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Shi Y, Wan J, Zhang X, Liang T, Yin Y. scCRT: a contrastive-based dimensionality reduction model for scRNA-seq trajectory inference. Brief Bioinform 2024; 25:bbae204. [PMID: 38701412 PMCID: PMC11066919 DOI: 10.1093/bib/bbae204] [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: 11/29/2023] [Revised: 03/28/2024] [Accepted: 04/15/2024] [Indexed: 05/05/2024] Open
Abstract
Trajectory inference is a crucial task in single-cell RNA-sequencing downstream analysis, which can reveal the dynamic processes of biological development, including cell differentiation. Dimensionality reduction is an important step in the trajectory inference process. However, most existing trajectory methods rely on cell features derived from traditional dimensionality reduction methods, such as principal component analysis and uniform manifold approximation and projection. These methods are not specifically designed for trajectory inference and fail to fully leverage prior information from upstream analysis, limiting their performance. Here, we introduce scCRT, a novel dimensionality reduction model for trajectory inference. In order to utilize prior information to learn accurate cells representation, scCRT integrates two feature learning components: a cell-level pairwise module and a cluster-level contrastive module. The cell-level module focuses on learning accurate cell representations in a reduced-dimensionality space while maintaining the cell-cell positional relationships in the original space. The cluster-level contrastive module uses prior cell state information to aggregate similar cells, preventing excessive dispersion in the low-dimensional space. Experimental findings from 54 real and 81 synthetic datasets, totaling 135 datasets, highlighted the superior performance of scCRT compared with commonly used trajectory inference methods. Additionally, an ablation study revealed that both cell-level and cluster-level modules enhance the model's ability to learn accurate cell features, facilitating cell lineage inference. The source code of scCRT is available at https://github.com/yuchen21-web/scCRT-for-scRNA-seq.
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Affiliation(s)
- Yuchen Shi
- Hangzhou Dianzi University, Hangzhou City, Zhejiang Province, China
| | - Jian Wan
- Hangzhou Dianzi University, the Key Laboratory of Biomedical Intelligent Computing Technology of Zhejiang Province, and Zhejiang University of Science and Technology, Hangzhou City, Zhejiang Province, China
| | - Xin Zhang
- Hangzhou Dianzi University, Hangzhou City, Zhejiang Province, China
| | - Tingting Liang
- Hangzhou Dianzi University, Hangzhou City, Zhejiang Province, China
| | - Yuyu Yin
- Hangzhou Dianzi University, Hangzhou City, Zhejiang Province, China
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2
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Cao Y, Tran A, Kim H, Robertson N, Lin Y, Torkel M, Yang P, Patrick E, Ghazanfar S, Yang J. Thinking process templates for constructing data stories with SCDNEY. F1000Res 2023; 12:261. [PMID: 38434622 PMCID: PMC10905113 DOI: 10.12688/f1000research.130623.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/08/2023] [Indexed: 03/05/2024] Open
Abstract
Background Globally, scientists now have the ability to generate a vast amount of high throughput biomedical data that carry critical information for important clinical and public health applications. This data revolution in biology is now creating a plethora of new single-cell datasets. Concurrently, there have been significant methodological advances in single-cell research. Integrating these two resources, creating tailor-made, efficient, and purpose-specific data analysis approaches can assist in accelerating scientific discovery. Methods We developed a series of living workshops for building data stories, using Single-cell data integrative analysis (scdney). scdney is a wrapper package with a collection of single-cell analysis R packages incorporating data integration, cell type annotation, higher order testing and more. Results Here, we illustrate two specific workshops. The first workshop examines how to characterise the identity and/or state of cells and the relationship between them, known as phenotyping. The second workshop focuses on extracting higher-order features from cells to predict disease progression. Conclusions Through these workshops, we not only showcase current solutions, but also highlight critical thinking points. In particular, we highlight the Thinking Process Template that provides a structured framework for the decision-making process behind such single-cell analyses. Furthermore, our workshop will incorporate dynamic contributions from the community in a collaborative learning approach, thus the term 'living'.
