This tutorial provides a comprehensive guide to the current best practices in single-cell RNA-seq (scRNA-seq) analysis, covering both pre-processing and downstream analysis steps. The authors detail the steps from raw data processing to cell- and gene-level analysis, emphasizing the importance of quality control (QC), normalization, data correction, feature selection, and dimensionality reduction. They recommend specific tools and methods for each step, based on independent comparison studies, and integrate these recommendations into a workflow that is applied to a public dataset. The tutorial aims to serve as a practical guide for new researchers entering the field and help established users update their analysis pipelines. Key challenges in standardization, such as the growing number of analysis methods and dataset sizes, are discussed, along with technical aspects like programming language choices. The tutorial also highlights common pitfalls and provides recommendations for best practices, including the importance of joint consideration of QC covariates, the use of non-linear normalization methods, and the appropriate use of measured, corrected, and reduced data for different downstream analyses.This tutorial provides a comprehensive guide to the current best practices in single-cell RNA-seq (scRNA-seq) analysis, covering both pre-processing and downstream analysis steps. The authors detail the steps from raw data processing to cell- and gene-level analysis, emphasizing the importance of quality control (QC), normalization, data correction, feature selection, and dimensionality reduction. They recommend specific tools and methods for each step, based on independent comparison studies, and integrate these recommendations into a workflow that is applied to a public dataset. The tutorial aims to serve as a practical guide for new researchers entering the field and help established users update their analysis pipelines. Key challenges in standardization, such as the growing number of analysis methods and dataset sizes, are discussed, along with technical aspects like programming language choices. The tutorial also highlights common pitfalls and provides recommendations for best practices, including the importance of joint consideration of QC covariates, the use of non-linear normalization methods, and the appropriate use of measured, corrected, and reduced data for different downstream analyses.