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..
Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder
Authors: Michael Bereket, Theofanis Karaletsos
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate SAMS-VAE both quantitatively and qualitatively on a range of tasks using two popular single cell sequencing datasets. |
| Researcher Affiliation | Collaboration | Michael Bereket insitro EMAIL Theofanis Karaletsos insitro* EMAIL Research supporting this publication conducted while authors were employed at insitro |
| Pseudocode | Yes | Algorithm 1 SAMS-VAE generative process |
| Open Source Code | Yes | Code availability Our code, which includes implementations of all models and experiment configurations, is available at https://github.com/insitro/sams-vae. |
| Open Datasets | Yes | Dataset To assess model generalization to held out samples under individual perturbations, we analyze a subset of the genome-wide CRISPR interference (CRISPRi) perturb-seq dataset from Replogle et al. [17]... We analyze the CRISPR activation (CRISPRa) perturb-seq screen from Norman et al. [15]... |
| Dataset Splits | Yes | We randomly sample train, validation, and test splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, memory, or cloud instance types used for experiments. |
| Software Dependencies | No | The paper mentions `scikit-learn [16]` but does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | Each model is trained with a 100 dimensional latent space and MLP encoders and decoders with a single hidden layer of dimension 400 (see Section A.4 for full training details). Based on validation performance and sparsity, a Beta(1, 2) prior was selected for the SVAE+ mask, and a Bern(0.001) prior was selected for SAMS-VAE. |