Isotonic PEP Estimation

Overview

pyIsotonicPEP provides a unified interface for estimating Posterior Error Probabilities (PEPs) using isotonic regression for identifications in shotgun proteomics. It supports two methods:

• q2pep: Estimate non-decreasing PEPs from q-values.
• obs2pep: Estimate non-decreasing PEPs from a stream of target and decoy observations derived from target-decoy competition (TDC) method.

The package consists of two main Python files:

• IsotonicPEP.py: Implements isotonic regression and methods for PEP estimation.
• main.py: Provides a command-line interface (CLI) to run the isotonic regressor with various options.

Features

• Isotonic Regression: Enforces a non-decreasing constraint on probability estimates.
• q2pep Method: Converts q-values to PEP values.
• obs2pep Method: Processes target-decoy observations to compute PEP values.
• Optional PEP-based q-value Estimation: Calculates q-values from the estimated PEPs.
• Flexible Input Formats: For the obs2pep Method, supports both concatenated input files and separate target/decoy files.

Usage

Pull the docker image and run the container to display the help message

$ podman pull ghcr.io/statisticalbiotechnology/pyisotonicpep:main
$ podman run --rm -it pyisotonicpep:main -h

Command-Line Options

General Options

usage: main.py [-h] [--no-calc-q] [--verbose] {q2pep,obs2pep} ...

• {q2pep,obs2pep} Select the PEP estimation method.
  • q2pep Estimate PEP from q-values (q2pep method).
  • obs2pep Estimate PEP from target-decoy observations(obs2pep method).
• -h, --help Show this help message and exit.
• --no-calc-q Do not estimate q-values from calculated PEPs (default: calculate q-values).
• --verbose Print parameter information.

q2pep Mode Options

usage: main.py q2pep [-h] --input INPUT [--qcol QCOL] [--smooth] [--pava-method {basic,ip}] [--center-method {mean,median}] --output OUTPUT

• --input INPUT Path to the TSV file containing q-values.
• --qcol QCOL Column name for q-values in the input file (default: 'q-value').
• --smooth Apply block-merge pre-processing (default: False).
  • --pava-method {basic,ip} Choose PAVA method to use: 'basic' for PAVA regression or 'ip' for PAVA interpolation (default: basic).
• --center-method {mean,median} ChoosePAVA center method to use in PAVA interpolation (default: mean).
• --output OUTPUT Output file path (or output directory; if directory, a default name 'outputPEP.target.txt' will be used).

obs2pep Mode Options

usage: main.py obs2pep [-h] (--cat-file CAT_FILE | --target-file TARGET_FILE) [--decoy-file DECOY_FILE] [--score-col SCORE_COL] [--type-col TYPE_COL] [--target-label TARGET_LABEL] [--decoy-label DECOY_LABEL] -output OUTPUT

• --cat-file CAT_FILE Path to a concatenated TSV file containing score and label columns.
• --target-file TARGET_FILE Path to the TSV file containing target scores (used in separate input mode).
• --decoy-file DECOY_FILE Path to the TSV file containing decoy scores (required in separate input mode).
• --score-col SCORE_COL Column name for score (default: 'score').
• --type-col TYPE_COL Column name for target/decoy (default: 'label').
• --target-label TARGET_LABEL Target label in concatenated input file (default: 'target').
• --decoy-label DECOY_LABEL Decoy label in concatenated input file (default: 'decoy').
• --output OUTPUT Output file path or directory for target results. (Note: only target PEPs are saved.)

Running examples with the provided files

1. q2pep Mode

$ podman run --rm -it -v .:/data pyisotonicpep:main q2pep --input example/peptide.target.txt --output /data

2. obs2pep Mode

2.1 using a concatenated target and decoy input

$ podman run --rm -it -v .:/data pyisotonicpep:main obs2pep --cat-file example/peptide.cat.txt --score-col score --type-col type --target-label 0 --decoy-label 1 --output /data

2.2 using separate target and decoy inputs

$ podman run --rm -it -v .:/data pyisotonicpep:main obs2pep --target-file example/peptide.target.txt --decoy-file example/peptide.decoy.txt --score-col score --output /data