Single Cell RNA-seq Analysis Using Public Data
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Single Cell RNA-seq Analysis Using Public Data

Getting Started with Single Cell RNA-seq Analysis in R

About the Book

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.

About the Author

Table of Contents

    • Table of Contents
      • Preface
      • Chapter 1 Setting up the Environment for Single Cell RNA-seq Analysis
      • Chapter 2 Basics of R Programming Language
      • Chapter 3 Exploring Bioinformatics Analysis with R (Advanced)
      • Chapter 4 Learning the Fundamentals of Single Cell RNA-seq Analysis
      • Chapter 5 Hands-On! Analyzing Single Cell RNA-seq Data Using Public Datasets
      • Chapter 6 Processing Single Cell RNA-seq Data Downloaded from Public Databases
      • Chapter 7 Quality Control Methods for Single Cell RNA-seq Data
      • Afterword
    • 📘 Chapter 1: Setting Up the Environment for Single Cell RNA-seq Analysis
    • 📰 Setting up the Environment for Single Cell RNA-seq Analysis
      • About PC Specifications
    • 📰 Installation of R and RStudio
    • 📰 Installation of Docker
      • Installation of Docker
    • 📰 About R Package Manager Bioconductor
      • About R Package Manager Bioconductor
    • 📘 Chapter 2: Basics of R Programming
    • 📰 Basic Grammar of R Language
    • 📰 Handling DataFrames
    • 📰 How to Use Data Visualization Tool ggplot2
    • 📘 Chapter 3: Exploring Bioinformatics Analysis with R (Advanced)
    • 📰 PCA
      • What is Principal Component Analysis (PCA)?
      • Performing PCA on RNA-seq Data Using R
      • Executing the Code
    • 📰 Enrichment Analysis Using clusterProfiler
      • About Enrichment Analysis
      • Representation of Gene Names
    • 📰 Extracting DEGs from RNA-seq analysis data using DESeq2
      • What is DESeq2?
      • Running DESeq2
      • Checking DESeq2 Results
      • Reference
    • 📰 Enrichment analysis for Gene Ontology (GO) by BP, MF, and CC categories
      • Three Ontology Categories in the Gene Ontology (GO) System
      • Methods for Enrichment Analysis by BP, MF, and CC
      • Code Explanation
    • 📰 Enrichment Analysis Using Other Databases (KEGG, Reactome, etc.)
      • About the Databases Usable with clusterProfiler
      • KEGG
      • WikiPathways
      • Reactome
      • DOSE
    • 📘 Chapter 4: Learning the Fundamentals of Single Cell RNA-seq Analysis
    • 📰 Overview of Single Cell RNA-seq
      • What is Single Cell RNA-seq?
      • About scRNA-seq Databases
      • Choosing Between R and Python for scRNA-seq Analysis
    • 📰 Preparation for Analysis with Seurat
      • What is Seurat?
      • Installation and Environment Setup for Seurat
    • 📰 Steps for Data Preprocessing with Seurat
      • Creating SeuratObject and Importing Data
      • Quality Control (QC) and Filtering
      • Using Scatter Plots to Explore Correlations
      • Correlation Analysis Results
      • Data Normalization and Scaling
    • 📰 Data Analysis Procedure with Seurat
      • Extraction and Visualization of Variable Genes
      • Dimension Reduction and Cell Clustering
      • Determining the Dimensions of the Dataset
      • Dimension Reduction Using Nonlinear Methods
      • If you encounter errors even when following the commands…
    • 📘 Chapter 5: Let’s Practice! Single Cell RNA-seq Analysis Using Public Data
    • 📰 Finding Single Cell RNA-seq Data
    • Instructions for Analysis Preparation
    • 📰 Translating Japanese Markdown to English for International Publication
    • Translating Japanese Markdown to English for International Publication
    • 📰 Confirm gene expression with FeaturePlot
    • 📰 Merge Single Cell RNA-seq datasets
    • 📰 Correcting batch effects and comparing control group with stimulus group
    • 📰 Identification of marker genes for each cluster.
      • Preparation of scRNA-seq Data
      • Reference
    • 📘 Chapter 6: Processing scRNA-seq data downloaded from public databases
    • 📰 How to count gene expression levels of scRNA-seq data using Cell Ranger
      • What is Cell Ranger?
      • Implementation of Cell Ranger
      • Execute the mkfastq command with the following:
      • Reference
    • 📰 Processing NCBI’s Sequence Read Archive (SRA) Data with Cell Ranger
      • Aligning SRA Data File Names for Cell Ranger
      • Running Cell Ranger Count on SRA Data
    • 📘 Chapter 7: How to Perform Quality Control of Single Cell RNA-seq Data
    • 📰 QC for scRNA-seq Data
      • QC and Filtering Metrics
    • 📰 Detection of doublets by DoubletFinder
      • What is doublet detection?
      • Trying Out DoubletFinder
    • 📰 Background RNA removal by CellBender 📰
      • About HDF5 file format
      • Mechanism of Background RNA Removal by CellBender
      • Performing QC Using CellBender
      • Explanation and Notes
      • Points to Consider When Setting Parameters
    • Postscript

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