This lesson is still being designed and assembled (Pre-Alpha version)

Single-cell RNA-Seq Analysis

This 2-days lesson will discuss the computational analysis of the data obtained from scRNA-seq experiments.


The course is intended for those who have basic familiarity with Unix and the R scripting language. Please follow the instructions in Setup page.


Setup Download files required for the lesson
00:00 1. Introduction to scRNA-seq What is the advantages of RNA-seq?
What is the difference between bulk RNA-seq and scRNA-seq?
00:15 2. scRNA-seq Technologies What is the various technologies used for scRNA-seq?
00:30 3. Introduction to Bioconductor How to manage R packages using Bioconductor
00:45 4. Dataset and Workflow What is the workflow for scRNA-seq?
01:15 5. Convert SRA to FASTQ format What is The Sequence Read Archive (SRA)?
How to convert SRA to FASTQ using NCBI SRA Tools
01:45 6. Quality Control of FASTQ files Why do we check the quality of FASTQ?
How to generate FastQC report?
02:35 7. Mapping Reads What does pseudo alignment means?
What is the advantage of pseudi alignment?
What is the difference of UMI dataset with other sequencing datasets?
Which packages can be useful for mapping UMI datasets?
03:35 8. SingleCellExperiment Construction What is SingleCellExperiment?
Why it it important to clean expression matrix in scRNA-seq data analysis?
How to assess quality of reads and cells?
04:35 Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.