{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Su M"],"funding":["Marshal Initiative Funding of Hainan Medical University","Bioinformatics for Major Diseases Science Innovation Group of Hainan Medical University","Hainan Province Science and Technology Special Fund","Key Technologies Research and Development Program","Shenzhen Science and Technology Innovation Program","Hainan Province Clinical Medical Center","Start Fund for High-level Talents of Nanjing Medical University","Hainan Provincial Natural Science Foundation of China","National Natural Science Foundation of China","Start Fund for Specially Appointed Professor of Jiangsu Province"],"pagination":["68"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9716519"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["9(1)"],"pubmed_abstract":["The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications."],"journal":["Military Medical Research"],"pubmed_title":["Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications."],"pmcid":["PMC9716519"],"funding_grant_id":["JCYJ20210324140407021","JBGS202103","32170742","QWYH202175","ZDYF2021SHFZ051","820MS053","NMUR2020009","32060152","31970646","2022YFC2702500"],"pubmed_authors":["Chen QZ","Fan SC","Li YS","Yan HY","Pan T","Jiang CJ","Cairns MJ","Su M","Gong Y","Xu G","Li X","Li S","Zhou WW","Zhang Y","He X","Shi QZ","Wang X"],"additional_accession":[]},"is_claimable":false,"name":"Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications.","description":"The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Dec","modification":"2024-11-19T21:05:11.015Z","creation":"2024-11-19T21:05:11.015Z"},"accession":"S-EPMC9716519","cross_references":{"pubmed":["36461064"],"doi":["10.1186/s40779-022-00434-8"]}}