1
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Chu X, Li X, Zhang Y, Dang G, Miao Y, Xu W, Wang J, Zhang Z, Cheng S. Integrative single-cell analysis of human colorectal cancer reveals patient stratification with distinct immune evasion mechanisms. NATURE CANCER 2024:10.1038/s43018-024-00807-z. [PMID: 39147986 DOI: 10.1038/s43018-024-00807-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 07/16/2024] [Indexed: 08/17/2024]
Abstract
The tumor microenvironment (TME) considerably influences colorectal cancer (CRC) progression, therapeutic response and clinical outcome, but studies of interindividual heterogeneities of the TME in CRC are lacking. Here, by integrating human colorectal single-cell transcriptomic data from approximately 200 donors, we comprehensively characterized transcriptional remodeling in the TME compared to noncancer tissues and identified a rare tumor-specific subset of endothelial cells with T cell recruitment potential. The large sample size enabled us to stratify patients based on their TME heterogeneity, revealing divergent TME subtypes in which cancer cells exploit different immune evasion mechanisms. Additionally, by associating single-cell transcriptional profiling with risk genes identified by genome-wide association studies, we determined that stromal cells are major effector cell types in CRC genetic susceptibility. In summary, our results provide valuable insights into CRC pathogenesis and might help with the development of personalized immune therapies.
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Affiliation(s)
| | | | - Yu Zhang
- Changping Laboratory, Beijing, China
| | - Guohui Dang
- Changping Laboratory, Beijing, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | | | - Wenbin Xu
- Changping Laboratory, Beijing, China
| | | | - Zemin Zhang
- BIOPIC, Beijing Advanced Innovation Center for Genomics, School of Life Sciences, Peking University, Beijing, China.
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.
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2
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Cao X, Huang YA, You ZH, Shang X, Hu L, Hu PW, Huang ZA. scPriorGraph: constructing biosemantic cell-cell graphs with prior gene set selection for cell type identification from scRNA-seq data. Genome Biol 2024; 25:207. [PMID: 39103856 DOI: 10.1186/s13059-024-03357-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 07/29/2024] [Indexed: 08/07/2024] Open
Abstract
Cell type identification is an indispensable analytical step in single-cell data analyses. To address the high noise stemming from gene expression data, existing computational methods often overlook the biologically meaningful relationships between genes, opting to reduce all genes to a unified data space. We assume that such relationships can aid in characterizing cell type features and improving cell type recognition accuracy. To this end, we introduce scPriorGraph, a dual-channel graph neural network that integrates multi-level gene biosemantics. Experimental results demonstrate that scPriorGraph effectively aggregates feature values of similar cells using high-quality graphs, achieving state-of-the-art performance in cell type identification.
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Affiliation(s)
- Xiyue Cao
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Yu-An Huang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Lun Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Peng-Wei Hu
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Zhi-An Huang
- Research Office, City University of Hong Kong (Dongguan), Dongguan, 523000, China
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3
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Szałata A, Hrovatin K, Becker S, Tejada-Lapuerta A, Cui H, Wang B, Theis FJ. Transformers in single-cell omics: a review and new perspectives. Nat Methods 2024; 21:1430-1443. [PMID: 39122952 DOI: 10.1038/s41592-024-02353-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 06/07/2024] [Indexed: 08/12/2024]
Abstract
Recent efforts to construct reference maps of cellular phenotypes have expanded the volume and diversity of single-cell omics data, providing an unprecedented resource for studying cell properties. Despite the availability of rich datasets and their continued growth, current single-cell models are unable to fully capitalize on the information they contain. Transformers have become the architecture of choice for foundation models in other domains owing to their ability to generalize to heterogeneous, large-scale datasets. Thus, the question arises of whether transformers could set off a similar shift in the field of single-cell modeling. Here we first describe the transformer architecture and its single-cell adaptations and then present a comprehensive review of the existing applications of transformers in single-cell analysis and critically discuss their future potential for single-cell biology. By studying limitations and technical challenges, we aim to provide a structured outlook for future research directions at the intersection of machine learning and single-cell biology.
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Affiliation(s)
- Artur Szałata
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
| | - Karin Hrovatin
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Sören Becker
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
- Munich Center of Machine Learning, Munich, Germany
| | - Alejandro Tejada-Lapuerta
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
| | - Haotian Cui
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
- Peter Munk Cardiac Center, University Health Network, Toronto, Ontario, Canada
| | - Bo Wang
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada
- Peter Munk Cardiac Center, University Health Network, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- AI Hub, University Health Network, Toronto, Ontario, Canada
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- School of Computing, Information and Technology, Technical University of Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
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4
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Xia Y, Liu Y, Li T, He S, Chang H, Wang Y, Zhang Y, Ge W. Assessing parameter efficient methods for pre-trained language model in annotating scRNA-seq data. Methods 2024; 228:12-21. [PMID: 38759908 DOI: 10.1016/j.ymeth.2024.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 04/28/2024] [Accepted: 05/10/2024] [Indexed: 05/19/2024] Open
Abstract
Annotating cell types of single-cell RNA sequencing (scRNA-seq) data is crucial for studying cellular heterogeneity in the tumor microenvironment. Recently, large-scale pre-trained language models (PLMs) have achieved significant progress in cell-type annotation of scRNA-seq data. This approach effectively addresses previous methods' shortcomings in performance and generalization. However, fine-tuning PLMs for different downstream tasks demands considerable computational resources, rendering it impractical. Hence, a new research branch introduces parameter-efficient fine-tuning (PEFT). This involves optimizing a few parameters while leaving the majority unchanged, leading to substantial reductions in computational expenses. Here, we utilize scBERT, a large-scale pre-trained model, to explore the capabilities of three PEFT methods in scRNA-seq cell type annotation. Extensive benchmark studies across several datasets demonstrate the superior applicability of PEFT methods. Furthermore, downstream analysis using models obtained through PEFT showcases their utility in novel cell type discovery and model interpretability for potential marker genes. Our findings underscore the considerable potential of PEFT in PLM-based cell type annotation, presenting novel perspectives for the analysis of scRNA-seq data.
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Affiliation(s)
- Yucheng Xia
- Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, 610209, China
| | - Yuhang Liu
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Tianhao Li
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Sihan He
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Hong Chang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Yaqing Wang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Yongqing Zhang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Wenyi Ge
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China.
