1
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Ferrena A, Zheng XY, Jackson K, Hoang B, Morrow BE, Zheng D. scDAPP: a comprehensive single-cell transcriptomics analysis pipeline optimized for cross-group comparison. NAR Genom Bioinform 2024; 6:lqae134. [PMID: 39345754 PMCID: PMC11437360 DOI: 10.1093/nargab/lqae134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 07/07/2024] [Accepted: 09/18/2024] [Indexed: 10/01/2024] Open
Abstract
Single-cell transcriptomics profiling has increasingly been used to evaluate cross-group (or condition) differences in cell population and cell-type gene expression. This often leads to large datasets with complex experimental designs that need advanced comparative analysis. Concurrently, bioinformatics software and analytic approaches also become more diverse and constantly undergo improvement. Thus, there is an increased need for automated and standardized data processing and analysis pipelines, which should be efficient and flexible too. To address these, we develop the single-cell Differential Analysis and Processing Pipeline (scDAPP), a R-based workflow for comparative analysis of single cell (or nucleus) transcriptomic data between two or more groups and at the levels of single cells or 'pseudobulking' samples. The pipeline automates many steps of pre-processing using data-learnt parameters, uses previously benchmarked software, and generates comprehensive intermediate data and final results that are valuable for both beginners and experts of scRNA-seq analysis. Moreover, the analytic reports, augmented by extensive data visualization, increase the transparency of computational analysis and parameter choices, while facilitate users to go seamlessly from raw data to biological interpretation. scDAPP is freely available under the MIT license, with source code, documentation and sample data at the GitHub (https://github.com/bioinfoDZ/scDAPP).
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Affiliation(s)
- Alexander Ferrena
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
- Institute for Clinical and Translational Research, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Xiang Yu Zheng
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Kevyn Jackson
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Bang Hoang
- Department of Orthopedic Surgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Bernice E Morrow
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
- Departments of Obstetrics and Gynecology, and Pediatrics, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Deyou Zheng
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, USA
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2
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Yarlagadda S, Giorgio TD. A guide to single-cell RNA sequencing analysis using web-based tools for non-bioinformatician. FEBS J 2024; 291:2545-2561. [PMID: 38148322 DOI: 10.1111/febs.17036] [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: 10/12/2023] [Accepted: 12/14/2023] [Indexed: 12/28/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a technique that has proven to be a powerful tool for a wide range of fields and research studies. However, scRNA-seq data analysis has been dominated by scientists highly trained in bioinformatics or those with extensive computational experience and understanding. Recently, this trend has begun to shift as more user-friendly web-based scRNA-seq analysis tools have been developed that require little computational experience to use. However, barriers persist for nonbioinformaticians in using this technique. Complex, unfamiliar language and scarce comprehensive literature guidance to provide a framework for understanding scRNA-seq analysis outputs are among the obstacles. This work introduces many popular web-based tools for scRNA-seq and provides a general overview of their user interfaces and features. Then, a comprehensive start-to-finish introductory scRNA-seq analysis pipeline is described in detail, which aims to enable researchers to carry out scRNA-seq analysis, regardless of computational experience. Companion video tutorials can be found at "EasyScRNAseqTutorials" on YouTube (https://www.youtube.com/@scrnaseqtutorials). However, as scRNA-seq continues to penetrate new fields and expand in importance, there remains a need for more literature to help overcome barriers to its use by explaining further the highly complex and advanced analyses that are introduced within this paper.
