University of Toronto researchers present scGPT: a basic model for single cell biology based on a generative transformer pretrained on a pool of more than 33 million cells

Natural language processing and computer vision are just examples of the fields in which pretrained generative models have been incredibly successful. In particular, a viable strategy for building base models is to combine large-scale varied data sets with pre-trained transformers. The study investigates the feasibility of basic models for future research in cell biology and genetics by establishing connections between language and biological constructs (where texts constitute genes and characterize words and cells respectively). Researchers have been at the forefront of building scGPT, a basic model for single-cell biology based on a pretrained generative transformer spanning a pool of more than one million cells, using the growing body of single-cell sequencing data. The results show that scGPT, a pretrained generative transformer, efficiently extracts key biological insights related to genes and cells. The script can be enhanced for use in various applications using transfer learning in new ways. These challenges include the inference of gene networks, the prediction of genetic perturbations, and the integration of multiple batches. Watch the scGPT source code.

By facilitating detailed characterization of individual cell types and improving our understanding of disease pathogenesis, single cell RNA sequencing (scRNA-seq) paves the way for investigation of cellular heterogeneity, lineage tracing, the elucidation of pathogenic mechanisms and the development of patient-specific therapeutic approaches.

Given the exponential growth of sequencing data, it is urgent to create methods that can effectively take advantage of, improve on, and adapt to these new trends. Generative pre-training of foundation models is an effective strategy to overcome this difficulty. Learning from massive data sets, generative pretraining has recently seen extraordinary success in several domains. Popular uses include NLG (natural language generation) and computer vision. These baseline models, including DALL-E2 and GPT-4, are based on the principle of pre-training transformers on large-scale heterogeneous data sets that can be easily tailored to specific downstream tasks and scenarios. Not only that, but these pre-trained generative models always perform better than their custom-trained counterparts.

Researchers draw on NLG’s self-monitored pretraining method to improve modeling of massive amounts of single-cell sequencing data. The self-service transformer has been shown to be a useful and efficient framework for modeling text input tokens.

Using generative pretraining on more than a million cells, these scientists offer the first attempt to build a basic model of a single cell, dubbed scGPT. They present novel approaches for pretraining massive amounts of single-cell omics data, addressing the engineering and methodological issues that arise. They use a fast-access in-memory data structure to store hundreds of data sets, allowing them to handle large amounts of data. They modify the transformer architecture to learn cell and gene representations simultaneously and build a unified generative pretraining approach tailored to non-sequential omics data. To enable the use of the pre-trained model in various downstream tasks, they also provide standard pipelines with task-specific targets for model fine-tuning.

Through these three components, the scGPT model highlights the revolutionary potential of the single-cell based concept. That starts with scGPT, the first large-scale generative basic model to support transfer of learning to various downstream activities. They demonstrate the effectiveness of the “universal pretraining, tuning on demand” approach as a generalist solution for computational applications in single-cell omics by achieving state-of-the-art performance in cell type annotation, genetic perturbation prediction, batch correction, and multiomics integration.

Notably, scGPT is the only base model capable of incorporating scATAC-seq data and other single-cell omics. Second, scGPT reveals important biological information about condition-specific gene-gene interactions by comparing gene embeddings and attentional weights between refined and raw pretrained models. Third, the results show a scaling law: better pretraining embeddings and higher performance in post-tasks result from using more data in the pretraining phase. This discovery underscores the promising possibility that basic models can be constantly improved as more and more sequencing data becomes available to the research community. In light of these results, they hypothesize that the use of pretrained baseline models will significantly increase our knowledge of cell biology and lay the groundwork for future advances in the field. Making scGPT models and workflows publicly available allows for strengthening and accelerating research in these and related fields.

The script is a novel pretrained generative base model that uses pretrained transformers to make sense of a large volume of data from a single cell, as described by the study authors. Self-supervised pre-training has been shown to be effective in language models such as chatGPT and GPT4. In the study of single cells, they used the same strategy to decipher intricate biological connections. To better model the different facets of cellular processes, scGPT uses transformers to learn gene and cell embeddings simultaneously. Single-Cell GPT (scGPT) captures gene-to-gene interactions at the single-cell level, adding a new degree of interpretability by using the attention mechanism of transformers.

The researchers used extensive studies in fine-tuning and zero-shot scenarios to demonstrate the value of pre-training. The trained model is already a feature extractor for any data set. It demonstrates impressive extrapolation ability, showing substantial cell clustering in zero-shot studies. Furthermore, there is a high degree of congruence between the gene networks learned in scGPT and previously established functional relationships. We are confident in the model’s ability to discover relevant discoveries in single cell biology because it captures gene-gene interactions and reflects known biological information effectively. Also, with some tweaking, the information learned by the pretrained model can be used for various downstream tasks. The optimized scGPT model regularly outperforms models trained from scratch in tasks such as cell type annotation, multi-omics and multi-batch integration. This shows how the pretrained model benefits downstream tasks by improving accuracy and biological relevance. Overall, the evidence demonstrates the utility of pretraining in scGPT, demonstrating its ability to generalize, capture gene networks, and improve performance on subsequent tasks using transfer learning.

key features

• The generalist approach allows for integrated multi-omics analysis and disturbance prediction using a single model for a single cell study.
• We can identify condition-specific gene-gene interactions using learned attentional weights and gene embeddings.
• He identified a scaling law that demonstrates continuous improvement in model performance with increasing data load.
• There are now many pre-trained basic models for different solid organs available in the scGPT model zoo (see github) and a comprehensive pan-cancer model. Start digging into the data using the best possible starting point model.

The pretraining is expected to be performed on a much larger dataset that includes multi-omics, spatial-omics, and a wide range of disease states. The model can learn causal links and estimate how genes and cells respond over time if perturbations and temporal data are included in the pre-training phase. To better understand and interpret the learnings of the pretrained model, it would be ideal to validate the model on a broader set of biologically significant tasks. Furthermore, its goal is to investigate context-aware knowledge for single-cell data. The pretrained model should understand and adapt to new jobs and environments without further adjustment in a zero-trigger setup. They can improve the usefulness and applicability of scGPT in numerous study contexts by teaching you to understand the subtleties and unique needs of various studies. They hope that the pretraining paradigm will be easily implemented in single cell research and that it lays the groundwork for capitalizing on the knowledge accumulated in rapidly expanding cell atlases.

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