Single Cell Sequencing
Bern, 12-13 October 2016
University of Bern
Wednesday 12th October: Hochschulstrasse 4, Room 331 3.OG/West
Thursday 13th October: Schanzeneckstrasse 1, Room A301/ UniS
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The course will run every day from 9h to 12h30 and then from 13h30 to 17h, and will include two coffee breaks per day.
Morning: Overview of laboratory prep and sequence analysis
- Overview of different wet side preps (SmartSeq2, DropSeq, 10X)
- Overview of the types of sequences generated from SmartSeq and pipeline for analysis
- Overview of DropSeq sequence and analysis pipeline
- Overview of 10X sequences and analysis pipeline
- Sequence level quality control
Afternoon: Characteristics of expression data and QC
- What does single cell expression data look like and why?
- Introduction to RStudio
- Initial data exploration
- Quality control for expression matrices: filtering genes and samples, considerations in data analysis when using UMIs
- Why normalize gene expression and common types of normalization: using Scone for normalization
Morning: Plotting Single Cell RNA-Seq data
- Using Seurat to plot genes: plotting (a priori known) marker gene lists to confirm known cell types
- Why do we need dimensionality reduction and how is this used to plot samples (PCA and tSNE)?
- Plotting Samples in Seurat
- Batch Effects: what is a technical batch effect and how to identify them? What new biological batches exist in single cell data? Confounding by study design
Afternoon: Evaluating and defining cell populations
- Moving from clusters to populations of cells (defining clusters given ordinations): Seurat (and RaceID)
- Differential Expression (SCDE): the different between differential and discriminant expression
- Pathway Analysis: Pagoda, FastProject
- Overview of available methodology
- Resources online for further growth (online tutorials)