1
|
Alsaggaf I, Buchan D, Wan C. Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning. Brief Funct Genomics 2024; 23:441-451. [PMID: 38242863 DOI: 10.1093/bfgp/elad059] [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: 07/01/2023] [Revised: 12/14/2023] [Accepted: 12/18/2023] [Indexed: 01/21/2024] Open
Abstract
Cell type identification is an important task for single-cell RNA-sequencing (scRNA-seq) data analysis. Many prediction methods have recently been proposed, but the predictive accuracy of difficult cell type identification tasks is still low. In this work, we proposed a novel Gaussian noise augmentation-based scRNA-seq contrastive learning method (GsRCL) to learn a type of discriminative feature representations for cell type identification tasks. A large-scale computational evaluation suggests that GsRCL successfully outperformed other state-of-the-art predictive methods on difficult cell type identification tasks, while the conventional random genes masking augmentation-based contrastive learning method also improved the accuracy of easy cell type identification tasks in general.
Collapse
Affiliation(s)
- Ibrahim Alsaggaf
- School of Computing and Mathematical Sciences, Birkbeck, University of London, Malet Street, WC1E 7HX, London, United Kingdom
| | - Daniel Buchan
- Department of Computer Science, University College London, Gower Street, WC1E 6BT, London, United Kingdom
| | - Cen Wan
- School of Computing and Mathematical Sciences, Birkbeck, University of London, Malet Street, WC1E 7HX, London, United Kingdom
| |
Collapse
|
2
|
Zhang T, Ren J, Li L, Wu Z, Zhang Z, Dong G, Wang G. scZAG: Integrating ZINB-Based Autoencoder with Adaptive Data Augmentation Graph Contrastive Learning for scRNA-seq Clustering. Int J Mol Sci 2024; 25:5976. [PMID: 38892162 PMCID: PMC11172799 DOI: 10.3390/ijms25115976] [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: 03/24/2024] [Revised: 04/08/2024] [Accepted: 05/28/2024] [Indexed: 06/21/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) is widely used to interpret cellular states, detect cell subpopulations, and study disease mechanisms. In scRNA-seq data analysis, cell clustering is a key step that can identify cell types. However, scRNA-seq data are characterized by high dimensionality and significant sparsity, presenting considerable challenges for clustering. In the high-dimensional gene expression space, cells may form complex topological structures. Many conventional scRNA-seq data analysis methods focus on identifying cell subgroups rather than exploring these potential high-dimensional structures in detail. Although some methods have begun to consider the topological structures within the data, many still overlook the continuity and complex topology present in single-cell data. We propose a deep learning framework that begins by employing a zero-inflated negative binomial (ZINB) model to denoise the highly sparse and over-dispersed scRNA-seq data. Next, scZAG uses an adaptive graph contrastive representation learning approach that combines approximate personalized propagation of neural predictions graph convolution (APPNPGCN) with graph contrastive learning methods. By using APPNPGCN as the encoder for graph contrastive learning, we ensure that each cell's representation reflects not only its own features but also its position in the graph and its relationships with other cells. Graph contrastive learning exploits the relationships between nodes to capture the similarity among cells, better representing the data's underlying continuity and complex topology. Finally, the learned low-dimensional latent representations are clustered using Kullback-Leibler divergence. We validated the superior clustering performance of scZAG on 10 common scRNA-seq datasets in comparison to existing state-of-the-art clustering methods.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Guohua Wang
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China; (T.Z.); (J.R.); (L.L.); (Z.W.); (Z.Z.); (G.D.)
| |
Collapse
|
3
|
Qiu Y, Yang L, Jiang H, Zou Q. scTPC: a novel semisupervised deep clustering model for scRNA-seq data. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae293. [PMID: 38684178 DOI: 10.1093/bioinformatics/btae293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 04/14/2024] [Accepted: 04/26/2024] [Indexed: 05/02/2024]
Abstract
MOTIVATION Continuous advancements in single-cell RNA sequencing (scRNA-seq) technology have enabled researchers to further explore the study of cell heterogeneity, trajectory inference, identification of rare cell types, and neurology. Accurate scRNA-seq data clustering is crucial in single-cell sequencing data analysis. However, the high dimensionality, sparsity, and presence of "false" zero values in the data can pose challenges to clustering. Furthermore, current unsupervised clustering algorithms have not effectively leveraged prior biological knowledge, making cell clustering even more challenging. RESULTS This study investigates a semisupervised clustering model called scTPC, which integrates the triplet constraint, pairwise constraint, and cross-entropy constraint based on deep learning. Specifically, the model begins by pretraining a denoising autoencoder based on a zero-inflated negative binomial distribution. Deep clustering is then performed in the learned latent feature space using triplet constraints and pairwise constraints generated from partial labeled cells. Finally, to address imbalanced cell-type datasets, a weighted cross-entropy loss is introduced to optimize the model. A series of experimental results on 10 real scRNA-seq datasets and five simulated datasets demonstrate that scTPC achieves accurate clustering with a well-designed framework. AVAILABILITY AND IMPLEMENTATION scTPC is a Python-based algorithm, and the code is available from https://github.com/LF-Yang/Code or https://zenodo.org/records/10951780.
