scGen predicts single-cell perturbation responses

By Mohammad Lotfollahi, F. Alexander Wolf, Fabian J Theis. Nature Methods, 2019

653 citations recorded by the source corpus.

Subfields: Biophysics, Immunology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/44823090f43acd3a6a89e8869cf922233ed54af6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. Kexin Huang
2. Jure Leskovec
3. Yusuf H. Roohani
4. Malte D. Luecken
5. Fabian J Theis
6. Luke Zappia
7. H. Schiller
8. David Lopez-Paz

In Immunology

1. Jure Leskovec
2. Kexin Huang
3. Yusuf H. Roohani
4. Cole Trapnell
5. Gunnar Rätsch
6. Fabian J Theis

In Molecular Biology

1. Malte D. Luecken
2. Fabian J Theis
3. Kexin Huang
4. Cole Trapnell
5. Jure Leskovec
6. Yusuf H. Roohani
7. F. Alexander Wolf
8. H. Schiller

Citations in the corpus

• Predicting transcriptional outcomes of novel multigene perturbations with GEARS (2023, positive): “GEARS functionalizes this graph-based inductive bias using a graph neural network (GNN) architecture 33 .”
• Best practices for single-cell analysis across modalities (2023, methodology): “3).”
• Predicting cellular responses to complex perturbations in high‐throughput screens (2023, methodology): “Aside from CPA, existing methods (Lotfollahi et al , 2020; Russ-kikh et al , 2020) such as scGen (Lotfollahi et al , 2019) have also been shown capable of predicting single-cell perturbation responses when the dataset contains no combinatorial treatment or dose-dependent perturbations.”
• Learning single-cell perturbation responses using neural optimal transport (2021, background): “Current state-of-the-art methods (Lopez et al., 2018; Lotfollahi et al., 2019; Yang et al., 2020) predict perturbation responses via linear shifts in a learned low-dimensional latent space.”