Quickstart

This quickstart shows the minimal steps to run a FLiPPR analysis programmatically.

Installation

Install from source in a modern Python (see pyproject.toml):

uv sync

Install the published package into an existing environment:

uv pip install flippr

Basic example

The typical workflow uses Study to register FragPipe output directories, add one or more processes, and run them.

from flippr import Study

# Create a Study for a LiP dataset (optional TrP normalization can be provided)
study = Study(lip="/path/to/LiP_LFQ", trp=None, method="dda")

# Add a process: pid, control name, test name, replicates
study.add_process(pid="exp1", lip_ctrl="CTRL", lip_test="TREAT", n_rep=3)

# Run all processes
results = study.run()

# Access a result and its tables
res = results["exp1"]
ion_df = res.ion  # FLiPPR ion-level dataframe (polars.DataFrame)
peptides = res.peptide
proteins = res.protein_summary

Replicate argument

The n_rep (and trp_n_rep) argument supports several shapes:

  • An integer for equal numbers of control/test replicates: 3

  • A 2-tuple for different counts: (2, 3)

  • A pair of index tuples to reference specific replicate numbers: ((1,2), (1,2,3))