Collapse
Affiliation(s)
- Yushan Qiu
- School of Mathematical Sciences, Shenzhen University, Shenzhen, Guangdong 518000, China
| | - Lingfei Yang
- School of Mathematical Sciences, Shenzhen University, Shenzhen, Guangdong 518000, China
| | - Hao Jiang
- School of Mathematics, Renmin University of China, Haidian District, Beijing 100872, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610056, China
| |
Collapse
|
4
|
Liu F, Shi F, Du F, Cao X, Yu Z. CoT: a transformer-based method for inferring tumor clonal copy number substructure from scDNA-seq data. Brief Bioinform 2024; 25:bbae187. [PMID: 38670159 PMCID: PMC11052634 DOI: 10.1093/bib/bbae187] [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: 12/06/2023] [Revised: 03/08/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
Single-cell DNA sequencing (scDNA-seq) has been an effective means to unscramble intra-tumor heterogeneity, while joint inference of tumor clones and their respective copy number profiles remains a challenging task due to the noisy nature of scDNA-seq data. We introduce a new bioinformatics method called CoT for deciphering clonal copy number substructure. The backbone of CoT is a Copy number Transformer autoencoder that leverages multi-head attention mechanism to explore correlations between different genomic regions, and thus capture global features to create latent embeddings for the cells. CoT makes it convenient to first infer cell subpopulations based on the learned embeddings, and then estimate single-cell copy numbers through joint analysis of read counts data for the cells belonging to the same cluster. This exploitation of clonal substructure information in copy number analysis helps to alleviate the effect of read counts non-uniformity, and yield robust estimations of the tumor copy numbers. Performance evaluation on synthetic and real datasets showcases that CoT outperforms the state of the arts, and is highly useful for deciphering clonal copy number substructure.
Collapse
Affiliation(s)
- Furui Liu
- School of Information Engineering, Ningxia University, 750021, Ningxia, China
| | - Fangyuan Shi
- School of Information Engineering, Ningxia University, 750021, Ningxia, China
- Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, 750021, Ningxia, China
| | - Fang Du
- School of Information Engineering, Ningxia University, 750021, Ningxia, China
- Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, 750021, Ningxia, China
| | - Xiangmei Cao
- Basic Medical School, Ningxia Medical University, 750001, Ningxia, China
| | - Zhenhua Yu
- School of Information Engineering, Ningxia University, 750021, Ningxia, China
- Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-founded by Ningxia Municipality and Ministry of Education, Ningxia University, 750021, Ningxia, China
| |
Collapse
|
5
|
Tan D, Yang C, Wang J, Su Y, Zheng C. scAMAC: self-supervised clustering of scRNA-seq data based on adaptive multi-scale autoencoder. Brief Bioinform 2024; 25:bbae068. [PMID: 38426327 PMCID: PMC10905526 DOI: 10.1093/bib/bbae068] [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: 10/20/2023] [Revised: 01/15/2024] [Accepted: 01/26/2024] [Indexed: 03/02/2024] Open
Abstract
Cluster assignment is vital to analyzing single-cell RNA sequencing (scRNA-seq) data to understand high-level biological processes. Deep learning-based clustering methods have recently been widely used in scRNA-seq data analysis. However, existing deep models often overlook the interconnections and interactions among network layers, leading to the loss of structural information within the network layers. Herein, we develop a new self-supervised clustering method based on an adaptive multi-scale autoencoder, called scAMAC. The self-supervised clustering network utilizes the Multi-Scale Attention mechanism to fuse the feature information from the encoder, hidden and decoder layers of the multi-scale autoencoder, which enables the exploration of cellular correlations within the same scale and captures deep features across different scales. The self-supervised clustering network calculates the membership matrix using the fused latent features and optimizes the clustering network based on the membership matrix. scAMAC employs an adaptive feedback mechanism to supervise the parameter updates of the multi-scale autoencoder, obtaining a more effective representation of cell features. scAMAC not only enables cell clustering but also performs data reconstruction through the decoding layer. Through extensive experiments, we demonstrate that scAMAC is superior to several advanced clustering and imputation methods in both data clustering and reconstruction. In addition, scAMAC is beneficial for downstream analysis, such as cell trajectory inference. Our scAMAC model codes are freely available at https://github.com/yancy2024/scAMAC.
