below limit of detection (LOD) are coded as 1 for the compounds.
....
```
In the datasets each row indicates a measurement on a day (`sample_code`) and person (`subjectid`). The datasets can be joined on these variables.

The other data files (`annotations.xslx`, `ancestors_annotations.xlsx`, `annotations_plus_kegg_pathways.csv`) contain the annotations, ancestors of the annotations (to assign a class to a compound based on ChEBI ontology, see our paper for details), annotations plus KEGG pathways respectively.