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Guo X, Ning J, Chen Y, Liu G, Zhao L, Fan Y, Sun S. Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies. Brief Funct Genomics 2024; 23:95-109. [PMID: 37022699 DOI: 10.1093/bfgp/elad011] [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: 07/08/2022] [Revised: 12/09/2022] [Accepted: 03/10/2023] [Indexed: 04/07/2023] Open
Abstract
Differential expression (DE) analysis is a necessary step in the analysis of single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data. Unlike traditional bulk RNA-seq, DE analysis for scRNA-seq or SRT data has unique characteristics that may contribute to the difficulty of detecting DE genes. However, the plethora of DE tools that work with various assumptions makes it difficult to choose an appropriate one. Furthermore, a comprehensive review on detecting DE genes for scRNA-seq data or SRT data from multi-condition, multi-sample experimental designs is lacking. To bridge such a gap, here, we first focus on the challenges of DE detection, then highlight potential opportunities that facilitate further progress in scRNA-seq or SRT analysis, and finally provide insights and guidance in selecting appropriate DE tools or developing new computational DE methods.
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Affiliation(s)
- Xiya Guo
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Jin Ning
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Yuanze Chen
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Guoliang Liu
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Liyan Zhao
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Yue Fan
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Shiquan Sun
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
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Network assisted analysis of de novo variants using protein-protein interaction information identified 46 candidate genes for congenital heart disease. PLoS Genet 2022; 18:e1010252. [PMID: 35671298 PMCID: PMC9205499 DOI: 10.1371/journal.pgen.1010252] [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: 12/13/2021] [Revised: 06/17/2022] [Accepted: 05/12/2022] [Indexed: 11/19/2022] Open
Abstract
De novo variants (DNVs) with deleterious effects have proved informative in identifying risk genes for early-onset diseases such as congenital heart disease (CHD). A number of statistical methods have been proposed for family-based studies or case/control studies to identify risk genes by screening genes with more DNVs than expected by chance in Whole Exome Sequencing (WES) studies. However, the statistical power is still limited for cohorts with thousands of subjects. Under the hypothesis that connected genes in protein-protein interaction (PPI) networks are more likely to share similar disease association status, we developed a Markov Random Field model that can leverage information from publicly available PPI databases to increase power in identifying risk genes. We identified 46 candidate genes with at least 1 DNV in the CHD study cohort, including 18 known human CHD genes and 35 highly expressed genes in mouse developing heart. Our results may shed new insight on the shared protein functionality among risk genes for CHD. The topologic information in a pathway may be informative to identify functionally interrelated genes and help improve statistical power in DNV studies. Under the hypothesis that connected genes in PPI networks are more likely to share similar disease association status, we developed a novel statistical model that can leverage information from publicly available PPI databases. Through simulation studies under multiple settings, we proved our method can increase statistical power in identifying additional risk genes compared to methods without using the PPI network information. We then applied our method to a real example for CHD DNV data, and then visualized the subnetwork of candidate genes to find potential functional gene clusters for CHD.
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Zhu B, Li H, Zhang L, Chandra SS, Zhao H. A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data. Brief Bioinform 2022; 23:6581434. [PMID: 35514182 PMCID: PMC9487630 DOI: 10.1093/bib/bbac166] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 04/02/2022] [Accepted: 04/13/2022] [Indexed: 11/13/2022] Open
Abstract
The development of single-cell RNA-sequencing (scRNA-seq) technologies has offered insights into complex biological systems at the single-cell resolution. In particular, these techniques facilitate the identifications of genes showing cell-type-specific differential expressions (DE). In this paper, we introduce MARBLES, a novel statistical model for cross-condition DE gene detection from scRNA-seq data. MARBLES employs a Markov Random Field model to borrow information across similar cell types and utilizes cell-type-specific pseudobulk count to account for sample-level variability. Our simulation results showed that MARBLES is more powerful than existing methods to detect DE genes with an appropriate control of false positive rate. Applications of MARBLES to real data identified novel disease-related DE genes and biological pathways from both a single-cell lipopolysaccharide mouse dataset with 24 381 cells and 11 076 genes and a Parkinson's disease human data set with 76 212 cells and 15 891 genes. Overall, MARBLES is a powerful tool to identify cell-type-specific DE genes across conditions from scRNA-seq data.
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Affiliation(s)
- Biqing Zhu
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06511, USA
| | - Hongyu Li
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, 06511, USA
| | - Le Zhang
- Department of Neurology, School of Medicine, Yale University, New Haven, CT, 06511, USA
| | - Sreeganga S Chandra
- Department of Neurology, School of Medicine, Yale University, New Haven, CT, 06511, USA,Department of Neuroscience, School of Medicine, Yale University, New Haven, CT, 06511, USA
| | - Hongyu Zhao
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06511, USA,Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, 06511, USA,Corresponding author. Hongyu Zhao, 300 George Street, Ste 503, New Haven, CT 06511. E-mail:
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