Predicting transcriptional outcomes of novel multigene perturbations with GEARS

By Yusuf H. Roohani, Kexin Huang, Jure Leskovec. Nature Biotechnology, 2023

368 citations recorded by the source corpus.

Understanding cellular responses to genetic perturbation is central to numerous biomedical applications, from identifying genetic interactions involved in cancer to developing methods for regenerative medicine. However, the combinatorial explosion in the number of possible multigene perturbations severely limits experimental interrogation. Here, we present graph-enhanced gene activation and repression simulator (GEARS), a method that integrates deep learning with a knowledge graph of gene–gene relationships to predict transcriptional responses to both single and multigene perturbations using single-cell RNA-sequencing data from perturbational screens. GEARS is able to predict outcomes of perturbing combinations consisting of genes that were never experimentally perturbed. GEARS exhibited 40% higher precision than existing approaches in predicting four distinct genetic interaction subtypes in a combinatorial perturbation screen and identified the strongest interactions twice as well as prior approaches. Overall, GEARS can predict phenotypically distinct effects of multigene perturbations and thus guide the design of perturbational experiments. GEARS predicts transcriptional changes of multigene perturbations even for genes that were never experimentally perturbed.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/85906593661911c9a076cbb96391b9bb61a996bc

How researchers see this paper

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

In Molecular Biology

1. Xuegong Zhang
2. Jianzhu Ma
3. Jinbo Xu
4. Sheng Wang

Citations in the corpus

• Large Scale Foundation Model on Single-cell Transcriptomics (2023, positive): “Then, we trained the GEARS baseline model by setting epoch to 15 and batch size to 30.”