| Metadata details for analysis AN002777 | |
|---|---|
| Study ID | ST001705 |
| Analysis ID | AN002777 |
| Study Title | Machine learning-enabled renal cell carcinoma status prediction using multi-platform urine-based metabolomics (part-I) |
| Institute | University of Georgia |
| Species | Homo sapiens |
| Ion_mode | POSITIVE |
| MS type | ESI |
| MS Instrument Name | Thermo Q Exactive HF hybrid Orbitrap |
| MS Instrument Type | Orbitrap |
| Chromatography Instrument Name | Q Exactive HF |
| Chromatography Type | HILIC |
| Chromatography Column | Waters ACQUITY UPLC BEH HILIC (75 x 2.1mm,1.7um) |
| Solvent A | |
| Solvent B | |
| Gradient | |
| Flow rate | |
| Column Temperature | |
| Retention time units | Minutes |
- UCSD Metabolomics Workbench, a resource sponsored by the Common Fund of the National Institutes of Health
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