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Affiliation(s)
| | - Todd D Giorgio
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
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3
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Wei W, Xia X, Li T, Chen Q, Feng X. Shaoxia: a web-based interactive analysis platform for single cell RNA sequencing data. BMC Genomics 2024; 25:402. [PMID: 38658838 PMCID: PMC11040744 DOI: 10.1186/s12864-024-10322-1] [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: 01/09/2024] [Accepted: 04/18/2024] [Indexed: 04/26/2024] Open
Abstract
BACKGROUND In recent years, Single-cell RNA sequencing (scRNA-seq) is increasingly accessible to researchers of many fields. However, interpreting its data demands proficiency in multiple programming languages and bioinformatic skills, which limited researchers, without such expertise, exploring information from scRNA-seq data. Therefore, there is a tremendous need to develop easy-to-use software, covering all the aspects of scRNA-seq data analysis. RESULTS We proposed a clear analysis framework for scRNA-seq data, which emphasized the fundamental and crucial roles of cell identity annotation, abstracting the analysis process into three stages: upstream analysis, cell annotation and downstream analysis. The framework can equip researchers with a comprehensive understanding of the analysis procedure and facilitate effective data interpretation. Leveraging the developed framework, we engineered Shaoxia, an analysis platform designed to democratize scRNA-seq analysis by accelerating processing through high-performance computing capabilities and offering a user-friendly interface accessible even to wet-lab researchers without programming expertise. CONCLUSION Shaoxia stands as a powerful and user-friendly open-source software for automated scRNA-seq analysis, offering comprehensive functionality for streamlined functional genomics studies. Shaoxia is freely accessible at http://www.shaoxia.cloud , and its source code is publicly available at https://github.com/WiedenWei/shaoxia .
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Affiliation(s)
- Weideng Wei
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Research Unit of Oral Carcinogenesis and Management & Chinese Academy of Medical Sciences, West China Hospital of Stomatology, Sichuan University, No. 14, 3rd Section of Ren Min Nan Rd., Chengdu, Sichuan, 610041, China
| | - Xiaoqiang Xia
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Research Unit of Oral Carcinogenesis and Management & Chinese Academy of Medical Sciences, West China Hospital of Stomatology, Sichuan University, No. 14, 3rd Section of Ren Min Nan Rd., Chengdu, Sichuan, 610041, China
| | - Taiwen Li
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Research Unit of Oral Carcinogenesis and Management & Chinese Academy of Medical Sciences, West China Hospital of Stomatology, Sichuan University, No. 14, 3rd Section of Ren Min Nan Rd., Chengdu, Sichuan, 610041, China
| | - Qianming Chen
- Key Laboratory of Oral Biomedical Research of Zhejiang Province, Affiliated Stomatology Hospital, Zhejiang University School of Stomatology, Hangzhou, Zhejiang, 310006, China
| | - Xiaodong Feng
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Research Unit of Oral Carcinogenesis and Management & Chinese Academy of Medical Sciences, West China Hospital of Stomatology, Sichuan University, No. 14, 3rd Section of Ren Min Nan Rd., Chengdu, Sichuan, 610041, China.
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4
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Islam MT, Liu Y, Hassan MM, Abraham PE, Merlet J, Townsend A, Jacobson D, Buell CR, Tuskan GA, Yang X. Advances in the Application of Single-Cell Transcriptomics in Plant Systems and Synthetic Biology. BIODESIGN RESEARCH 2024; 6:0029. [PMID: 38435807 PMCID: PMC10905259 DOI: 10.34133/bdr.0029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 01/28/2024] [Indexed: 03/05/2024] Open
Abstract
Plants are complex systems hierarchically organized and composed of various cell types. To understand the molecular underpinnings of complex plant systems, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for revealing high resolution of gene expression patterns at the cellular level and investigating the cell-type heterogeneity. Furthermore, scRNA-seq analysis of plant biosystems has great potential for generating new knowledge to inform plant biosystems design and synthetic biology, which aims to modify plants genetically/epigenetically through genome editing, engineering, or re-writing based on rational design for increasing crop yield and quality, promoting the bioeconomy and enhancing environmental sustainability. In particular, data from scRNA-seq studies can be utilized to facilitate the development of high-precision Build-Design-Test-Learn capabilities for maximizing the targeted performance of engineered plant biosystems while minimizing unintended side effects. To date, scRNA-seq has been demonstrated in a limited number of plant species, including model plants (e.g., Arabidopsis thaliana), agricultural crops (e.g., Oryza sativa), and bioenergy crops (e.g., Populus spp.). It is expected that future technical advancements will reduce the cost of scRNA-seq and consequently accelerate the application of this emerging technology in plants. In this review, we summarize current technical advancements in plant scRNA-seq, including sample preparation, sequencing, and data analysis, to provide guidance on how to choose the appropriate scRNA-seq methods for different types of plant samples. We then highlight various applications of scRNA-seq in both plant systems biology and plant synthetic biology research. Finally, we discuss the challenges and opportunities for the application of scRNA-seq in plants.