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Affiliation(s)
- Yue Cao
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Andy Tran
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Hani Kim
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
- Children's Medical Research Institute, The University of Sydney, Westmead, NSW, 2145, Australia
| | - Nick Robertson
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Yingxin Lin
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Marni Torkel
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Pengyi Yang
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
- Children's Medical Research Institute, The University of Sydney, Westmead, NSW, 2145, Australia
| | - Ellis Patrick
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Shila Ghazanfar
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Jean Yang
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
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3
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Wang Z, Xie X, Liu S, Ji Z. scFseCluster: a feature selection-enhanced clustering for single-cell RNA-seq data. Life Sci Alliance 2023; 6:e202302103. [PMID: 37788907 PMCID: PMC10547911 DOI: 10.26508/lsa.202302103] [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: 04/20/2023] [Revised: 09/21/2023] [Accepted: 09/22/2023] [Indexed: 10/05/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) enables researchers to reveal previously unknown cell heterogeneity and functional diversity, which is impossible with bulk RNA sequencing. Clustering approaches are widely used for analyzing scRNA-seq data and identifying cell types and states. In the past few years, various advanced computational strategies emerged. However, the low generalization and high computational cost are the main bottlenecks of existing methods. In this study, we established a novel computational framework, scFseCluster, for scRNA-seq clustering analysis. scFseCluster incorporates a metaheuristic algorithm (Feature Selection based on Quantum Squirrel Search Algorithm) to extract the optimal gene set, which largely guarantees the performance of cell clustering. We conducted simulation experiments in several aspects to verify the performance of the proposed approach. scFseCluster performed very well on eight benchmark scRNA-seq datasets because of the optimal gene sets obtained using the Feature Selection based on Quantum Squirrel Search Algorithm. The comparative study demonstrated the significant advantages of scFseCluster over seven State-of-the-Art algorithms. In addition, our analysis shows that feature selection on high-variable genes can significantly improve clustering performance. In conclusion, our study demonstrates that scFseCluster is a highly versatile tool for enhancing scRNA-seq data clustering analysis.
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Affiliation(s)
- Zongqin Wang
- https://ror.org/05td3s095 College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
| | - Xiaojun Xie
- https://ror.org/05td3s095 College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
- https://ror.org/05td3s095 Center for Data Science and Intelligent Computing, Nanjing Agricultural University, Nanjing, China
| | - Shouyang Liu
- https://ror.org/05td3s095 Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, China
| | - Zhiwei Ji
- https://ror.org/05td3s095 College of Artificial Intelligence, Nanjing Agricultural University, Nanjing, China
- https://ror.org/05td3s095 Center for Data Science and Intelligent Computing, Nanjing Agricultural University, Nanjing, China
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4
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Xu K, Huang S, Yang Z, Zhang Y, Fang Y, Zheng G, Lin B, Zhou M, Sun J. Automatic detection and differential diagnosis of age-related macular degeneration from color fundus photographs using deep learning with hierarchical vision transformer. Comput Biol Med 2023; 167:107616. [PMID: 37922601 DOI: 10.1016/j.compbiomed.2023.107616] [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: 08/08/2023] [Revised: 10/14/2023] [Accepted: 10/23/2023] [Indexed: 11/07/2023]
Abstract
Age-related macular degeneration (AMD) is a leading cause of vision loss in the elderly, highlighting the need for early and accurate detection. In this study, we proposed DeepDrAMD, a hierarchical vision transformer-based deep learning model that integrates data augmentation techniques and SwinTransformer, to detect AMD and distinguish between different subtypes using color fundus photographs (CFPs). The DeepDrAMD was trained on the in-house WMUEH training set and achieved high performance in AMD detection with an AUC of 98.76% in the WMUEH testing set and 96.47% in the independent external Ichallenge-AMD cohort. Furthermore, the DeepDrAMD effectively classified dryAMD and wetAMD, achieving AUCs of 93.46% and 91.55%, respectively, in the WMUEH cohort and another independent external ODIR cohort. Notably, DeepDrAMD excelled at distinguishing between wetAMD subtypes, achieving an AUC of 99.36% in the WMUEH cohort. Comparative analysis revealed that the DeepDrAMD outperformed conventional deep-learning models and expert-level diagnosis. The cost-benefit analysis demonstrated that the DeepDrAMD offers substantial cost savings and efficiency improvements compared to manual reading approaches. Overall, the DeepDrAMD represents a significant advancement in AMD detection and differential diagnosis using CFPs, and has the potential to assist healthcare professionals in informed decision-making, early intervention, and treatment optimization.
