Transforming Biology with AI: The scGPT Revolution
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Chapter 1: The Intersection of AI and Biology
In recent years, generative models have made remarkable strides across various fields, from image processing to natural language tasks, exemplified by technologies like Stable Diffusion and ChatGPT. However, a pressing question arises: Why does the medical and biomedical research sector appear to lag in this technological evolution?
While companies like Google have initiated efforts, such as developing a medical model and the lighter PMC-LLaMA based on LLaMA, these models share a commonality—they are language models trained on scientific literature.
As we stand on the brink of the omics revolution, we are inundated with genomic data. Yet, despite this wealth of information, the number of algorithms available for analysis remains limited.
A groundbreaking technique, single-cell sequencing, has emerged, enabling researchers to capture the genetic profile of individual cells, encompassing DNA and RNA. This advancement has substantially increased the precision of our insights, but the need for novel algorithms to analyze this data is more critical than ever.
The DNA, RNA, and proteins can be viewed as sequences of characters that convey significant information. Therefore, it is conceivable to apply natural language processing algorithms to interpret these biological data types. Just as text is composed of words, one could analogously treat a cell's gene expression as a form of text. This innovative concept is precisely what a recent publication has proposed.
The authors of this study employed a modified transformer specifically designed for this purpose—scGPT: a generative transformer tailored for single-cell omics.
The first video highlights the integration of AI and biology, showcasing the possibilities of scGPT in the realm of genomic analysis.
Section 1.1: Understanding scGPT
Single-cell sequencing captures a cell's genetic profile at a specific moment, akin to taking a snapshot. From this snapshot, a wealth of information can be extracted about the cell's identity, functionality, and overall state. Over the years, we've amassed millions of these snapshots, creating extensive archives. The question then arises: Why not utilize a model to analyze this data?
The authors considered using a generative pre-training model similar to GPT, but with single-cell data as the input. The model's structure remains largely the same—featuring a transformer with multi-head attention that learns embeddings. However, the input mechanism had to be adapted to accommodate the distinct nature of genetic data.
The input to scGPT comprises three primary components: (1) gene tokens, (2) expression values, and (3) condition tokens. Instead of using subwords as tokens, genes are treated as tokens, where the gene's name acts as the token, accompanied by additional special tokens for padding and other purposes. Each gene token is linked to a numerical value that indicates its abundance within the cell, similar to word frequency in text.
Additionally, a condition token provides essential metadata related to each gene, such as functional pathways or alterations from perturbation experiments. This token functions like a contextual note, enriching the model's training process.
Subsection 1.1.1: Addressing Unique Challenges
The authors also acknowledged that, unlike words in a sentence, the order of genes within a cell is not fixed. There is no concept of predicting the "next gene," which complicates the direct application of causal masking techniques used in GPT models. To tackle this challenge, they developed a specialized attention masking mechanism for scGPT that determines prediction order based on attention scores.
To accommodate the unique characteristics of the data, the authors adapted multi-head self-attention and trained the model on profiles from over 10 million cells, further fine-tuning it on various cell types.
The second video delves into the future of foundation models in biology, emphasizing the innovative potential of scGPT.
Section 1.2: Advancements in Gene Representation
To enhance gene representation learning, the authors employed Gene Expression Prediction (GEP) as a self-supervised objective, iteratively predicting gene expression values of unknown tokens from known ones. This method enables the model to learn latent representations of cells or genes, allowing it to extract features from unseen data or be fine-tuned for different tasks.
The scGPT model excels in visualizing various cells and demonstrates superior clustering capabilities compared to traditional methods.
Identifying cells is a crucial step when working with single-cell data, and scGPT has shown improved accuracy over alternative approaches. The model achieved remarkable precision in predicting most cell types, with the exception of rare types characterized by minimal representation in the reference dataset.
Furthermore, the model can assist in understanding cellular responses through perturbation experiments, facilitating the integration of diverse sequencing methods.
Conclusions
In summary, scGPT represents a significant leap in the application of AI to biology, having been trained on millions of single-cell profiles. Drawing inspiration from the successes of natural language processing, the authors have crafted a self-supervised model that adeptly handles single-cell data.
They demonstrated the model's advantages in both zero-shot and fine-tuning contexts, showcasing its applicability to previously unseen data. The authors envision the pre-training paradigm becoming an integral part of single-cell research, unlocking the potential of existing cell atlases for groundbreaking discoveries.
In essence, models like scGPT could pave the way for new hypotheses and significant advancements in our understanding of biology. It is fascinating to witness how methodologies developed in one domain can seamlessly translate into another.
For those interested, the model is available on GitHub.