Introduction
#SingleCellOmics has revolutionized our understanding of molecular processes by offering insights into the intricate workings of individual cells. This emerging field enables researchers to precisely analyze complex biological data, revealing cellular heterogeneity previously obscured by traditional bulk analysis methods. However, this technological leap brings the challenge of data complexity Deep learning has shown immense promise in addressing this challenge, yet its lack of interpretability has led to a growing demand for models that can both predict and explain underlying biological phenomena.
Researchers have addressed this very issue in a recent research paper. The authors discuss how interpretable deep learning models can be harnessed to decode the complexities of single-cell omics data, providing actionable insights for biological research. Here, we summarize their key findings and discuss the relevance of their work to the pharmaceutical industry.
Single-Cell and Multimodal Single-Cell ‘Omics
Single-cell omics encompasses a range of high-resolution techniques for studying individual cells. For example, single-cell RNA sequencing (scRNA-seq) focuses on gene expression within individual cells, while single-cell ATAC-seq explores chromatin accessibility. Unlike traditional bulk profiling methods, which average signals across cell populations, single-cell technologies explore cellular diversity, providing a clearer picture of how different cells function, respond to stimuli, and contribute to diseases.
The emergence of multi-modal single cell omics has has further advanced this field by enabling simultaneous measurement of multiple molecular attributes, such as RNA transcripts and epigenetic markers, within the same cell. This detailed view is key to unlocking a holistic understanding of cellular behavior, but the resulting data complexity necessitates sophisticated analytical tools.
The Power and Problem of Deep Learning
Deep learning models have become invaluable for analyzing the high-dimensional, noisy, and heterogeneous data generated by single-cell omics. These models excel at tasks such as dimensionality reduction, data imputation, and cell type classification. Yet, the very architecture that makes deep learning so powerful—its multi-layered, non-linear design—also makes it difficult to interpret. In many cases, researchers are left with highly accurate predictions but little understanding of how the model arrived at those results.
This “black box” problem is particularly troubling in biological research, where understanding the mechanisms behind a prediction is as important as the prediction itself. This has spurred a surge of interest in developing interpretable deep learning models, which aim to bridge the gap between prediction and explanation.
Interpretable Deep Learning in Action
This paper highlights several innovative approachesto making deep learning more interpretable in the context of single-cell omics. These approaches can be broadly categorized into two major groups of interpretability: intrinsic vs post-hoc, and model-specific vs model agnostic.
- Intrinsic vs Post-hoc:
- Intrinsic: Interpretability that is built into the model itself. This involves designing the model architecture in a way that it is inherently understandable. For example, certain neural network architectures can be designed to highlight which parts of the data the model focuses on during decision-making.
- Post-hoc: Techniques that are applied after the model has been trained to extract interpretable information from it. These methods don’t change the model itself but rather analyze its outputs to provide explanations. Examples include feature attribution techniques like Shapley values or LIME (Local Interpretable Model-agnostic Explanations).
- Model-specific vs Model-agnostic:
- Model-specific: Approaches that are tailored to a specific type of model or neural network architecture. These techniques leverage the unique properties of a particular model to enhance interpretability. For instance, a model-specific approach might be designed exclusively for convolutional neural networks (CNNs) or transformers.
- Model-agnostic: Methods that can be applied across different types of models, regardless of their underlying architecture. These approaches are more flexible as they are not dependent on the specific structure of the model being interpreted. Feature attribution techniques like Shapley values can be considered model-agnostic because they can be applied to various types of models.
Applications: From Cell Identity to Gene Regulatory Networks
One of the key applications of interpretable deep learning in single-cell omics is the identification of cell identity genes—those genes that distinguish one cell type from another. Methods like scDeepFeatures apply deep learning to classify cell types based on scRNA-seq data and then use post-hoc feature attribution techniques to determine which genes were critical for the classification. This not only aids in better understanding cell types but also provides a pathway for identifying potential therapeutic targets.
Another exciting application is the use of gene regulatory networks (GRNs). GRNs describe how genes interact to control cell behavior, and deep learning models have shown great promise in reconstructing these networks from single-cell data. For example, the DeepMAPS method integrates multimodal data to infer cell-type-specific GRNs, offering insights into how different genes regulate cellular processes.
Challenges and Future Directions
Despite the progress made, several challenges remain. The paper notes that most interpretable deep learning models in single-cell omics focus on tasks like cell type annotation and gene selection. However, there is a growing need for models that can tackle more complex tasks such as cell–cell interactions and cellular trajectory inference.
Additionally, as single-cell omics moves towards multi-sample and multi-condition datasets, interpretable models will need to evolve to handle these more intricate data structures. This is particularly important for understanding how cells respond to different conditions, such as drug treatments or disease states.
Another frontier in single-cell research is spatial omics, which adds the dimension of spatial context to molecular data. Integrating this spatial information with deep learning models could provide new insights into how cells communicate and organize within tissues.
Conclusion
The future of single-cell omics research lies not just in generating more data but in making sense of that data in a meaningful and actionable way. Interpretable deep learning holds the promise of unlocking the full potential of single-cell technologies by providing models that can not only predict but also explain the underlying biological mechanisms. As researchers continue to develop more interpretable models, we can expect a deeper understanding of cellular processes and new breakthroughs in areas like precision medicine and disease treatment. For a detailed exploration of this cutting-edge research, you can access the full paper here.
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Tyler Kolisnik, PhD, Data Scientist, Bridge Informatics
In his role as Data Scientist, Tyler helps clients transform complex data into actionable insights. A specialist in bioinformatics, his expertise includes high-throughput sequencing, data analytics, pipeline development, SQL databasing, and R and Python programming.
Tyler previously worked as a Bioinformatician at Imagia-Canexia Health, Rancho Biosciences, and GenomeDx Biosciences. He completed his PhD at Massey University in Auckland, New Zealand in collaboration with the Genome Sciences Centre in Vancouver. His research focused on the development of machine learning models and tools for improving cancer prognosis and treatment. If you’re interested in reaching out, please email [email protected] or [email protected]
