The Workflow for Computational Analysis of Single-cell RNA-sequencing Data

• Published in: Korean Journal of Clinical Laboratory Science 2024, 56(1), , pp.10-20
• Main Authors: WOO, Sung-Hun, JUNG, Byung Chul
• Format: Article
• Language: English
• Subjects: big data; computational biology; single-cell gene expression analysis; 기타자연과학
• ISSN: 1738-3544; 2288-1662
• DOI: 10.15324/kjcls.2024.56.1.10

RNA-sequencing (RNA-seq) is a technique used for providing global patterns of transcriptomes in samples. However, it can only provide the average gene expression across cells and does not address the heterogeneity within the samples. The advances in single-cell RNA sequencing (scRNA-seq) technology have revolutionized our understanding of heterogeneity and the dynamics of gene expression at the single-cell level. For example, scRNA-seq allows us to identify the cell types in complex tissues, which can provide information regarding the alteration of the cell population by perturbations, such as genetic modification. Since its initial introduction, scRNA-seq has rapidly become popular, leading to the development of a huge number of bioinformatic tools. However, the analysis of the big dataset generated from scRNA-seq requires a general understanding of the preprocessing of the dataset and a variety of analytical techniques. Here, we present an overview of the workflow involved in analyzing the scRNA-seq dataset. First, we describe the preprocessing of the dataset, including quality control, normalization, and dimensionality reduction. Then, we introduce the downstream analysis provided with the most commonly used computational packages. This review aims to provide a workflow guideline for new researchers interested in this field.