Monet: An open-source Python package for analyzing and integrating scRNA-Seq data using PCA-based latent spaces

Single-cell RNA-Seq is a powerful technology that enables the transcriptomic profiling of the different cell populations that make up complex tissues. However, the noisy and high-dimensional nature of the generated data poses significant challenges for its analysis and integration. Here, I describe Monet, an open-source Python package designed to provide effective and computationally efficient solutions to some of the most common challenges encountered in scRNA-Seq data analysis, and to serve as a toolkit for scRNA-Seq method development. At its core, Monet implements algorithms to infer the dimensionality and construct a PCA-based latent space from a given dataset. This latent space, represented by a MonetModel object, then forms the basis for data analysis and integration. In addition to validating these core algorithms, I provide demonstrations of some more advanced analysis tasks currently supported, such as batch correction and label transfer, which are useful for analyzing multiple datasets from the same tissue. Monet is available at https://github.com/flo-compbio/monet. Ongoing work is focused on providing electronic notebooks with tutorials for individual analysis tasks, and on developing interoperability with other Python scRNA-Seq software. The author welcomes suggestions for future improvements.