pyIsoPEP 0.1.2

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) 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. In I‑Spline regression, for d2pep, scores are adaptively binned to balance decoy counts per bin, with tighter bins at lower scores. Linear weight rescaling is applied to emphasize early (low-PEP) regions.

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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
--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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