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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]...

  qpx command line interface for proteomics data processing.

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

Commands:
  convert    Convert proteomics data formats to QPX format
  project    Project management commands
  stats      Statistical analysis commands
  transform  Data transformation commands
  visualize  Visualization commands

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-psm \
    --msms-file msms.txt \
    --output-folder ./output \
    --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 --output-folder ./output

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

# Generate statistics
qpxc stats analyze psm --parquet-path ./output/psm.parquet

# Create visualizations
qpxc visualize plot ibaq-distribution --ibaq-path ./output/ae.parquet

Need Help?