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Zheng Y, Zhong Y, Hu J, Shang X. SCC: an accurate imputation method for scRNA-seq dropouts based on a mixture model. BMC Bioinformatics 2021; 22:5. [PMID: 33407064 PMCID: PMC7788948 DOI: 10.1186/s12859-020-03878-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Accepted: 11/13/2020] [Indexed: 01/14/2023] Open
Abstract
Background Single-cell RNA sequencing (scRNA-seq) enables the possibility of many in-depth transcriptomic analyses at a single-cell resolution. It’s already widely used for exploring the dynamic development process of life, studying the gene regulation mechanism, and discovering new cell types. However, the low RNA capture rate, which cause highly sparse expression with dropout, makes it difficult to do downstream analyses. Results We propose a new method SCC to impute the dropouts of scRNA-seq data. Experiment results show that SCC gives competitive results compared to two existing methods while showing superiority in reducing the intra-class distance of cells and improving the clustering accuracy in both simulation and real data. Conclusions SCC is an effective tool to resolve the dropout noise in scRNA-seq data. The code is freely accessible at https://github.com/nwpuzhengyan/SCC.
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Affiliation(s)
- Yan Zheng
- School of Computer Science, Northwestern Polytechnical University, West Youyi Road 127, Xi'an, 710072, China
| | - Yuanke Zhong
- School of Computer Science, Northwestern Polytechnical University, West Youyi Road 127, Xi'an, 710072, China
| | - Jialu Hu
- School of Computer Science, Northwestern Polytechnical University, West Youyi Road 127, Xi'an, 710072, China.
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, West Youyi Road 127, Xi'an, 710072, China.
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Zhao K, Oualkacha K, Lakhal-Chaieb L, Labbe A, Klein K, Ciampi A, Hudson M, Colmegna I, Pastinen T, Zhang T, Daley D, Greenwood CMT. A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation. Biometrics 2020; 77:424-438. [PMID: 32438470 PMCID: PMC8359306 DOI: 10.1111/biom.13307] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 02/28/2020] [Accepted: 05/08/2020] [Indexed: 01/24/2023]
Abstract
Identifying disease-associated changes in DNA methylation can help us gain a better understanding of disease etiology. Bisulfite sequencing allows the generation of high-throughput methylation profiles at single-base resolution of DNA. However, optimally modeling and analyzing these sparse and discrete sequencing data is still very challenging due to variable read depth, missing data patterns, long-range correlations, data errors, and confounding from cell type mixtures. We propose a regression-based hierarchical model that allows covariate effects to vary smoothly along genomic positions and we have built a specialized EM algorithm, which explicitly allows for experimental errors and cell type mixtures, to make inference about smooth covariate effects in the model. Simulations show that the proposed method provides accurate estimates of covariate effects and captures the major underlying methylation patterns with excellent power. We also apply our method to analyze data from rheumatoid arthritis patients and controls. The method has been implemented in R package SOMNiBUS.
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Affiliation(s)
- Kaiqiong Zhao
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.,Lady Davis Institute for Medical Research, Montreal, QC, Canada
| | - Karim Oualkacha
- Département de Mathématiques, Université du Québec à Montrèal, Montreal, QC, Canada
| | - Lajmi Lakhal-Chaieb
- Département de Mathématiques et de Statistique, Université Laval, Quebec City, QC, Canada
| | - Aurélie Labbe
- Département des Sciences de la Décision, HEC Montrèal, Montreal, QC, Canada
| | - Kathleen Klein
- Lady Davis Institute for Medical Research, Montreal, QC, Canada
| | - Antonio Ciampi
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.,Lady Davis Institute for Medical Research, Montreal, QC, Canada
| | - Marie Hudson
- Lady Davis Institute for Medical Research, Montreal, QC, Canada.,Department of Medicine, McGill University, Montreal, QC, Canada
| | - Inés Colmegna
- Department of Medicine, McGill University, Montreal, QC, Canada.,The Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Tomi Pastinen
- Center for Pediatric Genomic Medicine, Children's Mercy Kansas City, Kansas City, MO, USA
| | - Tieyuan Zhang
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Denise Daley
- The Centre for Heart Lung Innovation, and Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Celia M T Greenwood
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.,Lady Davis Institute for Medical Research, Montreal, QC, Canada.,Department of Human Genetics and Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada
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Kharoubi R, Oualkacha K, Mkhadri A. The cluster correlation-network support vector machine for high-dimensional binary classification. J STAT COMPUT SIM 2019. [DOI: 10.1080/00949655.2019.1575382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Rachid Kharoubi
- Department of Mathematics, Université du Québec À Montréal, Montreal, QC, Canada
| | - Karim Oualkacha
- Department of Mathematics, Université du Québec À Montréal, Montreal, QC, Canada
| | - Abdallah Mkhadri
- Department of Mathematics, Cadi Ayyad University, Marrakech, Morocco
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