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Foundational Models in Single-Cell Omics

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Agniruudrra R. Sinha, Bioinformatics Analyst | Mohamed Kassam, Head of Bioinformatics   |   5mins

A New Era of Biological Understanding

The field of single-cell omics has been revolutionized by the advent of foundational models, ushering in a new era of biological understanding and analysis. These powerful machine learning tools are transforming how we interpret and utilize the vast amounts of data generated by single-cell technologies, offering unprecedented insights into cellular biology and gene expression patterns.

What Are Foundational Models in Single-Cell Omics?

Foundational models in single-cell omics are large-scale machine learning models, typically based on transformer architectures, that are pretrained on massive datasets of single-cell and spatial transcriptomics data [1]. These models aim to learn a unified cell representation that captures complex relationships between genes and cells across various tissues and conditions.

Foundation Model

Figure 1: Foundational models in omics analysis: A snapshot of their diverse applications and basic steps.

A deep dive into foundational models reveals their technical marvels. Transformer architectures, originally designed for natural language processing, have been adapted for single-cell data analysis. In these models, each cell is treated as a "sentence," with its genes representing "words." The models use special tokens to denote cell identity and position, with gene expression values often encoded as rank values. This approach allows the model to capture the complex interplay of gene expression patterns within a cell. The self-attention mechanism in transformers enables the model to consider the relationships between all genes simultaneously, making it particularly well-suited for capturing the intricate dependencies in biological systems.

Transformer architectures in single-cell omics models have been further refined to handle the unique challenges of biological data. The self-attention mechanism has been adapted to capture gene-gene interactions, treating genes as tokens and computing attention scores between them. This allows the model to identify influential genes for predicting others' expression, effectively modeling gene interaction networks. Some models, like scMoFormer, employ multiple transformers to handle different data modalities, with a cross-modality aggregation component bridging these transformers. To address computational challenges, linearized transformers have been implemented to reduce complexity when dealing with large numbers of cells.

Additionally, these models often incorporate domain-specific knowledge about genes and proteins, enhancing their biological relevance. The tokenization of gene expression data, where each cell is represented as a sequence of gene tokens ordered by relative expression levels, helps preserve gene-gene relationships while mitigating technical batch effects. This sophisticated approach enables foundational models in single-cell omics to capture complex patterns across various cell types and conditions, leading to improved performance in tasks such as cell type annotation, gene regulatory network inference, and prediction of cellular responses to perturbations.

Key characteristics of these models include:

  • 1. Large-scale pretraining: Models like scFoundation and Nicheformer are trained on tens of millions of cells from diverse tissues and organisms [2].
  • 2. Multimodal integration: Some models, such as scGPT and Nicheformer, combine data from both dissociated single-cell and spatial transcriptomics or proteomics technologies [3].
  • 3. Flexible architecture: Most models utilize transformer-based architectures, allowing them to capture complex contextual relationships among genes [3].
  • 4. Transfer learning capabilities: These models can be fine-tuned or used for zero-shot learning on various downstream tasks [3].

Use Cases

Use case Description Models
Cell Type Annotation Models can accurately classify cells into different types based on their gene expression profiles scGPT, Geneformer, scaLR
Multi Batch Integration Foundational models excel at integrating data from multiple experimental batches, reducing technical variability COSMOS, scGPT, Geneformer
Perturbation Response Prediction These models can predict how cells will respond to various perturbations, such as drug treatments or genetic modifications scGPT, Geneformer
Gene Network Inference By capturing complex relationships between genes, these models can help to infer gene regulatory networks scGPT, Geneformer
Spatial Analysis Predict spatial context and composition, enabling the transfer of rich spatial information to scRNA-seq datasets Nicheformer, COSMOS

Challenges

  • 1. Computational resources: These models can be very resource-intensive, often requiring multiple GPUs to train and run [5].
  • 2. Interpretability and explainable AI: Foundational models, while state-of-the-art, might fall victim to the “black box” phenomenon. Developing explainable AI methods is crucial for deeper biological insights.
  • 3. Data quality and bias: As with all machine learning models, the quality of output depends on the quality of input data. These models require heavy preprocessing and their robustness depends on the diversity of the training data.
  • 4. Training data diversity: The quality and diversity of the training data are crucial for developing robust and unbiased models.

Emerging Trends

  • 1. Graph-based models: Integrating graph-based approaches with foundational models shows promise for capturing cellular heterogeneity and molecular patterns more effectively. Previous deep learning and graph-based approaches like DeepMAPS have shown great success in identifying gene networks [7]. Incorporating foundational models can enhance these approaches to provide more comprehensive representations of cellular interactions and gene regulatory networks.
  • 2. Extensive feature selection: Models like scaLR drastically reduce computational resources by selecting only the most important features for analysis [6].
  • 3. Multimodal integration: Future models are likely to incorporate even more diverse data types, including proteomics and epigenomics data [3].
  • 4. Spatial Awareness: Models like Nicheformer are paving the way for spatially aware representations of cellular variation at scale [4].

Conclusion

Foundational models mark a pivotal advancement in single-cell omics analysis. These sophisticated tools, built on extensive datasets and cutting-edge machine learning, offer unparalleled insights into cellular biology. As this field progresses, we anticipate the emergence of increasingly powerful and adaptable models. These innovations will significantly enhance our comprehension of intricate biological systems and catalyze breakthroughs in biomedical research and drug development.

Please reach out to the Zifo Bioinformatics team, info@zifornd.com, to set up a discussion about your research and application requirements. We are more than happy to arrange a call to explore your scientific needs in more detail.

References:

  • 1. https://frontlinegenomics.com/single-cell-foundational-models-the-next-big-thing/
  • 2. Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Zhang, X., Song, L. Large-scale foundation model on single-cell transcriptomics. Nat Methods. 2024 Aug;21(8):1481-1491. doi: 10.1038/s41592-024-02305-7. Epub 2024 Jun 6. PMID: 38844628.
  • 3. Cui, H., Wang, C., Maan, H. et al. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat Methods 21, 1470–1480 (2024). https://doi.org/10.1038/s41592-024-02201-0
  • 4. Schaar, A. C., Tejada-Lapuerta, A., Palla, G., Gutgesell, R., Halle, L., Minaeva, M., Vornholz, L., Dony, L., Drummer, F., Bahrami, M., & Theis, F. J. (2024). Nicheformer: a foundation model for single-cell and spatial omics. bioRxiv. https://doi.org/10.1101/2024.04.15.589472
  • 5. Ma, Q., Jiang, Y., Cheng, H. et al. Harnessing the deep learning power of foundation models in single-cell omics. Nat Rev Mol Cell Biol 25, 593–594 (2024). https://doi.org/10.1038/s41580-024-00756-6
  • 6. Jogani, S., Pol, A.S., Prajapati, M., Samal, A., Bhatia, K., Parmar, J., Patel, U., Shah, F., Vyas, N., Gupta, S. (2024). scaLR: A low-resource deep neural network-based platform for single cell analysis and biomarker discovery. bioRxiv: https://doi.org/10.1101/2024.09.19.613226.
  • 7. Ma, A., Wang, X., Li, J. et al. Single-cell biological network inference using a heterogeneous graph transformer. Nat Commun 14, 964 (2023). https://doi.org/10.1038/s41467-023-36559-0