Abstract
We present the Python package Monod for the analysis of single-cell RNA sequencing count data through chemical master equation models. Monod can effectively identify biological and technical components of noise, enabling insights into potential pitfalls of standard normalization techniques. By parameterizing multidimensional distributions with biophysical variables, it 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.
Competing Interest Statement
The authors have declared no competing interest.
Copyright
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Posted June 12, 2022.