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5
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Hao M, Gong J, Zeng X, Liu C, Guo Y, Cheng X, Wang T, Ma J, Zhang X, Song L. Large-scale foundation model on single-cell transcriptomics. Nat Methods 2024; 21:1481-1491. [PMID: 38844628 DOI: 10.1038/s41592-024-02305-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 05/10/2024] [Indexed: 08/10/2024]
Abstract
Large pretrained models have become foundation models leading to breakthroughs in natural language processing and related fields. Developing foundation models for deciphering the 'languages' of cells and facilitating biomedical research is promising yet challenging. Here we developed a large pretrained model scFoundation, also named 'xTrimoscFoundationα', with 100 million parameters covering about 20,000 genes, pretrained on over 50 million human single-cell transcriptomic profiles. scFoundation is a large-scale model in terms of the size of trainable parameters, dimensionality of genes and volume of training data. Its asymmetric transformer-like architecture and pretraining task design empower effectively capturing complex context relations among genes in a variety of cell types and states. Experiments showed its merit as a foundation model that achieved state-of-the-art performances in a diverse array of single-cell analysis tasks such as gene expression enhancement, tissue drug response prediction, single-cell drug response classification, single-cell perturbation prediction, cell type annotation and gene module inference.
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Affiliation(s)
- Minsheng Hao
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China
- BioMap, Beijing, China
| | | | | | | | | | | | | | - Jianzhu Ma
- Department of Electrical Engineering, Tsinghua University, Beijing, China.
- Institute for AI Industry Research, Tsinghua University, Beijing, China.
| | - Xuegong Zhang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, BNRIST, Department of Automation, Tsinghua University, Beijing, China.
- School of Life Sciences and School of Medicine, Center for Synthetic and Systems Biology, Tsinghua University, Beijing, China.
| | - Le Song
- BioMap, Beijing, China.
- Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE.
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6
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Cui H, Wang C, Maan H, Pang K, Luo F, Duan N, Wang B. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat Methods 2024; 21:1470-1480. [PMID: 38409223 DOI: 10.1038/s41592-024-02201-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 01/30/2024] [Indexed: 02/28/2024]
Abstract
Generative pretrained models have achieved remarkable success in various domains such as language and computer vision. Specifically, the combination of large-scale diverse datasets and pretrained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between language and cellular biology (in which texts comprise words; similarly, cells are defined by genes), our study probes the applicability of foundation models to advance cellular biology and genetic research. Using burgeoning single-cell sequencing data, we have constructed a foundation model for single-cell biology, scGPT, based on a generative pretrained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT effectively distills critical biological insights concerning genes and cells. Through further adaptation of transfer learning, scGPT can be optimized to achieve superior performance across diverse downstream applications. This includes tasks such as cell type annotation, multi-batch integration, multi-omic integration, perturbation response prediction and gene network inference.
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Affiliation(s)
- Haotian Cui
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontartio, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Chloe Wang
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontartio, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Hassaan Maan
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontartio, Canada
- Vector Institute, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Kuan Pang
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Fengning Luo
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Nan Duan
- Microsoft Research, Redmond, WA, USA
| | - Bo Wang
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontartio, Canada.
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
- Vector Institute, Toronto, Ontario, Canada.
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.
- AI Hub, University Health Network, Toronto, Ontario, Canada.
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7
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Lin Y, Pan Z, Zeng Y, Yang Y, Dai Z. Detecting novel cell type in single-cell chromatin accessibility data via open-set domain adaptation. Brief Bioinform 2024; 25:bbae370. [PMID: 39073828 DOI: 10.1093/bib/bbae370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 06/27/2024] [Accepted: 07/15/2024] [Indexed: 07/30/2024] Open
Abstract
Recent advances in single-cell technologies enable the rapid growth of multi-omics data. Cell type annotation is one common task in analyzing single-cell data. It is a challenge that some cell types in the testing set are not present in the training set (i.e. unknown cell types). Most scATAC-seq cell type annotation methods generally assign each cell in the testing set to one known type in the training set but neglect unknown cell types. Here, we present OVAAnno, an automatic cell types annotation method which utilizes open-set domain adaptation to detect unknown cell types in scATAC-seq data. Comprehensive experiments show that OVAAnno successfully identifies known and unknown cell types. Further experiments demonstrate that OVAAnno also performs well on scRNA-seq data. Our codes are available online at https://github.com/lisaber/OVAAnno/tree/master.
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Affiliation(s)
- Yuefan Lin
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China
| | - Zixiang Pan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China
| | - Yuansong Zeng
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China
| | - Zhiming Dai
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China
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8
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Fu Q, Dong C, Liu Y, Xia X, Liu G, Zhong F, Liu L. A comparison of scRNA-seq annotation methods based on experimentally labeled immune cell subtype dataset. Brief Bioinform 2024; 25:bbae392. [PMID: 39120646 PMCID: PMC11312369 DOI: 10.1093/bib/bbae392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 07/04/2024] [Accepted: 07/30/2024] [Indexed: 08/10/2024] Open
Abstract
Cell-type annotation is a critical step in single-cell data analysis. With the development of numerous cell annotation methods, it is necessary to evaluate these methods to help researchers use them effectively. Reference datasets are essential for evaluation, but currently, the cell labels of reference datasets mainly come from computational methods, which may have computational biases and may not reflect the actual cell-type outcomes. This study first constructed an experimentally labeled immune cell-subtype single-cell dataset of the same batch and systematically evaluated 18 cell annotation methods. We assessed those methods under five scenarios, including intra-dataset validation, immune cell-subtype validation, unsupervised clustering, inter-dataset annotation, and unknown cell-type prediction. Accuracy and ARI were evaluation metrics. The results showed that SVM, scBERT, and scDeepSort were the best-performing supervised methods. Seurat was the best-performing unsupervised clustering method, but it couldn't fully fit the actual cell-type distribution. Our results indicated that experimentally labeled immune cell-subtype datasets revealed the deficiencies of unsupervised clustering methods and provided new dataset support for supervised methods.
