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PyProphet export (version 2.1.4.1)

Input file:

This file needs to be in OSW format (--in)

Export format, either matrix, legacy_split, legacy_merged (mProphet/PyProphet) or score_plots format:

Report aggregated transition-level quantification:

(--transition_quantification / --no-transition_quantification)

Maximum PEP to retain scored transitions for quantification (requires transition-level scoring):

Should IPF results be reported if present? 'peptidoform': Report results on peptidoform-level, 'augmented': Augment OpenSWATH results with IPF scores, 'disable': Ignore IPF results:

IPF: Filter results to maximum run-specific peptidoform-level PEP:

Filter results to maximum run-specific peak group-level q-value:

Filter results to maximum global peptide-level q-value:

ilter results to maximum global protein-level q-value:

Append peptide-level error-rate estimates if available:

(--peptide / --no-peptide)

Append protein-level error-rate estimates if available:

(--protein / --no-protein)

Use swath2stats to export file for statsics:

Study design tabular file:

Needs to have columns with Filename, Condition, BioReplicate, Run

Filter for fdr and number of replicates:

FFT. Ratio of false positives to true negatives, q-values from pyProphet stats output:

As an approximation, the q-values of multiple runs are averaged and supplied as argument FFT. Numeric from 0 to 1.

Option to set the number of magnitude for which the m_score threshold is decreased:

FDR target.:

An m_score cutoff achieving and FDR smaller fdr_target will be selected. Calculated as FDR = decoys*FFT/targets

Number Replicates.:

Number of measurements within at least one condition that have to pass the mscore threshold for this transition.

Filter for a maximum number of peptides per protein:

Maximum number of peptides per protein.:

Maximum number of highest intense peptides to filter the data on.

Filter for a proteins that are supported by a minimum number of peptides:

Minimum number of peptides per protein:

Number of minimal number of peptide IDs associated with a protein ID in order to be kept in the dataset.

What it does

PyProphet: Semi-supervised learning and scoring of OpenSWATH results.

Export tabular (tsv) tables. By default, both peptide- and transition-level quantification is reported, which is necessary for requantification or SWATH2stats. If peptide and protein inference in the global context was conducted, the results will be filtered to 1% FDR by default.

Optional SWATH2stats output. SWATH2stats is intended to transform SWATH data from the OpenSWATH software into a format readable by other statistics packages while performing filtering, annotation and FDR estimation.

Study desing file for SWATH2stats

Tabular file with columns that are named: Filename, Condition, BioReplicate, Run.

The Filename should be part or the same as the original filenames used in OpenSWATH workflow

The Condition will be used for statistical analysis. In case multiple conditions are of interest for statistical analysis (e.g. diagnosis and age), this tool has to be run multiple times as SWATH2stats can only handle one condition at a time

The BioReplicate is corresponds to the biological replicate

The Run is the number of the MS run in which the sample was measured
Example for one replicate per patient
  Filename       Condition    BioReplicate     Run
healthy1.mzml     healthy         1             1
healthy2.mzml     healthy         2             2
diseased1.mzml    diseased        3             3
diseased2.mzml    diseased        4             4
    ...
    ...
Example for two replicates per patient
  Filename       Condition    BioReplicate     Run
healthy1.mzml     healthy         1             1
healthy2.mzml     healthy         1             2
diseased1.mzml    diseased        2             3
diseased2.mzml    diseased        2             4
    ...
    ...

PyProphet is a Python re-implementation of the mProphet algorithm (Reiter 2010 Nature Methods) optimized for SWATH-MS data acquired by data-independent acquisition (DIA). The algorithm was originally published in (Telemann 2014 Bioinformatics) and has since been extended to support new data types and analysis modes (Rosenberger 2017, Nature biotechnology and Nature methods).

For more information, visit http://openswath.org/en/latest/docs/pyprophet.html