Topic outline

  • General

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    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 training@sib.swiss.

  • 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)