Single cell RNA-seq data analysis

This course introduces single cell RNA-seq data analysis methods, tools and file formats. It covers the preprocessing steps of DropSeq data from raw reads to a digital gene expression matrix (DGE), and how to find sub-populations of cells using clustering with the Seurat tools. Both DropSeq and 10X Genomics data are used in the exercises. The user-friendly Chipster software is used in the exercises, so no Unix or R experience is required and the course is thus suitable for everybody. The course takes one day. You will learn how to

  • check the quality of reads with FastQC
  • tag reads with molecular and cellular barcodes
  • trim reads
  • align reads to the reference genome with HISAT2 and STAR
  • tag reads with gene names
  • visualize aligned reads in genomic context using the Chipster genome browser
  • estimate the number of usable cells by checking the inflection point
  • detect bead synthesis errors
  • create and filter DGE
  • regress out unwanted variability
  • detect variable genes and perform principle component analysis
  • cluster cells and find marker genes for a cluster

Course material (2018) is available at the course website and it includes:

  • slides
  • lecture and exercise videos
  • exercises (the data is available on Chipster server in the example sessions listed in the exercise sheet, and we also provide ready-made analysis sessions which you can use as a reference when doing exercises on your own).

Resource type: course materials, Video

Difficulty level: Beginner

Authors: Eija Korpelainen, Maria Lehtivaara

Contributors: Eija Korpelainen

External resources:

Chipster

Single cell RNA-seq data analysis https://tess.elixir-europe.org/materials/single-cell-rna-seq-data-analysis-with-chipster This course introduces single cell RNA-seq data analysis methods, tools and file formats. It covers the preprocessing steps of DropSeq data from raw reads to a digital gene expression matrix (DGE), and how to find sub-populations of cells using clustering with the Seurat tools. The user-friendly Chipster software is used in the exercises, so no Unix or R experience is required and the course is thus suitable for everybody. Eija Korpelainen
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