Topic outline

  • General

    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 

    This page is addressed to registered participants. To access event description, exact location and application form, please click here.

    For any assistance, please contact

    • Programme

      The course will run every day from 9h to 12h30 and then from 13h30 to 17h, and will include two coffee breaks per day.

      Day 1:

      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

      Day 2:

      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)