Background

The single-cell perturbation prediction problem is a pivotal aspect of modern computational biology, particularly in understanding cellular responses to various perturbations. These perturbations, defined as temporary or permanent changes caused by external influences, can significantly impact gene expression and cellular behavior [1]. The fundamental objective is to predict the omics signatures after a perturbation, using data on control and treatment conditions [1]. This prediction is crucial for optimizing experimental designs and identifying effective drugs and treatments [2]. Traditional single-cell sequencing is destructive, meaning the same cell cannot be measured before and after a perturbation [1]. As a result, researchers rely on groups of treated cells to evaluate how well predicted cell populations align with actual responses. One approach involves embedding control, predicted, and actual cells in principal component analysis (PCA) space to qualitatively assess predictions [1]. Negative feedback regulation is a common mechanism in homeostatic processes, including temperature control. Nerve cells relay information about body temperature to the hypothalamus, exemplifying how feedback mechanisms maintain specific parameters within a setpoint range [9]. Similarly, single-cell perturbation responses can be modeled computationally by predicting transcriptional changes and evaluating the separability of perturbed and unperturbed cell populations [1]. Recent advances in machine learning and the development of a perturbation atlas have facilitated deeper exploration of perturbation response modeling [10]. These approaches leverage large datasets, such as the UMAP representation of the combosciplex dataset, to generate and interpret gene regulatory rules in predicted gene–gene perturbations [2]. Despite these advances, exploring the vast perturbation space of combinatorial gene knock-outs or drug combinations remains challenging [1]. The attention mechanism in language models has also been applied to this field. By focusing on relevant parts of the input text, models can generate accurate outputs, aiding in the prediction of single-cell perturbation responses [5]. This interdisciplinary approach integrates machine learning with biological data, providing robust tools for understanding cellular responses to perturbations.

Machine Learning in Biology

Machine learning (ML) is increasingly pivotal in biological research, transforming complex biological phenomena into algorithmically tractable tasks. This capability extends significantly to single-cell perturbation modeling, where computational methods span from classical statistical inference to sophisticated machine and deep learning architectures. Among these, shallow models, autoencoders, and biologically informed approaches based on gene regulatory networks are particularly noteworthy[3]. Moreover, combinatorial efforts reminiscent of ensemble learning enhance predictive accuracy and robustness. Recent advances have introduced large foundational models in single-cell perturbation modeling, inspired by large language models (LLMs)[3]. These models help streamline predictions based on various single-cell datasets, making them accessible to both experimental biologists and computational scientists. However, the rapid growth in tools and datasets poses a challenge in keeping track of these advancements[3]. LLMs exhibit versatility in performing various tasks such as named entity recognition, text generation, summarization, question-answering, sentiment analysis, and translation[6]. This flexibility is leveraged to improve the performance and applicability of single-cell perturbation models. For instance, LLM-based systems often employ both online and offline evaluations to comprehensively understand and enhance model quality throughout the development and deployment lifecycle[6]. Offline evaluations, in particular, scrutinize LLMs against specific datasets to verify that features meet performance standards before deployment[6]. Moreover, robust and accessible tools for leveraging ML in single-cell perturbation are still under development. For example, methods like Augur and scGen have been applied to predict transcriptional responses and identify cell types most affected by perturbations[1]. These tools utilize datasets such as Kang et al., 2018 to qualitatively evaluate predictions by comparing control, predicted, and actual cell populations using techniques like principal component analysis (PCA)[1].

Large Language Models (LLMs)

Large Language Models (LLMs) are a category of artificial intelligence algorithms that utilize deep learning techniques and vast datasets to understand, summarize, generate, and predict new content[4]. These models, which are a type of generative AI, are specifically designed to assist in creating text-based content. LLMs function by enabling users to generate human-readable text based on input provided by the user[11]. Essentially, these models attempt to predict a word or a sequence of words, given the preceding words in the context they have encountered during training. This predictive capability is made possible through an underlying architecture known as the transformer model[5]. The process involves encoding the input, followed by decoding to generate an output prediction. Before an LLM can generate meaningful text, it requires comprehensive training on large datasets to perform general functions, and subsequent fine-tuning to execute specific tasks effectively[5]. One of the key components that enhance the accuracy of LLMs is the attention mechanism. This mechanism allows the model to concentrate on pertinent parts of the input text, which is crucial for producing precise outputs[5]. LLMs have a wide array of applications including generating code, powering chatbots and conversational AI, completing sentences, answering questions, and summarizing text[5][6]. In practical use, LLMs can perform tasks such as Named Entity Recognition (NER), text generation, summarization, question-answering, sentiment analysis, and translation[6]. The evaluation of LLMs can be divided into online and offline assessments, providing a comprehensive understanding of their performance and quality throughout the development and deployment lifecycle[6]. The integration of search engines with LLMs enhances their ability to extract relevant context from the web, which is then used to answer user queries effectively[12]. For instance, the Granite models developed by IBM Research utilize a decoder architecture that enables LLMs to predict the next word in a sequence, thereby supporting a variety of business applications such as conversational search and enterprise content discovery[13].

