asari-metabolomics 1.4.2

LC-MS metabolomics data preprocessing

Asari

Trackable and scalable Python program for high-resolution LC-MS metabolomics data preprocessing,

• Taking advantage of high mass resolution to prioritize mass separation and alignment
• Peak detection on a composite map instead of repeated on individual samples
• Statistics guided peak dection, based on local maxima and prominence, selective use of smoothing
• Reproducible, track and backtrack between features and EICs
• Tracking peak quality, selectiviy metrics on m/z, chromatography and annotation databases
• Performance conscious, memory and CPU uses scalable
• Fast assembly and annotation of metabolomes using chainable databases

Install

• From PyPi repository: pip3 install asari-metabolomics. Add --upgrade to update to new versions.

• Or clone from source code: https://github.com/shuzhao-li/asari . One can run it as a Python module by calling Python interpreter.

Use

If installed from pip, one can run asari as a command in a terminal, followed by a subcommand for specific tasks.

Help information is evoked without argument:

asari

To process all mzML files under directory mydir/projectx_dir:

asari process --mode pos --input mydir/projectx_dir

To get statistical description on a single file (useful to understand data and parameters):

asari analyze --input mydir/projectx_dir/file_to_analyze.mzML

To get annotation on a tab delimited feature table:

asari annotate --mode pos --ppm 10 --input mydir/projectx_dir/feature_table_file.tsv

To add automatic esitmation of min peak height, add this argument:

--autoheight True

Alternative to a standalone command, to run as a module via Python interpreter, one needs to point to module location, e.g.:

python3 -m asari.main process --mode pos --input mydir/projectx_dir

Parameters

Only one parameter in asari requires attention, i.e., precision ppm is set at 5 by default. Most modern instruments are fine with 5 ppm, but one may want to change if needed.

For the adventurous: Default parameters are set in defaul_parameters.py. They can be (work in progress) overwritten by user supplied parameter file in JSON. Lastly, parameters specified in command line overwrite all the above.

Algorithms

Basic data concepts follow https://github.com/shuzhao-li/metDataModel, organized as

├── Experiment
   ├── Sample
       ├── MassTrack
           ├── Peak
           ├── Peak
       ├── MassTrack 
           ├── Peak
           ├── Peak
    ...
   ├── Sample 
    ...
   ├── Sample 

A sample here corresponds to an injection file in LC-MS experiments. A MassTrack is an extracted chromatogram for a specific m/z measurement, governing full retention time. Therefore, a MassTrack may include multiple mass traces, or EICs/XICs, as referred by literature. Peak (an elution peak at specific m/z) is specific to a sample, but a feature is defined at the level of an experiment after correspondence.

Additional details:

• Use of MassTracks simplifies m/z correspondence
• Chromatogram construction is based on m/z values via flexible bins and frequency counts (in lieu histograms).
• Each sample is checked for mass precision, computational calibrations recorded for mass and retention time
• Elution peak alignment is based on LOWESS
• Use integers for RT scan numbers and intensities for computing efficiency
• Avoid mathematical curves whereas possible for computing efficiency

Selectivity is tracked for

• mSelectivity, how distinct are m/z measurements
• cSelectivity, how distinct are chromatograhic elution peaks
• dSelectivity, how distinct are database records

This package uses mass2chem and JMS for mass search and annotation functions.

Links

Source code: https://github.com/shuzhao-li/asari

Package Repository: https://pypi.org/project/asari-metabolomics/

Related projects:

Mummichog: metabolomics pathway/network analysis

metDataModel: data models for metabolomics, used by mummichog and Azimuth DB

mass2chem: common utilities in interpreting mass spectrometry data, annotation

JMS: Json's Metabolite Services