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Affiliation(s)
- Qiqing Fu
- Institutes of Biomedical Sciences, Fudan University, 200032 Shanghai, P.R. China
| | - Chenyu Dong
- Institutes of Biomedical Sciences, Fudan University, 200032 Shanghai, P.R. China
| | - Yunhe Liu
- Institutes of Biomedical Sciences, Fudan University, 200032 Shanghai, P.R. China
| | - Xiaoqiong Xia
- Institutes of Biomedical Sciences, Fudan University, 200032 Shanghai, P.R. China
| | - Gang Liu
- Institutes of Biomedical Sciences, Fudan University, 200032 Shanghai, P.R. China
| | - Fan Zhong
- Intelligent Medicine Institute, Fudan University, 200032 Shanghai, P.R. China
| | - Lei Liu
- Intelligent Medicine Institute, Fudan University, 200032 Shanghai, P.R. China
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9
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Xie J, Chen Y, Luo S, Yang W, Lin Y, Wang L, Ding X, Tong M, Yu R. Tracing unknown tumor origins with a biological-pathway-based transformer model. CELL REPORTS METHODS 2024; 4:100797. [PMID: 38889685 PMCID: PMC11228371 DOI: 10.1016/j.crmeth.2024.100797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 02/01/2024] [Accepted: 05/21/2024] [Indexed: 06/20/2024]
Abstract
Cancer of unknown primary (CUP) represents metastatic cancer where the primary site remains unidentified despite standard diagnostic procedures. To determine the tumor origin in such cases, we developed BPformer, a deep learning method integrating the transformer model with prior knowledge of biological pathways. Trained on transcriptomes from 10,410 primary tumors across 32 cancer types, BPformer achieved remarkable accuracy rates of 94%, 92%, and 89% in primary tumors and primary and metastatic sites of metastatic tumors, respectively, surpassing existing methods. Additionally, BPformer was validated in a retrospective study, demonstrating consistency with tumor sites diagnosed through immunohistochemistry and histopathology. Furthermore, BPformer was able to rank pathways based on their contribution to tumor origin identification, which helped to classify oncogenic signaling pathways into those that are highly conservative among different cancers versus those that are highly variable depending on their origins.
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Affiliation(s)
- Jiajing Xie
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China
| | - Ying Chen
- School of Informatics, Xiamen University, Xiamen, Fujian 361005, China
| | - Shijie Luo
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China
| | - Wenxian Yang
- Aginome Scientific, Xiamen, Fujian 361005, China
| | - Yuxiang Lin
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China
| | - Liansheng Wang
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China; School of Informatics, Xiamen University, Xiamen, Fujian 361005, China
| | - Xin Ding
- Department of Pathology, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian 361004, China.
| | - Mengsha Tong
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China; State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian 361102, China.
| | - Rongshan Yu
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China; School of Informatics, Xiamen University, Xiamen, Fujian 361005, China; Aginome Scientific, Xiamen, Fujian 361005, China.
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10
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Wagle MM, Long S, Chen C, Liu C, Yang P. Interpretable deep learning in single-cell omics. Bioinformatics 2024; 40:btae374. [PMID: 38889275 PMCID: PMC11211213 DOI: 10.1093/bioinformatics/btae374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 05/11/2024] [Accepted: 06/12/2024] [Indexed: 06/20/2024] Open
Abstract
MOTIVATION Single-cell omics technologies have enabled the quantification of molecular profiles in individual cells at an unparalleled resolution. Deep learning, a rapidly evolving sub-field of machine learning, has instilled a significant interest in single-cell omics research due to its remarkable success in analysing heterogeneous high-dimensional single-cell omics data. Nevertheless, the inherent multi-layer nonlinear architecture of deep learning models often makes them 'black boxes' as the reasoning behind predictions is often unknown and not transparent to the user. This has stimulated an increasing body of research for addressing the lack of interpretability in deep learning models, especially in single-cell omics data analyses, where the identification and understanding of molecular regulators are crucial for interpreting model predictions and directing downstream experimental validations. RESULTS In this work, we introduce the basics of single-cell omics technologies and the concept of interpretable deep learning. This is followed by a review of the recent interpretable deep learning models applied to various single-cell omics research. Lastly, we highlight the current limitations and discuss potential future directions.
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Affiliation(s)
- Manoj M Wagle
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- School of Mathematics and Statistics, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Siqu Long
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- School of Mathematics and Statistics, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Carissa Chen
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Chunlei Liu
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Pengyi Yang
- Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
- School of Mathematics and Statistics, Faculty of Science, The University of Sydney, Camperdown, NSW 2006, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, NSW 2006, Australia
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11
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Guselnikova O, Trelin A, Kang Y, Postnikov P, Kobashi M, Suzuki A, Shrestha LK, Henzie J, Yamauchi Y. Pretreatment-free SERS sensing of microplastics using a self-attention-based neural network on hierarchically porous Ag foams. Nat Commun 2024; 15:4351. [PMID: 38806498 PMCID: PMC11133413 DOI: 10.1038/s41467-024-48148-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 04/21/2024] [Indexed: 05/30/2024] Open
Abstract
Low-cost detection systems are needed for the identification of microplastics (MPs) in environmental samples. However, their rapid identification is hindered by the need for complex isolation and pre-treatment methods. This study describes a comprehensive sensing platform to identify MPs in environmental samples without requiring independent separation or pre-treatment protocols. It leverages the physicochemical properties of macroporous-mesoporous silver (Ag) substrates templated with self-assembled polymeric micelles to concurrently separate and analyze multiple MP targets using surface-enhanced Raman spectroscopy (SERS). The hydrophobic layer on Ag aids in stabilizing the nanostructures in the environment and mitigates biofouling. To monitor complex samples with multiple MPs and to demultiplex numerous overlapping patterns, we develop a neural network (NN) algorithm called SpecATNet that employs a self-attention mechanism to resolve the complex dependencies and patterns in SERS data to identify six common types of MPs: polystyrene, polyethylene, polymethylmethacrylate, polytetrafluoroethylene, nylon, and polyethylene terephthalate. SpecATNet uses multi-label classification to analyze multi-component mixtures even in the presence of various interference agents. The combination of macroporous-mesoporous Ag substrates and self-attention-based NN technology holds potential to enable field monitoring of MPs by generating rich datasets that machines can interpret and analyze.
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Affiliation(s)
- Olga Guselnikova
- National Institute for Materials Science (NIMS), Tsukuba, Ibaraki, Japan.
- Research School of Chemistry and Applied Biomedical Sciences, Tomsk Polytechnic University, Tomsk, Russian Federation.
| | - Andrii Trelin
- Department of Solid-State Engineering, University of Chemistry and Technology, Prague, Czech Republic
| | - Yunqing Kang
- National Institute for Materials Science (NIMS), Tsukuba, Ibaraki, Japan
- Department of Materials Process Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Japan
| | - Pavel Postnikov
- Research School of Chemistry and Applied Biomedical Sciences, Tomsk Polytechnic University, Tomsk, Russian Federation
- Department of Solid-State Engineering, University of Chemistry and Technology, Prague, Czech Republic
| | - Makoto Kobashi
- Department of Materials Process Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Japan
| | - Asuka Suzuki
- Department of Materials Process Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Japan
| | - Lok Kumar Shrestha
- National Institute for Materials Science (NIMS), Tsukuba, Ibaraki, Japan
- Department of Materials Science, Institute of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Joel Henzie
- National Institute for Materials Science (NIMS), Tsukuba, Ibaraki, Japan.
| | - Yusuke Yamauchi
- Department of Materials Process Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Japan.
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD, Australia.