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Affiliation(s)
- Ke Xu
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China
| | - Shenghai Huang
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China
| | - Zijian Yang
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China
| | - Yibo Zhang
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China
| | - Ye Fang
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China
| | - Gongwei Zheng
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China
| | - Bin Lin
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China.
| | - Meng Zhou
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China.
| | - Jie Sun
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China.
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5
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Shi Y, Wan J, Zhang X, Yin Y. CL-Impute: A contrastive learning-based imputation for dropout single-cell RNA-seq data. Comput Biol Med 2023; 164:107263. [PMID: 37531858 DOI: 10.1016/j.compbiomed.2023.107263] [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: 05/09/2023] [Revised: 06/27/2023] [Accepted: 07/16/2023] [Indexed: 08/04/2023]
Abstract
BACKGROUND Single-cell RNA-sequencing (scRNA-seq) technology has revolutionized the study of cell heterogeneity and biological interpretation at the single-cell level. However, the dropout events commonly present in scRNA-seq data can markedly reduce the reliability of downstream analysis. Existing imputation methods often overlook the discrepancy between the established cell relationship from dropout noisy data and reality, which limits their performances due to the learned untrustworthy cell representations. METHOD Here, we propose a novel approach called the CL-Impute (Contrastive Learning-based Impute) model for estimating missing genes without relying on preconstructed cell relationships. CL-Impute utilizes contrastive learning and a self-attention network to address this challenge. Specifically, the proposed CL-Impute model leverages contrastive learning to learn cell representations from the self-perspective of dropout events, whereas the self-attention network captures cell relationships from the global-perspective. RESULTS Experimental results on four benchmark datasets, including quantitative assessment, cell clustering, gene identification, and trajectory inference, demonstrate the superior performance of CL-Impute compared with that of existing state-of-the-art imputation methods. Furthermore, our experiment reveals that combining contrastive learning and masking cell augmentation enables the model to learn actual latent features from noisy data with a high rate of dropout events, enhancing the reliability of imputed values. CONCLUSIONS CL-Impute is a novel contrastive learning-based method to impute scRNA-seq data in the context of high dropout rate. The source code of CL-Impute is available at https://github.com/yuchen21-web/Imputation-for-scRNA-seq.
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Affiliation(s)
- Yuchen Shi
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China; Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education, Ministry of Education, China
| | - Jian Wan
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China; School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, China
| | - Xin Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China; Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education, Ministry of Education, China.
| | - Yuyu Yin
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China; Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education, Ministry of Education, China.
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6
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Massimino M, Martorana F, Stella S, Vitale SR, Tomarchio C, Manzella L, Vigneri P. Single-Cell Analysis in the Omics Era: Technologies and Applications in Cancer. Genes (Basel) 2023; 14:1330. [PMID: 37510235 PMCID: PMC10380065 DOI: 10.3390/genes14071330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023] Open
Abstract
Cancer molecular profiling obtained with conventional bulk sequencing describes average alterations obtained from the entire cellular population analyzed. In the era of precision medicine, this approach is unable to track tumor heterogeneity and cannot be exploited to unravel the biological processes behind clonal evolution. In the last few years, functional single-cell omics has improved our understanding of cancer heterogeneity. This approach requires isolation and identification of single cells starting from an entire population. A cell suspension obtained by tumor tissue dissociation or hematological material can be manipulated using different techniques to separate individual cells, employed for single-cell downstream analysis. Single-cell data can then be used to analyze cell-cell diversity, thus mapping evolving cancer biological processes. Despite its unquestionable advantages, single-cell analysis produces massive amounts of data with several potential biases, stemming from cell manipulation and pre-amplification steps. To overcome these limitations, several bioinformatic approaches have been developed and explored. In this work, we provide an overview of this entire process while discussing the most recent advances in the field of functional omics at single-cell resolution.
