.. _command_line_interface: Command Line Interface ========================= .. automodule:: pyprophet.cli :members: :undoc-members: :show-inheritance: `pyprophet` is the main command-line interface for PyProphet, with subcommands for scoring, IPF, levels context inference, and other utility functions. .. click:: pyprophet.main:cli :prog: pyprophet :nested: none Semi-Supervised Scoring of Peak-Groups -------------------------------------- .. _cli_score: PyProphet provides a command-line interface for scoring peak-groups using the `score` subcommand. This provides a re-implementation of the original `mProphet `_ algorithm, which is a semi-supervised machine learning approach for scoring peak-groups in SRM mass spectrometry data. .. currentmodule:: pyprophet.cli.score .. click:: pyprophet.cli.score:score :prog: pyprophet score :nested: full The :program:`score` command has several advanced options that can be seen using the :option:`--helphelp` flag. Inference of Peptidoforms ------------------------- .. _cli_ipf: For PTM analyses, PyProphet provides the :program:`infer peptidoform` subcommand. This command allows you to perform inference of peptidoforms, for site-localization of peptidoforms in large-scale DIA experiments. Refer to `Rosenberger, G. et. al. (2017) `_ to learn more about the inference of peptidoforms workflow. .. currentmodule:: pyprophet.cli.ipf .. click:: pyprophet.cli.ipf:ipf :prog: pyprophet infer peptidoform :nested: none For glycoform inference, you can use the :program:`infer glycoform` subcommand, which is specifically designed for glycopeptide analyses. .. currentmodule:: pyprophet.cli.ipf .. click:: pyprophet.cli.ipf:glycoform :prog: pyprophet infer glycoform :nested: none Refer to `Yang, Y. et. al. (2021) `_ to learn more about the glycoform inference workflow. Peptide / Protein / Gene Inference ---------------------------------- .. _cli_levels_context: To perform inference at different levels of biological context and different experimental contexts (global, experiment-wide and run-specific), PyProphet provides the :program:`infer` subcommand. This command allows you to infer peptide, glycopeptide, protein, and gene levels from your data. Refer to `Rosenberger, G. et. al. (2017) `_ to learn more about the levels context inference. For more information about glycopeptide inference, refer to `Yang, Y.. et. al. (2021) `_. .. currentmodule:: pyprophet.cli.levels_context .. click:: pyprophet.cli.levels_context:peptide :prog: pyprophet infer peptide :nested: none The :program:`peptide` command accepts a :option:`helphelp` argument to display its advanced options that are not shown here. .. click:: pyprophet.cli.levels_context:glycopeptide :prog: pyprophet infer glycopeptide :nested: none .. click:: pyprophet.cli.levels_context:protein :prog: pyprophet infer protein :nested: none The :program:`protein` command accepts a :option:`helphelp` argument to display its advanced options that are not shown here. .. click:: pyprophet.cli.levels_context:gene :prog: pyprophet infer gene :nested: none The :program:`gene` command accepts a :option:`helphelp` argument to display its advanced options that are not shown here. Exporters --------- PyProphet provides several export utilities to export between different file formats for OpenSwath's (*.osw* / *.sqMass*sqlite-based formats) and experimental parquet formats, as well as exporting PDF reports of the data. .. currentmodule:: pyprophet.cli.export TSV Results (Proteomics) ^^^^^^^^^^^^^^^^^^^^^^^^ .. _export_tsv_proteomics: To export results from a post-scoring workflow (using the *.osw* input workflow) to a tab-separated values (TSV) file, you can use the :program:`export tsv` subcommand. This is useful for exporting results in a format that can be easily read and processed by other tools or scripts. .. click:: pyprophet.cli.export:export_tsv :prog: pyprophet export tsv :nested: none TSV Results (Small Molecules) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ This is similar to the TSV export for proteomics, but specifically designed for small molecule data. It allows you to export results in a tab-separated values (TSV) format, which can be easily read and processed by other tools or scripts. .. click:: pyprophet.cli.export:export_compound :prog: pyprophet export compound :nested: none TSV Results (Glycoform) ^^^^^^^^^^^^^^^^^^^^^^^ This is similar to the TSV export for proteomics, but specifically designed for glycoform data. It allows you to export results in a tab-separated values (TSV) format, which can be easily read and processed by other tools or scripts. .. click:: pyprophet.cli.export:export_glyco :prog: pyprophet export glyco :nested: none TSV Quantification Matrices (Proteomics) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. _export_matrix_proteomics: To export quantification matrices from a post-scoring workflow to a tab-separated values (TSV) file, you can use the :program:`export matrix` subcommand. This is useful for exporting quantification data in a format that can be easily read and processed by other tools or scripts. .. click:: pyprophet.cli.export:export_matrix :prog: pyprophet export matrix :nested: none Convert OSW to Parquet ^^^^^^^^^^^^^^^^^^^^^^ To convert OpenSwath's *.osw* / *.sqMass* format to a parquet format, you can use the :program:`export parquet` subcommand. This is useful for converting results from the *.osw* / *.sqMass* format to a more efficient and space saving data storage format. This subcommand has the option to convert the entire *.osw* file to a snigle parquet file (with both precursor and transition data) or to split the parquet file into a separate precursors_features.parquet file and a transition_features.parquet file. There is the option to further split by run, which is useful for large datasets. .. click:: pyprophet.cli.export:export_parquet :prog: pyprophet export parquet :nested: none Export Feature Score Plots ^^^^^^^^^^^^^^^^^^^^^^^^^^^ To export the distribution of feature scores (VAR_ columns) and, if available, scorer scores (SCORE columns), you can use the :program:`export feature-scores` subcommand. This command works with all file formats (OSW, Parquet, and Split Parquet): - **For unscored files**: Plots only VAR_ columns (feature variables) - **For scored files**: Applies RANK==1 filtering and plots both SCORE and VAR_ columns This is useful for investigating the distribution and quality of scores for target-decoy separation. .. click:: pyprophet.cli.export:export_feature_scores :prog: pyprophet export feature-scores :nested: none Export Score Plots (Deprecated) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. deprecated:: 3.1 Use :program:`pyprophet export feature-scores` instead. The :program:`export score-plots` command is deprecated and will be removed in a future version. It has been replaced by the more flexible :program:`export feature-scores` command which works with all file formats. .. click:: pyprophet.cli.export:export_score_plots :prog: pyprophet export score-plots :nested: none Export Results Report ^^^^^^^^^^^^^^^^^^^^ To export a PDF report of the results, you can use the :program:`export score-report` subcommand. This is useful for generating a report that summarizes the results of your analysis, including scores and identifications, and other relevant information. .. click:: pyprophet.cli.export:export_scored_report :prog: pyprophet export score-report :nested: none Merge files ----------- PyProphet provides a command to merge multiple files into a single file. This is useful for combining results from different analyses or runs into a single file for further processing or analysis. .. currentmodule:: pyprophet.cli.merge Merge OSW Files ^^^^^^^^^^^^^^^^ To merge multiple OSW files into a single OSW file, you can use the :program:`merge osw` subcommand. .. click:: pyprophet.cli.merge:merge_osw :prog: pyprophet merge osw :nested: none Merge Parquet Files ^^^^^^^^^^^^^^^^^^^ .. _merge_parquet: To merge multiple Parquet files into a single Parquet file, you can use the :program:`merge parquet` subcommand. .. click:: pyprophet.cli.merge:merge_parquet :prog: pyprophet merge parquet :nested: none