Single Cell Analysis Course

This course will cover detailed analysis for Single Cell RNA-seq data

Pre-requisites: Familiarity with Unix and clusters, job submission and running some lsf files, shell scripts. Familiarity with R programming (Functions, control structures, data frames, apply)


  • What is scRNA-seq and Why do we need it? (Lecture 1)
  • Protocols and methodology for scRNA-seq setup (Lecture 1)
  • Platforms & Computational Analysis, Alignment (Lecture 2)
  • This course will deal with the computational analysis of the data obtained from scRNA-seq experiments.
  • Quality Control (Lecture 3)
  • Identification and filtering of samples to ensure robust downstream analysis (Lecture 3)
  • Seurat Part 1 (Lecture 4)
  • Introduction to linear dimensionality reduction (Lecture 4)
  • Determining the principal sources of variance between the cells and separation of cells into clusters
  • Seurat Part 2 (Lecture 5)
  • Clustering the cells
  • Non-linear dimensionality reduction & T-SNE Plots
  • Seurat Part 3 (Differential expression between clusters) (Lecture 6)
  • Finding markers for each cluster and using these for diff expr (Lecture 6)

Software used during the course

  • Salmon (Alignment of reads)
  • Seurat┬áis an R package designed for QC, analysis, and exploration of single cell RNA-seq data.

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