Benchmarking Transcriptomics Foundation Models for Perturbation Analysis : one PCA still rules them all (2410.13956v2)

Abstract: Understanding the relationships among genes, compounds, and their interactions in living organisms remains limited due to technological constraints and the complexity of biological data. Deep learning has shown promise in exploring these relationships using various data types. However, transcriptomics, which provides detailed insights into cellular states, is still underused due to its high noise levels and limited data availability. Recent advancements in transcriptomics sequencing provide new opportunities to uncover valuable insights, especially with the rise of many new foundation models for transcriptomics, yet no benchmark has been made to robustly evaluate the effectiveness of these rising models for perturbation analysis. This article presents a novel biologically motivated evaluation framework and a hierarchy of perturbation analysis tasks for comparing the performance of pretrained foundation models to each other and to more classical techniques of learning from transcriptomics data. We compile diverse public datasets from different sequencing techniques and cell lines to assess models performance. Our approach identifies scVI and PCA to be far better suited models for understanding biological perturbations in comparison to existing foundation models, especially in their application in real-world scenarios.

• The paper introduces a benchmarking framework that evaluates transcriptomics models using metrics like iLISI, kNN, and structural integrity.
• The evaluation shows that classical methods like PCA and models such as scVI consistently outperform advanced foundation models in perturbation tasks.
• The study emphasizes the need for biologically tailored training objectives to boost model robustness and drive innovations in drug discovery and gene studies.

Benchmarking Transcriptomics Foundation Models for Perturbation Analysis

The paper "Benchmarking Transcriptomics Foundation Models for Perturbation Analysis: one PCA still rules them all" addresses the nuanced application of foundation models in the domain of transcriptomics, specifically targeting perturbation analysis. The authors introduce a comprehensive benchmarking framework to evaluate these models against classical techniques, emphasizing the significance of transcriptomics data in understanding cellular responses to various biological interventions.

Problem Statement

The complexity of biological interactions and technological limitations have traditionally impeded a deep understanding of gene-compound interactions within living organisms. Transcriptomics, despite providing intricate insights into cellular states, remains underutilized due to its inherent noise and limited data. The proliferation of foundation models trained on vast amounts of transcriptomics data necessitates a standardized evaluation framework to assess their efficacy in perturbation tasks.

Evaluation Framework

The authors present a biologically motivated evaluation framework comprising a hierarchy of tasks. This framework aims to offer a structured assessment across various dimensions to ensure models not only excel in technical tasks but also demonstrate real-world applicability:

1. Batch Effect Reduction: Using the iLISI metric, the evaluation measures a model’s ability to integrate data from multiple batches.
2. Latent Space Separability: Employing linear probing to test if perturbations can be linearly separated.
3. Perturbation Consistency: Validating the model's robustness through the Perturbation Consistency metric.
4. Latent Space Organization: Using k-Nearest Neighbors (kNN) to assess the local organization of perturbation clusters.
5. Retrieval of Biological Relationships: Analyzing the model’s ability to recall known gene interactions.
6. Latent Space Interpretability: Examining how well latent embeddings can be reconstructed into gene expression profiles, introducing Structural Integrity as a novel evaluation metric.

Experimental Insights

Utilizing datasets from various sequencing techniques, the paper benchmarks foundation models—such as scVI, PCA, Geneformer, scGPT, UCE, and others—on real-world perturbation tasks. Key findings include:

• scVI and PCA Superiority: The paper reveals that simplic models like scVI, whether from scratch or in zero-shot scenarios, and PCA consistently outperform foundation models in most perturbation-related tasks.
• Foundation Models' Limitations: Current foundation models, though optimal for tasks like batch effect reduction, fall short in capturing biological complexities inherent in perturbation tasks.
• Data Scalability: The robust performance of scVI, even with minimal training data, highlights its applicability in limited data regimes, thus showcasing valuable scaling properties in single-cell perturbation contexts.

Implications and Future Directions

The research underscores the need for biologically tailored training objectives in foundation models to enhance performance on perturbation tasks. The benchmarking framework outlined by the authors provides a rigorous standard for evaluating future models, emphasizing the balance between technical proficiency and biological relevance.

The implications are multifaceted: improvements in model robustness and interpretability could revolutionize drug discovery, therapeutic interventions, and a deeper biological understanding of gene functionalities. Future developments may explore hybrid model approaches integrating the strengths of foundation models while retaining biological authenticity akin to simpler models like PCA and scVI.

Conclusion

This paper contributes significantly to the domain of transcriptomics by presenting a detailed benchmarking protocol vital for the evolution of foundation models. It advocates for a paradigm shift, urging researchers to revisit and refine these models, ensuring their applicability extends beyond theoretical optimization to solving real-world biological challenges.