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asari-metabolomics-1.9.8
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مشاهده بیشترتوضیحات
LC-MS metabolomics data preprocessing
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | asari-metabolomics-1.9.8 |
| نام | asari-metabolomics |
| نسخه کتابخانه | 1.9.8 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Shuzhao Li |
| ایمیل نویسنده | shuzhao.li@gmail.com |
| آدرس صفحه اصلی | https://github.com/shuzhao-li/asari |
| آدرس اینترنتی | https://pypi.org/project/asari-metabolomics/ |
| مجوز | BSD 3-Clause |
Asari
=====
[](https://asari.readthedocs.io/en/latest/?badge=latest)
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
- Scalable, performance conscious, disciplined use of memory and CPU
- Transparent, JSON centric data structures, easy to chain other tools
Input data are centroied mzML files.
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.
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.
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 `doc/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/
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
نیازمندی
| مقدار | نام |
|---|---|
| - | metDatamodel |
| - | mass2chem |
| >=0.5.6 | khipu-metabolomics |
| >=0.4.10 | jms-metabolite-services |
| - | pymzml |
| - | numpy |
| - | scipy |
| - | statsmodels |
| - | pyyaml |
| - | panel |
| - | holoviews |
| - | hvplot |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl asari-metabolomics-1.9.8:
pip install asari-metabolomics-1.9.8.whl
نصب پکیج tar.gz asari-metabolomics-1.9.8:
pip install asari-metabolomics-1.9.8.tar.gz