pyIsoPEP 0.1.3

Estimate PEPs from empirical null models using isotonic regression.

pyIsoPEP: Isotonic PEP Estimator

Overview

pyIsoPEP provides both a Docker image and a Python API with a unified interface for estimating smooth, non‑decreasing Posterior Error Probabilities (PEPs) directly from empirical null models using isotonic regression for target identifications in shotgun proteomics.

Two workflows are available:

workflow	scheme	starting point
q2pep	rank-based	a list of target identifications with q-values
d2pep	score-based	target‑decoy competition (TDC) output: a list of target and decoy identifications with scores

Internally, both workflows use isotonic regression - implemented via either the Pool-Adjacent-Violators Algorithm (PAVA) or I‑Splines. Optional post-processing can be applied to derive q-values from the estimated PEPs.

pyIsoPEP substantially enhances the robustness of PEP estimation and supports post-processing for both DDA and DIA data. The same implementation is also incorporated into Percolator (C++).

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Installation

PyPI

pip install pyIsoPEP

Docker image

podman pull ghcr.io/statisticalbiotechnology/pyisotonicpep:main
# or
docker pull ghcr.io/statisticalbiotechnology/pyisotonicpep:main

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Input & output

Input: tab‑separated value (TSV) files; column names are configurable.
Output: the original target rows plus up to four extra columns:

column	present when	description
pyIsoPEP FDR	--calc-q-from-fdr	estimated False Discovery Rates (FDRs)
pyIsoPEP q-value from FDR	--calc-q-from-fdr	FDR-derived q-values
pyIsoPEP PEP	always	estimated PEPs
pyIsoPEP q-value from PEP	--calc-q-from-pep	PEP-derived q-values

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Command-line reference

Top‑level help:

pyisopep -h
# or
podman run --rm -it pyisotonicpep:main -h

Workflow‑specific help:

pyisopep q2pep -h
pyisopep d2pep -h
# or
podman run --rm -it pyisotonicpep:main q2pep -h
podman run --rm -it pyisotonicpep:main d2pep -h

Common flags (valid for both workflows)

flag	default	description
--cat-file FILE, --target-file FILE	required	Select concatenated vs. separate input
--decoy-file FILE	—	Required only with separate target & decoy lists
--score-col COL	score	Score column
--label-col COL	label	Column distinguishing targets/decoys in a concatenated file
--target-label STR	target	String marking target rows
--decoy-label STR	decoy	String marking decoy rows
--regression-algo {PAVA,ispline}	ispline	Monotone regression backend
--calc-q-from-fdr	off	Derive q‑values from running FDRs (needs decoys)
--calc-q-from-pep	off	Derive q‑values after estimating PEPs
-k, --keep-input-order	off	Write output in original input order
--output FILE|DIR	required	Write a target‑only list; if DIR, a default name is used
--verbose	off	Echo all parsed parameters

Additional flags – q2pep only

flag	default	description
--qcol COL	q-value	Column containing the input q‑values
--ip	off	Smooth PAVA step‑function with a monotone spline
--ip-algo {ispline,pchip}	ispline	Interpolator used with --ip
--center-method {mean,median}	mean	x‑coordinate of each PAVA block center when interpolating

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Examples

PyPI

q2pep

Example 1: a target input file with q‑values

pyisopep q2pep --target-file example/peptide.target.txt --qcol q-value --calc-q-from-pep --output example/results

Example 2: separate target and decoy input files, derive q‑values from FDR first

pyisopep q2pep --target-file example/peptide.target.txt --decoy-file example/peptide.decoy.txt --score-col score --label-col type --target-label 0 --decoy-label 1 --calc-q-from-fdr --calc-q-from-pep --output results/

Example 3: a concatenated target and decoy input file with q‑values

pyisopep q2pep --cat-file example/peptide.cat.txt --qcol q-value --score-col score --label-col type --target-label 0 --decoy-label 1 --calc-q-from-pep --output results/

d2pep

Example 1: separate target and decoy input files

pyisopep d2pep --target-file example/peptide.target.txt --decoy-file example/peptide.decoy.txt --score-col score --label-col type --target-label 0 --decoy-label 1 --calc-q-from-fdr --calc-q-from-pep --output results/

Example 2: a concatenated target and decoy input file

pyisopep d2pep --cat-file example/peptide.cat.txt --qcol q-value --score-col score --label-col type --target-label 0 --decoy-label 1 --calc-q-from-pep --output results/

Docker image

Pull the Docker image from GitHub Container Registry

podman pull ghcr.io/statisticalbiotechnology/pyisotonicpep:main

q2pep

podman run --rm -it -v .:/data pyisotonicpep:main q2pep --target-file /example/peptide.target.txt --decoy-file /example/peptide.decoy.txt --score-col score --label-col type --target-label 0 --decoy-label 1 --calc-q-from-fdr --calc-q-from-pep --output /data

d2pep

podman run --rm -it -v .:/data pyisotonicpep:main d2pep --cat-file /example/peptide.cat.txt --qcol q-value --score-col score --label-col type --target-label 0 --decoy-label 1 --calc-q-from-pep --output /data

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Links

• PyPI package: https://pypi.org/project/pyIsoPEP/
• Docker image: https://github.com/statisticalbiotechnology/smooth_q_to_pep/pkgs/container/pyisotonicpep
• Github repository: https://github.com/statisticalbiotechnology/smooth_q_to_pep

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