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Quick Start

Get started with qpx in minutes - from installation to your first data conversion.

Quick Start Flow

Quick Start Flow

Prerequisites

Before installing qpx, ensure you have:

  • Python 3.10 or higher - Check with python --version
  • pip - Python package manager (included with Python)
  • Optional: conda/mamba for environment management

Installation

pip install qpx

# Create a new environment
conda create -n qpx python=3.10
conda activate qpx

# Install qpx
pip install qpx

git clone https://github.com/bigbio/qpx.git
cd qpx
pip install .

Verify Installation

After installation, verify qpx is working correctly:

# Check version
qpxc --version

# View available commands
qpxc --help

You should see output similar to:

Usage: qpxc [OPTIONS] COMMAND [ARGS]...

  qpxc -- quantitative proteomics data format tools.

Options:
  --version   Show the version and exit.
  -h, --help  Show this message and exit.

Commands:
  convert    Convert external tool outputs to QPX format.
  info       Show information about a QPX dataset.
  ontology   Manage CV ontology data (PSI-MS, PRIDE CV).
  query      Query and inspect QPX datasets.
  transform  Transform QPX data into derived representations.
  validate   Validate a QPX dataset or structure against the canonical...

Your First Conversion

Let's convert some sample MaxQuant data to QPX format.

Step 1: Download Sample Data

# Create a working directory
mkdir qpx-tutorial && cd qpx-tutorial

# Download sample MaxQuant msms.txt file
curl -L -o msms.txt \
  "https://raw.githubusercontent.com/bigbio/qpx/main/tests/examples/maxquant/maxquant_simple/msms.txt"

Step 2: Convert to QPX Format

# Convert MaxQuant PSM data to QPX parquet format
qpxc convert maxquant \
    --msms-file msms.txt \
    --output-folder ./output \
    --structures psm \
    --verbose

Step 3: Verify the Output

# List the generated files
ls -la output/

# You should see a file like: psm-{uuid}.psm.parquet

Step 4: Inspect the Data (Optional)

# Using Python to read the parquet file
import pyarrow.parquet as pq

table = pq.read_table("output/psm-*.psm.parquet")
df = table.to_pandas()

print(f"Total PSMs: {len(df)}")
print(f"Columns: {list(df.columns)}")
print(df.head())

What's Next?

Now that you've completed your first conversion, explore more:

Next Step Description
Examples Overview More conversion and analysis examples
Convert Commands All available data converters
Transform Commands Data transformation tools
Format Specification Understanding QPX data formats

Common Commands

# Convert DIA-NN data
qpxc convert diann --report-path report.tsv --sdrf-file data.sdrf.tsv --output-folder ./output

# Convert FragPipe data
qpxc convert fragpipe --psm-file psm.tsv --output-folder ./output

# Validate a QPX dataset
qpxc validate --dataset-folder ./output

# Query a QPX dataset
qpxc query --dataset-folder ./output --sql "SELECT * FROM psm LIMIT 10"

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