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Biswas B, Kumar N, Sugimoto M, Hoque MA. scHD4E: Novel ensemble learning-based differential expression analysis method for single-cell RNA-sequencing data. Comput Biol Med 2024; 178:108769. [PMID: 38897145 DOI: 10.1016/j.compbiomed.2024.108769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/14/2024] [Accepted: 06/15/2024] [Indexed: 06/21/2024]
Abstract
Differential expression (DE) analysis between cell types for scRNA-seq data by capturing its complicated features is crucial. Recently, different methods have been developed for targeting the scRNA-seq data analysis based on different modeling frameworks, assumptions, strategies and test statistic in considering various data features. The scDEA is an ensemble learning-based DE analysis method developed recently, yielding p-values using Lancaster's combination, generated by 12 individual DE analysis methods, and producing more accurate and stable results than individual methods. The objective of our study is to propose a new ensemble learning-based DE analysis method, scHD4E, using top performers in only 4 separate methods. The top performer 4 methods have been selected through an evaluation process using six real scRNA-seq data sets. We conducted comprehensive experiments for five experimental data sets to evaluate our proposed method based on the sample size effects, batch effects, type I error control, gene ontology enrichment analysis, runtime, identified matched DE genes, and semantic similarity measurement between methods. We also perform similar analyses (except the last 3 terms) and compute performance measures like accuracy, F1 score, Mathew's correlation coefficient etc. for a simulated data set. The results show that scHD4E is performs better than all the individual and scDEA methods in all the above perspectives. We expect that scHD4E will serve the modern data scientists for detecting the DEGs in scRNA-seq data analysis. To implement our proposed method, a Github R package scHD4E and its shiny application has been developed, and available in the following links: https://github.com/bbiswas1989/scHD4E and https://github.com/bbiswas1989/scHD4E-Shiny.
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Affiliation(s)
- Biplab Biswas
- Department of Statistics, Faculty of Science, Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Gopalganj, 8100, Bangladesh; Department of Statistics, Faculty of Science, University of Rajshahi, Rajshahi, 6205, Bangladesh.
| | - Nishith Kumar
- Department of Statistics, Faculty of Science, Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Gopalganj, 8100, Bangladesh.
| | - Masahiro Sugimoto
- Institute for Advanced Biosciences, Keio University 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata, 997-0052, Japan.
| | - Md Aminul Hoque
- Department of Statistics, Faculty of Science, University of Rajshahi, Rajshahi, 6205, Bangladesh.
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Chen H, Zuo H, Huang J, Liu J, Jiang L, Jiang C, Zhang S, Hu Q, Lai H, Yin B, Yang G, Mai G, Li B, Chi H. Unravelling infiltrating T-cell heterogeneity in kidney renal clear cell carcinoma: Integrative single-cell and spatial transcriptomic profiling. J Cell Mol Med 2024; 28:e18403. [PMID: 39031800 PMCID: PMC11190954 DOI: 10.1111/jcmm.18403] [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: 03/26/2024] [Revised: 05/02/2024] [Accepted: 05/07/2024] [Indexed: 07/15/2024] Open
Abstract
Kidney renal clear cell carcinoma (KIRC) pathogenesis intricately involves immune system dynamics, particularly the role of T cells within the tumour microenvironment. Through a multifaceted approach encompassing single-cell RNA sequencing, spatial transcriptome analysis and bulk transcriptome profiling, we systematically explored the contribution of infiltrating T cells to KIRC heterogeneity. Employing high-density weighted gene co-expression network analysis (hdWGCNA), module scoring and machine learning, we identified a distinct signature of infiltrating T cell-associated genes (ITSGs). Spatial transcriptomic data were analysed using robust cell type decomposition (RCTD) to uncover spatial interactions. Further analyses included enrichment assessments, immune infiltration evaluations and drug susceptibility predictions. Experimental validation involved PCR experiments, CCK-8 assays, plate cloning assays, wound-healing assays and Transwell assays. Six subpopulations of infiltrating and proliferating T cells were identified in KIRC, with notable dynamics observed in mid- to late-stage disease progression. Spatial analysis revealed significant correlations between T cells and epithelial cells across varying distances within the tumour microenvironment. The ITSG-based prognostic model demonstrated robust predictive capabilities, implicating these genes in immune modulation and metabolic pathways and offering prognostic insights into drug sensitivity for 12 KIRC treatment agents. Experimental validation underscored the functional relevance of PPIB in KIRC cell proliferation, invasion and migration. Our study comprehensively characterizes infiltrating T-cell heterogeneity in KIRC using single-cell RNA sequencing and spatial transcriptome data. The stable prognostic model based on ITSGs unveils infiltrating T cells' prognostic potential, shedding light on the immune microenvironment and offering avenues for personalized treatment and immunotherapy.
