# Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder

arXiv: https://arxiv.org/abs/2311.02794 · HTML: https://mlsanity.com/paper/2311.02794/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 5 Nov 2023.

## Builds on (most cited first, 8 of 8)

- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations](https://mlsanity.com/paper/1811.12359/) (2018)
- [Importance Weighted Autoencoders](https://mlsanity.com/paper/1509.00519/) (2015)
- [Towards Causal Representation Learning](https://mlsanity.com/paper/2102.11107/) (2021)
- [Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA](https://mlsanity.com/paper/2107.10098/) (2021)
- [Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling](https://mlsanity.com/paper/2211.03553/) (2022)
- [Bayesian representation learning with oracle constraints](https://mlsanity.com/paper/1506.05011/) (2015)
- [Holdout predictive checks for Bayesian model criticism](https://mlsanity.com/paper/1908.00882/) (2019)

## Built on by (most cited first, 30 of 38)

- [How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities](https://mlsanity.com/paper/2409.11654/) (2024)
- [PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis](https://mlsanity.com/paper/2408.10609/) (2024)
- [Multi-Modal and Multi-Attribute Generation of Single Cells with CFGen](https://mlsanity.com/paper/2407.11734/) (2024)
- [Automated Discovery of Pairwise Interactions from Unstructured Data](https://mlsanity.com/paper/2409.07594/) (2024)
- [Statistical and structural identifiability in representation learning](https://mlsanity.com/paper/2603.11970/) (2026)
- [Learning Identifiable Factorized Causal Representations of Cellular Responses](https://mlsanity.com/paper/2410.22472/) (2024)
- [Doloris: Dual Conditional Diffusion Implicit Bridges with Sparsity Masking Strategy for Unpaired Single-Cell Perturbation Estimation](https://mlsanity.com/paper/2506.21107/) (2025)
- [STRAND: Sequence-Conditioned Transport for Single-Cell Perturbations](https://mlsanity.com/paper/2602.10156/) (2026)
- [HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts](https://mlsanity.com/paper/2603.01396/) (2026)
- [What Makes a Representation Good for Single-Cell Perturbation Prediction?](https://mlsanity.com/paper/2605.19343/) (2026)
- [Efficient Fine-Tuning of Single-Cell Foundation Models Enables Zero-Shot Molecular Perturbation Prediction](https://mlsanity.com/paper/2412.13478/) (2024)
- [GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype](https://mlsanity.com/paper/2505.03853/) (2025)
- [MorphGen: Controllable and Morphologically Plausible Generative Cell-Imaging](https://mlsanity.com/paper/2510.01298/) (2025)
- [Departures: Distributional Transport for Single-Cell Perturbation Prediction with Neural Schr"odinger Bridges](https://mlsanity.com/paper/2511.13124/) (2025)
- [CRADLE-VAE: Enhancing Single-Cell Gene Perturbation Modeling with Counterfactual Reasoning-based Artifact Disentanglement](https://mlsanity.com/paper/2409.05484/) (2024)
- [Modeling Gene Expression Distributional Shifts for Unseen Genetic Perturbations](https://mlsanity.com/paper/2507.02980/) (2025)
- [PRESCRIBE: Predicting Single-Cell Responses with Bayesian Estimation](https://mlsanity.com/paper/2510.07964/) (2025)
- [Scalable Single-Cell Gene Expression Generation with Latent Diffusion Models](https://mlsanity.com/paper/2511.02986/) (2025)
- [CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning](https://mlsanity.com/paper/2603.21743/) (2026)
- [PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling](https://mlsanity.com/paper/2607.23447/) (2026)
- [sc-OTGM: Single-Cell Perturbation Modeling by Solving Optimal Mass Transport on the Manifold of Gaussian Mixtures](https://mlsanity.com/paper/2405.03726/) (2024)
- [Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space](https://mlsanity.com/paper/2411.12010/) (2024)
- [CFM-GP: Unified Conditional Flow Matching to Learn Gene Perturbation Across Cell Types](https://mlsanity.com/paper/2508.08312/) (2025)
- [Shortest-Path Flow Matching with Mixture-Conditioned Bases for OOD Generalization to Unseen Conditions](https://mlsanity.com/paper/2601.11827/) (2026)
- [Multi-Domain Empirical Bayes for Linearly-Mixed Causal Representations](https://mlsanity.com/paper/2603.18404/) (2026)
- [PRiMeFlow: Capturing Complex Expression Heterogeneity in Perturbation Response Modelling](https://mlsanity.com/paper/2604.13986/) (2026)
- [Learning Latent Dynamical Causal Processes for Single-Cell Perturbation Prediction](https://mlsanity.com/paper/2605.25581/) (2026)
- [D2R2: Discrete Diffusion with Regulation Reinforcement for Single-Cell Perturbation Prediction](https://mlsanity.com/paper/2608.15288/) (2026)
- [GPO-VAE: Modeling Explainable Gene Perturbation Responses utilizing GRN-Aligned Parameter Optimization](https://mlsanity.com/paper/2501.18973/) (2025)
- [Efficient Data Selection for Training Genomic Perturbation Models](https://mlsanity.com/paper/2503.14571/) (2025)

## Abstract (arXiv)

Generative models of observations under interventions have been a vibrant topic of interest across machine learning and the sciences in recent years. For example, in drug discovery, there is a need to model the effects of diverse interventions on cells in order to characterize unknown biological mechanisms of action. We propose the Sparse Additive Mechanism Shift Variational Autoencoder, SAMS-VAE, to combine compositionality, disentanglement, and interpretability for perturbation models. SAMS-VAE models the latent state of a perturbed sample as the sum of a local latent variable capturing sample-specific variation and sparse global variables of latent intervention effects. Crucially, SAMS-VAE sparsifies these global latent variables for individual perturbations to identify disentangled, perturbation-specific latent subspaces that are flexibly composable. We evaluate SAMS-VAE both quantitatively and qualitatively on a range of tasks using two popular single cell sequencing datasets. In order to measure perturbation-specific model-properties, we also introduce a framework for evaluation of perturbation models based on average treatment effects with links to posterior predictive checks. SAMS-VAE outperforms comparable models in terms of generalization across in-distribution and out-of-distribution tasks, including a combinatorial reasoning task under resource paucity, and yields interpretable latent structures which correlate strongly to known biological mechanisms. Our results suggest SAMS-VAE is an interesting addition to the modeling toolkit for machine learning-driven scientific discovery.