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12
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Chen H, Lu Y, Dai Z, Yang Y, Li Q, Rao Y. Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledge. Brief Bioinform 2024; 25:bbae314. [PMID: 38960404 PMCID: PMC11221887 DOI: 10.1093/bib/bbae314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 12/13/2023] [Accepted: 06/20/2024] [Indexed: 07/05/2024] Open
Abstract
Recent advances in microfluidics and sequencing technologies allow researchers to explore cellular heterogeneity at single-cell resolution. In recent years, deep learning frameworks, such as generative models, have brought great changes to the analysis of transcriptomic data. Nevertheless, relying on the potential space of these generative models alone is insufficient to generate biological explanations. In addition, most of the previous work based on generative models is limited to shallow neural networks with one to three layers of latent variables, which may limit the capabilities of the models. Here, we propose a deep interpretable generative model called d-scIGM for single-cell data analysis. d-scIGM combines sawtooth connectivity techniques and residual networks, thereby constructing a deep generative framework. In addition, d-scIGM incorporates hierarchical prior knowledge of biological domains to enhance the interpretability of the model. We show that d-scIGM achieves excellent performance in a variety of fundamental tasks, including clustering, visualization, and pseudo-temporal inference. Through topic pathway studies, we found that d-scIGM-learned topics are better enriched for biologically meaningful pathways compared to the baseline models. Furthermore, the analysis of drug response data shows that d-scIGM can capture drug response patterns in large-scale experiments, which provides a promising way to elucidate the underlying biological mechanisms. Lastly, in the melanoma dataset, d-scIGM accurately identified different cell types and revealed multiple melanin-related driver genes and key pathways, which are critical for understanding disease mechanisms and drug development.
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Affiliation(s)
- Hegang Chen
- School of Computer Science and Engineering, Sun Yat-sen University, 132 Waihuan East Road, Guangzhou University Town, 510006, Guangzhou, China
| | - Yuyin Lu
- School of Computer Science and Engineering, Sun Yat-sen University, 132 Waihuan East Road, Guangzhou University Town, 510006, Guangzhou, China
| | - Zhiming Dai
- School of Computer Science and Engineering, Sun Yat-sen University, 132 Waihuan East Road, Guangzhou University Town, 510006, Guangzhou, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, 132 Waihuan East Road, Guangzhou University Town, 510006, Guangzhou, China
| | - Qing Li
- Department of Computing, The Hong Kong Polytechnic University, PQ806, Mong Man Wai Building, 999077, Hong Kong SAR
| | - Yanghui Rao
- School of Computer Science and Engineering, Sun Yat-sen University, 132 Waihuan East Road, Guangzhou University Town, 510006, Guangzhou, China
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13
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Xu J, Huang D, Zhang X. scmFormer Integrates Large-Scale Single-Cell Proteomics and Transcriptomics Data by Multi-Task Transformer. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307835. [PMID: 38483032 PMCID: PMC11109621 DOI: 10.1002/advs.202307835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/24/2024] [Indexed: 05/23/2024]
Abstract
Transformer-based models have revolutionized single cell RNA-seq (scRNA-seq) data analysis. However, their applicability is challenged by the complexity and scale of single-cell multi-omics data. Here a novel single-cell multi-modal/multi-task transformer (scmFormer) is proposed to fill up the existing blank of integrating single-cell proteomics with other omics data. Through systematic benchmarking, it is demonstrated that scmFormer excels in integrating large-scale single-cell multimodal data and heterogeneous multi-batch paired multi-omics data, while preserving shared information across batchs and distinct biological information. scmFormer achieves 54.5% higher average F1 score compared to the second method in transferring cell-type labels from single-cell transcriptomics to proteomics data. Using COVID-19 datasets, it is presented that scmFormer successfully integrates over 1.48 million cells on a personal computer. Moreover, it is also proved that scmFormer performs better than existing methods on generating the unmeasured modality and is well-suited for spatial multi-omic data. Thus, scmFormer is a powerful and comprehensive tool for analyzing single-cell multi-omics data.
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Affiliation(s)
- Jing Xu
- Key Laboratory of Plant Germplasm Enhancement and Specialty AgricultureWuhan Botanical GardenChinese Academy of SciencesWuhan430074China
- University of Chinese Academy of SciencesBeijing100049China
| | - De‐Shuang Huang
- Eastern Institute for Advanced StudyEastern Institute of TechnologyNingbo315200China
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty AgricultureWuhan Botanical GardenChinese Academy of SciencesWuhan430074China
- Center of Economic BotanyCore Botanical GardensChinese Academy of SciencesWuhan430074China
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14
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Ren L, Huang D, Liu H, Ning L, Cai P, Yu X, Zhang Y, Luo N, Lin H, Su J, Zhang Y. Applications of single‑cell omics and spatial transcriptomics technologies in gastric cancer (Review). Oncol Lett 2024; 27:152. [PMID: 38406595 PMCID: PMC10885005 DOI: 10.3892/ol.2024.14285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/19/2024] [Indexed: 02/27/2024] Open
Abstract
Gastric cancer (GC) is a prominent contributor to global cancer-related mortalities, and a deeper understanding of its molecular characteristics and tumor heterogeneity is required. Single-cell omics and spatial transcriptomics (ST) technologies have revolutionized cancer research by enabling the exploration of cellular heterogeneity and molecular landscapes at the single-cell level. In the present review, an overview of the advancements in single-cell omics and ST technologies and their applications in GC research is provided. Firstly, multiple single-cell omics and ST methods are discussed, highlighting their ability to offer unique insights into gene expression, genetic alterations, epigenomic modifications, protein expression patterns and cellular location in tissues. Furthermore, a summary is provided of key findings from previous research on single-cell omics and ST methods used in GC, which have provided valuable insights into genetic alterations, tumor diagnosis and prognosis, tumor microenvironment analysis, and treatment response. In summary, the application of single-cell omics and ST technologies has revealed the levels of cellular heterogeneity and the molecular characteristics of GC, and holds promise for improving diagnostics, personalized treatments and patient outcomes in GC.