Collapse
Affiliation(s)
- Dayu Tan
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, 230601 Hefei, China
| | - Cheng Yang
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, 230601 Hefei, China
| | - Jing Wang
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, 230601 Hefei, China
| | - Yansen Su
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, 230601 Hefei, China
| | - Chunhou Zheng
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, 230601 Hefei, China
| |
Collapse
|
6
|
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.
Collapse
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
| |
Collapse
|
7
|
Fang Z, Zheng R, Li M. scMAE: a masked autoencoder for single-cell RNA-seq clustering. Bioinformatics 2024; 40:btae020. [PMID: 38230824 PMCID: PMC10832357 DOI: 10.1093/bioinformatics/btae020] [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: 09/14/2023] [Revised: 01/07/2024] [Accepted: 01/12/2024] [Indexed: 01/18/2024] Open
Abstract
MOTIVATION Single-cell RNA sequencing has emerged as a powerful technology for studying gene expression at the individual cell level. Clustering individual cells into distinct subpopulations is fundamental in scRNA-seq data analysis, facilitating the identification of cell types and exploration of cellular heterogeneity. Despite the recent development of many deep learning-based single-cell clustering methods, few have effectively exploited the correlations among genes, resulting in suboptimal clustering outcomes. RESULTS Here, we propose a novel masked autoencoder-based method, scMAE, for cell clustering. scMAE perturbs gene expression and employs a masked autoencoder to reconstruct the original data, learning robust and informative cell representations. The masked autoencoder introduces a masking predictor, which captures relationships among genes by predicting whether gene expression values are masked. By integrating this masking mechanism, scMAE effectively captures latent structures and dependencies in the data, enhancing clustering performance. We conducted extensive comparative experiments using various clustering evaluation metrics on 15 scRNA-seq datasets from different sequencing platforms. Experimental results indicate that scMAE outperforms other state-of-the-art methods on these datasets. In addition, scMAE accurately identifies rare cell types, which are challenging to detect due to their low abundance. Furthermore, biological analyses confirm the biological significance of the identified cell subpopulations. AVAILABILITY AND IMPLEMENTATION The source code of scMAE is available at: https://zenodo.org/records/10465991.
Collapse
Affiliation(s)
- Zhaoyu Fang
- School of Computer Science and Engineering, Central South University, 932 South Lushan Road, Yuelu District, Changsha 410083, China
| | - Ruiqing Zheng
- School of Computer Science and Engineering, Central South University, 932 South Lushan Road, Yuelu District, Changsha 410083, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, 932 South Lushan Road, Yuelu District, Changsha 410083, China
| |
Collapse
|
8
|
Liu J, Zeng W, Kan S, Li M, Zheng R. CAKE: a flexible self-supervised framework for enhancing cell visualization, clustering and rare cell identification. Brief Bioinform 2023; 25:bbad475. [PMID: 38145950 PMCID: PMC10749894 DOI: 10.1093/bib/bbad475] [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: 10/07/2023] [Revised: 11/13/2023] [Accepted: 11/30/2023] [Indexed: 12/27/2023] Open
Abstract
Single cell sequencing technology has provided unprecedented opportunities for comprehensively deciphering cell heterogeneity. Nevertheless, the high dimensionality and intricate nature of cell heterogeneity have presented substantial challenges to computational methods. Numerous novel clustering methods have been proposed to address this issue. However, none of these methods achieve the consistently better performance under different biological scenarios. In this study, we developed CAKE, a novel and scalable self-supervised clustering method, which consists of a contrastive learning model with a mixture neighborhood augmentation for cell representation learning, and a self-Knowledge Distiller model for the refinement of clustering results. These designs provide more condensed and cluster-friendly cell representations and improve the clustering performance in term of accuracy and robustness. Furthermore, in addition to accurately identifying the major type cells, CAKE could also find more biologically meaningful cell subgroups and rare cell types. The comprehensive experiments on real single-cell RNA sequencing datasets demonstrated the superiority of CAKE in visualization and clustering over other comparison methods, and indicated its extensive application in the field of cell heterogeneity analysis. Contact: Ruiqing Zheng. (rqzheng@csu.edu.cn).