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Affiliation(s)
- Michele Massimino
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Federica Martorana
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Stefania Stella
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Silvia Rita Vitale
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Cristina Tomarchio
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Livia Manzella
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
| | - Paolo Vigneri
- Department of Clinical and Experimental Medicine, University of Catania, 95123 Catania, Italy
- Center of Experimental Oncology and Hematology, A.O.U. Policlinico "G. Rodolico-S. Marco", 95123 Catania, Italy
- Humanitas Istituto Clinico Catanese, University Oncology Department, 95045 Catania, Italy
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Huang Z, Wang J, Lu X, Mohd Zain A, Yu G. scGGAN: single-cell RNA-seq imputation by graph-based generative adversarial network. Brief Bioinform 2023; 24:7024714. [PMID: 36733262 DOI: 10.1093/bib/bbad040] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/21/2022] [Accepted: 01/18/2023] [Indexed: 02/04/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) data are typically with a large number of missing values, which often results in the loss of critical gene signaling information and seriously limit the downstream analysis. Deep learning-based imputation methods often can better handle scRNA-seq data than shallow ones, but most of them do not consider the inherent relations between genes, and the expression of a gene is often regulated by other genes. Therefore, it is essential to impute scRNA-seq data by considering the regional gene-to-gene relations. We propose a novel model (named scGGAN) to impute scRNA-seq data that learns the gene-to-gene relations by Graph Convolutional Networks (GCN) and global scRNA-seq data distribution by Generative Adversarial Networks (GAN). scGGAN first leverages single-cell and bulk genomics data to explore inherent relations between genes and builds a more compact gene relation network to jointly capture the homogeneous and heterogeneous information. Then, it constructs a GCN-based GAN model to integrate the scRNA-seq, gene sequencing data and gene relation network for generating scRNA-seq data, and trains the model through adversarial learning. Finally, it utilizes data generated by the trained GCN-based GAN model to impute scRNA-seq data. Experiments on simulated and real scRNA-seq datasets show that scGGAN can effectively identify dropout events, recover the biologically meaningful expressions, determine subcellular states and types, improve the differential expression analysis and temporal dynamics analysis. Ablation experiments confirm that both the gene relation network and gene sequence data help the imputation of scRNA-seq data.
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Affiliation(s)
- Zimo Huang
- MEng student at School of Software, Shandong University, China
| | - Jun Wang
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, China
| | - Xudong Lu
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, China
| | | | - Guoxian Yu
- School of Software, Shandong University, China
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8
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Wang R, Chung CR, Huang HD, Lee TY. Identification of species-specific RNA N6-methyladinosine modification sites from RNA sequences. Brief Bioinform 2023; 24:7008797. [PMID: 36715277 DOI: 10.1093/bib/bbac573] [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: 09/07/2022] [Revised: 11/11/2022] [Accepted: 11/24/2022] [Indexed: 01/31/2023] Open
Abstract
N6-methyladinosine (m6A) modification is the most abundant co-transcriptional modification in eukaryotic RNA and plays important roles in cellular regulation. Traditional high-throughput sequencing experiments used to explore functional mechanisms are time-consuming and labor-intensive, and most of the proposed methods focused on limited species types. To further understand the relevant biological mechanisms among different species with the same RNA modification, it is necessary to develop a computational scheme that can be applied to different species. To achieve this, we proposed an attention-based deep learning method, adaptive-m6A, which consists of convolutional neural network, bi-directional long short-term memory and an attention mechanism, to identify m6A sites in multiple species. In addition, three conventional machine learning (ML) methods, including support vector machine, random forest and logistic regression classifiers, were considered in this work. In addition to the performance of ML methods for multi-species prediction, the optimal performance of adaptive-m6A yielded an accuracy of 0.9832 and the area under the receiver operating characteristic curve of 0.98. Moreover, the motif analysis and cross-validation among different species were conducted to test the robustness of one model towards multiple species, which helped improve our understanding about the sequence characteristics and biological functions of RNA modifications in different species.