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Affiliation(s)
- Liping Ren
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| | - Danni Huang
- Department of Radiology, Central South University Xiangya School of Medicine Affiliated Haikou People's Hospital, Haikou, Hainan 570208, P.R. China
| | - Hongjiang Liu
- School of Computer Science and Technology, Aba Teachers College, Aba, Sichuan 624099, P.R. China
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
| | - Peiling Cai
- School of Basic Medical Sciences, Chengdu University, Chengdu, Sichuan 610106, P.R. China
| | - Xiaolong Yu
- Hainan Yazhou Bay Seed Laboratory, Sanya Nanfan Research Institute, Material Science and Engineering Institute of Hainan University, Sanya, Hainan 572025, P.R. China
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, P.R. China
| | - Nanchao Luo
- School of Computer Science and Technology, Aba Teachers College, Aba, Sichuan 624099, P.R. China
| | - Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, P.R. China
| | - Jinsong Su
- Research Institute of Integrated Traditional Chinese Medicine and Western Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, P.R. China
| | - Yinghui Zhang
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, Sichuan 611844, P.R. China
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15
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Meng D, Zhang S, Huang Y, Mao K, Han JDJ. Application of AI in biological age prediction. Curr Opin Struct Biol 2024; 85:102777. [PMID: 38310737 DOI: 10.1016/j.sbi.2024.102777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/12/2023] [Accepted: 01/15/2024] [Indexed: 02/06/2024]
Abstract
The development of anti-aging interventions requires quantitative measurement of biological age. Machine learning models, known as "aging clocks," are built by leveraging diverse aging biomarkers that vary across lifespan to predict biological age. In addition to traditional aging clocks harnessing epigenetic signatures derived from bulk samples, emerging technologies allow the biological age estimating at single-cell level to dissect cellular diversity in aging tissues. Moreover, imaging-based aging clocks are increasingly employed with the advantage of non-invasive measurement, making it suitable for large-scale human cohort studies. To fully capture the features in the ever-growing multi-modal and high-dimensional aging-related data and uncover disease associations, deep-learning based approaches, which are effective to learn complex and non-linear relationships without relying on pre-defined features, are increasingly applied. The use of big data and AI-based aging clocks has achieved high accuracy, interpretability and generalizability, guiding clinical applications to delay age-related diseases and extend healthy lifespans.
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Affiliation(s)
- Dawei Meng
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 100871, China
| | - Shiqiang Zhang
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 100871, China
| | - Yuanfang Huang
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 100871, China
| | - Kehang Mao
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 100871, China
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 100871, China.
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16
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Xie Y, Chen H, Chellamuthu VR, Lajam ABM, Albani S, Low AHL, Petretto E, Behmoaras J. Comparative Analysis of Single-Cell RNA Sequencing Methods with and without Sample Multiplexing. Int J Mol Sci 2024; 25:3828. [PMID: 38612639 PMCID: PMC11011421 DOI: 10.3390/ijms25073828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 03/20/2024] [Accepted: 03/21/2024] [Indexed: 04/14/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique for investigating biological heterogeneity at the single-cell level in human systems and model organisms. Recent advances in scRNA-seq have enabled the pooling of cells from multiple samples into single libraries, thereby increasing sample throughput while reducing technical batch effects, library preparation time, and the overall cost. However, a comparative analysis of scRNA-seq methods with and without sample multiplexing is lacking. In this study, we benchmarked methods from two representative platforms: Parse Biosciences (Parse; with sample multiplexing) and 10x Genomics (10x; without sample multiplexing). By using peripheral blood mononuclear cells (PBMCs) obtained from two healthy individuals, we demonstrate that demultiplexed scRNA-seq data obtained from Parse showed similar cell type frequencies compared to 10x data where samples were not multiplexed. Despite relatively lower cell capture affecting library preparation, Parse can detect rare cell types (e.g., plasmablasts and dendritic cells) which is likely due to its relatively higher sensitivity in gene detection. Moreover, a comparative analysis of transcript quantification between the two platforms revealed platform-specific distributions of gene length and GC content. These results offer guidance for researchers in designing high-throughput scRNA-seq studies.
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Affiliation(s)
- Yi Xie
- Programme in Cardiovascular and Metabolic Disorders and Centre for Computational Biology, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore; (Y.X.)
| | - Huimei Chen
- Programme in Cardiovascular and Metabolic Disorders and Centre for Computational Biology, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore; (Y.X.)
| | - Vasuki Ranjani Chellamuthu
- Translational Immunology Institute, SingHealth/Duke-NUS Academic Medical Centre, Academia, Singapore 169856, Singapore; (V.R.C.)
| | - Ahmad bin Mohamed Lajam
- Translational Immunology Institute, SingHealth/Duke-NUS Academic Medical Centre, Academia, Singapore 169856, Singapore; (V.R.C.)
| | - Salvatore Albani
- Translational Immunology Institute, SingHealth/Duke-NUS Academic Medical Centre, Academia, Singapore 169856, Singapore; (V.R.C.)
| | - Andrea Hsiu Ling Low
- Department of Rheumatology and Immunology, Singapore General Hospital, Academia, Singapore 169856, Singapore;
- SingHealth Duke-NUS Medicine Academic Clinical Programme, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Enrico Petretto
- Programme in Cardiovascular and Metabolic Disorders and Centre for Computational Biology, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore; (Y.X.)
- Institute for Big Data and Artificial Intelligence in Medicine, School of Science, China Pharmaceutical University, Nanjing 210009, China
| | - Jacques Behmoaras
- Programme in Cardiovascular and Metabolic Disorders and Centre for Computational Biology, Duke-NUS Medical School, 8 College Road, Singapore 169857, Singapore; (Y.X.)
- Department of Immunology and Inflammation, Centre for Inflammatory Disease, Imperial College London, Hammersmith Hospital, London W12 0NN, UK
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17
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Liu T, Li K, Wang Y, Li H, Zhao H. Evaluating the Utilities of Foundation Models in Single-cell Data Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.08.555192. [PMID: 38464157 PMCID: PMC10925156 DOI: 10.1101/2023.09.08.555192] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Foundation Models (FMs) have made significant strides in both industrial and scientific domains. In this paper, we evaluate the performance of FMs in single-cell sequencing data analysis through comprehensive experiments across eight downstream tasks pertinent to single-cell data. By comparing ten different single-cell FMs with task-specific methods, we found that single-cell FMs may not consistently excel in all tasks than task-specific methods. However, the emergent abilities and the successful applications of cross-species/cross-modality transfer learning of FMs are promising. In addition, we present a systematic evaluation of the effects of hyper-parameters, initial settings, and stability for training single-cell FMs based on a proposed scEval framework, and provide guidelines for pre-training and fine-tuning. Our work summarizes the current state of single-cell FMs and points to their constraints and avenues for future development.
