This course provides a comprehensive end-to-end analysis of single cell RNA sequencing (scRNA-seq) data, tailored to guide beginners without prior programming or Linux knowledge. The course emphasizes the use of command-line tools and R packages such as Seurat and scType, and focuses on the fundamental differences between scRNA-seq and bulk RNA-seq, highlighting the necessity for scRNA-seq in investigating cellular and tumor heterogeneity. The course provides a thorough overview of all available scRNA technologies, with particular emphasis on 10x genomics and Smart-seq2, and offers both theoretical and hands-on training for every step in the data analysis pipeline.
Students are instructed on how to process raw scRNA datasets, including UMI, cell barcode filtering, alignment against reference genome, and deduplication and quantification to generate a count matrix. They also learn how to perform quality control on the data, remove unwanted dead cells, normalize and scale the data, perform data imputation, and perform dimension reduction and visualization through UMAP, tSNE, and PCA plots. The course covers cell clustering and subpopulation identification, including cell annotation, and also provides instruction on cell lineage and trajectory analysis. Finally, students learn how to perform differential gene expression analysis, identifying marker genes expressed only in their chosen subpopulations of cells. Overall, this course provides a comprehensive and practical introduction to scRNA-seq analysis for beginners, enabling them to gain the necessary skills to conduct their own analyses.
What Will You Learn?
- In-depth Introduction to scRNA-seq
- Complete end-to-end scRNA-seq analysis
- Cellular and Tumor Heterogeneity
- Bulk vs. Single Cell RNA-seq Analysis
- Single-Cell RNA-seq Technologies
- 10x Genomics Complete Pipeline
- From Raw Datasets to Cell Sub-populations
- Normalization, Quality Control and Dimension Reduction
- Cell Clustering and Cell Annotation
- Differential Gene Expression Analysis
- Downstream Analysis
- Seurat Package Complete Guidelines
In-depth Introduction to Single Cell RNA-Sequencing, Pipeline and Single-Cell Technologies
Introduction to Single-Cell RNA-seq, Its Pipeline and Analysis23:34
Gene Expression and Its Significance21:42
Cellular and Tumor Heterogeneity09:10
Bulk RNA-sequencing vs. Single-Cell RNA-sequencing21:50
Single-Cell RNA-seq Technologies (10x Genomics, Smart-Seq, Drop-seq and more)23:09
Cell Isolation and Cell Lysis Protocols for Single-Cell Genomics09:53
In-depth Introduction to Single-Cell RNA-sequencing Analysis Pipeline24:45
Droplet Technologies: 10x Genomics14:10
Full-Length Transcript Technologies: Smart-Seq217:55
Hands-on Single-Cell RNA-seq Data Analysis (From Raw Reads to Cell Subpopulation Identification and DEGs Identification)
Additional Supplementary Lectures
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