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arXiv:10.1038/s41587-026-03113-4[2026]

TxPert: using multiple knowledge graphs for prediction of transcriptomic perturbation effects

xeuron.com/p/txpert-using-multiple-knowledge-graphs-for-prediction-of-tra·u/george16152·DOI·Source·PDF

AI Summary

Accurately predicting cellular responses to genetic perturbations is essential for understanding disease mechanisms and designing effective therapies. Yet, exhaustively exploring the space of possible perturbations (for example, multigene perturbations or across tissues and cell types) is prohibitively expensive, motivating methods that can generalize to unseen conditions. We present TxPert, a latent-transfer-based deep learning method that uses multiple knowledge graphs of gene (product)–gene (product) relationships to predict transcriptomic perturbation effects. Different knowledge graphs encode complementary information and we show that a combination of graphs derived from biological databases and high-throughput perturbation screens yields the best performance. For predictions of single unseen perturbations, TxPert approaches the performance of split-half experimental reproducibility. For double unseen perturbations and single perturbations in a different cell line, its predictions increase Person Δ for unseen single perturbations by 8–25% over existing methods.

AI Metadata Extraction

Extract authors, key findings, references, and an executive summary using AI.

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Executive Summary

TxPert is a deep learning framework developed to predict the transcriptomic responses of cells to genetic perturbations. Accurate prediction of such responses is critical for understanding disease mechanisms and drug discovery but is hampered by the high cost of experimental screens and the limited generalizability of existing computational models. TxPert addresses these challenges by integrating a basal state encoder with a graph neural network (GNN) that leverages diverse biological knowledge graphs—including curated databases and proprietary high-throughput screening data—to predict the outcomes of unseen single or combinatorial perturbations and perturbations across different cellular contexts. Rigorous benchmarking demonstrates that TxPert consistently outperforms existing methods like GEARS and scLAMBDA. The study introduces a robust training and evaluation framework that accounts for batch-matched controls and experimental noise. Notably, TxPert's predictions for unseen single perturbations reach levels of accuracy comparable to split-half experimental reproducibility, providing a competitive benchmark for human-level performance in this domain. The model's success is largely attributed to the synergistic use of complementary biological knowledge graphs, with architectural innovations like the Exphormer-MG graph transformer facilitating effective multi-graph integration. While robust, the model reveals specific performance limitations, such as a reduced ability to accurately predict the downregulation of the unseen perturbation target gene itself. Furthermore, performance is influenced by both the size of the perturbation effect and the existing level of knowledge regarding the targeted gene. By providing a reusable, extendable framework and highlighting these strengths and weaknesses, the authors offer a foundational tool for the scientific community to improve the design of in silico screens, ultimately aiming to accelerate the development of effective therapeutic interventions and personalized medicine.

Authors

Frederik WenkelFirst Author

Valence Labs, Montréal, Quebec, Canada

frederik@valencelabs.com

Wilson Tu

Valence Labs, Montréal, Quebec, Canada

ali@valencelabs.com

Cassandra Masschelein

Valence Labs, Montréal, Quebec, Canada

Hamed Shirzad

Valence Labs, Montréal, Quebec, Canada

Liam Hodgson

Valence Labs, Montréal, Quebec, Canada

Ihab Bendidi

Valence Labs, Montréal, Quebec, Canada

Cian Eastwood

Valence Labs, Montréal, Quebec, Canada

Shawn T. Whitfield

Valence Labs, Montréal, Quebec, Canada

Craig Russell

Valence Labs, Montréal, Quebec, Canada

Yassir El Mesbahi

Valence Labs, Montréal, Quebec, Canada

Jiarui Ding

Computer Science, University of British Columbia, Vancouver, British Columbia, Canada

