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You may have an interest in scRNA-seq analysis but not know where to start... Some may even have outsourced the analysis altogether. ScRNA-seq analysis is an innovative technology that analyzes gene expression at the individual cell level, and it is believed that advanced knowledge of bioinformatics is required for this type of analysis. In this book, based on the R language, you can learn how to analyze public database-registered scRNA-seq data using the Seurat library.

Learning scRNA-seq analysis based on fragmented and inconsistent internet articles can be extremely challenging. I have years of experience conducting research in the laboratory, and I understand that studying programming while busy with wet lab analysis can be quite difficult. Therefore, I felt the need to clearly summarize the process of learning scRNA-seq analysis in a technical book format for efficient learning. By incorporating scRNA-seq analysis using public data into your own research, you can enhance your papers by combining them with experiments in your wet lab.

This book is particularly useful for the following types of readers:

Those who are interested in Single Cell RNA-seq but don't know where to start
Individuals who have outsourced the data analysis of Single Cell RNA-seq to specialized external organizations
People who are busy with general wet lab experiments and do not have time to learn programming or statistics
Researchers who want to incorporate Single Cell RNA-seq into their own studies
Working professionals who want to write papers based on independent research or students who want to enhance their achievements in graduate school

No prior knowledge of programming or statistics is required. This book has been carefully crafted to help readers without programming experience get started, covering PC setup methods, how to use R language, and installation of Seurat.

By utilizing this book, you will be able to take the first step towards incorporating scRNA-seq analysis into your research. Additionally, you will gain knowledge on creating new figures and tables for your papers when incorporating analysis results using the ggplot2 library.

Furthermore, this book can also be used as a guide for independent researchers to carry out analysis on their home PCs. If the results are used to write papers and provide support for those aiming to obtain a doctoral degree or advance to doctoral studies as working professionals, the author would be honored.

It is hoped that this book will serve as a help in turning aspirations and interests in scRNA-seq into concrete actions, and be a useful introductory guide for acquiring analytical skills in this field.

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