LLM Models for Single Cell Perturbation Prediction

Recent advances in machine learning, particularly with large language models (LLMs), have started to address the challenges of predicting molecular responses in single cells upon various perturbations, including chemical, genetic, and mechanical influences[14][10]. Understanding these responses is a core question in biology, yet obtaining single-cell measurements typically involves destructive methods, making it difficult to learn heterogeneous perturbation responses from unpaired distributions of perturbed and non-perturbed cells[14]. To tackle this problem, LLMs can be employed to predict perturbation responses by leveraging the extensive datasets generated from single-cell omics studies. For instance, Augur is a computational approach that trains a machine learning model to predict experimental labels for each cell type across multiple cross-validation runs. The model then prioritizes cell type responses based on metric scores that measure the accuracy of the predictions[1]. This model uses the area under the curve as the default metric for categorical data and the concordance correlation coefficient as a proxy for numerical data accuracy, thereby approximating perturbation responses[1]. The integration of LLMs in this domain has been enabled by the development of a vast array of datasets and benchmarks designed to evaluate model capabilities on more specific downstream tasks, such as question answering and sentiment analysis[8][4]. For instance, Schaeffer et al. argue that emergent abilities in LLMs are predictably acquired according to a smooth scaling law, which can be adapted to predict perturbation responses by constructing an informative perturbation latent space[8][13]. This can be achieved by querying the LLM with prompts to generate responses that might include newly generated text, summarized text, or even sentiment analysis reports relevant to single-cell perturbation data[4]. Furthermore, encoder-decoder models, such as those used in BERT, can be pre-trained with objectives like masked language modeling and next sentence prediction to generate new sentences conditioned on a given input. These models are particularly suited for tasks like summarization, translation, and generative question answering, which are essential for processing and understanding large single-cell perturbation datasets[15].

Case Studies

The application of Large Language Models (LLMs) in single-cell perturbation prediction has been explored through various case studies, demonstrating their efficacy and potential. These models, rooted in advanced machine learning and deep learning architectures, are being used to predict cellular responses to various perturbations.

Online and Offline Evaluations

One notable case study emphasizes the importance of blending both online and offline evaluations to ensure robust LLM performance. This dual approach allows developers to gather insights from real-world usage while maintaining reliability and efficiency through controlled, automated assessments. Offline evaluations involve scrutinizing LLMs against specific datasets to verify that performance standards are met, particularly in aspects like entailment and factuality[6].

Generating and Evaluating Datasets

A crucial step in utilizing LLMs for single-cell perturbation prediction involves generating and evaluating relevant datasets. For example, in a comprehensive study, a dataset was created from 100 questions and context derived from Databricks documents. This dataset was used to prompt various language models, such as GPT-4, GPT-3.5, Claude-v1, Llama2-70b-chat, Vicuna-33b, and mpt-30b-chat, to generate answers. The resulting question-context-answer pairs were stored in a dataset called “answer sheets” for further analysis[16]. This methodology illustrates the potential for LLMs to handle complex scientific queries by leveraging curated datasets.

Real-World Applications

The implementation of LLMs in real-world applications reveals the significant advantages organizations can gain. For instance, fetching data to create a vector store as context for an LLM to answer questions has been highlighted as a successful strategy. This approach enables organizations to utilize LLMs for intricate tasks, including single-cell perturbation prediction, thereby gaining a competitive edge in their respective fields[13].

Emergent Abilities in LLMs

Another study explored the emergent abilities of LLMs, arguing that these capabilities are predictably acquired according to a smooth scaling law. The researchers used a toy statistical model to solve multiple-choice questions and adapted it to other types of tasks relevant to LLMs. This included identifying offensive content in mixed-language paragraphs and translating Kiswahili proverbs to English. Such findings are crucial for understanding how LLMs can be fine-tuned and applied to specific problems, including those in single-cell perturbation prediction[8].

The Role of Foundational Models

The rising trend of large foundational models in computational methods for single-cell perturbation modeling has been a subject of extensive review. These models extend from classical statistical methodologies to various machine learning and deep learning architectures. The integration of biologically informed approaches based on gene regulatory networks and combinatorial efforts akin to ensemble learning is particularly noteworthy[3]. Such foundational models provide a comprehensive framework for accurately predicting cellular responses to perturbations.