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Affiliation(s)
- Haiqing Chen
- Department of General Surgery (Hepatopancreatobiliary Surgery), The Affiliated HospitalSouthwest Medical UniversityLuzhouChina
- School of Clinical Medicine, The Affiliated HospitalSouthwest Medical UniversityLuzhouChina
| | - Haoyuan Zuo
- Department of General Surgery (Hepatopancreatobiliary Surgery), The Affiliated HospitalSouthwest Medical UniversityLuzhouChina
- Department of General Surgery (Hepatopancreatobiliary Surgery)Deyang People's HospitalDeyangChina
| | - Jinbang Huang
- School of Clinical Medicine, The Affiliated HospitalSouthwest Medical UniversityLuzhouChina
| | - Jie Liu
- Department of General Surgery (Hepatopancreatobiliary Surgery), The Affiliated HospitalSouthwest Medical UniversityLuzhouChina
- Department of General SurgeryDazhou Central HospitalDazhouChina
| | - Lai Jiang
- School of Clinical Medicine, The Affiliated HospitalSouthwest Medical UniversityLuzhouChina
| | - Chenglu Jiang
- School of Clinical Medicine, The Affiliated HospitalSouthwest Medical UniversityLuzhouChina
| | - Shengke Zhang
- School of Clinical Medicine, The Affiliated HospitalSouthwest Medical UniversityLuzhouChina
| | - Qingwen Hu
- School of Clinical Medicine, The Affiliated HospitalSouthwest Medical UniversityLuzhouChina
| | - Haotian Lai
- School of Clinical Medicine, The Affiliated HospitalSouthwest Medical UniversityLuzhouChina
| | - Bangchao Yin
- Department of PathologySixth People's Hospital of YibinYibinChina
| | - Guanhu Yang
- Department of Specialty MedicineOhio UniversityAthensOhioUSA
| | - Gang Mai
- Department of General Surgery (Hepatopancreatobiliary Surgery), The Affiliated HospitalSouthwest Medical UniversityLuzhouChina
- Department of General Surgery (Hepatopancreatobiliary Surgery)Deyang People's HospitalDeyangChina
| | - Bo Li
- Department of General Surgery (Hepatopancreatobiliary Surgery), The Affiliated HospitalSouthwest Medical UniversityLuzhouChina
| | - Hao Chi
- School of Clinical Medicine, The Affiliated HospitalSouthwest Medical UniversityLuzhouChina
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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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Patil AR, Kumar G, Zhou H, Warren L. scViewer: An Interactive Single-Cell Gene Expression Visualization Tool. Cells 2023; 12:1489. [PMID: 37296611 PMCID: PMC10253102 DOI: 10.3390/cells12111489] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 05/09/2023] [Accepted: 05/25/2023] [Indexed: 06/12/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) is an attractive technology for researchers to gain valuable insights into the cellular processes and cell type diversity present in all tissues. The data generated by the scRNA-seq experiment are high-dimensional and complex in nature. Several tools are now available to analyze the raw scRNA-seq data from public databases; however, simple and easy-to-explore single-cell gene expression visualization tools focusing on differential expression and co-expression are lacking. Here, we present scViewer, an interactive graphical user interface (GUI) R/Shiny application designed to facilitate the visualization of scRNA-seq gene expression data. With the processed Seurat RDS object as input, scViewer utilizes several statistical approaches to provide detailed information on the loaded scRNA-seq experiment and generates publication-ready plots. The major functionalities of scViewer include exploring cell-type-specific gene expression, co-expression analysis of two genes, and differential expression analysis with different biological conditions considering both cell-level and subject-level variations using negative binomial mixed modeling. We utilized a publicly available