Code to reproduce the figures from 'Analysis of multi-condition single-cell data with latent embedding multivariate regression'

Ahlmann-Eltze C, Huber W (2025). “Analysis of multi-condition single-cell data with latent embedding multivariate regression.” Nature Genetics (2025). doi:10.1038/s41588-024-01996-0.

This repository contains the code to reproduce all figures from the manuscript. It is structured into 5 folders and several additional files which I will explain below.

• illustrations/: contains the illustrator, png, and pdf files of the experimental design.
• plots/: contains the finished figures from the main and supplementary text.
• notebooks/: contains Rmarkdown files and rendered html notebooks which create the plots.
• benchmark/: contains the compute intense code that was run on EMBL's cluster system for efficiency reasons.
• renv/activate.R, renv.lock: the configuration files to recreate the computation environment in R (see renv package).
• copy_benchmark_project.sh: script that copies the files from the EMBL cluster system to my local computer.
• render_notebooks.sh: script that calls rmarkdown's html rendering for each file in the notebooks/ folder.

Rendered Notebooks

• Preparation and visualization of panobinostat treatment in glioblastoma.
• Preparation and visualization of the Alzheimer plaque densities
• Preparation and visualization of the zebrafish embryonic development
• Presentation of the benchmark results
• Visualization of a LEMUR fit on simulated data

Software

The R package used in the analysis is available at https://bioconductor.org/packages/lemur/. The source code is at https://github.com/const-ae/lemur. A Python implementation of the LEMUR model is available at https://github.com/const-ae/pylemur.

For a simplified implementation of the LEMUR algorithm, see our blog post at const-ae.name/post/2025-01-05_lemur_simplified/lemur-simplified/.