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18
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Wei Z, Chenjun W, Feiyang X, Mingfeng J, Yixuan Z, Qi L, Zhuoxing S, Qi D. scHybridBERT: integrating gene regulation and cell graph for spatiotemporal dynamics in single-cell clustering. Brief Bioinform 2024; 25:bbae018. [PMID: 38517692 PMCID: PMC10959234 DOI: 10.1093/bib/bbae018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 12/19/2023] [Accepted: 01/09/2024] [Indexed: 03/24/2024] Open
Abstract
Graph learning models have received increasing attention in the computational analysis of single-cell RNA sequencing (scRNA-seq) data. Compared with conventional deep neural networks, graph neural networks and language models have exhibited superior performance by extracting graph-structured data from raw gene count matrices. Established deep neural network-based clustering approaches generally focus on temporal expression patterns while ignoring inherent interactions at gene-level as well as cell-level, which could be regarded as spatial dynamics in single-cell data. Both gene-gene and cell-cell interactions are able to boost the performance of cell type detection, under the framework of multi-view modeling. In this study, spatiotemporal embedding and cell graphs are extracted to capture spatial dynamics at the molecular level. In order to enhance the accuracy of cell type detection, this study proposes the scHybridBERT architecture to conduct multi-view modeling of scRNA-seq data using extracted spatiotemporal patterns. In this scHybridBERT method, graph learning models are employed to deal with cell graphs and the Performer model employs spatiotemporal embeddings. Experimental outcomes about benchmark scRNA-seq datasets indicate that the proposed scHybridBERT method is able to enhance the accuracy of single-cell clustering tasks by integrating spatiotemporal embeddings and cell graphs.
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Affiliation(s)
- Zhang Wei
- Zhejiang Sci-Tech University, 310028, Hangzhou, China
| | - Wu Chenjun
- Zhejiang Sci-Tech University, 310028, Hangzhou, China
| | - Xing Feiyang
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, 200092, Shanghai, China
| | | | - Zhang Yixuan
- Zhejiang Sci-Tech University, 310028, Hangzhou, China
| | - Liu Qi
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, 200092, Shanghai, China
| | - Shi Zhuoxing
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, 510060, Guangzhou, China
| | - Dai Qi
- Zhejiang Sci-Tech University, 310028, Hangzhou, China
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19
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Wan H, Yuan M, Fu Y, Deng M. Continually adapting pre-trained language model to universal annotation of single-cell RNA-seq data. Brief Bioinform 2024; 25:bbae047. [PMID: 38388681 PMCID: PMC10883808 DOI: 10.1093/bib/bbae047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 12/29/2023] [Accepted: 01/18/2024] [Indexed: 02/24/2024] Open
Abstract
MOTIVATION Cell-type annotation of single-cell RNA-sequencing (scRNA-seq) data is a hallmark of biomedical research and clinical application. Current annotation tools usually assume the simultaneous acquisition of well-annotated data, but without the ability to expand knowledge from new data. Yet, such tools are inconsistent with the continuous emergence of scRNA-seq data, calling for a continuous cell-type annotation model. In addition, by their powerful ability of information integration and model interpretability, transformer-based pre-trained language models have led to breakthroughs in single-cell biology research. Therefore, the systematic combining of continual learning and pre-trained language models for cell-type annotation tasks is inevitable. RESULTS We herein propose a universal cell-type annotation tool, called CANAL, that continuously fine-tunes a pre-trained language model trained on a large amount of unlabeled scRNA-seq data, as new well-labeled data emerges. CANAL essentially alleviates the dilemma of catastrophic forgetting, both in terms of model inputs and outputs. For model inputs, we introduce an experience replay schema that repeatedly reviews previous vital examples in current training stages. This is achieved through a dynamic example bank with a fixed buffer size. The example bank is class-balanced and proficient in retaining cell-type-specific information, particularly facilitating the consolidation of patterns associated with rare cell types. For model outputs, we utilize representation knowledge distillation to regularize the divergence between previous and current models, resulting in the preservation of knowledge learned from past training stages. Moreover, our universal annotation framework considers the inclusion of new cell types throughout the fine-tuning and testing stages. We can continuously expand the cell-type annotation library by absorbing new cell types from newly arrived, well-annotated training datasets, as well as automatically identify novel cells in unlabeled datasets. Comprehensive experiments with data streams under various biological scenarios demonstrate the versatility and high model interpretability of CANAL. AVAILABILITY An implementation of CANAL is available from https://github.com/aster-ww/CANAL-torch. CONTACT dengmh@pku.edu.cn. SUPPLEMENTARY INFORMATION Supplementary data are available at Journal Name online.
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Affiliation(s)
- Hui Wan
- School of Mathematical Sciences, Peking University, Beijing, China, 100871
| | - Musu Yuan
- Center for Quantitative Biology, Peking University, Beijing, China, 100871
| | - Yiwei Fu
- School of Mathematical Sciences, Peking University, Beijing, China, 100871
| | - Minghua Deng
- School of Mathematical Sciences, Peking University, Beijing, China, 100871
- Center for Quantitative Biology, Peking University, Beijing, China, 100871
- Center for Statistical Science, Peking university, Beijing, China, 100871
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20
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Zhou S, Li Y, Wu W, Li L. scMMT: a multi-use deep learning approach for cell annotation, protein prediction and embedding in single-cell RNA-seq data. Brief Bioinform 2024; 25:bbad523. [PMID: 38300515 PMCID: PMC10833085 DOI: 10.1093/bib/bbad523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 11/27/2023] [Accepted: 12/19/2023] [Indexed: 02/02/2024] Open
Abstract
Accurate cell type annotation in single-cell RNA-sequencing data is essential for advancing biological and medical research, particularly in understanding disease progression and tumor microenvironments. However, existing methods are constrained by single feature extraction approaches, lack of adaptability to immune cell types with similar molecular profiles but distinct functions and a failure to account for the impact of cell label noise on model accuracy, all of which compromise the precision of annotation. To address these challenges, we developed a supervised approach called scMMT. We proposed a novel feature extraction technique to uncover more valuable information. Additionally, we constructed a multi-task learning framework based on the GradNorm method to enhance the recognition of challenging immune cells and reduce the impact of label noise by facilitating mutual reinforcement between cell type annotation and protein prediction tasks. Furthermore, we introduced logarithmic weighting and label smoothing mechanisms to enhance the recognition ability of rare cell types and prevent model overconfidence. Through comprehensive evaluations on multiple public datasets, scMMT has demonstrated state-of-the-art performance in various aspects including cell type annotation, rare cell identification, dropout and label noise resistance, protein expression prediction and low-dimensional embedding representation.
