Manifold Interpolating Optimal-Transport Flows for Trajectory Inference
Authors: Guillaume Huguet, Daniel Sumner Magruder, Alexander Tong, Oluwadamilola Fasina, Manik Kuchroo, Guy Wolf, Smita Krishnaswamy
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on simulated data with bifurcations and merges, as well as sc RNA-seq data from embryoid body differentiation, and acute myeloid leukemia treatment. We compare our results to those other methods that preform population flows including Trajectory Net [44] which is based on a CNF that is regularized to achieve efficient paths, and Diffusion Schrödinger s Bridge (DSB) [10] which is an optimal transport framework for generative modeling. The baseline measure in the quantitative results corresponds to the average distance between the previous and next timepoints for a given time t. |
| Researcher Affiliation | Academia | Guillaume Huguet1 D.S. Magruder2 Alexander Tong1 Oluwadamilola Fasina2 Manik Kuchroo2 Guy Wolf1 Smita Krishnaswamy2 1Université de Montréal; Mila Quebec AI Institute 2 Yale University |
| Pseudocode | Yes | We describe the training procedure of the GAE in algorithm 2 in the supplementary material. We describe the overall training procedure of MIOFlow in algorithm 1. |
| Open Source Code | Yes | Code is available here: https://github.com/KrishnaswamyLab/MIOFlow |
| Open Datasets | Yes | Code and links to publicly available data are provided in the supplemental material. We use Dyngen [6] to simulate a sc RNA-seq dataset from a dynamical cellular process. |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] All training details are provided in the supplement. |
| Hardware Specification | No | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] Compute resources are discussed in the supplement. |
| Software Dependencies | No | In practice, to compute the Wasserstein between discrete distributions, we use the implementation from the library Python Optimal Transport [14]. |
| Experiment Setup | No | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] All training details are provided in the supplement. |