Challenges and Limitations

The application of large language models (LLMs) to single cell perturbation prediction faces several significant challenges and limitations. One of the primary challenges is the limitation in contextual understanding, which often leads to misinformation and inaccuracies in the results. These issues necessitate ongoing research and advancements to enhance the accuracy and reliability of LLM outputs, ensuring they provide more trustworthy results. Furthermore, the reliance of LLMs on their training data poses a risk of incorporating potentially outdated information. For example, models like ChatGPT cannot accurately address queries requiring knowledge of developments beyond their last training cut-off date in 2021[7]. Another critical limitation is the iterative nature of developing and refining evaluation criteria for LLM outputs. Predefined evaluation criteria are often incomplete, and the iterative process of evaluation, reevaluation, and criteria updates is necessary to better align LLM outputs with desired results. This process involves updating prompts to increase the probability of generating good responses and decreasing the probability of bad ones, reflecting the difficulty in predicting LLM behavior and human preferences without direct observation of the outputs[17]. Additionally, LLMs may exhibit hallucinations, where the model generates false information or outputs that do not match the user's intent. These hallucinations highlight the models' limitations in interpreting human meaning accurately, as they primarily predict the next syntactically correct word or phrase rather than truly understanding the content[5]. The evaluation of LLMs for single cell perturbation prediction also presents particular challenges. LLMs are usually evaluated by their perplexity on a test set of unseen data to gauge their ability to generalize beyond their training data. However, this approach can be problematic as language models may overfit to their training data, making it difficult to assess their true performance on novel tasks[8]. Various benchmarks and datasets have been developed to evaluate specific capabilities of LLMs, such as general knowledge, commonsense reasoning, and mathematical problem-solving, but these are not exhaustive and may not fully capture the nuances required for single cell perturbation prediction[8].

Future Directions

As research into single-cell perturbation prediction progresses, several promising avenues are being explored to enhance the capabilities of predictive models. One of the primary focuses is on improving Out-Of-Distribution (OOD) prediction. OOD prediction enables researchers to study the effects of various perturbations, including different treatment doses, combination therapies, multiple genetic knockouts, and temporal changes, even when these combinations were not part of the original experimental data[2]. This capability could significantly advance the field by allowing more accurate and comprehensive modeling of cellular responses. In parallel, advancements in large language models (LLMs) offer substantial potential for single-cell perturbation prediction. Future developments in LLMs are likely to emphasize improved contextual understanding, enhanced reasoning abilities, and reduced biases[7]. These enhancements could make LLMs more proficient in interpreting complex biological data and generating precise predictions based on diverse and previously unseen perturbations. The integration of LLMs into predictive models for cellular perturbations will necessitate robust evaluation frameworks. Current practices involve the use of extensive testing datasets and benchmarks to assess the models' performance on specific tasks, such as question answering and commonsense reasoning[8]. These evaluations ensure that LLMs can reliably handle the intricacies of biological data and provide accurate predictions. The computational efficiency of LLMs has improved significantly, making it feasible to train large models within reasonable timeframes and costs[8]. This progress is critical for the practical application of LLMs in biological research. Moreover, specialized LLMs designed for specific tasks may outperform more generalized models. For instance, models like GPT-3.5 have demonstrated superior performance in context-specific question-answering tasks compared to other large models such as Vicuna-33B[16]. This suggests that tailoring LLMs to the unique requirements of single-cell perturbation prediction could yield better results than using more generic models.

Resources

[1] What are Large Language Models (LLMs)? | Definition from TechTarget. Link

[2] Large Language Models 101. This guide does not propose new… | by Nicholas Beaudoin | Eviden Data Science and Engineering Community | Medium. Link

[3] What is a Large Language Model? | A Comprehensive LLMs Guide | Elastic. Link

[4] Evaluating Large Language Model (LLM) systems: Metrics, challenges, and best practices | by Jane Huang | Data Science at Microsoft | Medium. Link

[5] How Large Language Models Work. From zero to ChatGPT | by Andreas Stöffelbauer | Medium | Data Science at Microsoft. Link

[6] What Are Large Language Models (LLMs)? | IBM. Link

[7] Predicting cellular responses to complex perturbations in high‐throughput screens - PMC. Link

[8] CH103 - Chapter 8: Homeostasis and Cellular Function - Chemistry. Link

[9] 19. Perturbation modeling — Single-cell best practices. Link

[10] Machine learning for perturbational single-cell omics - ScienceDirect. Link

[11] A mini-review on perturbation modelling across single-cell omic modalities - ScienceDirect. Link

[12] Learning single-cell perturbation responses using neural optimal transport | Nature Methods. Link

[13] Large language model - Wikipedia. Link

[14] Large Language Models: A Survey. Link

[15] Large Language Models (LLMs): Challenges, Predictions, Tutorial. Link

[16] What We’ve Learned From A Year of Building with LLMs – Applied LLMs. Link

[17] Best Practices for LLM Evaluation of RAG Applications. Link