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Affiliation(s)
- Songqi Zhou
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
- Chongqing School, University of Chinese Academy of Sciences, Chongqing, China
| | - Yang Li
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
- Chongqing School, University of Chinese Academy of Sciences, Chongqing, China
- Chongqing Research Institute of Big Data, Peking University, Chongqing, China
| | - Wenyuan Wu
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
- Chongqing School, University of Chinese Academy of Sciences, Chongqing, China
| | - Li Li
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
- Chongqing School, University of Chinese Academy of Sciences, Chongqing, China
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21
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Liu J, Yang M, Yu Y, Xu H, Li K, Zhou X. Large language models in bioinformatics: applications and perspectives. ARXIV 2024:arXiv:2401.04155v1. [PMID: 38259343 PMCID: PMC10802675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Large language models (LLMs) are a class of artificial intelligence models based on deep learning, which have great performance in various tasks, especially in natural language processing (NLP). Large language models typically consist of artificial neural networks with numerous parameters, trained on large amounts of unlabeled input using self-supervised or semi-supervised learning. However, their potential for solving bioinformatics problems may even exceed their proficiency in modeling human language. In this review, we will present a summary of the prominent large language models used in natural language processing, such as BERT and GPT, and focus on exploring the applications of large language models at different omics levels in bioinformatics, mainly including applications of large language models in genomics, transcriptomics, proteomics, drug discovery and single cell analysis. Finally, this review summarizes the potential and prospects of large language models in solving bioinformatic problems.
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Affiliation(s)
- Jiajia Liu
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, 77030, USA
| | - Mengyuan Yang
- School of Life Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China
| | - Yankai Yu
- School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan 611756, China
| | - Haixia Xu
- The Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Kang Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, 77030, USA
- McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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22
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Zhang Y, Sun H, Zhang W, Fu T, Huang S, Mou M, Zhang J, Gao J, Ge Y, Yang Q, Zhu F. CellSTAR: a comprehensive resource for single-cell transcriptomic annotation. Nucleic Acids Res 2024; 52:D859-D870. [PMID: 37855686 PMCID: PMC10767908 DOI: 10.1093/nar/gkad874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/12/2023] [Accepted: 09/27/2023] [Indexed: 10/20/2023] Open
Abstract
Large-scale studies of single-cell sequencing and biological experiments have successfully revealed expression patterns that distinguish different cell types in tissues, emphasizing the importance of studying cellular heterogeneity and accurately annotating cell types. Analysis of gene expression profiles in these experiments provides two essential types of data for cell type annotation: annotated references and canonical markers. In this study, the first comprehensive database of single-cell transcriptomic annotation resource (CellSTAR) was thus developed. It is unique in (a) offering the comprehensive expertly annotated reference data for annotating hundreds of cell types for the first time and (b) enabling the collective consideration of reference data and marker genes by incorporating tens of thousands of markers. Given its unique features, CellSTAR is expected to attract broad research interests from the technological innovations in single-cell transcriptomics, the studies of cellular heterogeneity & dynamics, and so on. It is now publicly accessible without any login requirement at: https://idrblab.org/cellstar.
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Affiliation(s)
- Ying Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Huaicheng Sun
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Wei Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Tingting Fu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shijie Huang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jinsong Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yichao Ge
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Qingxia Yang
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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23
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Hou W, Ji Z. Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.16.537094. [PMID: 37131626 PMCID: PMC10153208 DOI: 10.1101/2023.04.16.537094] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Cell type annotation is an essential step in single-cell RNA-seq analysis. However, it is a time-consuming process that often requires expertise in collecting canonical marker genes and manually annotating cell types. Automated cell type annotation methods typically require the acquisition of high-quality reference datasets and the development of additional pipelines. We assessed the performance of GPT-4, a highly potent large language model, for cell type annotation, and demonstrated that it can automatically and accurately annotate cell types by utilizing marker gene information generated from standard single-cell RNA-seq analysis pipelines. Evaluated across hundreds of tissue types and cell types, GPT-4 generates cell type annotations exhibiting strong concordance with manual annotations and has the potential to considerably reduce the effort and expertise needed in cell type annotation. We also developed GPTCelltype, an open-source R software package to facilitate cell type annotation by GPT-4.
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Affiliation(s)
- Wenpin Hou
- Department of Biostatistics, The Mailman School of Public Health, Columbia University, New York City, NY, USA
| | - Zhicheng Ji
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
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24
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Yin Q, Chen L. CellTICS: an explainable neural network for cell-type identification and interpretation based on single-cell RNA-seq data. Brief Bioinform 2023; 25:bbad449. [PMID: 38061196 PMCID: PMC10703497 DOI: 10.1093/bib/bbad449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 10/30/2023] [Accepted: 11/14/2023] [Indexed: 12/18/2023] Open
Abstract
Identifying cell types is crucial for understanding the functional units of an organism. Machine learning has shown promising performance in identifying cell types, but many existing methods lack biological significance due to poor interpretability. However, it is of the utmost importance to understand what makes cells share the same function and form a specific cell type, motivating us to propose a biologically interpretable method. CellTICS prioritizes marker genes with cell-type-specific expression, using a hierarchy of biological pathways for neural network construction, and applying a multi-predictive-layer strategy to predict cell and sub-cell types. CellTICS usually outperforms existing methods in prediction accuracy. Moreover, CellTICS can reveal pathways that define a cell type or a cell type under specific physiological conditions, such as disease or aging. The nonlinear nature of neural networks enables us to identify many novel pathways. Interestingly, some of the pathways identified by CellTICS exhibit differential expression "variability" rather than differential expression across cell types, indicating that expression stochasticity within a pathway could be an important feature characteristic of a cell type. Overall, CellTICS provides a biologically interpretable method for identifying and characterizing cell types, shedding light on the underlying pathways that define cellular heterogeneity and its role in organismal function. CellTICS is available at https://github.com/qyyin0516/CellTICS.
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Affiliation(s)
- Qingyang Yin
- Department of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, United States
| | - Liang Chen
- Department of Quantitative and Computational Biology, University of Southern California, 1050 Childs Way, Los Angeles, CA 90089, United States
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25
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Lazaros K, Vlamos P, Vrahatis AG. Methods for cell-type annotation on scRNA-seq data: A recent overview. J Bioinform Comput Biol 2023; 21:2340002. [PMID: 37743364 DOI: 10.1142/s0219720023400024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
The evolution of single-cell technology is ongoing, continually generating massive amounts of data that reveal many mysteries surrounding intricate diseases. However, their drawbacks continue to constrain us. Among these, annotating cell types in single-cell gene expressions pose a substantial challenge, despite the myriad of tools at our disposal. The rapid growth in data, resources, and tools has consequently brought about significant alterations in this area over the years. In our study, we spotlight all note-worthy cell type annotation techniques developed over the past four years. We provide an overview of the latest trends in this field, showcasing the most advanced methods in taxonomy. Our research underscores the demand for additional tools that incorporate a biological context and also predicts that the rising trend of graph neural network approaches will likely lead this research field in the coming years.
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Affiliation(s)
- Konstantinos Lazaros
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Panagiotis Vlamos
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
| | - Aristidis G Vrahatis
- Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece
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26
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Shi Q, Chen X, Zhang Z. Decoding Human Biology and Disease Using Single-cell Omics Technologies. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:926-949. [PMID: 37739168 PMCID: PMC10928380 DOI: 10.1016/j.gpb.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 05/22/2023] [Accepted: 06/08/2023] [Indexed: 09/24/2023]
Abstract
Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.