Marta M. Fay

Recursion, Salt Lake City, UT, USA

Berton Earnshaw

Valence Labs, Montréal, Quebec, Canada

Emmanuel Noutahi

Valence Labs, Montréal, Quebec, Canada

Alisandra K. Denton

Valence Labs, Montréal, Quebec, Canada

ali@valencelabs.com

Abstract

Accurately predicting cellular responses to genetic perturbations is essential for understanding disease mechanisms and designing effective therapies. Yet, exhaustively exploring the space of possible perturbations (for example, multigene perturbations or across tissues and cell types) is prohibitively expensive, motivating methods that can generalize to unseen conditions. We present TxPert, a latent-transfer-based deep learning method that uses multiple knowledge graphs of gene (product)–gene (product) relationships to predict transcriptomic perturbation effects. Different knowledge graphs encode complementary information and we show that a combination of graphs derived from biological databases and high-throughput perturbation screens yields the best performance. For predictions of single unseen perturbations, TxPert approaches the performance of split-half experimental reproducibility. For double unseen perturbations and single perturbations in a different cell line, its predictions increase Person Δ for unseen single perturbations by 8–25% over existing methods.

Key Findings (20)

  1. 1

    Key finding 1: TxPert uses a latent-transfer-based deep learning architecture for transcriptomic perturbation prediction.

  2. 2

    Key finding 2: Incorporating multiple knowledge graphs (STRING, GO, PxMap, TxMap) consistently improves predictive performance.

  3. 3

    Key finding 3: TxPert outperforms existing methods like GEARS and scLAMBDA across various out-of-distribution (OOD) tasks.

  4. 4

    Key finding 4: Batch effects and confounding significantly impact model performance, requiring batch-matched control strategies.

  5. 5

    Key finding 5: Retrieval metrics are superior to traditional differentially expressed gene selection for evaluation.

  6. 6

    Key finding 6: TxPert approaches the performance of split-half experimental reproducibility for single unseen perturbations.

  7. 7

    Key finding 7: For double unseen perturbations, TxPert achieves a substantial performance lead over GEARS and scLAMBDA.

  8. 8

    Key finding 8: TxPert effectively generalizes to new cell lines without seen perturbations.

  9. 9

    Key finding 9: The Exphormer-MG graph transformer architecture provides optimal results for integrating multiple graphs.

  10. 10

    Key finding 10: Performance is sensitive to the degradation of biological graph structure, showing robustness only until ~60% edge removal.

  11. 11

    Key finding 11: There is a measurable correlation between perturbation target knowledge level (Pharos rank) and prediction accuracy.

  12. 12

    Key finding 12: TxPert exhibits a failure mode in predicting the downregulation of the unseen perturbation target itself.

  13. 13

    Key finding 13: Combinations of STRING and PxMap graphs perform consistently better than STRING alone across all knowledge levels.

  14. 14

    Key finding 14: Hybrid-BMP message-passing architecture demonstrated top performance for single perturbation tasks in K562 cells.

  15. 15

    Key finding 15: Baseline performance (mean baseline) is a strong predictor, especially for essential genes.

  16. 16

    Key finding 16: Gene perturbation stress responses shift cellular states from growth toward quiescence and recycling.

  17. 17

    Key finding 17: Multi-graph integration strategies (GAT-Hybrid, Exphormer-MG, GAT-MLG, Hybrid-BMP) all show strong performance gains.

  18. 18

    Key finding 18: Cross-batch control correlation is significantly lower than within-batch correlation.

  19. 19

    Key finding 19: The use of proprietary phenomics-derived graphs (PxMap/TxMap) enhances predictive capacity.

  20. 20

    Key finding 20: TxPert provides a reusable framework that sets a new standard for benchmarking transcriptomic perturbation models.

Discussion & Future Directions

The authors acknowledge that independent benchmarks have recently raised questions about the performance of foundation models in biology. TxPert addresses these issues through rigorous evaluation, utilizing strong baselines and showing competitive performance with split-half experimental reproducibility. Future work aims to leverage even larger perturbation datasets, transition toward few-shot or active learning, and improve generalization to primary human tissues. The authors emphasize the need for standardization in benchmarking and the development of metrics that explicitly assess the conditionality and specificity of perturbation effects.

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