2022 Vol. 8, No. 3

Cover Story

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, the authors present an overview of the workflow for computational analysis of scRNA-seq data. They detail the steps of a typi-cal 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, trajec-tory inference and cell–cell communication. The authors provide guidelines according to their best practice. This review will be helpful for the experi-mentalists interested in analyzing their data, and will aid the users seeking to update their analysis pipelines.

Spatial transcriptomics: new dimension of understanding biological complexity
Single-cell multi-omics sequencing and its applications in studying the nervous system
Measuring the size and growth of single cells
Practical bioinformatics pipelines for single-cell RNA-seq data analysis
Brain structure and structural basis of neurodegenerative diseases