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Affiliation(s)
- Qiang Shi
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Xueyan Chen
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Zemin Zhang
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Changping Laboratory, Beijing 102206, China.
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27
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Cheng C, Chen W, Jin H, Chen X. A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell-Cell Communication. Cells 2023; 12:1970. [PMID: 37566049 PMCID: PMC10417635 DOI: 10.3390/cells12151970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/10/2023] [Accepted: 07/21/2023] [Indexed: 08/12/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of cellular heterogeneity, identification of rare but significant cell types, and exploration of cell-cell communications and interactions. Its broad applications span both basic and clinical research domains. In this comprehensive review, we survey the current landscape of scRNA-seq analysis methods and tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, and the inference of cell-cell communication. We review the challenges encountered in scRNA-seq analysis, including issues of sparsity or low expression, reliability of cell annotation, and assumptions in data integration, and discuss the potential impact of suboptimal clustering and differential expression analysis tools on downstream analyses, particularly in identifying cell subpopulations. Finally, we discuss recent advancements and future directions for enhancing scRNA-seq analysis. Specifically, we highlight the development of novel tools for annotating single-cell data, integrating and interpreting multimodal datasets covering transcriptomics, epigenomics, and proteomics, and inferring cellular communication networks. By elucidating the latest progress and innovation, we provide a comprehensive overview of the rapidly advancing field of scRNA-seq analysis.
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Affiliation(s)
- Changde Cheng
- Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA;
| | - Wenan Chen
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; (W.C.); (H.J.)
| | - Hongjian Jin
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; (W.C.); (H.J.)
| | - Xiang Chen
- Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA;
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28
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Zhu H, Chen J, Liu K, Gao L, Wu H, Ma L, Zhou J, Liu Z, Han JDJ. Human PBMC scRNA-seq-based aging clocks reveal ribosome to inflammation balance as a single-cell aging hallmark and super longevity. SCIENCE ADVANCES 2023; 9:eabq7599. [PMID: 37379396 DOI: 10.1126/sciadv.abq7599] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 05/23/2023] [Indexed: 06/30/2023]
Abstract
Quantifying aging rate is important for evaluating age-associated decline and mortality. A blood single-cell RNA sequencing dataset for seven supercentenarians (SCs) was recently generated. Here, we generate a reference 28-sample aging cohort to compute a single-cell level aging clock and to determine the biological age of SCs. Our clock model placed the SCs at a blood biological age to between 80.43 and 102.67 years. Compared to the model-expected aging trajectory, SCs display increased naive CD8+ T cells, decreased cytotoxic CD8+ T cells, memory CD4+ T cells, and megakaryocytes. As the most prominent molecular hallmarks at the single-cell level, SCs contain more cells and cell types with high ribosome level, which is associated with and, according to Bayesian network inference, contributes to a low inflammation state and slow aging of SCs. Inhibiting ribosomal activity or translation in monocytes validates such translation against inflammation balance revealed by our single-cell aging clock.
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Affiliation(s)
- Hongming Zhu
- Translational Medical Center for Stem Cell Therapy & Institute for Regenerative Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, P.R. China
| | - Jiawei Chen
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 100871, P.R. China
| | - Kangping Liu
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 100871, P.R. China
| | - Lei Gao
- Translational Medical Center for Stem Cell Therapy & Institute for Regenerative Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, P.R. China
- Division of Life Sciences and Medicine, University of Science and Technology of China (USTC), Hefei, Anhui 230001, P.R. China
| | - Haiyan Wu
- Translational Medical Center for Stem Cell Therapy & Institute for Regenerative Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, P.R. China
| | - Liangliang Ma
- Department of Health Management, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, P.R. China
| | - Jieru Zhou
- Department of Health Management, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, P.R. China
| | - Zhongmin Liu
- Translational Medical Center for Stem Cell Therapy & Institute for Regenerative Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, P.R. China
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing 100871, P.R. China
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29
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Xu J, Zhang A, Liu F, Chen L, Zhang X. CIForm as a Transformer-based model for cell-type annotation of large-scale single-cell RNA-seq data. Brief Bioinform 2023:7169137. [PMID: 37200157 DOI: 10.1093/bib/bbad195] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/03/2023] [Accepted: 04/30/2023] [Indexed: 05/20/2023] Open
Abstract
Single-cell omics technologies have made it possible to analyze the individual cells within a biological sample, providing a more detailed understanding of biological systems. Accurately determining the cell type of each cell is a crucial goal in single-cell RNA-seq (scRNA-seq) analysis. Apart from overcoming the batch effects arising from various factors, single-cell annotation methods also face the challenge of effectively processing large-scale datasets. With the availability of an increase in the scRNA-seq datasets, integrating multiple datasets and addressing batch effects originating from diverse sources are also challenges in cell-type annotation. In this work, to overcome the challenges, we developed a supervised method called CIForm based on the Transformer for cell-type annotation of large-scale scRNA-seq data. To assess the effectiveness and robustness of CIForm, we have compared it with some leading tools on benchmark datasets. Through the systematic comparisons under various cell-type annotation scenarios, we exhibit that the effectiveness of CIForm is particularly pronounced in cell-type annotation. The source code and data are available at https://github.com/zhanglab-wbgcas/CIForm.
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Affiliation(s)
- Jing Xu
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Aidi Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
| | - Fang Liu
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
| | - Liang Chen
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
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30
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Hou W, Ji Z. Reference-free and cost-effective automated cell type annotation with GPT-4 in single-cell RNA-seq analysis. RESEARCH SQUARE 2023:rs.3.rs-2824971. [PMID: 37205379 PMCID: PMC10187429 DOI: 10.21203/rs.3.rs-2824971/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Cell type annotation is an essential step in single-cell RNA-seq analysis. However, it is a time-consuming process that often requires expertise in collecting canonical marker genes and manually annotating cell types. Automated cell type annotation methods typically require the acquisition of high-quality reference datasets and the development of additional pipelines. We demonstrate that GPT-4, a highly potent large language model, can automatically and accurately annotate cell types by utilizing marker gene information generated from standard single-cell RNA-seq analysis pipelines. Evaluated across hundreds of tissue types and cell types, GPT-4 generates cell type annotations exhibiting strong concordance with manual annotations, and has the potential to considerably reduce the effort and expertise needed in cell type annotation.
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Affiliation(s)
- Wenpin Hou
- Department of Biostatistics, The Mailman School of Public Health, Columbia University, New York City, NY, USA
| | - Zhicheng Ji
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
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