coremstools 0.0.8.4

A package for processing CoreMS assignments of ultrahigh mass resolution LC-ESI-MS data

CoreMS LC Modules

A Python package for processing CoreMS assignments of ultrahigh mass resolution LC-ESI-MS data.

Functionality

Functions that do not require CoreMS:

• Alignment of assigned features across a dataset
• Calculation of average assignment & feature parameters across a dataset:
  1. measured m/z
  2. calibrated m/z
  3. resolving power
  4. m/z error
  5. feature S/N
  6. confidence score
• Gap filling of ambiguous assignments
• Stoichiometric classifications
• NOSC calucations
• O/C, H/C, N/C calculations
• Identification of significant assignment errors in a dataset, based on rolling average and standard deviation

Functions that require CoreMS:

• Determination of a feature's chromatographic dispersity
• Generation of calibrant list(s) for data calibration
• QC checks of retention and intensity of an internal standard across a dataset

Installation

Local editable installation

git clone https://github.com/deweycw/corems-tools.git corems-tools
cd corems-tools/src
python -m pip install -e . # from root directory of project  

Install pkg from TestPyPi

python -m pip install --index-url https://test.pypi.org/simple/ --no-deps coremstools

Build and upload to TestPyPi

See: https://packaging.python.org/en/latest/tutorials/packaging-projects/

Build prior to uploading to archives

python -m pip install -U build
python -m build

Upload to TestPyPi with Twine

python -m pip install -U twine
python -m twine upload --repository testpypi dist/*

Example Workflow

Phase 1: Assignments & initial QC

1. Generate calibrants using calibrant generator function (requires CoreMS)
2. Perform assignments (requires CoreMS)
3. Calculate dispersity (requires CoreMS)
4. Evaluate retention and intensity of internal standard (requires CoreMS)
5. Generate assignment error plots (error v. m/z; error dist in each time window)

Phase 2: Alignment, gapfilling, blank correction, error flags

6. Align features across dataset
7. Gapfill across dataset
8. Perform blank correction
9. Flag features with potentially signficant assignment error (i.e., feature is flagged if difference between rolling average of assignment error (across dataset) and error of individual assignment exceeds 4x the standard deviation of the assignment error for the specific feature)

Phase 3: Additional classifications

10. Determine O/C, H/C, N/C & NOSC for features
11. Determine stoichiometric classifcations