Development for python driven QC calculation (QCCalculator)
pip install -U git+https://github.com/bigbio/qccalculator.git#egg=QCCalculator
- biopython
- click
- mzqc-pylib
- pandas
- plotly-express
- pronto
- pyopenms
- requests
- toposort
See setup.py for most recent listing.
Structure: QCCalculator is structured as a click applications project.
The cli.py module contains the application code calling the metric calculation (from the other modules), data input (from pyopenms and pandas), and mzQC assembly (from mzqc-pylib).
The other modules contain metric calculation code for metric calculation and value collection split per topic of metrics.
This folder contains classes and methods to calculate (qccalculator) QC metrics and create corresponding MZQC objects and their visualisation (qcplots).
The set of basic metrics are created by getBasicQuality which will consume at least one MS run and will encapsulate the created basic metrics in an MZQC.RunQuality. Other metric calculating methods however are producing MZQC.QualityMetrics and care must be taken to add these to the correct RunQuality elements.
Spectrum_acquisition_metrics_MS1 (Spectrum acquisition metric values - MS1): RT, SN, peakcount, int spectrum_acquisition_metrics_MS2 (Spectrum acquisition metric values - MS2): -""- spectrum_topn (Spectra topn ranks): RT, rank tandem_spectrum_metrics_MS2 (Tandem spectrum metric values - MS2): RT, precursor_int, surveyscan_int, precursor_err, precursor_mz, precursor_c, surveyscan_max trap_metrics_MS1 (Trap metric values - MS1): RT, iontraptime trap_metrics_MS2 (Trap metric values - MS2): RT, iontraptime tic_tab (Total ion current chromatogram): RT, int
identification_accuracy_metrics (Identifications accuracy metric values): RT, MZ, delta_ppm, abs_err identification_scoring_metrics (Identification scoring metric values): RT, c, score identification_sequence_metrics (Identifications sequence metric values): RT, peptide, target hydrophobicity_metrics (Hydrophobicity metric values): RT, gravy
For example, to add a new input from mzTab, you need a new module mztab.py to read the identification data. You also need to extend the command function for full QC in cli.py. The code should accept the results of mztab.py, optimally directly using the umbrella function getIDQuality. If that is not possible, you'd need to add a idqcmztab.py module to reuse the metric calculations with more direct input of values.