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Affiliation(s)
- Rulan Wang
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, Longgang District, 51872, Shenzhen, P.R. China
| | - Chia-Ru Chung
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, Longgang District, 51872, Shenzhen, P.R. China
- School of Life Sciences, University of Science and Technology of China, 230026, Hefei, Anhui, P.R. China
| | - Hsien-Da Huang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, Longgang District, 51872, Shenzhen, P.R. China
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, Longgang District, 51872, Shenzhen, P.R. China
| | - Tzong-Yi Lee
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, Longgang District, 51872, Shenzhen, P.R. China
- School of Medicine, The Chinese University of Hong Kong, Shenzhen, 2001 Longxiang Road, Longgang District, 51872, Shenzhen, P.R. China
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9
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Qi R, Zou Q. Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level. RESEARCH (WASHINGTON, D.C.) 2023; 6:0050. [PMID: 36930772 PMCID: PMC10013796 DOI: 10.34133/research.0050] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 12/27/2022] [Indexed: 01/12/2023]
Abstract
Cancer treatments always face challenging problems, particularly drug resistance due to tumor cell heterogeneity. The existing datasets include the relationship between gene expression and drug sensitivities; however, the majority are based on tissue-level studies. Study drugs at the single-cell level are perspective to overcome minimal residual disease caused by subclonal resistant cancer cells retained after initial curative therapy. Fortunately, machine learning techniques can help us understand how different types of cells respond to different cancer drugs from the perspective of single-cell gene expression. Good modeling using single-cell data and drug response information will not only improve machine learning for cell-drug outcome prediction but also facilitate the discovery of drugs for specific cancer subgroups and specific cancer treatments. In this paper, we review machine learning and deep learning approaches in drug research. By analyzing the application of these methods on cancer cell lines and single-cell data and comparing the technical gap between single-cell sequencing data analysis and single-cell drug sensitivity analysis, we hope to explore the trends and potential of drug research at the single-cell data level and provide more inspiration for drug research at the single-cell level. We anticipate that this review will stimulate the innovative use of machine learning methods to address new challenges in precision medicine more broadly.
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Affiliation(s)
- Ren Qi
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China.,Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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10
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Xia D, Wang Y, Xiao Y, Li W. Applications of single-cell RNA sequencing in atopic dermatitis and psoriasis. Front Immunol 2022; 13:1038744. [PMID: 36505405 PMCID: PMC9732227 DOI: 10.3389/fimmu.2022.1038744] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 10/27/2022] [Indexed: 11/27/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) is a novel technology that characterizes molecular heterogeneity at the single-cell level. With the development of more automated, sensitive, and cost-effective single-cell isolation methods, the sensitivity and efficiency of scRNA-seq have improved. Technological advances in single-cell analysis provide a deeper understanding of the biological diversity of cells present in tissues, including inflamed skin. New subsets of cells have been discovered among common inflammatory skin diseases, such as atopic dermatitis (AD) and psoriasis. ScRNA-seq technology has also been used to analyze immune cell distribution and cell-cell communication, shedding new light on the complex interplay of components involved in disease responses. Moreover, scRNA-seq may be a promising tool in precision medicine because of its ability to define cell subsets with potential treatment targets and to characterize cell-specific responses to drugs or other stimuli. In this review, we briefly summarize the progress in the development of scRNA-seq technologies and discuss the latest scRNA-seq-related findings and future trends in AD and psoriasis. We also discuss the limitations and technical problems associated with current scRNA-seq technology.
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Affiliation(s)
- Dengmei Xia
- Department of Dermatology, Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China,Department of Dermatology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China,Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yiyi Wang
- Department of Dermatology, Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China,Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yue Xiao
- Department of Dermatology, Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China,Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wei Li
- Department of Dermatology, Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China,Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China,*Correspondence: Wei Li,
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Carangelo G, Magi A, Semeraro R. From multitude to singularity: An up-to-date overview of scRNA-seq data generation and analysis. Front Genet 2022; 13:994069. [PMID: 36263428 PMCID: PMC9575985 DOI: 10.3389/fgene.2022.994069] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 09/15/2022] [Indexed: 11/23/2022] Open
Abstract
Single cell RNA sequencing (scRNA-seq) is today a common and powerful technology in biomedical research settings, allowing to profile the whole transcriptome of a very large number of individual cells and reveal the heterogeneity of complex clinical samples. Traditionally, cells have been classified by their morphology or by expression of certain proteins in functionally distinct settings. The advent of next generation sequencing (NGS) technologies paved the way for the detection and quantitative analysis of cellular content. In this context, transcriptome quantification techniques made their advent, starting from the bulk RNA sequencing, unable to dissect the heterogeneity of a sample, and moving to the first single cell techniques capable of analyzing a small number of cells (1–100), arriving at the current single cell techniques able to generate hundreds of thousands of cells. As experimental protocols have improved rapidly, computational workflows for processing the data have also been refined, opening up to novel methods capable of scaling computational times more favorably with the dataset size and making scRNA-seq much better suited for biomedical research. In this perspective, we will highlight the key technological and computational developments which have enabled the analysis of this growing data, making the scRNA-seq a handy tool in clinical applications.
