Cradle-VAE: Enhancing Single-Cell Gene Perturbation Modeling with Counterfactual Reasoning-based Artifact Disentanglement
Authors: Seungheun Baek, Soyon Park, Yan Ting Chok, Junhyun Lee, Jueon Park, Mogan Gim, Jaewoo Kang
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that this approach improves not only treatment effect estimation performance but also generative quality as well. Table 2 shows the quantitative results on the four Perturb-seq datasets. According to the results, CRADLE-VAE overall surpassed all of its baselines in the three evaluation metrics that measure the model s ability to accurately predict cellular responses. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, Korea University, Seoul, South Korea 2Department of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, South Korea 3AIGEN Sciences, Seoul 04778, South Korea |
| Pseudocode | Yes | Algorithm 1: CRADLE-VAE Encoding Process. Algorithm 2: CRADLE-VAE Decoding Process. Algorithm 3: CRADLE-VAE Generative Process. |
| Open Source Code | Yes | Code https://github.com/dmis-lab/CRADLE-VAE |
| Open Datasets | Yes | We evaluated CRADLE-VAE on four Perturb-seq datasets, i.e. Norman dataset (Norman et al. 2019), Dixit dataset (Dixit et al. 2016), Replogle dataset (Replogle et al. 2022), and Adamson dataset (Adamson et al. 2016). |
| Dataset Splits | Yes | For datasets involving multi-gene perturbations, the test set was constructed using combinations not encountered during training, representing approximately 25% of the total possible combinations. Conversely, for datasets involving single perturbations, the evaluation emphasized the models ability to capture trends in the observed data within the context of single-perturbation scenarios. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models or memory specifications used for experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | The paper describes experiment settings including datasets, baselines, and evaluation metrics, but it does not specify concrete hyperparameter values (e.g., learning rate, batch size, epochs, optimizer settings) for training. |