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Affiliation(s)
- Md Torikul Islam
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Yang Liu
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Md Mahmudul Hassan
- Department of Genetics and Plant Breeding,
Patuakhali Science and Technology University, Dumki, Patuakhali 8602, Bangladesh
| | - Paul E. Abraham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Jean Merlet
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research and Graduate Education,
University of Tennessee Knoxville, Knoxville, TN 37996, USA
| | - Alice Townsend
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- Bredesen Center for Interdisciplinary Research and Graduate Education,
University of Tennessee Knoxville, Knoxville, TN 37996, USA
| | - Daniel Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - C. Robin Buell
- Center for Applied Genetic Technologies,
University of Georgia, Athens, GA 30602, USA
- Department of Crop and Soil Sciences,
University of Georgia, Athens, GA 30602, USA
- Institute of Plant Breeding, Genetics, and Genomics,
University of Georgia, Athens, GA 30602, USA
| | - Gerald A. Tuskan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Xiaohan Yang
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
- The Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
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5
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Klamann C, Lau CJ, Ruiz-Ramírez J, Schwartz GW. TooManyCellsInteractive: A visualization tool for dynamic exploration of single-cell data. Gigascience 2024; 13:giae056. [PMID: 39172544 PMCID: PMC11340645 DOI: 10.1093/gigascience/giae056] [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: 12/13/2023] [Revised: 05/27/2024] [Accepted: 07/15/2024] [Indexed: 08/24/2024] Open
Abstract
BACKGROUND As single-cell sequencing technologies continue to advance, the growing volume and complexity of the ensuing data present new analytical challenges. Large cellular populations from single-cell atlases are more difficult to visualize and require extensive processing to identify biologically relevant subpopulations. Managing these workflows is also laborious for technical users and unintuitive for nontechnical users. RESULTS We present TooManyCellsInteractive (TMCI), a browser-based JavaScript application for interactive exploration of cell populations. TMCI provides an intuitive interface to visualize and manipulate a radial tree representation of hierarchical cell subpopulations and allows users to easily overlay, filter, and compare biological features at multiple resolutions. Here we describe the software architecture and demonstrate how we used TMCI in a pan-cancer analysis to identify unique survival pathways among drug-tolerant persister cells. CONCLUSIONS TMCI will facilitate exploration and visualization of large-scale sequencing data in a user-friendly way. TMCI is freely available at https://github.com/schwartzlab-methods/too-many-cells-interactive. An example tree from data within this article is available at https://tmci.schwartzlab.ca/.
