Representation and Quantification of Module Activity in single-cell and bulk transcriptomics

• Compute module activity scores for single-cell and bulk RNA-seq data
• Seamless integration with AnnData objects (scanpy)
• Support for GMT pathway files (e.g., MSigDB hallmark gene sets)
• Lightweight and easy to extend

conda env create -f environment.yml
conda activate pyroma

pip install roma-analysis

git clone https://github.com/altyn-bulmers/pyroma.git
cd pyroma
pip install -e .

import pyroma

# Initialize ROMA
roma = pyroma.ROMA()

# Assign your AnnData object and GMT file
roma.adata = adata  # AnnData from scanpy

hallmarks_gmt_path = pyroma.genesets.use_hallmarks()
roma.gmt   = hallmarks_gmt_path

# Compute module activity scores
roma.compute()

# Inspect results
roma.adata.uns['ROMA_active_modules']

Comprehensive tutorials are available on Read the Docs:

• Getting started
• Advanced usage
• Advanced usage (cell-type setting)

If you need submodule content (e.g., additional scripts or data):

git clone --recurse-submodules [email protected]:altyn-bulmers/pyroma.git

Companion notebooks and detailed workflows are maintained in a dedicated repository (will be available soon):

• pyroma_reproducibility

Core datasets are sourced from rRoma_comp and included as TSV files in the datasets/ directory.

1. Martignetti L, Calzone L, Bonnet E, Barillot E, Zinovyev A (2016). ROMA: Representation and Quantification of Module Activity from Target Expression Data. Front. Genet. 7:18.
2. Najm M, Cornet M, Albergante L, et al. (2024). Representation and quantification of module activity from omics data with rROMA. npj Syst Biol Appl. 10:8.