There are several scenarios for running the ChemRICH analysis. Below are the few common cases. A navigation tree is provided below to guide a new user which ChemRICH approach will fit best for their study.
1) ChemRICH with any type of set definitions - This is the basic use of ChemRICH. Users can provide any set definitions. It needs the minimum input file. To use it, follow the instructions here. If you do not have the SMILES code for the detected compounds, use this approach. LINK
2) ChemRICH (Chemical Class as Set Definition) . This approach needs SMILES codes for the input compounds. There are two variants 1) SMILES codes with user-provided chemical classes and 2) SMILES codes with predicted MeSH classes. LINK
3) ChemRICH for Lipids . Many lipids are not yet covered in several databases. Work around is to use a lipid class specific SMILES codes. LINK
4) ChemRICH (Sub-class). In case you have interpreted MS/MS spectra to get class level information, you can use this approach. LINK
5) ChemRICH with Unknown Metabolites. Using correlation modules we can include unknown metabolites into a set analysis. This can be helpful if you have no compound libraries and have no MS/MS spectra. LINK
6) ChemRICH for Metabolon's data. Metabolon provides non-overlapping pathway ontology terms for each measured metabolite (named), which makes it easier to run ChemRICH analysis for their datasets. LINK
7) ChemRICH for multiple statistical tests : In many studies, we might have multiple conditions to compare or have built many regression models. In this step, we can run ChemRICH for those conditions in a batch mode. LINK