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Affiliation(s)
- Conor Klamann
- Data Sciences Institute, University of Toronto, Toronto, ON M5G 1Z5, Canada
| | - Christie J Lau
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Javier Ruiz-Ramírez
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Gregory W Schwartz
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
- Vector Institute, Toronto, ON M5G 1M1, Canada
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6
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Nouri N, Kurlovs AH, Gaglia G, de Rinaldis E, Savova V. Scaling up single-cell RNA-seq data analysis with CellBridge workflow. Bioinformatics 2023; 39:btad760. [PMID: 38113416 PMCID: PMC10751228 DOI: 10.1093/bioinformatics/btad760] [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: 07/31/2023] [Revised: 12/04/2023] [Accepted: 12/18/2023] [Indexed: 12/21/2023] Open
Abstract
SUMMARY Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of gene expression at the individual cell level, unraveling unprecedented insights into cellular heterogeneity. However, the analysis of scRNA-seq data remains a challenging and time-consuming task, often demanding advanced computational expertise, rendering it impractical for high-volume environments and applications. We present CellBridge, an automated workflow designed to simplify the standard procedures entailed in scRNA-seq data analysis, eliminating the need for specialized computational expertise. CellBridge utilizes state-of-the-art computational methods, integrating a range of advanced functionalities, covering the entire process from raw unaligned sequencing reads to cell type annotation. Hence, CellBridge accelerates the pace of discovery by seamlessly enabling insights into vast volumes of scRNA-seq data, without compromising workflow control and reproducibility. AVAILABILITY AND IMPLEMENTATION The source code, detailed documentation, and materials required to reproduce the results are available on GitHub and archived in Zenodo. For the CellBridge pre-processing step (v1.0.0), access the GitHub repository at https://github.com/Sanofi-Public/PMCB-ToBridge and the Zenodo archive at https://zenodo.org/records/10246161. For the CellBridge processing step (v1.0.0), visit the GitHub repository at https://github.com/Sanofi-Public/PMCB-CellBridge and the Zenodo archive at https://zenodo.org/records/10246046.
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Affiliation(s)
- Nima Nouri
- Precision Medicine and Computational Biology, Sanofi, Cambridge, MA 02141, United States
| | - Andre H Kurlovs
- Precision Medicine and Computational Biology, Sanofi, Cambridge, MA 02141, United States
| | - Giorgio Gaglia
- Precision Medicine and Computational Biology, Sanofi, Cambridge, MA 02141, United States
| | - Emanuele de Rinaldis
- Precision Medicine and Computational Biology, Sanofi, Cambridge, MA 02141, United States
| | - Virginia Savova
- Precision Medicine and Computational Biology, Sanofi, Cambridge, MA 02141, United States
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7
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Pan L, Mou T, Huang Y, Hong W, Yu M, Li X. Ursa: A Comprehensive Multiomics Toolbox for High-Throughput Single-Cell Analysis. Mol Biol Evol 2023; 40:msad267. [PMID: 38091963 PMCID: PMC10752348 DOI: 10.1093/molbev/msad267] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 09/08/2023] [Accepted: 11/03/2023] [Indexed: 12/28/2023] Open
Abstract
The burgeoning amount of single-cell data has been accompanied by revolutionary changes to computational methods to map, quantify, and analyze the outputs of these cutting-edge technologies. Many are still unable to reap the benefits of these advancements due to the lack of bioinformatics expertise. To address this issue, we present Ursa, an automated single-cell multiomics R package containing 6 automated single-cell omics and spatial transcriptomics workflows. Ursa allows scientists to carry out post-quantification single or multiomics analyses in genomics, transcriptomics, epigenetics, proteomics, and immunomics at the single-cell level. It serves as a 1-stop analytic solution by providing users with outcomes to quality control assessments, multidimensional analyses such as dimension reduction and clustering, and extended analyses such as pseudotime trajectory and gene-set enrichment analyses. Ursa aims bridge the gap between those with bioinformatics expertise and those without by providing an easy-to-use bioinformatics package for scientists in hoping to accelerate their research potential. Ursa is freely available at https://github.com/singlecellomics/ursa.