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Affiliation(s)
- Giulia Carangelo
- Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, Florence, Italy
| | - Alberto Magi
- Department of Information Engineering, University of Florence, Florence, Italy
| | - Roberto Semeraro
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
- *Correspondence: Roberto Semeraro,
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Brendel M, Su C, Bai Z, Zhang H, Elemento O, Wang F. Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:814-835. [PMID: 36528240 PMCID: PMC10025684 DOI: 10.1016/j.gpb.2022.11.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Revised: 08/17/2022] [Accepted: 11/24/2022] [Indexed: 12/23/2022]
Abstract
Single-cell RNA sequencing (scRNA-seq) has become a routinely used technique to quantify the gene expression profile of thousands of single cells simultaneously. Analysis of scRNA-seq data plays an important role in the study of cell states and phenotypes, and has helped elucidate biological processes, such as those occurring during the development of complex organisms, and improved our understanding of disease states, such as cancer, diabetes, and coronavirus disease 2019 (COVID-19). Deep learning, a recent advance of artificial intelligence that has been used to address many problems involving large datasets, has also emerged as a promising tool for scRNA-seq data analysis, as it has a capacity to extract informative and compact features from noisy, heterogeneous, and high-dimensional scRNA-seq data to improve downstream analysis. The present review aims at surveying recently developed deep learning techniques in scRNA-seq data analysis, identifying key steps within the scRNA-seq data analysis pipeline that have been advanced by deep learning, and explaining the benefits of deep learning over more conventional analytic tools. Finally, we summarize the challenges in current deep learning approaches faced within scRNA-seq data and discuss potential directions for improvements in deep learning algorithms for scRNA-seq data analysis.
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Affiliation(s)
- Matthew Brendel
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; Institute for Computational Biomedicine, Caryl and Israel Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA
| | - Chang Su
- Department of Health Service Administration and Policy, Temple University, Philadelphia, PA 19122, USA.
| | - Zilong Bai
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA
| | - Hao Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA
| | - Olivier Elemento
- Institute for Computational Biomedicine, Caryl and Israel Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA.
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Wang L, Cai M, Song Y, Bai J, Sun W, Yu J, Du S, Lu J, Fu S. Multidimensional difference analysis in gastric cancer patients between high and low latitude. Front Genet 2022; 13:944492. [PMID: 35957688 PMCID: PMC9360553 DOI: 10.3389/fgene.2022.944492] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 06/27/2022] [Indexed: 12/24/2022] Open
Abstract
Genetic variation has been shown to affect tumor growth and progression, and the temperature at different latitudes may promote the evolution of genetic variation. Geographical data with latitudinal information is of importance to understand the interplay between genetic variants and environmental influence, such as the temperature, in gastric cancer (GC). In this study, we classified the GC samples from The Cancer Genome Atlas database into two groups based on the latitudinal information of patients and found that GC samples with low-latitude had better clinical outcomes. Further analyses revealed significant differences in other clinical factors such as disease stage and grade between high and low latitudes GC samples. Then, we analyzed the genomic and transcriptomic differences between the two groups. Furthermore, we evaluated the activity score of metabolic pathways and infiltrating immune cells in GC samples with different latitudes using the single-sample gene set enrichment analysis algorithm. These results showed that GC samples at low-latitude had lower tumor mutation burden and subclones as well as higher DNA repair activities. Meanwhile, we found that most immune cells were associated with the prognosis of low-latitude GC patients. At last, we constructed and validated an immune-related prognostic model to evaluate the prognosis of GC samples at different latitudes. This study has provided a further understanding of the geographical contribution to GC at the multiomic level and may benefit the individualized treatment of GC patients at different latitudes.
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Affiliation(s)
- Liqiang Wang
- Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, China
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, China
| | - Mengdi Cai
- Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, China
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, China
| | - Ying Song
- Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, China
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, China
| | - Jing Bai
- Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, China
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, China
| | - Wenjing Sun
- Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, China
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, China
| | - Jingcui Yu
- Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, China
- Scientific Research Centre, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Shuomeng Du
- Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, China
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, China
| | - Jianping Lu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- *Correspondence: Songbin Fu, ; Jianping Lu,
| | - Songbin Fu
- Key Laboratory of Preservation of Human Genetic Resources and Disease Control in China (Harbin Medical University), Ministry of Education, Harbin, China
- Laboratory of Medical Genetics, Harbin Medical University, Harbin, China
- *Correspondence: Songbin Fu, ; Jianping Lu,
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