markeR Python Bridge

This workspace provides two simple Python helpers for using the Bioconductor R package markeR via rpy2.

• markeR_to_python.py provides a minimal example workflow demonstrating how to load the example data and call a specific function, serving as a template for users who wish to structure their own analysis script in Python.
• run_marker_function.py is a generic command‑line wrapper that can invoke any function exported by the markeR package.

Prerequisites

• R (>=4.5) installed and on your PATH.

• A Python virtual environment. A requirements.txt file is provided in this folder listing the needed packages (rpy2, pandas, numpy plus optional ipython/jupyter for notebook usage). To set up the environment:

  python -m venv .venv
  source .venv/bin/activate
  pip install -r requirements.txt

  After activation you can run the helper scripts using python from the same environment.

Quick start

1. Run the tutorial workflow

python markeR_to_python.py --tutorial --output tutorial.png
open tutorial.png

This executes an example from the markeR vignette; consult the original R tutorial (link below) for a step‑by‑step description of each analysis step.

2. Call any markeR function

python run_marker_function.py PlotScores \
  --data counts_example \
  --metadata metadata_example \
  --gene_sets genesets_example \
  --Variable "Condition" \
  --method logmedian \
  --nrow 1 \
  --output my_plot.png

All parameters are passed as --name value pairs. Use --help-function or refer to the online reference manual for argument names. The two scripts are described briefly below.

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Tutorial script (markeR_to_python.py)

A minimal Python example illustrating how to call markeR from within a Python environment. It loads the example datasets, runs a selected function, and generates a corresponding plot. The script is intended as a template for users who wish to structure and extend their own analysis workflows in Python.

python markeR_to_python.py --tutorial [--output file.png]

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Flexible CLI caller (run_marker_function.py)

This wrapper constructs and executes R code on the fly, so you can run any markeR function without writing R. It handles type conversion, data loading and optional PNG output.

Basic syntax:

python run_marker_function.py FUNCTION_NAME [OPTIONS]

Useful options:

• --param value – named argument for the R function
• --output filename.png – capture plot output
• --width / --height – PNG dimensions (pixels; default 800×600)
• --help-function – show documentation link/usage hint
• --verbose – display generated R code prior to execution

Example (scores only, no plot):

python run_marker_function.py CalculateScores \
  --data counts_example \
  --metadata metadata_example \
  --gene_sets genesets_example \
  --method logmedian

Example saving a plot to PNG (with custom dimensions):

python run_marker_function.py PlotScores \
  --data counts_example \
  --metadata metadata_example \
  --gene_sets genesets_example \
  --Variable "Condition" \
  --method logmedian \
  --nrow 1 \
  --width 800 \
  --height 400 \
  --output my_plot.png

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Built‑in example data

counts_example, metadata_example and genesets_example are loaded automatically and mirror the objects used in the tutorial. They let you try commands without supplying your own datasets.

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Tips & troubleshooting

• Add --verbose to see the exact R code being run – handy when a parameter doesn’t behave as expected.
• If --output fails, check write permissions and ensure the directory exists.
• Installation problems usually indicate R is missing; make sure R is on your path before installing Python dependencies.

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References

• markeR on Bioconductor
• Official R tutorial
• Reference manual
• Paper: https://www.biorxiv.org/content/10.64898/2025.12.05.692517

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Notes: scripts install markeR via BiocManager if it’s not already present. Warnings from ggplot2 (e.g. about aes_string()) are harmless and come from the package itself.