dataset (brain cells from a study of Alzheimer's disease to demonstrate the utility of our tool. scViewer can be downloaded from GitHub as a Shiny app with local installation. Overall, scViewer is a user-friendly application that will allow researchers to visualize and interpret the scRNA-seq data efficiently for multi-condition comparison by performing gene-level differential expression and co-expression analysis on the fly. Considering the functionalities of this Shiny app, scViewer can be a great resource for collaboration between bioinformaticians and wet lab scientists for faster data visualizations.
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Affiliation(s)
- Abhijeet R. Patil
- Global Statistical and Data Sciences, Teva Pharmaceuticals, West Chester, PA 19380, USA
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Das S, Rai A, Rai SN. Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges. ENTROPY 2022; 24:e24070995. [PMID: 35885218 PMCID: PMC9315519 DOI: 10.3390/e24070995] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 06/25/2022] [Accepted: 07/09/2022] [Indexed: 01/11/2023]
Abstract
With the advent of single-cell RNA-sequencing (scRNA-seq), it is possible to measure the expression dynamics of genes at the single-cell level. Through scRNA-seq, a huge amount of expression data for several thousand(s) of genes over million(s) of cells are generated in a single experiment. Differential expression analysis is the primary downstream analysis of such data to identify gene markers for cell type detection and also provide inputs to other secondary analyses. Many statistical approaches for differential expression analysis have been reported in the literature. Therefore, we critically discuss the underlying statistical principles of the approaches and distinctly divide them into six major classes, i.e., generalized linear, generalized additive, Hurdle, mixture models, two-class parametric, and non-parametric approaches. We also succinctly discuss the limitations that are specific to each class of approaches, and how they are addressed by other subsequent classes of approach. A number of challenges are identified in this study that must be addressed to develop the next class of innovative approaches. Furthermore, we also emphasize the methodological challenges involved in differential expression analysis of scRNA-seq data that researchers must address to draw maximum benefit from this recent single-cell technology. This study will serve as a guide to genome researchers and experimental biologists to objectively select options for their analysis.
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Affiliation(s)
- Samarendra Das
- ICAR-Directorate of Foot and Mouth Disease, Arugul, Bhubaneswar 752050, India
- International Centre for Foot and Mouth Disease, Arugul, Bhubaneswar 752050, India
- Correspondence: or (S.D.); (S.N.R.)
| | - Anil Rai
- ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India;
| | - Shesh N. Rai
- School of Interdisciplinary and Graduate Studies, University of Louisville, Louisville, KY 40292, USA
- Biostatistics and Bioinformatics Facility, Brown Cancer Center, University of Louisville, Louisville, KY 40202, USA
- Biostatisitcs and Informatics Facility, Center for Integrative Environmental Health Sciences, University of Louisville, Louisville, KY 40202, USA
- Data Analysis and Sample Management Facility, The University of Louisville Super Fund Center, University of Louisville, Louisville, KY 40202, USA
- Hepatobiology and Toxicology Center, University of Louisville, Louisville, KY 40202, USA
- Christina Lee Brown Envirome Institute, University of Louisville, Louisville, KY 40202, USA
- Correspondence: or (S.D.); (S.N.R.)
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