Collapse
Affiliation(s)
- Jin Liu
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China
| | - Weixing Zeng
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China
| | - Shichao Kan
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China
| | - Ruiqing Zheng
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P.R. China
| |
Collapse
|
9
|
Wan H, Chen L, Deng M. scEMAIL: Universal and Source-free Annotation Method for scRNA-seq Data with Novel Cell-type Perception. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:939-958. [PMID: 36608843 PMCID: PMC10025768 DOI: 10.1016/j.gpb.2022.12.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 11/30/2022] [Accepted: 12/11/2022] [Indexed: 01/05/2023]
Abstract
Current cell-type annotation tools for single-cell RNA sequencing (scRNA-seq) data mainly utilize well-annotated source data to help identify cell types in target data. However, on account of privacy preservation, their requirements for raw source data may not always be satisfied. In this case, achieving feature alignment between source and target data explicitly is impossible. Additionally, these methods are barely able to discover the presence of novel cell types. A subjective threshold is often selected by users to detect novel cells. We propose a universal annotation framework for scRNA-seq data called scEMAIL, which automatically detects novel cell types without accessing source data during adaptation. For new cell-type identification, a novel cell-type perception module is designed with three steps. First, an expert ensemble system measures uncertainty of each cell from three complementary aspects. Second, based on this measurement, bimodality tests are applied to detect the presence of new cell types. Third, once assured of their presence, an adaptive threshold via manifold mixup partitions target cells into "known" and "unknown" groups. Model adaptation is then conducted to alleviate the batch effect. We gather multi-order neighborhood messages globally and impose local affinity regularizations on "known" cells. These constraints mitigate wrong classifications of the source model via reliable self-supervised information of neighbors. scEMAIL is accurate and robust under various scenarios in both simulation and real data. It is also flexible to be applied to challenging single-cell ATAC-seq data without loss of superiority. The source code of scEMAIL can be accessed at https://github.com/aster-ww/scEMAIL and https://ngdc.cncb.ac.cn/biocode/tools/BT007335/releases/v1.0.
Collapse
Affiliation(s)
- Hui Wan
- School of Mathematical Sciences, Peking University, Beijing 100871, China
| | - Liang Chen
- Huawei Technologies Co., Ltd., Beijing 100080, China.
| | - Minghua Deng
- School of Mathematical Sciences, Peking University, Beijing 100871, China; Center for Statistical Science, Peking University, Beijing 100871, China; Center for Quantitative Biology, Peking University, Beijing 100871, China.
| |
Collapse
|
10
|
Chen Y, Hu Y, Hu X, Feng C, Chen M. CoGO: a contrastive learning framework to predict disease similarity based on gene network and ontology structure. Bioinformatics 2022; 38:4380-4386. [PMID: 35900147 DOI: 10.1093/bioinformatics/btac520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 06/16/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Quantifying the similarity of human diseases provides guiding insights to the discovery of micro-scope mechanisms from a macro scale. Previous work demonstrated that better performance can be gained by integrating multiview data sources or applying machine learning techniques. However, designing an efficient framework to extract and incorporate information from different biological data using deep learning models remains unexplored. RESULTS We present CoGO, a Contrastive learning framework to predict disease similarity based on Gene network and Ontology structure, which incorporates the gene interaction network and gene ontology (GO) domain knowledge using graph deep learning models. First, graph deep learning models are applied to encode the features of genes and GO terms from separate graph structure data. Next, gene and GO features are projected to a common embedding space via a nonlinear projection. Then cross-view contrastive loss is applied to maximize the agreement of corresponding gene-GO associations and lead to meaningful gene representation. Finally, CoGO infers the similarity between diseases by the cosine similarity of disease representation vectors derived from related gene embedding. In our experiments, CoGO outperforms the most competitive baseline method on both AUROC and AUPRC, especially improves 19.57% in AUPRC (0.7733). The prediction results are significantly comparable with other disease similarity studies and thus highly credible. Furthermore, we conduct a detailed case study of top similar disease pairs which is demonstrated by other studies. Empirical results show that CoGO achieves powerful performance in disease similarity problem. AVAILABILITY AND IMPLEMENTATION https://github.com/yhchen1123/CoGO.
