Single cell RNA-seq data analysis with R

Programme

Monday 27.5.2019

Introduction to single cell RNA-seq (Jules Gilet)
Quality control and data preprocessing (Åsa Björklund)
Normalisation (Heli Pessa)
Removal of confounding factors (Bishwa Ghimire)
Data integration (CCA, MNN, dataset alignment) (Ahmed Mahfouz)

Tuesday 28.5.2019

Dimensionality reduction (PCA, tSNE and UMAP) (Paulo Czarnewski)
Clustering (Ahmed Mahfouz)
Differential gene expression analysis (Ståle Nygård)

Wednesday 29.5.2019

Cell type identification (Philip Lijnzaad)
Trajectories/Pseudo-time (Paulo Czarnewski and Jules Gilet)
Spatial transcriptomics (Lars Borm and Jeongbin Park)

Prerequisites

In order to follow this course you should have prior experience in using R.

Learning objectives

After this course you will be able to:

use a range of bioinformatics tools to analyze single cell RNA-seq data
discuss a variety of aspects of single cell RNA-seq data analysis
understand the advantages and limitations of single cell RNA-seq data analysis

Scientific topics: RNA-Seq

Keywords: RNA-Seq, Single Cell technologies, scRNA-seq

Resource type: course materials

Target audience: bioinformaticians, Biologists

Difficulty level: Intermediate

Contributors: Eija Korpelainen @eija, ekorpelainen@gmail.com

Single cell RNA-seq data analysis with R https://tess.elixir-europe.org/materials/single-cell-rna-seq-data-analysis-with-r-26ec3ec0-8f43-47db-9788-7f8f63eb447b This international hands-on course covers several aspects of single cell RNA-seq data analysis, ranging from clustering and differential gene expression analysis to trajectories, cell type identification and spatial transcriptomics. The course is kindly sponsored by the ELIXIR EXCELERATE project. Note: You can find all the course material including the R code and data files in the course [GitHub](https://github.com/NBISweden/excelerate-scRNAseq) repository, and the lecture videos are available as a [YouTube playlist](https://www.youtube.com/playlist?list=PLjiXAZO27elC_xnk7gVNM85I2IQl5BEJN). Eija Korpelainen @eija, ekorpelainen@gmail.com RNA-Seq RNA-Seq, Single Cell technologies, scRNA-seq bioinformaticians Biologists