Asari

Trackable and scalable Python program for high-resolution LC-MS metabolomics data preprocessing (Li et al. Nature Communications 14.1 (2023): 4113):

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
Scalable, performance conscious, disciplined use of memory and CPU
Transparent, JSON centric data structures, easy to chain other tools

A web server (https://asari.app) and full pipeline are available now.

A set of tutorials are hosted at https://github.com/shuzhao-li-lab/asari_pcpfm_tutorials/.

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. GitHub repo is often ahead of PyPi versions.
Requires Python 3.8+. Installation time ~ 5 seconds if common libraries already exist.
One can use the web version (https://asari.app) without local installation.

Input

Input data are centroid mzML files from LC-MS metabolomics. We use ThermoRawFileParser (https://github.com/compomics/ThermoRawFileParser) to convert Thermo .RAW files to .mzML. Msconvert in ProteoWizard (https://proteowizard.sourceforge.io/tools.shtml) can handle the conversion of most vendor data formats and .mzXML files.

MS/MS spectra are ignored by asari. Our pipeline (https://pypi.org/project/pcpfm/) has annotation steps to use MS/MS data.

Use

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

For help information:

asari -h

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

--autoheight True

To output additional extraction table on a targeted list of m/z values from target_mzs.txt:

asari extract --input mydir/projectx_dir --target target_mzs.txt

This is useful to add QC check during data processing, e.g. the target_mzs.txt file can be spike-in controls.

To launch a dashboard in your web browser after the project is processed into directory process_result_dir:

asari viz --input process_result_dir

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

Output

A typical run on disk may generatae a directory like this

rsvstudy_asari_project_427105156
├── Annotated_empricalCompounds.json
├── Feature_annotation.tsv
├── export
│   ├── _mass_grid_mapping.csv
│   ├── cmap.pickle
│   ├── full_Feature_table.tsv
│   └── unique_compound__Feature_table.tsv
├── pickle
│   ├── Blank_20210803_003.pickle
│   ├── ...
├── preferred_Feature_table.tsv
└── project.json

The recommended feature table is preferred_Feature_table.tsv.

All peaks are kept in export/full_Feature_table.tsv if they meet signal (snr) and shape standards (part of input parameters but default values are fine for most people). That is, if a feature is only present in one sample, it will be reported, as we think this is important for applications like exposome and personalized medicine. The filtering decisions are left to end users.

The pickle folder keeps intermediate files during processing. They are removed after the processing by default, to save disk space. Users can choose to keep them by specifying --pickle True.

Dashboard

After data are processed, users can use asari viz --input process_result_dir to launch a dashboard to inspect data, where 'process_result_dir' refers to the result folder. The dashboard uses these files under the result folder: 'project.json', 'export/cmap.pickle', 'export/epd.pickle' and 'export/full_Feature_table.tsv'. Thus, one can move around the folder, but modification of these files is not a good idea. Please note that pickle files are for internal use, and one should not trust pickle files from other people.

viz_screen_shot

Parameters

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

Default ionization mode is pos. Change to neg if needed, by specifying --mode neg in command line.

Users can supply a custom parameter file xyz.yaml, via --parameters xyz.yaml in command line. A template YAML file can be found at test/parameters.yaml.

When the above methods overlap, command line arguments take priority. That is, commandline overwrites xyz.yaml, which overwrites default asari parameters in defaul_parameters.py.

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, which results in a MassGrid
Two modes of m/z correspondence: a clustering method for studies >= N (default 10) samples; and a slower method based on landmark peaks and verifying mass precision.
Chromatogram construction is based on m/z values via flexible bins and frequency counts (in lieu histograms).
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

Step-by-step algorithms are explained in doc/README.md.

