Requirements

Before diving into this slide deck, we recommend you to have a look at:

• Introduction to Galaxy Analyses

• Sequence analysis
  • Quality Control: slides slides - tutorial hands-on
  • Mapping: slides slides - tutorial hands-on

question Questions

• What are the main steps of scRNA-seq?

• What kind of variation can confound an analysis?

objectives Objectives

• Learn the main stages of an scRNA-seq analysis

• Understand the methods and concepts underlying scRNA-seq

Single Cell RNA Pre-processing

Single Cell RNA Downstream Analysis

Barcoding Cells

Filtering: Cell and Gene

Normalisation: Technical Variation

Normalisation: Biological Variation

Dimension Reduction: Relatedness of Cells

Dimension Reduction: Projection

Community Clustering: Louvain

Aim: Maximise internal links and minimise external links

Community Clustering: Louvain

Pick a cell, place in neighbour, and accept if internal:external increases

Cell Type: Identifying Cluster Types

Clustering: Hard vs Soft

Clustering: Hard vs Soft

Continuous Phenotypes:

Interactive Environments: live.usegalaxy.eu

CellxGene Local Test

• We can probe clusters to see how they are so differentially expressed

   Copy
  pip3 install cellxgene
  cellxgene launch https://cellxgene-example-data.czi.technology/pbmc3k.h5ad
  

• Launch locally: http://127.0.0.1:5005

keypoints Key points

• Understanding the purpose of barcoding

• Knowing the difference between hard and soft clustering

• A KNN graph can be generated from a count matrix.

• Community clustering can be generated from a KNN graph.

• Interpreting scRNA-seq plots

Thank You!

This material is the result of a collaborative work. Thanks to the Galaxy Training Network and all the contributors!

Authors: Mehmet Tekman

This material is licensed under the Creative Commons Attribution 4.0 International License.