Abstract
FEBS Journal 2024, 291, 2545-2561
A guide to single-cell RNA sequencing analysis using web-based tools for non-bioinformatician
Yarlagadda S, Giorgio TD
Single-cell RNA sequencing (scRNA-seq) is a technique that has proven to be a powerful tool for a wide range of fields and research studies. However, scRNA-seq data analysis has been dominated by scientists highly trained in bioinformatics or those with extensive computational experience and understanding. Recently, this trend has begun to shift as more user-friendly web-based scRNA-seq analysis tools have been developed that require little computational experience to use. However, barriers persist for nonbioinformaticians in using this technique. Complex, unfamiliar language and scarce comprehensive literature guidance to provide a framework for understanding scRNA-seq analysis outputs are among the obstacles. This work introduces many popular web-based tools for scRNA-seq and provides a general overview of their user interfaces and features. Then, a comprehensive start-to-finish introductory scRNA-seq analysis pipeline is described in detail, which aims to enable researchers to carry out scRNA-seq analysis, regardless of computational experience. Companion video tutorials can be found at "EasyScRNAseqTutorials" on YouTube (). However, as scRNA-seq continues to penetrate new fields and expand in importance, there remains a need for more literature to help overcome barriers to its use by explaining further the highly complex and advanced analyses that are introduced within this paper.
Online tools have greatly increased accessibility to and simplified conducting single-cell RNA sequencing analysis (scRNA-seq), even for nonbioinformaticians. This paper lays a foundation for anyone to easily conduct scRNA-seq analysis using these online tools. Guided analysis and examples are provided involving automated quality control to improve data quality, creating visualizations for visual analysis, identifying cell types by cluster analysis, and gene set enrichment analysis to identify differential gene expression.