Abstract
We present the Python package Monod for the analysis of single-cell RNA sequencing count data through biophysical modeling. Monod naturally “integrates” unspliced and spliced count matrices, and provides a route to identifying and studying differential expression patterns that do not cause changes in average gene expression. The Monod framework is open-source and modular, and may be extended to more sophisticated models of variation and further experimental observables.
The Monod package can be installed from the command line using pip install monod. The source code is available and maintained at https://github.com/pachterlab/monod. A separate repository, which contains sample data and Python notebooks for analysis with Monod, is accessible at https://github.com/pachterlab/monod_examples/. Structured documentation and tutorials are hosted at https://monod-examples.readthedocs.io/.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Single-nucleus data integration, study of IdU perturbations, and summary of “Length Biases.”