Collapse
Affiliation(s)
- Yuhao Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Yanshi Hu
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Xiaotian Hu
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Cong Feng
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China.,Biomedical Big Data Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China.,Institute of Hematology, Zhejiang University, Hangzhou, 310058, China
| |
Collapse
|
11
|
Ding Q, Yang W, Luo M, Xu C, Xu Z, Pang F, Cai Y, Anashkina AA, Su X, Chen N, Jiang Q. CBLRR: a cauchy-based bounded constraint low-rank representation method to cluster single-cell RNA-seq data. Brief Bioinform 2022; 23:6649282. [DOI: 10.1093/bib/bbac300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 06/17/2022] [Accepted: 07/02/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
The rapid development of single-cel+l RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for exploring biological phenomena at the single-cell level. The discovery of cell types is one of the major applications for researchers to explore the heterogeneity of cells. Some computational methods have been proposed to solve the problem of scRNA-seq data clustering. However, the unavoidable technical noise and notorious dropouts also reduce the accuracy of clustering methods. Here, we propose the cauchy-based bounded constraint low-rank representation (CBLRR), which is a low-rank representation-based method by introducing cauchy loss function (CLF) and bounded nuclear norm regulation, aiming to alleviate the above issue. Specifically, as an effective loss function, the CLF is proven to enhance the robustness of the identification of cell types. Then, we adopt the bounded constraint to ensure the entry values of single-cell data within the restricted interval. Finally, the performance of CBLRR is evaluated on 15 scRNA-seq datasets, and compared with other state-of-the-art methods. The experimental results demonstrate that CBLRR performs accurately and robustly on clustering scRNA-seq data. Furthermore, CBLRR is an effective tool to cluster cells, and provides great potential for downstream analysis of single-cell data. The source code of CBLRR is available online at https://github.com/Ginnay/CBLRR.
Collapse
Affiliation(s)
- Qian Ding
- School of Life Science and Technology, Harbin Institute of Technology , Harbin, Heilongjiang, China
| | - Wenyi Yang
- School of Life Science and Technology, Harbin Institute of Technology , Harbin, Heilongjiang, China
| | - Meng Luo
- School of Life Science and Technology, Harbin Institute of Technology , Harbin, Heilongjiang, China
| | - Chang Xu
- School of Life Science and Technology, Harbin Institute of Technology , Harbin, Heilongjiang, China
| | - Zhaochun Xu
- School of Life Science and Technology, Harbin Institute of Technology , Harbin, Heilongjiang, China
| | - Fenglan Pang
- School of Life Science and Technology, Harbin Institute of Technology , Harbin, Heilongjiang, China
| | - Yideng Cai
- School of Life Science and Technology, Harbin Institute of Technology , Harbin, Heilongjiang, China
| | - Anastasia A Anashkina
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences , Moscow, Russia
| | - Xi Su
- Foshan Maternity & Child Healthcare Hospital, Southern Medical University , Foshan, Guangdong, China
| | - Na Chen
- Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University , Jinan, Shandong, China
| | - Qinghua Jiang
- School of Life Science and Technology, Harbin Institute of Technology , Harbin, Heilongjiang, China
| |
Collapse
|
12
|
A Contrastive Learning Pre-Training Method for Motif Occupancy Identification. Int J Mol Sci 2022; 23:ijms23094699. [PMID: 35563090 PMCID: PMC9103107 DOI: 10.3390/ijms23094699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 04/21/2022] [Accepted: 04/22/2022] [Indexed: 12/10/2022] Open
Abstract
Motif occupancy identification is a binary classification task predicting the binding of DNA motif instances to transcription factors, for which several sequence-based methods have been proposed. However, through direct training, these end-to-end methods are lack of biological interpretability within their sequence representations. In this work, we propose a contrastive learning method to pre-train interpretable and robust DNA encoding for motif occupancy identification. We construct two alternative models to pre-train DNA sequential encoder, respectively: a self-supervised model and a supervised model. We augment the original sequences for contrastive learning with edit operations defined in edit distance. Specifically, we propose a sequence similarity criterion based on the Needleman–Wunsch algorithm to discriminate positive and negative sample pairs in self-supervised learning. Finally, a DNN classifier is fine-tuned along with the pre-trained encoder to predict the results of motif occupancy identification. Both proposed contrastive learning models outperform the baseline end-to-end CNN model and SimCLR method, reaching AUC of 0.811 and 0.823, respectively. Compared with the baseline method, our models show better robustness for small samples. Specifically, the self-supervised model is proved to be practicable in transfer learning.
Collapse
|