Running mzOS as a script
Perform analysis
Installing mzOS made a script available in your current environnement. It is called mzos
. The aim of this script is to perform an analysis given a XCMS peaklist:
- Deisotoping
- Feature annotation (adducts, fragments)
- Database search (only HMDB and LMSD are supported for now)
- Annotation confidence estimation
- using network presence/missing metabolites information (Bayesian inference)
- using observed/theoritical isotopic pattern
Parameters
parser.add_argument("-x", "--xcms_pkl", type=str, help="path to the xcms peaklist", required=True)
parser.add_argument("-p", "--polarity", default='negative', choices=['negative', 'positive'], help='experiment polarity', required=True)
parser.add_argument("--mz_tol_ppm", type=float, default=10.0, help='mass over charge tolerance', required=False)
parser.add_argument("--dims", default=False, action='store_true', help='direct infusion MS experiment', required=False)
parser.add_argument('--db', default='hmdb', choices=['hmdb', 'lmsd', 'hmdb + lmsd'], required=False)
parser.add_argument("--output", type=str, default="annotations.tsv", required=False)
parser.add_argument("--bayes", default=True, required=False)
--xcms_pkl
Simply the path to the XCMS peaklist
--polarity
Polarity used to acquire spectra. Should be 'negative' or 'positive'
--mz_tol_ppm
Tolerance in mass precision used in algorithm (in ppm). Defautlt 10.
--dims
If the experiment is a direct infusion experiment True or False.
--db
Database to search for. 'hmdb' or 'lmsd' or 'hmdb + lmsd'
--ouput
Path to the result directory
--bayes
Perform Bayesian algorithm or not (can be time consuming). True or False.
Example
Be sure to activate the virtual environnement where you installed mzOS.
mzos --xcms_pkl C:\Users\M\xcms_results.tsv --polarity 'negative' --db 'hmdb + lmsd' --output '.\annotations.csv'
Result file
Matrix with several columns:
-
id: id of the feature.
-
mz: mass over charge
-
time: time elution
-
Main putative attribution: the most probable tag that can be assigned to this feature. It can be:
-
a monoistope + n isotopes + n adducts/fragments
-
an isotope (C13 ...)
-
an adduct
-
a fragment
-
nothing
-
-
Main attribution pattern composition: one of the most important column, showing all relations between features. For example:
-
feature detected as an isotope in Main putative tag shows Isotope C13 of 2546 for charge=1. Here you can find to which feature it has been detected as a C13 isotope. You have also a charge information.
-
feature detected as a monoisotope:
-
-
Putative secondary attributions: All possible other attributions (or tag) that algorithm found but set as secondary (main is considered as the most probable)
-
Putative annotation: matching metabolite(s)
-
Putative formula: chemical formula of the matching metabolite
-
Inchi: inchi metabolite formula
-
Database_id: Database Id such as HMDB_ID and KEGG_ID
-
Isotopic pattern matching score: RMSD between observed and theoritical isotopic pattern intensities
-
Annotation assignment probability: Using network analysis to infer a presence probability using a bayesian algorithm (see metsamp)
-
Annotation pattern composition: Isotopic pattern detected to use the Isotopic pattern matching score