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Affiliation(s)
- Lu Pan
- Institute of Environmental Medicine, Karolinska Institutet, Solna 171 65, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna 171 65, Sweden
| | - Tian Mou
- School of Biomedical Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
| | - Yue Huang
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna 171 65, Sweden
| | - Weifeng Hong
- Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Min Yu
- Department of General Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong 510515, China
| | - Xuexin Li
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Solna 171 65, Sweden
- Department of General Surgery, The Fourth Affiliated Hospital, China Medical University, Shenyang 110032, China
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8
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Tzaferis C, Karatzas E, Baltoumas FA, Pavlopoulos GA, Kollias G, Konstantopoulos D. SCALA: A complete solution for multimodal analysis of single-cell Next Generation Sequencing data. Comput Struct Biotechnol J 2023; 21:5382-5393. [PMID: 38022693 PMCID: PMC10651449 DOI: 10.1016/j.csbj.2023.10.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 10/16/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Analysis and interpretation of high-throughput transcriptional and chromatin accessibility data at single-cell (sc) resolution are still open challenges in the biomedical field. The existence of countless bioinformatics tools, for the different analytical steps, increases the complexity of data interpretation and the difficulty to derive biological insights. In this article, we present SCALA, a bioinformatics tool for analysis and visualization of single-cell RNA sequencing (scRNA-seq) and Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) datasets, enabling either independent or integrative analysis of the two modalities. SCALA combines standard types of analysis by integrating multiple software packages varying from quality control to the identification of distinct cell populations and cell states. Additional analysis options enable functional enrichment, cellular trajectory inference, ligand-receptor analysis, and regulatory network reconstruction. SCALA is fully parameterizable, presenting data in tabular format and producing publication-ready visualizations. The different available analysis modules can aid biomedical researchers in exploring, analyzing, and visualizing their data without any prior experience in coding. We demonstrate the functionality of SCALA through two use-cases related to TNF-driven arthritic mice, handling both scRNA-seq and scATAC-seq datasets. SCALA is developed in R, Shiny and JavaScript and is mainly available as a standalone version, while an online service of more limited capacity can be found at http://scala.pavlopouloslab.info or https://scala.fleming.gr.
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Affiliation(s)
- Christos Tzaferis
- Institute for Bioinnovation, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece
| | - Fotis A. Baltoumas
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece
- Research Institute of New Biotechnologies and Precision Medicine, National and Kapodistrian University of Athens, Greece
| | - George Kollias
- Institute for Bioinnovation, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece
- Research Institute of New Biotechnologies and Precision Medicine, National and Kapodistrian University of Athens, Greece
- Department of Physiology, Medical School, National and Kapodistrian University of Athens, Greece
| | - Dimitris Konstantopoulos
- Institute for Bioinnovation, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece
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9
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Landais Y, Vallot C. Multi-modal quantification of pathway activity with MAYA. Nat Commun 2023; 14:1668. [PMID: 36966153 PMCID: PMC10039856 DOI: 10.1038/s41467-023-37410-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 03/16/2023] [Indexed: 03/27/2023] Open
Abstract
Signaling pathways can be activated through various cascades of genes depending on cell identity and biological context. Single-cell atlases now provide the opportunity to inspect such complexity in health and disease. Yet, existing reference tools for pathway scoring resume activity of each pathway to one unique common metric across cell types. Here, we present MAYA, a computational method that enables the automatic detection and scoring of the diverse modes of activation of biological pathways across cell populations. MAYA improves the granularity of pathway analysis by detecting subgroups of genes within reference pathways, each characteristic of a cell population and how it activates a pathway. Using multiple single-cell datasets, we demonstrate the biological relevance of identified modes of activation, the robustness of MAYA to noisy pathway lists and batch effect. MAYA can also predict cell types starting from lists of reference markers in a cluster-free manner. Finally, we show that MAYA reveals common modes of pathway activation in tumor cells across patients, opening the perspective to discover shared therapeutic vulnerabilities.
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Affiliation(s)
| | - Céline Vallot
- CNRS UMR3244, Institut Curie, PSL University, Paris, France.
- Translational Research Department, Institut Curie, PSL University, Paris, France.
- Single Cell Initiative, Institut Curie, PSL University, Paris, France.
