scCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration

Bioinformatics has undergone a paradigm shift in artificial intelligence (AI), particularly through foundation models (FMs), which address longstanding challenges in bioinformatics such as limited annotated data and data noise. These AI techniques have demonstrated remarkable efficacy across various downstream validation tasks, effectively representing diverse biological entities and heralding a new era in computational biology. The primary goal of this survey is to conduct a general investigation and summary of FMs in bioinformatics, tracing their evolutionary trajectory, current research landscape, and methodological frameworks. Our primary focus is on elucidating the application of FMs to specific biological problems, offering insights to guide the research community in choosing appropriate FMs for tasks like sequence analysis, structure prediction, and function annotation. Each section delves into the intricacies of the targeted challenges, contrasting the architectures and advancements of FMs with conventional methods and showcasing their utility across different biological domains. Further, this review scrutinizes the hurdles and constraints encountered by FMs in biology, including issues of data noise, model interpretability, and potential biases. This analysis provides a theoretical groundwork for understanding the circumstances under which certain FMs may exhibit suboptimal performance. Lastly, we outline prospective pathways and methodologies for the future development of FMs in biological research, facilitating ongoing innovation in the field. This comprehensive examination not only serves as an academic reference but also as a roadmap for forthcoming explorations and applications of FMs in biology.

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Single-Cell Data Integration and Cell Type Annotation through Contrastive Adversarial Open-set Domain Adaptation

Aminzadeh,

Wu,

et al. 2024

Preprint

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Single-cell sequencing technologies have enabled in-depth analysis of cellular heterogeneity across tissues and disease contexts. However, as datasets increase in size and complexity, characterizing diverse cellular populations, integrating data across multiple modalities, and correcting batch effects remain challenges. We present SAFAARI (Single-cell Annotation and Fusion with Adversarial Open-Set Domain Adaptation Reliable for Data Integration), a unified deep learning framework designed for cell annotation, batch correction, and multi-omics integration. SAFAARI leverages supervised contrastive learning and adversarial domain adaptation to achieve domain-invariant embeddings and enables label transfer across datasets, addressing challenges posed by batch effects, biological domain shifts, and multi-omics modalities. SAFAARI identifies novel cell types and mitigates class imbalance to enhance the detection of rare cell types. Through comprehensive benchmarking, we evaluated SAFAARI against existing annotation and integration methods across real-world datasets exhibiting batch effects and domain shifts, as well as simulated and multi-omics data. SAFAARI demonstrated scalability and robust performance in cell annotation via label transfer across heterogeneous datasets, detection of unknown cell types, correction of batch effects, and cross-omics data integration while leveraging available annotations for improved integration. SAFAARIs innovative approach outperformed competing methods in both qualitative and quantitative metrics, offering a flexible, accurate, and scalable solution for single-cell analysis with broad applicability to diverse biological and clinical research questions.

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scCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration

Cited by 4 publications

References 101 publications

Droplet-based single-cell sequencing: Strategies and applications

Droplet-based single-cell sequencing: Strategies and applications

Progress and opportunities of foundation models in bioinformatics

Single-Cell Data Integration and Cell Type Annotation through Contrastive Adversarial Open-set Domain Adaptation

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