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Practical bioinformatics pipelines for single-cell RNA-seq data analysis

Single-cell RNA sequencing (scRNA-seq) is a revolutionary tool to explore cells. With an increasing number of scRNA-seq data analysis tools that have been developed, it is challenging for users to choose and compare their performance. Here, we present an overview of the workflow for computational an...

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Detalles Bibliográficos
Autores principales: He, Jiangping, Lin, Lihui, Chen, Jiekai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Biophysics Reports Editorial Office 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10189648/
https://www.ncbi.nlm.nih.gov/pubmed/37288243
http://dx.doi.org/10.52601/bpr.2022.210041
Descripción
Sumario:Single-cell RNA sequencing (scRNA-seq) is a revolutionary tool to explore cells. With an increasing number of scRNA-seq data analysis tools that have been developed, it is challenging for users to choose and compare their performance. Here, we present an overview of the workflow for computational analysis of scRNA-seq data. We detail the steps of a typical scRNA-seq analysis, including experimental design, pre-processing and quality control, feature selection, dimensionality reduction, cell clustering and annotation, and downstream analysis including batch correction, trajectory inference and cell–cell communication. We provide guidelines according to our best practice. This review will be helpful for the experimentalists interested in analyzing their data, and will aid the users seeking to update their analysis pipelines.