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10
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Umu SU, Rapp Vander-Elst K, Karlsen VT, Chouliara M, Bækkevold ES, Jahnsen FL, Domanska D. Cellsnake: a user-friendly tool for single-cell RNA sequencing analysis. Gigascience 2022; 12:giad091. [PMID: 37889009 PMCID: PMC10603768 DOI: 10.1093/gigascience/giad091] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 08/25/2023] [Accepted: 10/05/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptome data to understand the heterogeneity of cell populations at the single-cell level. The analysis of scRNA-seq data requires the utilization of numerous computational tools. However, nonexpert users usually experience installation issues, a lack of critical functionality or batch analysis modes, and the steep learning curves of existing pipelines. RESULTS We have developed cellsnake, a comprehensive, reproducible, and accessible single-cell data analysis workflow, to overcome these problems. Cellsnake offers advanced features for standard users and facilitates downstream analyses in both R and Python environments. It is also designed for easy integration into existing workflows, allowing for rapid analyses of multiple samples. CONCLUSION As an open-source tool, cellsnake is accessible through Bioconda, PyPi, Docker, and GitHub, making it a cost-effective and user-friendly option for researchers. By using cellsnake, researchers can streamline the analysis of scRNA-seq data and gain insights into the complex biology of single cells.
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Affiliation(s)
- Sinan U Umu
- Department of Pathology, Institute of Clinical Medicine, University of Oslo, Oslo 0372, Norway
| | | | - Victoria T Karlsen
- Department of Pathology, Oslo University Hospital-Rikshospitalet, Oslo 0372, Norway
| | - Manto Chouliara
- Department of Pathology, Oslo University Hospital-Rikshospitalet, Oslo 0372, Norway
| | - Espen Sønderaal Bækkevold
- Department of Pathology, Oslo University Hospital-Rikshospitalet, Oslo 0372, Norway
- Institute of Oral Biology, University of Oslo, Oslo 0372, Norway
| | - Frode Lars Jahnsen
- Department of Pathology, Institute of Clinical Medicine, University of Oslo, Oslo 0372, Norway
- Department of Pathology, Oslo University Hospital-Rikshospitalet, Oslo 0372, Norway
| | - Diana Domanska
- Department of Pathology, Oslo University Hospital-Rikshospitalet, Oslo 0372, Norway
- Department of Microbiology, University of Oslo, Rikshospitalet, Oslo 0372, Norway
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11
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Mikolajewicz N, Gacesa R, Aguilera-Uribe M, Brown KR, Moffat J, Han H. Multi-level cellular and functional annotation of single-cell transcriptomes using scPipeline. Commun Biol 2022; 5:1142. [PMID: 36307536 PMCID: PMC9616830 DOI: 10.1038/s42003-022-04093-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 10/11/2022] [Indexed: 11/08/2022] Open
Abstract
Single-cell RNA-sequencing (scRNA-seq) offers functional insight into complex biology, allowing for the interrogation of cellular populations and gene expression programs at single-cell resolution. Here, we introduce scPipeline, a single-cell data analysis toolbox that builds on existing methods and offers modular workflows for multi-level cellular annotation and user-friendly analysis reports. Advances to scRNA-seq annotation include: (i) co-dependency index (CDI)-based differential expression, (ii) cluster resolution optimization using a marker-specificity criterion, (iii) marker-based cell-type annotation with Miko scoring, and (iv) gene program discovery using scale-free shared nearest neighbor network (SSN) analysis. Both unsupervised and supervised procedures were validated using a diverse collection of scRNA-seq datasets and illustrative examples of cellular transcriptomic annotation of developmental and immunological scRNA-seq atlases are provided herein. Overall, scPipeline offers a flexible computational framework for in-depth scRNA-seq analysis.
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Affiliation(s)
- Nicholas Mikolajewicz
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Rafael Gacesa
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
| | - Magali Aguilera-Uribe
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Kevin R Brown
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Jason Moffat
- Donnelly Centre, University of Toronto, Toronto, ON, Canada.
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
- Institute for Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
| | - Hong Han
- Donnelly Centre, University of Toronto, Toronto, ON, Canada.
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada.
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