Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
GENOT: Entropic (Gromov) Wasserstein Flow Matching with Applications to Single-Cell Genomics
Authors: Dominik Klein, Théo Uscidda, Fabian Theis, Marco Cuturi
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments |
| Researcher Affiliation | Collaboration | Dominik Klein Helmholtz Munich EMAIL Théo Uscidda CREST-ENSAE EMAIL Fabian Theis Helmholtz Munich EMAIL Marco Cuturi Apple EMAIL |
| Pseudocode | Yes | Algorithm 1 U-GENOT. Skip teal steps for GENOT. |
| Open Source Code | Yes | The GENOT model along with the code to reproduce the experiments can be found at https: //github.com/MUCDK/genot, while a more modular implementation can be found in OTT-JAX [14]. Additionally, we implement applications in moscot [39]. |
| Open Datasets | Yes | The dataset of the developing mouse pancreas was published in Bastidas-Ponce et al. [2] and can be downloaded following the guidelines on https://www.ncbi.nlm.nih.gov/geo/query/acc. cgi?acc=GSE132188. |
| Dataset Splits | No | For each drug, we project the singlecell RNA-seq readout of the unperturbed and perturbed cells to a 50-dimensional PCA embedding. Subsequently, we split the data randomly to obtain a train and test set with a ratio of 60%/40%. |
| Hardware Specification | No | We only perform single-GPU training, thus we assume there is no limitation to reproduce single experiments / there is no environmental/societal effect due to a single experimental run. |
| Software Dependencies | No | The GENOT framework is implemented in JAX [6]. We use the discrete OT solvers provided by OTT-JAX [14]. |
| Experiment Setup | Yes | Batch size: n = 1024. Entropic regularization strength: ε = 10 2. By default, we do not scale the cost matrices passed to discrete OT solvers. Unbalancedness parameter: τ = (1, 1). This means that by default, we impose the hard marginal constraints. Number of training iterations: Titer = 10, 000. Optimizer: Adam W with learning rate lr = 10 4, and weight decay λ = 10 10. |