pyIsoPEP 0.1.4

Estimate PEPs from empirical null models using isotonic regression.

pyIsoPEP: Isotonic PEP Estimator

Overview

pyIsoPEP provides both a Docker image (Docker-, Podman-, and Apptainer-compatible) 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.

Six estimation combinations are available — three input methods × two regressors — mirroring the same design used in Percolator (C++):

#	method	x-axis	input	regressor	CLI
1	q2pep	rank	target q-values	I-Spline	q2pep
2	q2pep	rank	target q-values	PAVA	q2pep --pava
3	qns2pep	score	target q-values + scores	I-Spline	q2pep --score-based
4	qns2pep	score	target q-values + scores	PAVA	q2pep --score-based --pava
5	tdc2pep	score	TDC targets + decoys	I-Spline	d2pep
6	tdc2pep	score	TDC targets + decoys	PAVA	d2pep --pava

q2pep / qns2pep fit PEPs from target q-values only; only target statistics are reported.
tdc2pep fits p(decoy | score) over all PSMs and converts to PEP via p / (1 − p); only target PEPs are reported.
qns2pep and tdc2pep place I-Spline knots at quantiles of the score distribution, concentrating spline flexibility where observations are densest.

Optional post-processing derives q-values from estimated PEPs for both DDA and DIA data.

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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
# or
apptainer build pyisopep.sif docker://ghcr.io/statisticalbiotechnology/pyisotonicpep:main

---

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

Workflow‑specific help:

pyisopep q2pep -h
pyisopep d2pep -h

Common flags (valid for both subcommands)

flag	default	description
--cat-file FILE, --target-file FILE	required	Select concatenated vs. separate input
--decoy-file FILE	—	Decoy file (required in separate input mode)
--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
--pava	off	Use PAVA regression instead of the default I-Spline
--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
--score-based	off	Use raw scores as independent variable instead of rank (selects qns2pep); requires --score-col and decoy information
--trim-plateaus	off	Enable trimming of leading/trailing q‑value plateaus before isotonic regression (plateau trimming is off by default)
--no-pseudo-count	off	Disable the Jeffreys-style smoothing (value 0.5, distributed as 0.5 / n_mid per rank) that is added to raw PEPs before isotonic regression by default. Smoothing improves log-ratio calibration at very small q.

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Examples

PyPI

Combination 1 — q2pep, I-Spline (rank-based, default)

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

Combination 2 — q2pep, PAVA (rank-based)

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

Combination 3 — qns2pep, I-Spline (score-based q-values)

pyisopep q2pep --score-based \
  --target-file example/peptide.target.txt \
  --decoy-file  example/peptide.decoy.txt \
  --qcol q-value --score-col score \
  --calc-q-from-pep --output results/

Combination 4 — qns2pep, PAVA

pyisopep q2pep --score-based --pava \
  --target-file example/peptide.target.txt \
  --decoy-file  example/peptide.decoy.txt \
  --qcol q-value --score-col score \
  --calc-q-from-pep --output results/

Combination 5 — tdc2pep, I-Spline (score-based TDC)

pyisopep d2pep \
  --target-file example/peptide.target.txt \
  --decoy-file  example/peptide.decoy.txt \
  --score-col score --calc-q-from-pep --output results/

Combination 6 — tdc2pep, PAVA

pyisopep d2pep --pava \
  --target-file example/peptide.target.txt \
  --decoy-file  example/peptide.decoy.txt \
  --score-col score --calc-q-from-pep --output results/

Docker image

podman pull ghcr.io/statisticalbiotechnology/pyisotonicpep:main

# q2pep (rank-based, default)
podman run --rm -it -v .:/data pyisotonicpep:main \
  q2pep --target-file /example/peptide.target.txt \
  --decoy-file /example/peptide.decoy.txt \
  --score-col score --qcol q-value \
  --calc-q-from-fdr --calc-q-from-pep --output /data

# tdc2pep (score-based TDC)
podman run --rm -it -v .:/data pyisotonicpep:main \
  d2pep \
  --cat-file /example/peptide.cat.txt \
  --score-col score --label-col type --target-label 0 --decoy-label 1 \
  --calc-q-from-pep --output /data

---

Python API

import numpy as np
from pyIsoPEP.IsotonicPEP import IsotonicPEP

iso = IsotonicPEP()          # I-Spline by default
iso_pava = IsotonicPEP(pava=True)   # PAVA

# --- Combinations 1 & 2: q2pep (rank-based) ---
_, _, pep, q2 = iso.pep_regression(
    q_values=q_sorted, method="q2pep",
    pava=False,   # True for PAVA
    calc_q_from_pep=True,
)

# --- Combinations 3 & 4: qns2pep (score-based q-values) ---
_, _, pep, q2 = iso.pep_regression(
    q_values=q_sorted, obs=obs_all,   # obs shape (n, 2): [score, label]
    method="qns2pep",
    pava=False,   # True for PAVA
    calc_q_from_pep=True,
)

# --- Combinations 5 & 6: tdc2pep (score-based TDC) ---
_, _, pep, q2 = iso.pep_regression(
    obs=obs_all,
    method="tdc2pep",
    pava=False,   # True for PAVA
    calc_q_from_pep=True,
)

# Direct access — all PSMs (targets + decoys):
df_obs = iso.process_obs(obs_all)
pep_all = iso.tdc_to_pep(df_obs)            # I-Spline
pep_all = iso.tdc_to_pep(df_obs, pava=True) # PAVA

# Unified q_to_pep — rank-based (scores=None) or score-based (scores provided):
pep_rank  = iso.q_to_pep(q_sorted)
pep_score = iso.q_to_pep(q_sorted, scores=score_sorted)

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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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