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

Performance

Asari is designed to run > 1000 samples on a laptop computer. The performance is achieved via

Implementation of basic functions using discrete mathematics and avoiding continuous curves.
Main intensity values of each sample are not kept in memory.
Simple (and transparent) peak detection based on local maxima (no curve fitting until evaluation)
Composite mass tracks greatly reduce the run cycles on peak detection
Using Python numerical libraries and vector operations
Alignment of mass tracks uses clustering in larger sample size

When a study has N (default 10) or fewer samples, the MassGrid assembly uses a slower algorithm to compensate statistical distribution.

Future improvement can be made by implementing some functions, e.g. chromatogram building, in C.

Platforms, Anaconda and Docker

Desktop vs Cloud

Python itself is used on Windows, Mac and Linux. Users may encouter problems related to Python not to asari, in which cases your best option is to find your local IT friend. We are a small team of scientists. There is no plan to build a desktop graphic application, but we do a lot of cloud computing. If you don't like command lines (many people don't), please feel free to try out the web server (https://asari.app). The free server has a quota. Please contact us if you find yourself in need of substantial cloud resources.

Anaconda and conda, virtual environments

Anaconda has various channels to distribute conda pacages. After looking into conda distribution, I came to the conclusion that it's not worth the effort to maintain a separate package on conda-forge. The concern is that once we put in a conda package in public distribution, long-term maintenance of it and related packages will be potential issues. Pip is always in conda and one can use the same pip install asari-metabolomics in conda environment.

Conda is excellent in handling virtual environments. Because we often use tools of different dependencies, virtual environments are great for preventing conflicts. This screen shot shows asari 1.13.1 installed in conda "base" environment and 1.11.4 in my native system environment. conda_screen_shot

What happened to Docker?

My daily computer has an M2 chip, so that I haven't used Docker for a while. The use of virtual environments (see above) removes a lot of need of Docker.

There's a Dockerfile in GitHub repo and one can build an image from there. There's an older Docker image built on Intel chip at https://hub.docker.com/r/shuzhao/asari. This image includes mono and ThermoRawFileParser, which converts Thermo .raw files to .mzML files.

Example use To launch with volume mapping $ docker run -v /Users/shuzhao/data:/home -ti shuzhao/asari.

In the container, ThermoRawFileParser is under /usr/local/thermo/.

# mono /usr/local/thermo/ThermoRawFileParser.exe -d my_data_dir

# asari analyze --input tmp/file_008.mzML 

# asari process --mode neg --input tmp --output test99

Discussion and Future Plans

Known limitations

The current version was mostly developed for Orbitrap data, less tested on TOF.
Default elution peak detection is based on local maxima using statistically guided parameters. For highly complex local EIC, additional algorithm may be needed.

Next steps in development

Implementation of join function to facilitate better parallelization. The goal is to have 'native' level of matching features when large datasets are split and processed separately. This can be equivalent function of matching different datasets.
To improve TOF support. This maybe related to better construction of mass tracks when the m/z space gets too crowded.
Automated handling of sample clusters, since different sample types may be included in an experiment.
To add GC-MS support (mostly RT optimization, deconvolution and library search).

How accurate are my m/z values?

The mass tracks are scaffolds to assemble data. Very close m/z values may not be distinguished on a mass track. For example, when mass tracks are constructed for 5 ppm resolution, two m/z values of 3 ppm apart will be reported on the same mass track. This leads to a situation where the m/z values are not optimal. Asari is designed for reliable information retrieval. If the data are processed under 5 ppm, the information can be retrieved by 5 ppm. The true m/z values will be recovered via annotation, if the features are resolved by LC, when asari features are matched to annotation libraries.

As discussed in the manuscript, ppm is not perfect in modeling mass resolution and is not constant for all m/z ranges. It is a practical tool we currently work with. If two compounds are not resolved by LC and their m/z values are 4 ppm apart, asari processing by 5 ppm will treat them as one feature. If the mass resolution is justified, one can run asari using, for instance, 3 ppm. The default workflow in asari does not fine-tune the m/z values, because the split m/z peaks from centroiding are difficult to distinguish from real m/z peaks. We leave the fine-tuning to annotation or targeted extraction workflow.

We thank reviewer #1 for valuable discussions on this topic.