asari-metabolomics 1.13.1

LC-MS metabolomics data preprocessing

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.

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 centroied 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.

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.

If the individual files are large or the sample number is very high, it is easy to split the data and run asari separately. One can then use asari join to merge the results [in progress].

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

Docker image

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

Links

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

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

Test data: https://github.com/shuzhao-li/data/tree/main/data

Notebooks to reproduce publication figures: https://github.com/shuzhao-li/data/tree/main/notebooks

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.

Related projects

Mummichog: metabolomics pathway/network analysis

metDataModel: data models for metabolomics

mass2chem: common utilities in interpreting mass spectrometry data, annotation

khipu: a Python library for generalized, low-level annotation of MS metabolomics

JMS: Json's Metabolite Services